Electric power production operation environment risk early warning method and device and computer equipment

Abstract

The invention discloses an electric power production operation environment risk early warning method and device and computer equipment. Multi-dimensional data of the operation area is obtained, the multi-dimensional data comprises meteorological data, map data and field operation data, and the field operation data is at least used for determining an initial operation route; a preset model is adopted to analyze the multi-dimensional data, an analysis result is obtained, and the analysis result is used for indicating whether the initial operation route is in a target meteorological condition or not in the operation period; and generating an alarm message based on the target meteorological condition in the initial operation route in the operation period, the alarm message at least comprising a weather type in the initial operation route, the severity of the target meteorological condition, the duration of the target meteorological condition and a recommended operation route obtained after the initial operation route is corrected. According to the invention, the technical problem of low operation safety caused by low prediction accuracy of the meteorological condition of the operation area is solved.

Description

Technical Field

[0001] The present application relates to the technical field of power production operations, and in particular to a method, device and computer equipment for early warning of environmental risks in power production operations. Background Art

[0002] In the field of power production operations, especially in the operating environment of high-altitude and cold regions, the complexity and variability of meteorological conditions pose a major challenge to operational safety. The meteorological conditions in these areas are often complex and changeable, and the accuracy of meteorological warnings in local areas is not high, and it is impossible to effectively obtain short-term forecast weather, road conditions and other data information of precise geographical locations. Therefore, there is a high possibility of extreme weather such as heavy rain, hail, blizzards, strong winds, and heavy fog at the destination or route of the operation. There is also a risk of secondary disasters such as icy roads, large amounts of water and snow, landslides, and road slides around traffic sections or the work site. On-site operators are unable to effectively predict such phenomena in advance, which poses a serious threat to the safety of operators, the protection of power equipment, and the stability of power supply.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present application provide a method, device and computer equipment for early warning of environmental risks in power production operations, so as to at least solve the technical problem of low operation safety caused by low accuracy in forecasting meteorological conditions in the operation area.

[0005] According to one aspect of an embodiment of the present application, a method for early warning of environmental risks in power production operations is provided, comprising: acquiring multi-dimensional data of an operation area, the multi-dimensional data comprising: meteorological data, map data and on-site operation data, the on-site operation data being used at least to determine an initial operation route; analyzing the multi-dimensional data using a preset model to obtain an analysis result, the analysis result being used to indicate whether the initial operation route is in a target meteorological condition during an operation period; generating an alarm message based on the target meteorological condition in the initial operation route during the operation period, the alarm message comprising at least: the weather type in the initial operation route, the severity of the target meteorological condition, the duration of the target meteorological condition and a recommended operation route obtained after correcting the initial operation route.

[0006] Optionally, a preset model is used to analyze the multi-dimensional data, including: obtaining an internal data source and an external data source, wherein the internal data source includes at least: an electric power equipment maintenance plan, an electric power equipment emergency repair task order, an operation ticket and a work ticket and a power outage plan, and the external data source includes at least: historical weather conditions and traffic routes; storing the external data source in an external network server, and storing the internal data source in an internal network server, wherein the external network server and the internal network server are isolated from each other; extracting data from the external network server and the internal network server respectively to obtain a training data set, and using the training data set to train the preset model; after processing each type of data in the multi-dimensional data respectively, using the preset model to analyze the processed multi-dimensional data to obtain the weather conditions in the initial operation route during the operation period.

[0007] Optionally, each type of data in the multi-dimensional data is processed separately, including: dividing the operation area into multiple grid areas, and corresponding the meteorological data to the multiple grid areas one by one; obtaining time series information of the meteorological data, and superimposing the time series information of the meteorological data into corresponding grid areas; extracting meteorological information of multiple dimensions from the meteorological data, the meteorological information of the multiple dimensions including at least one of the following: temperature, humidity, air pressure, wind speed, wind direction, precipitation and meteorological disasters, and associating the meteorological information of the multiple dimensions with the multiple grid areas one by one; determining the grid area data associated with the meteorological data, the time series information and the meteorological information of the multiple dimensions as processed meteorological data; and processing the map data and field operation data in the multi-dimensional data in turn to obtain processed map data and processed field operation data.

[0008] Optionally, processing the map data in the multi-dimensional data includes: removing duplicate data, missing data or abnormal data in the map data in sequence to obtain initial map data; converting the initial map data into a unified format, performing feature extraction to obtain a variety of map features, and the multiple map features include at least one of the following: road type, terrain feature and population density; and binding the multiple map features to the locations in the map in sequence to obtain processed map data.

[0009] Optionally, the field operation data in the multi-dimensional data is processed, including: extracting the start and end locations of the operation from the field operation data; forming an operation path according to the start and end locations of the operation, and converting the operation path into a longitude and latitude coordinate sequence; associating the longitude and latitude coordinate sequence with the processed meteorological data according to the location information to obtain the initial operation route.

