Early warning method and device for substation operation risk

Abstract

The invention discloses an early warning method and device for substation operation risks, and relates to the technical field of electric power, in particular to the technical field of substations. The method comprises the following steps: acquiring data of N transformer substations; a target three-dimensional model of the transformer substation is determined according to the transformer substation data, the target three-dimensional model of the transformer substation changes along with changes of the transformer substation data, and the target three-dimensional model is used for representing a three-dimensional scene when an operator carries out construction operation on the transformer substation; determining operation risk information of the transformer substation according to the target three-dimensional model; and generating early warning information for the transformer substation according to the operation risk information of the transformer substation. The technical problem that in the prior art, a single static data source is used for monitoring the operation risk of the transformer substation, and information transmission and response in the early warning aspect are not timely, so that the risk monitoring effect is poor is solved.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, in particular to the field of substation technology, and specifically to an early warning method and device for substation operation risks. Background Art

[0002] With the development of power systems and the increasing complexity of substation operations, traditional risk monitoring methods face a series of challenges and limitations. Traditional risk monitoring methods mainly rely on limited data and simple risk assessment models, which cannot effectively deal with the complex scenarios and diverse risk factors in substation operations. At present, there are several major problems in substation operation risk monitoring:

[0003] First, most traditional monitoring methods are based on a single data source, such as manual observation or a limited number of sensor data, which cannot fully capture the true situation of the work site. This limitation leads to insufficient accuracy and comprehensiveness of the monitoring results, making it difficult to provide timely and effective warnings and processing of potential risks in complex working environments. Second, risk assessment models in existing technologies are usually based on static data and simple statistical analysis, ignoring the impact of temporal dynamics and spatial correlation on risks, which makes it impossible to accurately predict and identify specific risk events in substation operations, affecting the scientificity and effectiveness of safety management and decision-making. In addition, traditional technologies have problems with long information transmission and response time delays in risk warning and decision support. The monitoring system lacks real-time and high efficiency, and cannot respond to risk changes at the work site in a timely manner, affecting the effectiveness of emergency response and accident prevention.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The present application provides an early warning method and device for substation operation risks, so as to at least solve the technical problems in the prior art of using a single static data source for substation operation risk monitoring and untimely information transmission and response in early warning, resulting in poor risk monitoring effect.

[0006] In order to achieve the above-mentioned purpose, according to one aspect of the present application, there is provided an early warning method for substation operation risks, comprising: obtaining N substation data, wherein N is an integer greater than 1, and the N substation data at least include location information and behavior information of substation operators, location information and status information of substation equipment, and location information and usage status information of operators' operating tools; determining a target three-dimensional model of the substation based on the substation data, wherein the target three-dimensional model of the substation changes with changes in the substation data, and the target three-dimensional model is used to represent the three-dimensional scene of the operators when performing construction work on the substation; determining the operation risk information of the substation based on the target three-dimensional model; and generating early warning information for the substation based on the operation risk information of the substation.

[0007] Optionally, the target three-dimensional model of the substation is determined based on the substation data, including: adopting a time synchronization strategy, a space synchronization strategy and an information complementarity strategy to fuse N substation data to obtain work site information, wherein the time synchronization strategy is used to time align the N substation data based on the timestamp information of each substation data; the space synchronization strategy is used to convert the N substation data into the same spatial coordinate system; the information complementarity strategy is used to integrate the substation data with a preset logical relationship; and the target three-dimensional model of the substation is determined based on the work site information.

[0008] Optionally, determining a target three-dimensional model of the substation based on the work site information includes: obtaining device postures of multiple data acquisition devices, wherein the data acquisition devices are used to collect substation data, and the multiple data acquisition devices include lidar, cameras, and inertial sensors; fusing the device postures of the multiple data acquisition devices to obtain a target posture; determining the target three-dimensional model of the substation based on the work site information and the target posture.

[0009] Optionally, a target three-dimensional model of the substation is determined based on the work site information and the target posture, including: determining a depth map of the work site of the substation based on the work site information and the target posture; determining three-dimensional point cloud data of the work site based on the depth map and an image of the work site acquired by a camera; performing resolution enhancement processing on the three-dimensional point cloud data to obtain target three-dimensional point cloud data; and determining a target three-dimensional model based on the target three-dimensional point cloud data.

