Wind speed early warning method and device

Abstract

The invention provides a wind speed early warning method and device, relates to the field of electric power climbing operation, and can set a dynamic safe wind speed threshold according to a climbing operation area so as to improve the safety and efficiency of the electric power climbing operation. The method comprises the following steps: acquiring geographical environment parameters of a climbing operation area, wherein the geographical environment parameters comprise latitude and longitude of the climbing operation area, air pressure data and meteorological data in a preset time period; inputting geographical environment parameters of the climbing operation area into the trained wind speed prediction model, and outputting a predicted wind speed result within a preset time; the trained wind speed prediction model is a time sequence model or a deep learning model; a safe wind speed threshold value is obtained according to the predicted wind speed result within the preset time, the climbing operation data and the meteorological data within the preset time period; the climbing operation data comprises operation equipment data; and determining wind speed early warning information based on the magnitude relationship between the predicted wind speed result and the safe wind speed threshold within the preset time, and outputting and displaying the wind speed early warning information.

Description

Technical Field

[0001] The present disclosure relates to the field of electric power climbing operations, and in particular to a wind speed early warning method and device. Background Art

[0002] At present, many power companies determine whether to carry out height work based on fixed safety wind speed thresholds stipulated by the state or industry. For example, when the wind speed exceeds the safety wind speed threshold, the height work is suspended. However, these fixed safety wind speed thresholds may not be able to adapt to all complex working environments. If the safety wind speed threshold is too low, it may lead to low work efficiency. If the safety wind speed threshold is too high, it may bring safety hazards.

[0003] Existing high-altitude operation areas are usually equipped with wind speed monitoring equipment to obtain wind speed data in real time. However, this method can only monitor the current wind speed conditions and cannot predict future wind speed changes. During the operation, if there is a sudden change in wind speed, the operator cannot respond in time, increasing the risk of the operation. Summary of the invention

[0004] The present disclosure provides a wind speed warning method and device, which can not only accurately predict the wind speed, but also set a dynamic safe wind speed threshold according to the height operation area to improve the safety and efficiency of power height operations.

[0005] In order to achieve the above objectives, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present disclosure provides a wind speed warning method, which is applied to electronic equipment, including: obtaining geographical environment parameters of a climbing operation area, the geographical environment parameters including the longitude and latitude of the climbing operation area, air pressure data and meteorological data within a preset time period; inputting the geographical environment parameters of the climbing operation area into a trained wind speed prediction model, and outputting the predicted wind speed results within a preset time; the trained wind speed prediction model is a time series model or a deep learning model; deriving a safe wind speed threshold based on the predicted wind speed results within a preset time, the climbing operation data and the meteorological data within a preset time period; the climbing operation data includes operation equipment data; determining wind speed warning information based on the relationship between the predicted wind speed results within a preset time and the safe wind speed threshold, and outputting and displaying the wind speed warning information.

[0007] Based on the wind speed warning method of the first aspect, when the trained wind speed prediction model outputs the predicted wind speed results of the climbing work area, the safe wind speed threshold corresponding to the climbing work area can be dynamically and intelligently adjusted in combination with the climbing work data and the meteorological data within a preset time period, thereby ensuring that the setting of the safe wind speed threshold is both scientific and in line with the actual working environment, while also enhancing the work safety of the operators.

[0008] In combination with the first aspect, in another possible implementation, before using the trained wind speed prediction model, the method further includes: obtaining historical meteorological data and historical climbing operation data of the climbing operation area; the historical meteorological data includes historical wind speed data; performing feature extraction on the historical meteorological data and historical climbing operation data, and outputting the feature extraction results; using the feature extraction results to train the wind speed prediction model to obtain a trained wind speed prediction model. By obtaining historical meteorological data and historical climbing operation data to train the wind speed prediction model, the trained wind speed prediction model can have a higher prediction accuracy, significantly improving the prediction capabilities of short-term and long-term wind speed changes in the climbing operation area.

[0009] In combination with the first aspect, in another possible implementation manner, the method further includes: obtaining an actual wind speed result; and iteratively updating parameters of a trained wind speed prediction model based on the predicted wind speed result and the actual wind speed result within a preset time.

[0010] In combination with the first aspect, in another possible implementation, based on the size relationship between the predicted wind speed result within the preset time and the safe wind speed threshold, the wind speed warning information is determined, including: when the predicted wind speed result at the first preset time point within the preset time is greater than the safe wind speed threshold, the wind speed state at the first preset time point is determined to be the first state; according to the wind speed state at the first preset time point, the wind speed warning information is output, and the wind speed warning information includes the risk level, warning time, wind speed value and protective measures corresponding to the risk level, the warning time is the first preset time point, and the wind speed value is the predicted wind speed result at the first preset time point. When the predicted wind speed result exceeds the safe wind speed threshold, the wind speed warning information is set to instruct the operating personnel to take corresponding protective measures in time to prevent safety accidents caused by severe wind conditions, thereby enhancing the operating personnel's ability to take the initiative to prevent operational risks and improving operational safety.

[0011] In combination with the first aspect, in another possible implementation method, before extracting features from historical meteorological data and altitude operation data, the method also includes: preprocessing the historical meteorological data and historical altitude operation data to obtain processed historical meteorological data and altitude operation data, the preprocessing including data cleaning and / or data normalization.