[0010] Optionally, the preset model is used to analyze the processed multi-dimensional data, including: determining the meteorological information of multiple dimensions at different time periods on the initial working route based on the multi-dimensional data, the meteorological information of multiple dimensions including at least one of the following: temperature, humidity, air pressure, wind speed, wind direction, precipitation and meteorological disasters; determining the target meteorological conditions in the initial working route during the working period based on the meteorological information of multiple dimensions at different time periods on the initial working route, the target meteorological conditions including at least one of the following: rainy and snowy weather, high temperature weather, low temperature weather, strong wind weather; generating the alarm message when the target meteorological conditions occur in the initial working route during the working period.

[0011] Optionally, the method also includes: obtaining meteorological information of multiple dimensions of the working area, and obtaining location information and height information of the working area, the working area at least including: the top of the working tower; obtaining weights of wind speed and temperature corresponding to the location information and height information; adjusting the predicted wind speed and temperature of the working area according to the weights of the wind speed and temperature to obtain final meteorological information of the working area.

[0012] According to another aspect of an embodiment of the present application, there is also provided an environmental risk warning device for power production operations, comprising: an acquisition module for acquiring multi-dimensional data of an operation area, the multi-dimensional data comprising: meteorological data, map data and on-site operation data, the on-site operation data being at least used to determine an initial operation route; an analysis module for analyzing the multi-dimensional data using a preset model to obtain an analysis result, the analysis result being used to indicate whether the initial operation route is in a target meteorological condition during an operation period; an alarm module for generating an alarm message based on the target meteorological condition in the initial operation route during the operation period, the alarm message comprising at least: the weather type in the initial operation route, the severity of the target meteorological condition, the duration of the target meteorological condition and a recommended operation route obtained after correcting the initial operation route.

[0013] According to another aspect of the embodiment of the present application, a computer device is also provided, including: a memory and a processor, wherein the memory is used to store program instructions; the processor is connected to the memory and is used to execute the above-mentioned power production operation environmental risk warning method.

[0014] According to another aspect of the embodiment of the present application, a computer program product is also provided, including: computer instructions, where the computer instructions are executed by a processor to implement the above-mentioned power production operation environmental risk early warning method.

[0015] In an embodiment of the present application, multi-dimensional data of an operation area is obtained, and the multi-dimensional data includes: meteorological data, map data and on-site operation data, and the on-site operation data is at least used to determine an initial operation route; a preset model is used to analyze the multi-dimensional data to obtain an analysis result, and the analysis result is used to indicate whether the initial operation route is in a target meteorological condition during the operation period; based on the target meteorological condition in the initial operation route during the operation period, an alarm message is generated, and the alarm message includes at least: the weather type in the initial operation route, the severity of the target meteorological condition, the duration of the target meteorological condition and the recommended operation route obtained after correcting the initial operation route, thereby achieving the purpose of accurately predicting the meteorological condition in the operation area and giving an alarm message according to the meteorological condition, and achieving the technical effect of improving the accuracy of meteorological condition prediction and improving operation safety, thereby solving the technical problem of low operation safety caused by low accuracy of meteorological condition prediction in the operation area. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 It is a hardware structure block diagram of a computer terminal (or electronic device) for implementing a method for early warning of environmental risks in power production operations according to an embodiment of the present application;

[0018] Figure 2 It is a schematic diagram of a process of an early warning method for environmental risk in power production operations according to an embodiment of the present application;

[0019] Figure 3 It is a schematic diagram of an architecture of a method for implementing an early warning method for environmental risks in power production operations according to an embodiment of the present application;

[0020] Figure 4 It is a schematic diagram of data processing for realizing a method for early warning of environmental risks in power production operations according to an embodiment of the present application;

[0021] Figure 5 It is a schematic diagram of the structure of an environmental risk early warning system for power production operations according to an embodiment of the present application;

[0022] Figure 6 It is a structural schematic diagram of an environmental risk warning device for power production operations provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] Related technologies often rely on a single data source, such as using only weather station data, and lack the ability to integrate multi-source data such as geographic information systems (GIS) and traffic flow data. This limitation makes it impossible for the prediction model to fully capture the complexity of the operating environment, reduces the accuracy and practicality of the early warning, and leads to low accuracy in predicting meteorological conditions. At the same time, the data update and processing speed of the existing system are slow, and it is impossible to achieve a rapid response to sudden extreme weather. In high-altitude and cold areas, rapidly changing meteorological conditions require the system to be able to monitor and warn in real time. The degree of automation in the automatic release and response of early warning information is low, and many processes require manual intervention, which reduces efficiency. In order to solve the above problems, the embodiment of the present application provides a method for early warning of risks in the operating environment of power production.