[0010] Optionally, the substation operation risk information is determined based on the target three-dimensional model, and the early warning method for the substation operation risk also includes: determining the target data set based on N substation data, target posture, environmental parameters of the operation site, and operation data of the operators, wherein the operation data characterizes the operation behavior of the operators; obtaining association rules, which are used to determine the association characteristics between preset risk events and N substation data; determining the substation operation risk information based on the target data set, the association rules and the target three-dimensional model.

[0011] Optionally, determining substation operation risk information based on the target data set, association rules and target three-dimensional model also includes: determining a first type of risk factor based on the target data set and the target three-dimensional model, wherein the first type of risk factor is used to characterize abnormal behavior factors of equipment in the substation and abnormal behavior factors of operators when operating the substation; determining a second type of risk factor based on the association rules and the target three-dimensional model, wherein the second type of risk factor is used to characterize potential risk factors for preset risk events occurring at the operating site; and using the first type of risk factor and the second type of risk factor as substation operation risk information.

[0012] Optionally, early warning information for the substation is generated based on the substation operation risk information, including: obtaining weights and influence values ​​corresponding to the first type of risk factors and the second type of risk factors, respectively, wherein the weights and influence values ​​change with the change of substation data, and the influence values ​​are used to characterize the degree of influence of the first type of risk factors or the second type of risk factors on abnormal events occurring in the substation; determining the risk levels corresponding to the first type of risk factors and the second type of risk factors, respectively, based on the weights and influence values; and generating early warning information for the substation based on the risk levels and preset early warning thresholds.

[0013] According to another aspect of the present application, there is also provided an early warning device for substation operation risks, including: a first acquisition unit, which acquires N substation data, wherein N is an integer greater than 1, and the N substation data at least include location information and behavior information of substation operators, location information and status information of substation equipment, and location information and usage status information of operators' operating tools; a first determination unit, which determines a target three-dimensional model of the substation based on the substation data, wherein the target three-dimensional model of the substation changes with changes in the substation data, and the target three-dimensional model is used to characterize the three-dimensional scene of the operators when performing construction work on the substation; a second determination unit, which determines the operation risk information of the substation based on the target three-dimensional model; and a first generation unit, which generates early warning information for the substation based on the operation risk information of the substation.

[0014] According to another aspect of the present application, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed, the device where the computer-readable storage medium is located executes the above-mentioned early warning method for substation operation risks.

[0015] According to another aspect of the present application, an electronic device is also provided, wherein the electronic device includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above-mentioned early warning method for substation operation risks.

[0016] In an embodiment of the present application, a three-dimensional model of the substation operation site is constructed by acquiring data from multiple substations, wherein the three-dimensional model is updated in real time as the substation data changes, and then the operation risk information of the substation is determined based on the three-dimensional model. Finally, a corresponding risk warning is generated based on the operation risk information, thereby achieving the purpose of real-time monitoring of the substation operation site and timely risk warning, thereby achieving the technical effect of improving the risk monitoring effect of the substation, and further solving the technical problems in the prior art of poor risk monitoring effect due to the use of a single static data source for substation operation risk monitoring and untimely information transmission and response in the warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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:

[0018] Figure 1 is a flow chart of an optional early warning method for substation operation risks according to an embodiment of the present application;

[0019] Figure 2 It is a schematic diagram of an optional early warning device for substation operation risks according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] 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.

[0021] 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.

[0022] It should also be noted that the information and data collected by this application are authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of the relevant regions, take necessary confidentiality measures, do not violate public order and good customs, and provide corresponding operation portals for users to choose to authorize or refuse. For example, an interface is set up between this system and relevant users or institutions. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or institution through the interface, and obtain relevant information after receiving the consent information fed back by the aforementioned user or institution.

[0023] According to an embodiment of the present application, an embodiment of an early warning method for substation operation risks is provided. It should be noted that the steps shown in the flowchart of 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 can be executed in an order different from that shown here.