[0012] In combination with the first aspect, in another possible implementation, the risk level is determined based on a safe wind speed threshold.

[0013] In a second aspect, an embodiment of the present disclosure provides a wind speed warning device, which can be applied to an electronic device to implement the method in the first aspect. The functions of the wind speed warning device can be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions, such as an acquisition module, a prediction module, a processing module, and a display module.

[0014] Among them, the acquisition module is configured to obtain the geographical environment parameters of the climbing operation area, and the geographical environment parameters include the latitude and longitude of the climbing operation area, air pressure data, and meteorological data within a preset time period; the prediction module is configured to input the geographical environment parameters of the climbing operation area into a trained wind speed prediction model, and output the predicted wind speed results within a preset time; the trained wind speed prediction model is a time series model or a deep learning model. The processing module is configured to derive a safe wind speed threshold based on the predicted wind speed results within a preset time, the climbing operation data, and the meteorological data within a preset time period; the climbing operation data includes the operation equipment data; and based on the size relationship between the predicted wind speed results within a preset time and the safe wind speed threshold, determine the wind speed warning information. The display module is configured to output and display the wind speed warning information.

[0015] In conjunction with the second aspect, in a possible implementation, the acquisition module is further configured to acquire historical meteorological data and historical climbing operation data of the climbing operation area; the historical meteorological data includes historical wind speed data. The processing module is further configured to perform feature extraction on the historical meteorological data and the historical climbing operation data, and output the feature extraction result;

[0016] The wind speed prediction model is trained using the feature extraction results to obtain a trained wind speed prediction model.

[0017] In conjunction with the second aspect, in a possible implementation, the acquisition module is further configured to acquire an actual wind speed result. The processing module is further configured to iteratively update parameters of the trained wind speed prediction model based on the predicted wind speed result and the actual wind speed result within a preset time.

[0018] In combination with the second aspect, in a possible implementation method, the processing module is also configured to determine that the wind speed state at a first preset time point within the preset time is a first state when the predicted wind speed result at a first preset time point within the preset time is greater than the safety wind speed threshold; output wind speed warning information based on the wind speed state at the first preset time point, the wind speed warning information including the risk level, warning time, wind speed value and protective measures corresponding to the risk level, the warning time is the first preset time point, and the wind speed value is the predicted wind speed result at the first preset time point.

[0019] In combination with the second aspect, in a possible implementation, the processing module is also configured to pre-process the historical meteorological data and historical climbing operation data to obtain processed historical meteorological data and climbing operation data, and the pre-processing includes data cleaning and / or data normalization.

[0020] In conjunction with the second aspect, in a possible implementation, the risk level is determined according to a safe wind speed threshold.

[0021] In a third aspect, the present disclosure provides an electronic device, comprising: a memory, a display screen, and one or more processors; the memory, the display screen, and the processor are coupled. The memory is used to store computer program codes, and the computer program codes include computer instructions; when the electronic device is running, the processor is used to execute one or more computer instructions stored in the memory, so that the electronic device executes the wind speed warning method as described in any one of the first aspects above.

[0022] In a fourth aspect, the present disclosure provides a computer storage medium, comprising computer instructions, which, when executed on an electronic device, enables the electronic device to execute any of the wind speed warning methods in the first aspect.

[0023] In a fifth aspect, the present disclosure provides a computer program product. When the computer program product is executed on an electronic device, the electronic device executes the wind speed warning method as described in any one of the first aspects.

[0024] In a sixth aspect, a device (for example, the device may be a chip system) is provided, the device including a processor for supporting a first device to implement the functions involved in the first aspect. In one possible design, the device also includes a memory for storing program instructions and data necessary for the first device. When the device is a chip system, it may be composed of a chip, or may include a chip and other discrete devices.

[0025] It should be understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0027] Figure 1 This is one of the flow charts of a wind speed warning method according to an exemplary embodiment.

[0028] Figure 2 This is a second flow chart of a wind speed warning method according to an exemplary embodiment.

[0029] Figure 3 This is a third flow chart of a wind speed warning method according to an exemplary embodiment.

[0030] Figure 4 This is a fourth flow chart of a wind speed warning method according to an exemplary embodiment.

[0031] Figure 5 This is a fifth flow chart of a wind speed warning method according to an exemplary embodiment.

[0032] Figure 6 The figure is a structural block diagram of a wind speed warning device according to an exemplary embodiment.

[0033] Figure 7 It is a schematic structural diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0034] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0035] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure 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 disclosure described herein can be implemented in an order other than those illustrated or described herein. The data involved in the present disclosure may be data authorized by the user or fully authorized by all parties. The implementation methods described in the following exemplary embodiments do not represent all implementation methods consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the attached claims.

[0036] It should also be understood that the term “comprising” indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements and / or components.

[0037] In the scenario of high-altitude power operations, wind speed has a great impact on whether the operators can work safely. At present, relevant technologies can detect wind speed in a variety of ways. For example, before the operators of the power company perform high-altitude operations, they can refer to the wind speed data in the weather forecast information provided by the meteorological department to decide whether to operate. These wind speed data can only provide an approximate wind speed range with limited accuracy. For another example, it can be determined whether the operation can be carried out under the current wind speed based on the fixed safety wind speed threshold stipulated by the state or industry and the real-time wind speed monitoring data.