[0026] An embodiment of a method for early warning of environmental risks in power production operations provided in an embodiment of the present application may be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal for implementing a method for early warning of environmental risks in power production operations is shown. Figure 1As shown, the computer terminal 10 may include one or more (102a, 102b, ..., 102n are used to illustrate) processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions connected via a wired and / or wireless network. In addition, it may also include: a display, a keyboard, a cursor control device, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, and a BUS bus. Those skilled in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0027] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10. As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0028] The memory 104 can be used to store software programs and modules of application software, such as a program instruction / data storage device corresponding to a power production operation environment risk early warning method in an embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, realizing the above-mentioned power production operation environment risk early warning method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0029] The transmission module 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission module 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission module 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0030] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 .

[0031] It should be noted that, in some optional embodiments, the above Figure 1 The computer terminal shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. It should be noted that Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computer terminal described above.

[0032] In the above-mentioned operating environment, an embodiment of the present application provides an embodiment of a method for early warning of environmental risks in power production operations. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0033] Figure 2 is a flow chart of a method for early warning of environmental risks in power production operations according to an embodiment of the present application, such as Figure 2 As shown, the method comprises the following steps:

[0034] Step S202, acquiring multi-dimensional data of the operation area, wherein the multi-dimensional data includes: meteorological data, map data, and on-site operation data, wherein the on-site operation data is at least used to determine an initial operation route;

[0035] Step S204, using a preset model to analyze the multi-dimensional data to obtain an analysis result, where the analysis result is used to indicate whether the initial operation route is in a target meteorological condition during the operation period;

[0036] Step S206, generating an alarm message based on the target meteorological conditions in the initial operation route during the operation period, wherein the alarm message includes at least: the weather type in the initial operation route, the severity of the target meteorological conditions, the duration of the target meteorological conditions, and the recommended operation route obtained after correcting the initial operation route.

[0037] Through the above steps, multi-dimensional data of the operation area is obtained, and the multi-dimensional data includes: meteorological data, map data and on-site operation data, and the on-site operation data is at least used to determine the initial operation route; the multi-dimensional data is analyzed by using a preset model to obtain an analysis result, and the analysis result is used to indicate whether the initial operation route is in the target meteorological conditions during the operation period; based on the target meteorological conditions in the initial operation route during the operation period, an alarm message is generated, and the alarm message includes at least: the weather type in the initial operation route, the severity of the target meteorological conditions, the duration of the target meteorological conditions and the recommended operation route obtained after the initial operation route is corrected, so as to achieve the purpose of accurately predicting the meteorological conditions in the operation area and giving an alarm message according to the meteorological conditions, and achieve the technical effect of improving the accuracy of meteorological condition prediction and improving operation safety, thereby solving the technical problem of low operation safety caused by low accuracy of meteorological condition prediction in the operation area.

[0038] The power production operation environment risk warning method provided in this application comprehensively considers the complexity of the operating environment, and improves the accuracy and practicality of the prediction model by integrating multi-source data such as meteorology, geography, and transportation. Build a prediction model that can be generalized to different regions and meteorological conditions to improve the prediction ability of rare and emerging meteorological events. Through automation and intelligence, the automatic release and response of warning information is realized, and based on meteorological data, business data and road information, an efficient and accurate on-site operation warning service is built to realize real-time warning and monitoring of extreme meteorological and geological disasters for power transportation and on-site operations in high-altitude and cold areas, ensuring that operators can receive and understand warning information in a timely manner. Predict the road conditions and weather conditions during operation transportation and at the operation site in advance, and provide effective information to on-site operators in advance, so as to adjust the advantageous route or change the itinerary according to the actual road conditions, and change or adjust the operation plan in time, which can effectively ensure the safety of operators, power equipment and stable power supply.

[0039] The following further introduces the power production operation environmental risk warning method in steps S202 to S206 of the embodiment of the present application.

[0040] In step S204, the multi-dimensional data is analyzed using a preset model, including: obtaining an internal data source and an external data source, wherein the internal data source includes at least: an electric power equipment maintenance plan, an electric power equipment emergency repair task order, an operation ticket and a work ticket and a power outage plan, and the external data source includes at least: historical weather conditions and traffic routes; storing the external data source in an external network server, and storing the internal data source in an internal network server, wherein the external network server and the internal network server are isolated from each other; extracting data from the external network server and the internal network server respectively to obtain a training data set, and using the training data set to train the preset model; after processing each type of data in the multi-dimensional data respectively, using the preset model to analyze the processed multi-dimensional data to obtain the weather conditions in the initial operation route during the operation period.