[0024] It should be noted that an intelligent risk warning system is used as the execution subject of the early warning method for substation operation risk in the embodiment of the present application. It is understandable that the early warning method for substation operation risk provided in the embodiment of the present application can also be executed by other systems or devices, and the embodiment of the present application does not specifically limit this.

[0025] Figure 1 is a flow chart of an optional early warning method for substation operation risk according to an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0026] Step S101, obtaining data of N substations.

[0027] In step S101, N is an integer greater than 1, and the N substation data include at least location information and behavior information of substation operators, location information and status information of substation equipment, and location information and usage status information of operators' operating tools.

[0028] Optionally, the intelligent risk warning system can use Beidou high-precision positioning sensors, visual sensors, binocular vision sensors, and inertial sensors to collect data from the substation operation site. Users can install a variety of sensors for collecting data at appropriate locations according to the substation operation site conditions and calibrate them.

[0029] Optionally, the intelligent risk warning system can use wireless transmission technology to transmit the data collected by the sensor to the data processing center in real time, and establish a data storage system to store and back up the transmitted data.

[0030] Optionally, the intelligent risk warning system can perform preliminary processing on the collected substation data, including at least noise removal, data completion and data unified format conversion. For the noise removal operation, a filtering algorithm can be applied to remove the noise in the data, and for the data completion operation, an interpolation method or a prediction model can be used to complete the missing data.

[0031] Step S102: determining a target three-dimensional model of the substation according to the substation data.

[0032] In step S102, the target three-dimensional model of the substation changes with the change of the substation data. The target three-dimensional model is used to represent the three-dimensional scene when the operators perform construction work on the substation.

[0033] Optionally, when the substation data changes, the constructed target three-dimensional model will also change accordingly, and the target three-dimensional model changes in real time as the substation data changes.

[0034] Step S103, determining the operation risk information of the substation according to the target three-dimensional model.

[0035] Optionally, after constructing the target three-dimensional model, the intelligent risk warning system monitors the status of the entire substation in real time based on observing the target three-dimensional model, and determines the risk information of the substation in combination with the target three-dimensional model and related technologies.

[0036] Step S104: generating early warning information for the substation according to the operation risk information of the substation.

[0037] Optionally, when the operational risk information for the substation is generated, the intelligent risk warning system will promptly issue relevant warning information to remind the operator or manager to check and maintain in time. Among them, the intelligent risk warning system can display all risk conditions in the substation in the form of a visual page.

[0038] It can be seen from the contents of steps S101 to S104 that in the present application, firstly, N substation data are acquired, wherein N is an integer greater than 1, and the N substation data at least include the location information and behavior information of the substation operators, the location information and status information of the substation equipment, and the location information and usage status information of the operators' operating tools. Then, the target three-dimensional model of the substation is determined according to the substation data, wherein the target three-dimensional model of the substation changes with the change of the substation data, and the target three-dimensional model is used to characterize the three-dimensional scene of the operators when performing construction work on the substation. Then, the operation risk information of the substation is determined according to the target three-dimensional model, and finally, the early warning information for the substation is generated according to the operation risk information of the substation.

[0039] It can be seen from the above content that in an embodiment of the present application, a three-dimensional model of the substation operation site is constructed by acquiring data from multiple substations, wherein the three-dimensional model is updated in real time as the substation data changes, and then the operation risk information of the substation is determined based on the three-dimensional model. Finally, a corresponding risk warning method is generated based on the operation risk information, thereby achieving the purpose of real-time monitoring of the substation operation site and timely risk warning, thereby achieving the technical effect of improving the risk monitoring effect of the substation, and further solving the technical problem in the prior art of poor risk monitoring effect due to the use of a single static data source for substation operation risk monitoring and untimely information transmission and response in the warning.

[0040] In an optional embodiment, the intelligent risk warning system adopts a time synchronization strategy, a space synchronization strategy and an information complementarity strategy to fuse N substation data to obtain work site information, wherein the time synchronization strategy is used to time align the N substation data based on the timestamp information of each substation data; the space synchronization strategy is used to convert the N substation data into the same spatial coordinate system; the information complementarity strategy is used to integrate the substation data with a preset logical relationship, and then determine the target three-dimensional model of the substation based on the work site information.

[0041] Optionally, the intelligent risk warning system first aligns the timestamps of the data collected by different sensors, and then converts the data collected by different sensors into the same spatial coordinate system.