[0038] None of the above methods can predict the wind speed changes in the future. When the wind speed suddenly changes during the operation, the operators will not be able to respond in time, which increases the risk of the operation.

[0039] Based on the above problems, the embodiments of the present disclosure provide a wind speed warning method. When the trained wind speed prediction model outputs the predicted wind speed result of the high-altitude work area, the safety wind speed threshold corresponding to the high-altitude work area can be dynamically and intelligently adjusted in combination with the requirements of the high-altitude work, thereby ensuring that the setting of the safety wind speed threshold is both scientific and in line with the actual working environment, while also enhancing the working safety of the operators.

[0040] The wind speed warning method provided by the embodiment of the present disclosure is exemplarily described below:

[0041] The wind speed warning method provided by the present disclosure can be applied to electronic devices.

[0042] In some embodiments, the electronic device may be a server, a terminal, or other electronic devices used for voice recognition, which is not limited in the present disclosure.

[0043] The server may be a single server, or a server cluster consisting of multiple servers. In some implementations, the server cluster may also be a distributed cluster. The present disclosure does not limit the specific implementation of the server.

[0044] The terminal may be a mobile phone, tablet computer, desktop, laptop, handheld computer, notebook computer, ultra-mobile personal computer (UMPC), netbook, cellular phone, personal digital assistant (PDA), augmented reality (AR) and virtual reality (VR) devices, etc., which can install and use content community applications (such as Kuaishou). The present disclosure does not impose any special restrictions on the specific form of the electronic device. It can interact with the user through one or more methods such as keyboard, touchpad, touch screen, remote control, voice interaction or handwriting device.

[0045] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0046] like Figure 1 As shown, when the wind speed warning method is applied to an electronic device, the wind speed warning method may include:

[0047] Step 101: Obtain geographical environment parameters of the altitude operation area.

[0048] Among them, the geographical environment parameters include the longitude and latitude of the climbing operation area, air pressure data, and meteorological data within a preset time period.

[0049] In some examples, the electronic device can obtain the geographical environment parameters of the climbing operation area through a variety of different types of sensors. For example, a global positioning system (GPS) device can be used to select a suitable location in the climbing operation area for positioning measurement to obtain the latitude coordinates of the climbing operation area. In the climbing operation area, a barometer is used to measure the air pressure data of the climbing operation area. Meteorological data within a preset time period can also be obtained through meteorological pipe-side equipment, such as temperature, humidity, wind speed, wind direction, precipitation, etc. It can be understood that the meteorological data within the preset event segment can be shown in a variety of forms, such as statistical charts, data maps, etc.

[0050] By obtaining the geographical environment parameters of the climbing operation area, the subsequent predicted wind speed results can be more targeted and accurate.

[0051] Step 102: Input the geographical environment parameters of the climbing operation area into the trained wind speed prediction model, and output the predicted wind speed result within a preset time.

[0052] Among them, the trained wind speed prediction model is a time series model or a deep learning model.

[0053] The electronic device can use the geographical environment parameters of the climbing operation area as the input of the trained wind speed prediction model, and the trained wind speed prediction model outputs the predicted wind speed result within a preset time in the climbing operation area. The predicted wind speed result within the preset time can be a short-term prediction result or a long-term prediction result.

[0054] In some examples, after obtaining the geographical environment parameters of the climbing work area, the geographical environment parameters of the climbing work area can be encoded to obtain vector data of preset dimensions, and the vector data of preset dimensions can be input into a trained wind speed prediction model to output the predicted wind speed results within a preset time.

[0055] For example, the trained wind speed prediction model can be a Long Short-Term Memory (LSTM) network or a Support Vector Regression (SVR).

[0056] The change of wind speed is affected by many factors, such as atmospheric circulation, topography, seasonal changes, day and night alternation, etc., so a simple linear model cannot accurately describe the correlation between wind speed and various factors. The deep learning model is good at processing the nonlinear relationship between data. Therefore, this application uses a deep learning model to predict wind speed to improve the accuracy of prediction. In addition, a time series model can be used to learn the dependence and dynamic change law of wind speed on different time scales, so as to accurately describe the relationship between wind speed and time. The time series model can be used to derive the long-term trend and seasonal pattern of wind speed data.

[0057] Step 103, deriving a safe wind speed threshold value based on the predicted wind speed result within a preset time, the climbing operation data and the meteorological data within a preset time period.

[0058] Among them, the high-altitude operation data includes operation equipment data.

[0059] When the type of climbing operation (for example, line maintenance, equipment installation, etc.) is different, the corresponding climbing operation data is also different. Therefore, after obtaining the predicted wind speed result within the preset time of the climbing operation area, you can continue to refer to the climbing operation data and the meteorological data within the preset time period to obtain the safe wind speed threshold. Among them, the climbing operation data may include the operation height, operation equipment data, the urgency of the operation task, etc. The operation equipment data may include operation equipment parameters, performance parameters of the operation equipment (for example, maximum working height, carrying capacity, working radius, etc.), and safety parameters of the operation equipment (for example, maximum safe wind speed, safe braking distance, equipment stability index, etc.).