[0041] Wherein, each type of data in the multi-dimensional data is processed separately, including: dividing the operation area into multiple grid areas, and making the meteorological data correspond to the multiple grid areas one by one; obtaining the time series information of the meteorological data, and superimposing the time series information of the meteorological data into the corresponding grid areas; extracting meteorological information of multiple dimensions from the meteorological data, and the meteorological information of the multiple dimensions includes at least one of the following: temperature, humidity, air pressure, wind speed, wind direction, precipitation and meteorological disasters, and associating the meteorological information of the multiple dimensions with the multiple grid areas one by one; determining the grid area data associated with the meteorological data, the time series information and the meteorological information of the multiple dimensions as processed meteorological data; processing the map data and the field operation data in the multi-dimensional data in turn to obtain the processed map data and the processed field operation data.

[0042] In some embodiments of the present application, the process of processing the map data in the multi-dimensional data is as follows: removing duplicate data, missing data or abnormal data in the map data in turn to obtain initial map data; after converting the initial map data into a unified format, performing feature extraction to obtain a variety of map features, and the multiple map features include at least one of the following: road type, terrain features and population density; binding the multiple map features to the locations in the map in turn to obtain processed map data.

[0043] In some embodiments of the present application, the field operation data in the multidimensional data is processed, including: extracting the start and end locations of the operation from the field operation data; forming an operation path based on the start and end locations of the operation, and converting the operation path into a longitude and latitude coordinate sequence; associating the longitude and latitude coordinate sequence with the processed meteorological data according to the location information to obtain the initial operation route.

[0044] The process of analyzing the processed multi-dimensional data using the preset model can be broken down into the following process: determining the meteorological information of multiple dimensions at different time periods on the initial operation route based on the multi-dimensional data, the meteorological information of the multiple dimensions including at least one of the following: temperature, humidity, air pressure, wind speed, wind direction, precipitation and meteorological disasters; determining the target meteorological conditions in the initial operation route during the operation period based on the meteorological information of multiple dimensions at different time periods on the initial operation route, the target meteorological conditions including at least one of the following: rainy and snowy weather, high temperature weather, low temperature weather and strong wind weather; generating the alarm message when the target meteorological conditions occur in the initial operation route during the operation period.

[0045] Taking into account that power equipment operations are often at the top of pole towers, in order to more accurately predict the meteorological conditions in the actual operating area, the present application provides the following method: obtaining meteorological information of multiple dimensions of the operating area, and obtaining location information and height information of the operating area, the operating area at least including: the top of the operating pole tower; obtaining the weights of wind speed and temperature corresponding to the location information and height information; adjusting the predicted wind speed and temperature of the operating area according to the weights of the wind speed and temperature to obtain the final meteorological information of the operating area.

[0046] In order to better illustrate the implementation process of environmental risk warning for power production operations, Figure 3 A system architecture for implementing an early warning method for environmental risks in power production operations is shown. Figure 3 As shown, it includes: data access layer, data storage layer, data processing layer, data application layer and data display layer, among which,

[0047] Data access layer: access multi-dimensional data from the outside, including weather data, meteorological data, landforms, and map data, as the input data source for abnormal weather forecasts, and use historical weather, meteorology, geological disasters, and transportation as external data sources for prediction model training. In addition, access professional system data such as maintenance plans, emergency repair task orders, operation tickets, work tickets, power outage plans, etc. from the internal system as the internal data source for prediction model training. At the same time, the internal data source can also be used as the data source for travel routes and on-site operation data during emergency repair and maintenance operations.

[0048] Data storage layer: deploy external network servers to store external weather, meteorological, geological disaster, road network and map data. Deploy internal network servers to store data related to power field operations, such as maintenance plans, emergency repair task orders, operation tickets, work tickets and power outage plans.

[0049] It is understandable that the operation ticket is the written basis for equipment operation in the power system. Before any operation that changes the operation mode of the power system, the operator must follow the pre-prepared operation ticket. The operation ticket lists in detail the operation steps, required safety measures, name, number and status of the operating equipment, etc., to ensure the accuracy and safety of the operation and prevent accidents caused by misoperation. The operation ticket is usually written by the person in charge of the operation task, and after approval, it is executed by the operator. The work ticket is a written permission and safety guidance document for equipment maintenance, overhaul or construction in the power system. The work ticket will detail the scope, time, location, operator, safety measures, power outage scope, energized scope, operation risk assessment, etc. Before starting the operation, the operator must get the corresponding work ticket and perform the operation according to the requirements on the work ticket to ensure the safety of personnel and equipment during the operation.

[0050] Data processing layer: including data standardization processing and internal and external data fusion and interoperability processing. Standardize the external data according to the specifications and internal and external data interoperability requirements, establish data access specifications and processing specifications, interoperate data from different external sources, perform preliminary processing on abnormal data, and define the input and output standards of the prediction model to provide support for other system data applications. At the same time, establish internal and external data mapping relationships, perform internal and external data fusion, and complete the preparation of prediction model input data.

[0051] An optional fusion method may be internal and external data mapping: establishing a mapping relationship between internal and external data sources, for example: matching external meteorological data with geographic location information in the internal work plan, and associating external road network data with internal vehicle information to achieve reasonable data integration.