[0042] Optionally, the intelligent risk warning system uses a Bayesian estimation data fusion algorithm and a multi-sensor data fusion model in deep learning (such as a convolutional neural network) to fuse multi-source data.

[0043] From the above content, it can be seen that multi-source data are integrated through data fusion algorithms to form comprehensive work site information, and the accuracy and efficiency of data fusion are improved. At the same time, time synchronization, spatial alignment and information complementarity are considered in the fusion process, which improves the integrity and accuracy of the data.

[0044] In an optional embodiment, the intelligent risk warning system obtains the device postures of multiple data acquisition devices, where the data acquisition devices are used to collect substation data, and the multiple data acquisition devices include lidar, cameras, and inertial sensors. The device postures of the multiple data acquisition devices are then fused to obtain the target posture, and finally the target three-dimensional model of the substation is determined based on the work site information and the target posture.

[0045] Optionally, the intelligent risk warning system obtains the positions and postures of multiple data acquisition devices based on SLAM technology (Simultaneous Localization and Mapping), that is, the location information and posture information of the data acquisition devices.

[0046] Optionally, the position and posture of the laser radar are obtained by:

[0047] First, obtain the point cloud data P L , as shown in formula (1):

[0048] P L ={(x i ,y i , z i )|i=1,2...N (1)

[0049] Among them, (x i ,y i , z i ) represents the three-dimensional coordinates of each point obtained by the laser radar scan.

[0050] Then the pose T is obtained by the ICP algorithm. L , as shown in formula (2):

[0051] T L =ICP(P L )(2)

[0052] Among them, T L is the SLAM pose of the lidar, and ICP is the iterative closest point algorithm used for point cloud registration.

[0053] Optionally, the position and posture of a visual acquisition device (such as a camera) is obtained by:

[0054] First, the image data is obtained, and then the visual device posture T is obtained according to the image data processed by the ORB algorithm (Oriented FAST and Rotated BRIEF, fast robust feature extraction algorithm) and the BA algorithm (Bundle Adjustment, beam adjustment algorithm) V , as shown in formula (3):

[0055] T V =BA(ORB(images))(3)

[0056] Among them, T V is the visual SLAM pose.

[0057] Optionally, the pose T of the inertial sensor I It is obtained by the following method, as shown in formula (4):

[0058] T I =∫adt(4)

[0059] Among them, a is the acceleration data and dt is the time increment.

[0060] Optionally, the multi-source pose data is fused according to EKF (Extended Kalman Filter) to predict and update the pose, as shown in formula (5):

[0061] T=EKF(T L , T V , T I )(5)

[0062] Among them, T is the fused pose.

[0063] From the above content, it can be seen that by acquiring the posture information of multiple acquisition devices in real time based on SLAM technology and then fusing the acquired posture information, the effect of timely updating can be achieved. At the same time, according to the accurate posture status of the acquired acquisition devices, it can provide a basis for the subsequent construction of an accurate three-dimensional model.

[0064] In an optional embodiment, the intelligent risk warning system determines a depth map of the substation's work site based on the work site information and the target posture, and then determines three-dimensional point cloud data of the work site based on the depth map and the image of the work site captured by the camera, and then performs resolution enhancement processing on the three-dimensional point cloud data to obtain target three-dimensional point cloud data, and finally determines the target three-dimensional model based on the target three-dimensional point cloud data.

[0065] Optionally, the intelligent risk warning system generates a depth map D based on binocular vision. The generation process of the depth map D is shown in formula (6):

[0066] D=DepthEstimation(stereo_images)(6)

[0067] Among them, DepthEstimation is the depth estimation algorithm, and stereo_images is the binocular image.

[0068] Optionally, the intelligent risk warning system uses AtlasNet (AtlasNet: 3D Shape Generation from Boundaries, 3D reconstruction neural network) to generate a 3D point cloud P3D. The generation process of the 3D point cloud P3D is shown in formula (7):

[0069] P3D = AtlasNet(D, images)(7)

[0070] Optionally, the intelligent risk warning system uses SRCNN (Super-Resolution Convolutional Neural Network) to perform super-resolution enhancement processing on the three-dimensional point cloud to obtain the target three-dimensional point cloud P3D enhanced , the process is shown in formula (8):

[0071] P3D enhanced =SRCNN(P3D)(8)

[0072] Optionally, when generating a depth map based on binocular vision, accurate pose information of the acquisition device is required to ensure the accuracy of the data and the model.