[0060] In some examples, the process of deriving a safe wind speed threshold based on the predicted wind speed results within a preset time, the climbing operation data, and the meteorological data within a preset time period can be to determine the safe wind speed threshold based on the predicted wind speed results within a preset time, the working height in the climbing operation requirements, the working equipment data, the urgency of the working task, and the meteorological data within a preset time period.

[0061] According to the predicted wind speed results within the preset time, the working height in the climbing work requirements, the working equipment data, the urgency of the working task, and the meteorological data within the preset time period, the safe wind speed threshold can be determined by adjusting the predicted wind speed results within the preset time according to the working height, the working equipment data, the urgency of the working task, and the meteorological data within the preset time period to obtain the safe wind speed threshold. For example, if the working height is high and the equipment requirements in the working equipment data are high, the predicted wind speed results will be adjusted down to obtain the safe wind speed threshold. If the working height is low and the equipment requirements in the working equipment data are low, the predicted wind speed results will be adjusted up to obtain the safe wind speed threshold.

[0062] The safety wind speed threshold may include a maximum wind speed threshold and a warning wind speed threshold. The maximum wind speed threshold is used to instruct operators to stop working when the actual wind speed reaches the maximum wind speed threshold. The warning wind speed threshold is used to instruct operators to take additional safety measures before continuing working when the actual wind speed reaches the warning wind speed threshold.

[0063] Step 104: Based on the relationship between the predicted wind speed result within a preset time and the safe wind speed threshold, determine the wind speed warning information, output and display the wind speed warning information.

[0064] In some examples, after obtaining the predicted wind speed results within a preset time, the relationship between the predicted wind speed results at each predicted time point within the preset time and the safety wind speed threshold can be determined in turn, thereby obtaining wind speed warning information based on the relationship between the predicted wind speed results at each predicted time point and the safety wind speed threshold.

[0065] Among them, determining the size relationship between the predicted wind speed result of each predicted time point within the preset time and the safe wind speed threshold may include that the predicted wind speed value at the predicted time point is less than or equal to the safe wind speed threshold and the predicted wind speed value at the predicted time point is greater than the safe wind speed threshold.

[0066] In some examples, based on the magnitude relationship between the predicted wind speed result at each predicted time point and the safe wind speed threshold, the wind speed warning information may include: when the predicted wind speed value at the predicted time point is less than or equal to the safe wind speed threshold, the wind speed state at the time point may be marked as "safe". When the predicted wind speed value at the predicted time point is greater than the safe wind speed threshold, the wind speed state at the time point may be marked as "dangerous". When the wind speed state at the predicted time point is marked as "dangerous", the wind speed warning information corresponding to the predicted time point may be generated according to the degree of danger.

[0067] The wind speed warning information may include the risk level, warning time, wind speed value, and protective measures corresponding to the risk level. The warning time is the predicted time point when the wind speed result is marked as "dangerous". The wind speed value is the predicted wind speed result corresponding to the warning time.

[0068] For example, the risk level may include mild, moderate, and severe. The protective measures corresponding to the risk level may include suspending the high-altitude operation, strengthening the fixing of equipment, evacuating personnel, adopting additional safety protection measures, etc.

[0069] In addition, after the electronic device outputs the predicted wind speed result, the operation time and safe operation window period can be reasonably arranged according to the predicted wind speed result, so as to maximize the guarantee that the climbing operation is carried out under suitable climatic conditions, arrange the climbing operation time more scientifically, effectively avoid unnecessary downtime and waiting, and greatly improve the operation efficiency. Exemplarily, the predicted wind speed result can be shown in the form of a wind speed prediction curve (or a wind speed change curve), and can also be shown in other ways, which is not limited by the present disclosure.

[0070] In some examples, after the electronic device determines the wind speed warning information, it can output and display the wind speed warning information.

[0071] Exemplarily, the wind speed warning information may be output in the form of text, charts, sound and light signals, etc. When the wind speed drops to a safe range, the wind speed warning information will no longer be displayed.

[0072] After the electronic device outputs the wind speed warning information, relevant personnel can disseminate the wind speed warning information to the corresponding high-altitude workers through various channels (for example, text messages, e-mails, alarm systems at the work site, mobile applications), so that the high-altitude workers can obtain the wind speed warning information clearly and intuitively.

[0073] This application dynamically manages the predicted wind speed results and the safe wind speed threshold through the above-mentioned method, thereby improving the overall safety level of power climbing operations, reducing accidents caused by wind speed, and also improving work efficiency, ensuring that operations are carried out as efficiently as possible under the premise of safety.

[0074] Optional, such as Figure 2 As shown, before the geographical environment parameters of the climbing operation area are input into the trained wind speed prediction model in step 102, the method further includes:

[0075] Step 201: Obtain historical meteorological data and historical climbing operation data in the climbing operation area.

[0076] Among them, historical meteorological data include historical wind speed data, historical wind direction data, historical air pressure data, historical temperature data and historical humidity data; historical climbing operation data include historical height data, historical time data and historical location data.

[0077] In some examples, multiple wind speed sensors can be deployed at different heights in the climbing operation area, and the wind speed sensors are used to detect wind speed data in real time. Historical wind speed data can be obtained through wind speed sensors and can also be obtained through weather forecast data.