[0052] Fusion of data sets from different sources, including merging, correlating and integrating data, to form a more comprehensive analytical perspective. For example, combining meteorological data with power operation data to predict risks under specific operating conditions.

[0053] Through-processing: Dealing with interoperability issues between internal and external data to ensure that data can be transmitted smoothly between different systems.

[0054] Data application layer: Analyze the processed data through the prediction model algorithm, extract abnormal weather data of the route, abnormal weather data of the operation site, abnormal weather warning data of the route, abnormal weather warning data of the operation site, etc. to provide support for line repair and maintenance operations. At the same time, deploy data application microservices to provide support for other systems to apply abnormal weather data and abnormal weather warning data, and inform the line repair personnel through text messages to take preventive measures.

[0055] Data display layer: Abnormal weather forecast results are displayed intuitively using map overlays to provide decision-making basis for on-site task execution, including: display of weather conditions along the route, display of abnormal weather conditions around the work site, display of warning messages, and display of abnormal weather SMS messages.

[0056] In order to better demonstrate the power production operation environmental risk early warning method provided by the embodiment of the present application, Figure 4 A data processing flow chart is shown, such as Figure 4 As shown, including:

[0057] Step 1, data acquisition:

[0058] Meteorological data, including temperature, humidity, air pressure, wind speed, wind direction and precipitation, are updated to the intranet in a unidirectional and regular manner through the external network environment and the security gateway.

[0059] Map data, including area division, stake number, coordinates and other information, can be accessed directly through SDK / API and other methods.

[0060] On-site operation data, including operation area, operation time, operation personnel and work start and end locations, are regularly obtained through WebService and other methods according to data access specifications.

[0061] Step 2, data storage:

[0062] After parsing and processing, external data is stored in the server in the external network area. Security components are deployed in the external network server, and one-way inflow and personalized access policies are configured.

[0063] Step 3, data processing:

[0064] Meteorological data: With space as the main dimension, the meteorological data is gridded, and the time dimension is superimposed on the gridded data to form standardized meteorological data based on both space and time dimensions. Based on this, the meteorological data is further standardized into expressions of different dimensions such as temperature, humidity, air pressure, wind speed, wind direction, and precipitation.

[0065] For example, data gridding: Use a geographic information system (GIS) to divide the continuous geographic space into multiple grids, each grid representing a specific geographic area. This allows meteorological data to correspond to specific geographic locations, facilitating spatial analysis and prediction. For example, grids can be defined using longitude and latitude coordinates to ensure that each grid has clear boundaries. Time series overlay: On the basis of gridding, the time series information of meteorological data is overlaid on the grid data to form a dual dimension of space and time. This means that each grid not only has its spatial attributes, but also has meteorological data about a specific point in time, such as temperature, humidity, wind speed, etc.

[0066] Multi-dimensional standardization: Decompose meteorological data into different dimensions (such as temperature, humidity, air pressure, wind speed, wind direction, precipitation, etc.), and standardize the data in each dimension. Standardization can include data normalization, unit conversion, etc., to ensure the comparability and processability of data of different dimensions in the model.

[0067] Techniques such as removing outliers, filling missing values, and smoothing data can improve the quality of data and the accuracy of model predictions.

[0068] Map data: First, the acquired map data is processed initially, including removing duplicate data, missing data, abnormal data, etc. Secondly, the map data is converted from the original format to a standard format, and then the map data from different data sources are integrated together for analysis. Finally, the integrated map data is processed by statistical analysis, machine learning and other algorithms to extract useful map information and knowledge.

[0069] For example: Data cleaning: Check and remove duplicate, missing or abnormal records in map data to ensure the accuracy and completeness of the data. Format conversion: Convert map data into a unified standard format, such as GeoJSON, Shapefile, etc., to facilitate subsequent data fusion and analysis. Data integration: Integrate map data from different sources into a unified data set or database, and achieve efficient spatial data query and analysis by establishing indexes or geospatial databases such as PostGIS. Feature extraction and algorithm processing: Perform statistical analysis or machine learning processing on the integrated map data to extract key features, such as road types, terrain features, population density, etc., to build a richer geographic information background to provide support for subsequent risk prediction.

[0070] On-site operation data: The source data is stored in a structured manner according to the operation time, operation personnel, work start and end locations, etc. The travel path formed after path planning is stored in a structured manner. The meteorological data of the travel route is stored in a structured manner.

[0071] For example, structured storage: field operation data is structured and stored according to data fields (operation time, operator, operation location, etc.) to ensure the readability and analyzability of the data.

[0072] Path planning digitization: Convert the operation path planning into digital information, such as longitude and latitude coordinate sequences, to facilitate matching and analysis with map data and meteorological data.

[0073] Weather data association: The geographic location of the travel route and the work site is associated with the standardized weather data, and the weather conditions at specific points in time on the travel route and the work site are stored to evaluate the safety of the work environment.

[0074] Job data preprocessing: Preprocess the job data, such as checking the rationality of the job plan, evaluating the safety qualifications of the operators, etc., to ensure the accuracy of the data and the safety of the operation.

[0075] Step 4, data application:

[0076] Identification and warning of abnormal weather (target weather conditions) on the initial operation route, specifically,

[0077] Integrating power operation task information, map positioning, path planning capabilities, and weather forecast data, the Corrformer model technology is used to establish an abnormal weather recognition and alarm model for specific travel routes. The model is trained using historical meteorological data and route information to predict future weather conditions on specific routes. Based on real-time monitoring of meteorological data, detailed meteorological condition analysis is provided, and timely warnings are issued for disastrous meteorological conditions such as rain, snow, high temperature, low temperature, and strong winds to ensure driving safety. At the same time, routes with better meteorological conditions are recommended, and areas with abnormal meteorological conditions are automatically avoided to provide the best driving options and ensure driving safety. When the model detects that abnormal weather may occur on a specific route, an alarm notification is generated, including information such as weather type, severity, and duration, as well as recommended routes with better meteorological conditions.

[0078] Identification and warning of abnormal weather in the operation area, specifically,

[0079] Integrate power operation task information, map positioning, and destination weather forecast data, and use Corrformer model technology to establish an abnormal weather recognition and warning model for a specific destination. The model can be trained using historical meteorological data and destination information to predict future weather conditions near the destination. Intelligently identify the destination and provide accurate weather information within the agreed time period. By combining advanced meteorological data analysis and prediction models, key meteorological elements such as temperature, humidity, precipitation, and wind speed in the destination area are presented.

[0080] Alarm generation: When the model detects that abnormal weather may occur near the destination, an alarm notification is generated, including information such as weather type, severity, and duration, to assist in deciding whether to go to the work site to carry out on-site operations.

[0081] Abnormal weather identification and warning at the top of on-site operation towers: Based on the abnormal weather identification model for specific destinations, the weather propagation direction is automatically inferred according to meteorological observation data, and the wind speed and temperature weights at the operation pole location are predicted. At the same time, combined with the longitude and latitude of the operation pole location, the height of the operation pole and other parameters, various key data near the ground are analyzed and predicted to identify and predict abnormal weather at the top of the operation tower.

[0082] Figure 5A power production operation environmental risk early warning system is shown, comprising:

[0083] Data processing module, used to access, store and process meteorological data, map data and field operation data;

[0084] Model calculation module, used to provide strategic recommendations based on weather type, meteorological severity and duration provided by the model;

[0085] Visual display module, used to display the map overlay of abnormal information on the route and abnormal weather around the work site;

[0086] The early warning service module is used for abnormal weather warning services along the travel route.

[0087] According to an embodiment of the present application, an embodiment of an environmental risk warning device for power production operations is also provided. Figure 6 Schematic diagram of a structure of a power production operation environmental risk warning device provided according to an embodiment of the present application. Figure 6 As shown, the device comprises:

[0088] An acquisition module 60 is used to acquire multi-dimensional data of the operation area, wherein the multi-dimensional data includes: meteorological data, map data and on-site operation data, wherein the on-site operation data is at least used to determine an initial operation route;

[0089] An analysis module 62 is used to analyze the multi-dimensional data using a preset model to obtain an analysis result, which is used to indicate whether the initial operation route is in a target meteorological condition during the operation period;

[0090] The alarm module 64 is used to generate an alarm message based on the target meteorological conditions in the initial operation route during the operation period, and the alarm message includes at least: the weather type in the initial operation route, the severity of the target meteorological conditions, the duration of the target meteorological conditions and the recommended operation route obtained after correcting the initial operation route.

[0091] The above-mentioned power production operation environment risk early warning device adopts multi-dimensional data of the operation area, and the multi-dimensional data includes: meteorological data, map data and on-site operation data, and the on-site operation data is at least used to determine the initial operation route; the multi-dimensional data is analyzed by a preset model to obtain an analysis result, and the analysis result is used to indicate whether the initial operation route is in the target meteorological conditions during the operation period; based on the target meteorological conditions in the initial operation route during the operation period, an alarm message is generated, and the alarm message includes at least: the weather type in the initial operation route, the severity of the target meteorological conditions, the duration of the target meteorological conditions and the recommended operation route obtained after the initial operation route is corrected, so as to achieve the purpose of accurately predicting the meteorological conditions in the operation area and giving an alarm message according to the meteorological conditions, and achieve the technical effect of improving the accuracy of meteorological condition prediction and improving operation safety, thereby solving the technical problem of low operation safety caused by low accuracy of meteorological condition prediction in the operation area.