[0078] Step 202: extract features from historical meteorological data and historical altitude operation data, and output feature extraction results.

[0079] In some examples, feature extraction is performed on historical meteorological data and historical altitude operation data, and the process of outputting feature extraction results may include performing feature extraction on historical wind speed data, historical wind direction data, historical temperature data, historical humidity data, historical altitude data, historical time data, and historical location data, and outputting feature extraction results.

[0080] The feature extraction result includes at least one of a time series feature, a geographical environment feature, a meteorological condition feature, and a time feature. The time series feature may be a historical wind speed value, a historical wind speed change rate, a historical wind speed periodicity pattern, etc. The geographical environment feature may be a wind speed corresponding to different terrains, a wind speed corresponding to different altitudes, a wind speed corresponding to vegetation coverage, etc. The meteorological condition feature may be a wind speed corresponding to different temperatures, a wind speed corresponding to different humidity, a wind speed corresponding to different air pressures, etc. The time feature may be a wind speed corresponding to each hour, a wind speed corresponding to different seasons, etc.

[0081] After extracting features from historical meteorological data and historical altitude operation data, the feature extraction results can be represented and encoded in an appropriate manner to facilitate processing by the wind speed prediction model.

[0082] By extracting features from historical meteorological data and historical climbing operation data, and using the feature extraction results to train the wind speed prediction model, the generalization ability of the model can be optimized, so that the prediction accuracy of the trained wind speed prediction model is more accurate.

[0083] Step 203: train the wind speed prediction model using the feature extraction result to obtain a trained wind speed prediction model.

[0084] After obtaining the feature extraction result, the feature extraction result can be used as input data of the wind speed prediction model. The wind speed prediction model completes the training by learning the relationship between the feature extraction result and the wind speed to obtain a trained wind speed prediction model.

[0085] In some examples, after the feature extraction result is input into the wind speed prediction model, the wind speed prediction model may output an initial wind speed prediction result. Based on the initial wind speed prediction result and the actual wind speed result, the parameters of the wind speed prediction model are iteratively updated to obtain a trained wind speed prediction model.

[0086] In other examples, the process of iteratively updating the parameters of the wind speed prediction model based on the initial wind speed prediction results and the actual wind speed results to obtain a trained wind speed prediction model can be: the electronic device determines the loss value between the initial wind speed prediction results and the actual wind speed results based on a loss function; and then iteratively updates the parameters of the wind speed prediction model based on the loss value to obtain a trained wind speed prediction model.

[0087] Specifically, there may be multiple groups of initial wind speed prediction results and actual wind speed results. The electronic device may obtain a loss value based on the difference between one group of initial wind speed prediction results and actual wind speed results among the multiple groups of initial wind speed prediction results and actual wind speed results, and then adjust the parameters of the wind speed prediction model based on the loss value; and then continue to train the above wind speed prediction model with the next group of initial wind speed prediction results and actual wind speed results until the loss value is less than a preset threshold value, so as to obtain a trained wind speed prediction model.

[0088] By obtaining historical meteorological data and high-altitude operation data to train the wind speed prediction model, the trained wind speed prediction model can have a higher prediction accuracy and significantly improve the prediction ability of short-term and long-term wind speed changes in the high-altitude operation area.

[0089] Optional, such as Figure 3 As shown, the method also includes:

[0090] Step 301: Obtain actual wind speed results.

[0091] In some examples, the electronic device obtains actual wind speed results in the aerial work area.

[0092] Step 302: iteratively update the parameters of the trained wind speed prediction model based on the predicted wind speed results and the actual wind speed results within a preset time.

[0093] In some examples, after using the trained wind speed prediction model to output the predicted wind speed results within a preset time period, the actual wind speed results can be synchronized, and then the actual wind speed results and the predicted wind speed results can be used as a set of training data to continue training the trained wind speed prediction model to continuously update the parameters of the trained wind speed prediction model.

[0094] Optional, such as Figure 4 As shown, step 104 may include:

[0095] Step 401: When the predicted wind speed result at a first preset time point within a preset time is greater than a safe wind speed threshold, determine that the wind speed state at the first preset time point is a first state.

[0096] Since the predicted wind speed result at the first preset time point is greater than the safe wind speed threshold, it means that the predicted wind speed result at the first preset time point is not suitable for workers to work in the high-altitude operation area, so the wind speed state at the first preset time point can be set to the first state. Exemplarily, the first state can be a "dangerous state".

[0097] Step 402: output wind speed warning information according to the wind speed state at the first preset time point.

[0098] The wind speed warning information includes the risk level, warning time, wind speed value and protective measures corresponding to the risk level, the warning time is the first preset time point, and the wind speed value is the predicted wind speed result at the first preset time point. The risk level can be related to the wind speed state at the preset time point.

[0099] Within the preset time, if the predicted wind speed result output by the trained wind speed prediction model is greater than the preset safety wind speed threshold, the electronic device will display a wind speed warning message on the relevant display interface, which can prompt the operator to stop working.

[0100] Exemplarily, the electronic device may output wind speed warning information according to the wind speed status at a first preset time point, that is, instruct the operator to stop working at the first preset time point through the wind speed warning information such as "dangerous status".

[0101] Of course, it is understandable that the wind speed state at other preset time points within the preset time may also be the first state or the second state. For example, the second state may be a "normal state", and the second state may be used to indicate that the operator is working normally. The present application does not limit the wind speed state at other preset time points within the preset time, and the specific application shall be subject to actual use.

[0102] In the first example, the risk level may also be determined based on a safe wind speed threshold. The present application does not limit the method for determining the risk level.

[0103] If the predicted wind speed result output by the trained wind speed prediction model is less than or equal to the preset safety wind speed threshold, the electronic device may also display wind speed warning information on the relevant display interface. The wind speed warning information may prompt the operator to continue the operation. If the predicted wind speed result output by the trained wind speed prediction model is less than or equal to the preset safety wind speed threshold, the electronic device may not display the wind speed warning information, and the present disclosure does not limit this.

[0104] When the predicted wind speed exceeds the safe wind speed threshold, the wind speed warning information is set to instruct the operators to take corresponding protective measures in time to avoid safety accidents caused by severe wind conditions, enhance the operators' active prevention capabilities against operational risks, and improve operational safety.

[0105] Optional, such as Figure 5 As shown, before step 202, the method further includes:

[0106] Step 501: pre-process historical meteorological data and historical climbing operation data to obtain processed historical meteorological data and climbing operation data.

[0107] Among them, preprocessing includes data cleaning and / or data normalization.

[0108] In some examples, after obtaining historical meteorological data and climbing operation data of the climbing operation area, data cleaning can be performed on the historical meteorological data and the climbing operation data to check for missing values, abnormal values, and erroneous data in the historical meteorological data and the climbing operation data.

[0109] When historical meteorological data and altitude operation data include missing values, appropriate methods can be used to fill them in according to the characteristics of the data, such as mean filling, median filling, or using regression model prediction filling.

[0110] When historical meteorological data and altitude operation data include abnormal values, we can first determine the cause of the abnormal value. If it is caused by input errors or measurement errors, we should correct or delete the data. If the abnormal value is a real extreme situation, we should continue to analyze the specific impact of the data on wind speed.

[0111] In addition, after cleaning the data, in order to make data of different orders of magnitude comparable, the data can be standardized or normalized. Exemplarily, Z-score standardization, Min-Max normalization, etc. can be used for processing.

[0112] Combination of the above Figure 1-Figure 5 The method provided by the embodiment of the present disclosure is described in detail. In order to achieve the above functions, the wind speed warning device includes hardware structures and / or software modules corresponding to the execution of each function, and these hardware structures and / or software modules corresponding to the execution of each function can constitute a wind speed warning device. It should be easily appreciated by those skilled in the art that, in combination with the algorithm steps of each example described in the embodiments disclosed herein, the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present disclosure.

[0113] The embodiments of the present disclosure may divide the wind speed warning device into functional modules according to the above method examples. For example, the wind speed warning device may divide each functional module corresponding to each function, or may integrate two or more functions into one processing module. The above integrated modules may be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiments of the present disclosure is schematic and is only a logical functional division. There may be other division methods in actual implementation.

[0114] The following, combined Figure 6The wind speed warning device provided by the embodiment of the present disclosure is described in detail. It should be understood that the description of the device embodiment corresponds to the description of the method embodiment, so the contents not described in detail can be referred to the method embodiment above, and will not be repeated here for the sake of brevity.

[0115] Figure 6 1 is a logical structure diagram of a wind speed warning device according to an exemplary embodiment. Figure 6 The wind speed warning device includes: an acquisition module 610, a prediction module 620, a processing module 630 and a display module 640.

[0116] The acquisition module 610 is configured to acquire geographical environment parameters of the climbing operation area, where the geographical environment parameters include the longitude and latitude of the climbing operation area, air pressure data, and meteorological data within a preset time period. Figure 1 , the prediction module 620 can be used to execute step 101.

[0117] The prediction module 620 is configured to input the geographical environment parameters of the climbing operation area into a trained wind speed prediction model and output the predicted wind speed result within a preset time; the trained wind speed prediction model is a time series model or a deep learning model; for example, combined with Figure 1 , the prediction module 620 can be used to perform step 102.

[0118] The processing module 630 is configured to obtain a safe wind speed threshold according to the predicted wind speed result within a preset time, the climbing operation data and the meteorological data within a preset time period; the climbing operation data includes the operation equipment data; for example, in combination with Figure 1 , the processing module 630 can be used to execute step 103.

[0119] The display module 640 is further configured to determine the wind speed warning information based on the magnitude relationship between the predicted wind speed result within a preset time and the safe wind speed threshold, and output and display the wind speed warning information; for example, in combination with Figure 1 , the display module 640 can be used to execute step 104.

[0120] Optionally, the acquisition module 610 is configured to acquire historical meteorological data and historical climbing operation data of the climbing operation area; the historical meteorological data includes historical wind speed data; for example, combined with Figure 2 , the acquisition module 610 can be used to execute step 201.

[0121] The processing module 630 is further configured to extract features from the historical meteorological data and the historical climbing operation data, and output the feature extraction results; train the wind speed prediction model using the feature extraction results to obtain a trained wind speed prediction model; for example, combining Figure 2, the processing module 630 can be used to execute step 202 and step 203.