[0092] The analysis module 62 includes: an analysis submodule, which is used to analyze the multi-dimensional data using a preset model, including: obtaining an internal data source and an external data source, wherein the internal data source at least includes: an electric power equipment maintenance plan, an electric power equipment emergency repair task order, an operation ticket and a work ticket and a power outage plan, and the external data source at least includes: historical weather conditions and traffic routes; storing the external data source in an external network server, and storing the internal data source in an internal network server, and the external network server and the internal network server are isolated from each other; extracting data from the external network server and the internal network server respectively to obtain a training data set, and using the training data set to train the preset model; after processing each type of data in the multi-dimensional data respectively, using the preset model to analyze the processed multi-dimensional data to obtain the weather conditions in the initial operation route during the operation period.

[0093] The analysis submodule includes: a first processing unit, a second processing unit and a third processing unit, wherein the first processing unit is used to process each type of data in the multi-dimensional data respectively, including: dividing the operation area into multiple grid areas, and corresponding the meteorological data to the multiple grid areas one by one; obtaining the time series information of the meteorological data, and superimposing the time series information of the meteorological data into the corresponding grid areas; extracting meteorological information of multiple dimensions from the meteorological data, and the meteorological information of the multiple dimensions includes at least one of the following: temperature, humidity, air pressure, wind speed, wind direction, precipitation and meteorological disasters, and associating the meteorological information of the multiple dimensions with the multiple grid areas one by one; determining the grid area data associated with the meteorological data, the time series information and the meteorological information of the multiple dimensions as processed meteorological data; processing the map data and the field operation data in the multi-dimensional data in turn to obtain the processed map data and the processed field operation data.

[0094] The second processing unit is used to process the map data in the multi-dimensional data, including: removing duplicate data, missing data or abnormal data in the map data in sequence to obtain initial map data; after converting the initial map data into a unified format, performing feature extraction to obtain a variety of map features, the multiple map features include at least one of the following: road type, terrain feature and population density; and binding the multiple map features to the positions in the map in sequence to obtain processed map data.

[0095] The third processing unit is used to process the field operation data in the multi-dimensional data, including: extracting the start and end locations of the operation from the field operation data; forming an operation path according to the start and end locations of the operation, and converting the operation path into a longitude and latitude coordinate sequence; associating the longitude and latitude coordinate sequence with the processed meteorological data according to the location information to obtain the initial operation route.

[0096] The analysis module 62 also includes an early warning submodule, which is used to analyze the processed multi-dimensional data using the preset model, including: determining the meteorological information of multiple dimensions at different time periods on the initial operation route based on the multi-dimensional data, the meteorological information of the multiple dimensions including at least one of the following: temperature, humidity, air pressure, wind speed, wind direction, precipitation and meteorological disasters; determining the target meteorological conditions in the initial operation route during the operation period based on the meteorological information of multiple dimensions at different time periods on the initial operation route, the target meteorological conditions including at least one of the following: rainy and snowy weather, high temperature weather, low temperature weather, strong wind weather; generating the alarm message when the target meteorological conditions occur in the initial operation route during the operation period.

[0097] The early warning submodule includes a correction unit, which is used to obtain meteorological information of multiple dimensions of the working area, and obtain the location information and height information of the working area, and the working area at least includes: the top of the working tower; obtain the weights of the wind speed and temperature corresponding to the location information and height information; adjust the predicted wind speed and temperature of the working area according to the weights of the wind speed and temperature to obtain the final meteorological information of the working area.

[0098] It should be noted that the various modules in the above-mentioned power production operation environment risk warning device can be program modules (for example, a set of program instructions that implement a certain specific function) or hardware modules. For the latter, it can be expressed in the following forms, but is not limited to this: the expression form of each of the above-mentioned modules is a processor, or the functions of each of the above-mentioned modules are implemented by a processor.

[0099] It should be noted that the power production operation environment risk early warning device provided in this embodiment can be used to execute Figure 2 A method for early warning of environmental risks in power production operations is shown. Therefore, the relevant explanations and instructions on the above-mentioned method for early warning of environmental risks in power production operations are also applicable to the embodiments of the present application and will not be repeated here.

[0100] According to another aspect of the embodiment of the present application, a computer device is also provided, including: a memory and a processor, wherein the memory is used to store program instructions; the processor is connected to the memory and is used to execute the above-mentioned power production operation environmental risk warning method.

[0101] According to another aspect of the embodiment of the present application, a computer program product is also provided, including: computer instructions, where the computer instructions are executed by a processor to implement the above-mentioned power production operation environmental risk early warning method.

[0102] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0103] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0104] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0105] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0106] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0107] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), mobile hard disk, disk or CD-ROM and other media that can store program codes.