[0122] Optionally, the acquisition module 640 is further configured to obtain an actual wind speed result. Figure 3 , the processing module 640 can be used to execute step 301.

[0123] The processing module 630 is further configured to iteratively update the parameters of the trained wind speed prediction model based on the predicted wind speed results and the actual wind speed results within a preset time. Figure 3 , the processing module 640 can be used to execute step 302.

[0124] Optionally, the processing module 630 is further configured to determine that the wind speed state at the first preset time point is the first state when the predicted wind speed result at the first preset time point within the preset time is greater than the safe wind speed threshold; output wind speed warning information according to the wind speed state at the first preset time point, the wind speed warning information including the risk level, warning time, wind speed value and protective measures corresponding to the risk level, the warning time is the first preset time point, and the wind speed value is the predicted wind speed result at the first preset time point. For example, combined with Figure 4 , the processing module 640 can be used to execute step 401 and step 402.

[0125] Optionally, the processing module 630 is further configured to pre-process the historical meteorological data and the historical climbing operation data to obtain processed historical meteorological data and climbing operation data, wherein the pre-processing includes data cleaning and / or data normalization. Figure 5 , the processing module 640 can be used to execute step 501.

[0126] Optionally, the risk level is determined based on a safe wind speed threshold.

[0127] Of course, the path selection device provided by the embodiment of the present disclosure includes but is not limited to the above modules, for example, the path selection device may also include a storage module 650. The storage module 650 may be used to store the program code of the write path selection device, and may also be used to store data generated by the write path selection device during operation, such as data in a write request, etc.

[0128] Figure 7 FIG. 1 shows a possible structural diagram of the electronic device involved in the above embodiment. Figure 7 As shown, the electronic device 700 includes a processor 701 and a memory 702 .

[0129] Understandably, Figure 7The electronic device 700 shown can implement all functions of the above-mentioned speech recognition model generation method. The functions of each module in the above-mentioned speech recognition model generation device can be implemented in the processor 701 of the electronic device 700. The storage module of the speech recognition model generation device is equivalent to the memory 702 of the electronic device 700.

[0130] The processor 701 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 70 may include an application processor (AP), a modem processor, a graphics processor (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Different processing units may be independent devices or integrated in one or more processors.

[0131] The memory 702 may include one or more computer-readable storage media, which may be non-transitory. The memory 702 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 702 is used to store at least one instruction, which is used to be executed by the processor 701 to implement the speech recognition model generation method provided by the embodiment of the method of the present disclosure.

[0132] In some embodiments, the electronic device 700 may further optionally include: a peripheral device interface 703 and at least one peripheral device. The processor 701, the memory 702 and the peripheral device interface 703 may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface 703 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 704, a touch display screen 705, a camera 706, an audio circuit 707, a positioning component 708 and a power supply 707.

[0133] The peripheral device interface 703 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 701 and the memory 702. In some embodiments, the processor 701, the memory 702, and the peripheral device interface 703 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 701, the memory 702, and the peripheral device interface 703 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0134] The radio frequency circuit 704 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 704 communicates with communication networks and other communication devices through electromagnetic signals. The radio frequency circuit 704 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the radio frequency circuit 404 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The radio frequency circuit 704 can communicate with other speech recognition model generation devices through at least one wireless communication protocol. The wireless communication protocol includes but is not limited to: a metropolitan area network, various generations of mobile communication networks (2G, 3G, 4G and 6G), a wireless local area network and / or a Wi-Fi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 704 may also include circuits related to NFC (Near Field Communication), which is not limited in the present disclosure.

[0135] The display screen 705 is used to display the UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 705 is a touch display screen, the display screen 705 also has the ability to collect touch signals on the surface or above the surface of the display screen 705. The touch signal can be input to the processor 701 as a control signal for processing. At this time, the display screen 705 can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, the display screen 705 can be one, and the front panel of the electronic device 700 is set; the display screen 705 can be made of materials such as LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), etc.

[0136] The camera assembly 706 is used to collect images or videos. Optionally, the camera assembly 706 includes a front camera and a rear camera. Usually, the front camera is set on the front panel of the speech recognition model generation device, and the rear camera is set on the back of the speech recognition model generation device. The audio circuit 707 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals and input them into the processor 701 for processing, or input them into the radio frequency circuit 404 to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there can be multiple microphones, which are respectively arranged at different parts of the electronic device 700. The microphone can also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor 701 or the radio frequency circuit 704 into sound waves. The speaker can be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into a sound wave audible to humans, but also convert the electrical signal into a sound wave that cannot be heard by humans for purposes such as ranging. In some embodiments, the audio circuit 707 may also include a headphone jack.

[0137] Positioning component 708 is used to locate the current geographic location of electronic device 700 to implement navigation or LBS (Location Based Service). Positioning component 708 can be a positioning component based on the US GPS (Global Positioning System), China's Beidou system, Russia's Grenas system or the European Union's Galileo system.

[0138] The power supply 709 is used to power various components in the electronic device 700. The power supply 707 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 709 includes a rechargeable battery, the rechargeable battery can support wired charging or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0139] In some embodiments, the electronic device 700 further includes one or more sensors 710. The one or more sensors 710 include, but are not limited to: an acceleration sensor, a gyroscope sensor, a pressure sensor, a fingerprint sensor, an optical sensor, and a proximity sensor.

[0140] The acceleration sensor can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established with the electronic device 700. The gyroscope sensor can detect the body direction and rotation angle of the electronic device 700, and the gyroscope sensor can cooperate with the acceleration sensor to collect the user's 3D actions on the electronic device 700. The pressure sensor can be set on the side frame of the electronic device 700 and / or the lower layer of the touch display screen 706. When the pressure sensor is set on the side frame of the electronic device 700, the user's holding signal of the electronic device 700 can be detected. The fingerprint sensor is used to collect the user's fingerprint. The optical sensor is used to collect the ambient light intensity. The proximity sensor, also known as the distance sensor, is usually set on the front panel of the electronic device 700. The proximity sensor is used to collect the distance between the user and the front of the electronic device 700.

[0141] The present disclosure also provides a computer-readable storage medium having instructions stored thereon. When the instructions in the storage medium are executed by a processor of a speech recognition model generating device, the speech recognition model generating device is enabled to execute the speech recognition model generating method provided by the present disclosure.

[0142] The embodiment of the present disclosure also provides a computer program product containing instructions, which, when executed on a speech recognition model generation device, enables the speech recognition model generation device to execute the speech recognition model generation method provided by the present disclosure.

[0143] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0144] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A wind speed early warning method, characterized in that: The method comprises: Acquire geographical environment parameters of the climbing operation area, wherein the geographical environment parameters include the longitude and latitude of the climbing operation area, air pressure data, and meteorological data within a preset time period; Inputting the geographical environment parameters of the climbing operation area into the trained wind speed prediction model, and outputting the predicted wind speed result within a preset time; the trained wind speed prediction model is a time series model or a deep learning model; A safe wind speed threshold is obtained based on the predicted wind speed result within the preset time, the climbing operation data and the meteorological data within the preset time period; the climbing operation data includes the operation equipment data; Based on the magnitude relationship between the predicted wind speed result within the preset time and the safety wind speed threshold, wind speed warning information is determined, and the wind speed warning information is output and displayed.

2. The method according to claim 1, characterized in that Before inputting the geographical environment parameters of the climbing operation area into the trained wind speed prediction model, the method further includes: Acquire historical meteorological data and historical climbing operation data of the climbing operation area; the historical meteorological data includes historical wind speed data; Performing feature extraction on the historical meteorological data and the historical altitude operation data, and outputting feature extraction results; The wind speed prediction model is trained using the feature extraction result to obtain the trained wind speed prediction model.

3. The method according to claim 1 or 2, characterized in that: The method further comprises: Get the actual wind speed result; Based on the predicted wind speed result within the preset time and the actual wind speed result, the parameters of the trained wind speed prediction model are iteratively updated.

4. The method according to claim 1, characterized in that: The determining of wind speed warning information based on the magnitude relationship between the predicted wind speed result within the preset time and the safe wind speed threshold includes: When the predicted wind speed result at a first preset time point within the preset time is greater than the safe wind speed threshold, determining that the wind speed state at the first preset time point is a first state; According to the wind speed status at the first preset time point, wind speed warning information is output, and the wind speed warning information includes risk level, warning time, wind speed value and protective measures corresponding to the risk level. The warning time is the first preset time point, and the wind speed value is the predicted wind speed result at the first preset time point.

5. The method according to claim 2, characterized in that: Before extracting features from the historical meteorological data and the historical climbing operation data, the method further includes: The historical meteorological data and the historical climbing operation data are preprocessed to obtain processed historical meteorological data and climbing operation data, wherein the preprocessing includes data cleaning and / or data normalization.

6. The method according to claim 4, characterized in that The risk level is determined according to the safe wind speed threshold.

7. A wind speed warning device, characterized in that: The device comprises: The prediction module is configured to use the trained wind speed prediction model to predict the wind speed in the climbing operation area and output the predicted wind speed result within a preset time; A processing module is configured to obtain a safe wind speed threshold according to the predicted wind speed result within the preset time and the requirements of the climbing operation; The processing module is further configured to determine wind speed warning information based on the magnitude relationship between the predicted wind speed result within the preset time and the safe wind speed threshold; The display module is configured to output and display wind speed warning information.

8. The wind speed warning device according to claim 7, characterized in that: The device also includes: An acquisition module is configured to acquire historical meteorological data and climbing operation data of the climbing operation area; The processing module is further configured to perform feature extraction on the historical meteorological data and the altitude operation data, and output feature extraction results; The processing module is further configured to train a wind speed prediction model using the feature extraction result to obtain the trained wind speed prediction model.

9. An electronic device, characterized in that: include: A touch screen, comprising a touch sensor and a display screen; one or more processors; Memory; Wherein, one or more computer programs are stored in the memory, and the one or more computer programs include instructions. When the instructions are executed by the electronic device, the electronic device executes a wind speed warning method as described in any one of claims 1 to 6.

10. A computer-readable storage medium having computer program instructions stored thereon; characterized in that: When the computer program instructions are executed by an electronic device, the electronic device implements a wind speed warning method as claimed in any one of claims 1 to 6.

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