[0108] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for early warning of environmental risks in power production operations, characterized in that: include: Acquire multi-dimensional data of the operation area, the multi-dimensional data including: meteorological data, map data and on-site operation data, the on-site operation data at least used to determine an initial operation route; Analyzing the multi-dimensional data using a preset model to obtain an analysis result, wherein the analysis result is used to indicate whether the initial operation route is in a target meteorological condition during an operation period; Based on the target meteorological conditions in the initial working route during the working period, an alarm message is generated, and the alarm message includes at least: the weather type in the initial working route, the severity of the target meteorological conditions, the duration of the target meteorological conditions and the recommended working route obtained after correcting the initial working route.

2. The method according to claim 1, characterized in that The multi-dimensional data is analyzed using a preset model, including: Acquire internal data sources and external data sources, wherein the internal data sources at least include: power equipment maintenance plan, power equipment emergency repair task list, operation ticket and work ticket and power outage plan, and the external data sources at least include: historical meteorological conditions and traffic routes; The external data source is stored in an external network server, and the internal data source is stored in an internal network server, wherein the external network server and the internal network server are isolated from each other; Extracting data from the external network server and the internal network server respectively to obtain a training data set, and using the training data set to train to obtain the preset model; After processing each type of the multi-dimensional data respectively, the processed multi-dimensional data is analyzed using the preset model to obtain the meteorological conditions in the initial operation route during the operation period.

3. The method according to claim 2, characterized in that Processing each type of the multi-dimensional data separately includes: Divide the operation area into a plurality of grid areas, and correspond the meteorological data to the plurality of grid areas one by one; Acquire the time series information of the meteorological data, and superimpose the time series information of the meteorological data into the corresponding grid area; Extracting meteorological information of multiple dimensions from the meteorological data, wherein the meteorological information of the multiple dimensions includes at least one of the following: temperature, humidity, air pressure, wind speed, wind direction, precipitation, and meteorological disasters, and associating the meteorological information of the multiple dimensions with the multiple grid areas one by one; Determine the grid area data associated with the meteorological data, the time series information, and the multiple dimensional meteorological information as processed meteorological data; The map data and the field operation data in the multi-dimensional data are processed in sequence to obtain processed map data and processed field operation data.

4. The method according to claim 3, characterized in that Processing the map data in the multi-dimensional data includes: Removing duplicate data, missing data or abnormal data in the map data in sequence to obtain initial map data; After converting the initial map data into a unified format, feature extraction is performed to obtain a plurality of map features, wherein the plurality of map features include at least one of the following: road type, terrain feature, and population density; The various map features are sequentially bound to locations in the map to obtain processed map data.

5. The method according to claim 3, characterized in that: Processing the field operation data in the multi-dimensional data includes: Extracting the operation start and end locations from the field operation data; Forming an operation path according to the operation start and end locations, and converting the operation path into a longitude and latitude coordinate sequence; The latitude and longitude coordinate sequence is associated with the processed meteorological data according to the location information to obtain the initial operation route.

6. The method according to claim 1, characterized in that The preset model is used to analyze the processed multi-dimensional data, including: Determine, based on the multi-dimensional data, meteorological information of multiple dimensions at different time periods on the initial operation route, wherein the meteorological information of multiple dimensions includes at least one of the following: temperature, humidity, air pressure, wind speed, wind direction, precipitation, and meteorological disasters; Determine the target meteorological conditions in the initial operation route during the operation period based on the meteorological information of multiple dimensions in different time periods on the initial operation route, wherein the target meteorological conditions include at least one of the following: rainy and snowy weather, high temperature weather, low temperature weather, and strong wind weather; When the target meteorological condition occurs during the operation period in the initial operation route, the warning message is generated.

7. The method according to claim 6, characterized in that The method further comprises: Acquire meteorological information of multiple dimensions of the operation area, and acquire location information and height information of the operation area, wherein the operation area at least includes: a tower top of an operation tower; Obtaining weights of wind speed and temperature corresponding to the location information and height information; The predicted wind speed and temperature of the operation area are adjusted according to the weights of the wind speed and temperature to obtain final meteorological information of the operation area.

8. A power production operation environmental risk early warning device, characterized in that: An acquisition module, used to acquire multi-dimensional data of the operation area, wherein the multi-dimensional data includes: meteorological data, map data and on-site operation data, wherein the on-site operation data is at least used to determine an initial operation route; An analysis module, used to analyze the multi-dimensional data using a preset model to obtain an analysis result, wherein the analysis result is used to indicate whether the initial operation route is in a target meteorological condition during an operation period; An alarm module is used to generate an alarm message based on the target meteorological conditions in the initial operation route during the operation period, and the alarm message includes at least: the weather type in the initial operation route, the severity of the target meteorological conditions, the duration of the target meteorological conditions and the recommended operation route obtained after correcting the initial operation route.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory is used to store program instructions; The processor is connected to the memory and is used to execute the power production operation environmental risk early warning method as described in any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by the processor, the power production operation environmental risk early warning method described in any one of claims 1 to 7 is implemented.

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