[0073] Optionally, when the substation data changes, the changed area ΔI in the substation is obtained by monitoring, and the changed area ΔI can be obtained according to formula (9):

[0074] ΔI=Difference(I t , I t-1 )(9)

[0075] Among them, I t is the current moment image, I t-1 is the image at the previous moment, and Difference is the image difference algorithm.

[0076] After obtaining ΔI, a changed depth map ΔD is generated, as shown in formula (10):

[0077] ΔD=DepthEstimation(ΔI)(10)

[0078] Based on the changed depth map ΔD, the changed 3D point cloud data ΔP is generated. 3D , as shown in formula (11):

[0079] ΔP 3D =PointCloudGeneration(ΔD)(11)

[0080] Among them, PointCloudGeneration is the point cloud generation algorithm.

[0081] As shown in formula (12), the intelligent risk warning system converts the target three-dimensional point cloud P3D before the change enhanced And the 3D point cloud ΔP generated based on the depth map change 3D Summarize to get new 3D point cloud data P 3D new ,When the 3D point cloud data changes, the target 3D model will also change accordingly.

[0082] P 3D new =P 3D enhanced +ΔP 3D (12)

[0083] From the above content, it can be seen that the three-dimensional model is constructed by collecting the specific posture information and depth map of the equipment. When the depth map changes, the three-dimensional model will also change, so that an accurate three-dimensional model can be generated in a dynamic environment. That is, the three-dimensional model can be used to monitor the situation at the substation operation site in real time. At the same time, the accuracy of the target three-dimensional model is improved by enhancing the resolution of the three-dimensional point cloud data.

[0084] In an optional embodiment, the intelligent risk warning system determines a target data set based on N substation data, target posture, environmental parameters of the work site, and operating data of operators, wherein the operating data characterizes the operating behavior of the operators, and then obtains association rules, which are used to determine the association features between preset risk events and N substation data, and then determines the substation operation risk information based on the target data set, association rules, and target three-dimensional model.

[0085] Optionally, the intelligent risk warning system performs standardization processing on the target data set so that the data has a mean of 0 and a variance of 1. The processing process is shown in formula (13):

[0086]

[0087] Among them, x t is the input data at time t, μ is the mean, and σ is the standard deviation.

[0088] Optionally, the intelligent risk warning system organizes the characteristic data of the substation operation site into a transaction set D. Each transaction T∈D contains a set of features I. The Apriori algorithm in the association rule mining technology is used to mine frequent item sets. The frequent item set F is found in an iterative manner. The frequent item set F represents the set of all item sets I whose support is greater than or equal to min_support. This set includes all item sets that are considered to appear frequently in the data set, as shown in formula (14):

[0089] F={I|Support(I)≥min_support}(14)

[0090] Among them, Support(I) is a metric used to measure the frequency of item set I appearing in all events.

[0091] Generate association rules R based on frequent item sets F, as shown in formula (15):

[0092] R={A→B∣Confidence(A→B)≥min_confidence}(15)

[0093] Among them, A and B are part of the frequent item set F, Confidence(A→B) represents the confidence that A leads to B, which is a metric, and min_confidence represents the minimum confidence threshold.

[0094] Optionally, association rules R help discover the relationship between different features, such as the association between specific operating modes, environmental conditions or equipment states and risk events, and help understand which combinations of features may lead to high risks. For example, if a certain feature A and a certain risk or fault B meet the judgment in the association rule, that is, A→B is established, it means that A and B are highly correlated, which means that this feature A is likely to cause the occurrence of risk events, which can help discover potential risk events.

[0095] From the above content, it can be seen that potential risk events in the substation can be discovered through association rules. According to the potential risk events, managers or operators can check or maintain in advance to prevent the occurrence of risk events. Finally, the operational risk information of the entire substation can be determined through the acquired target data set, association rules and target three-dimensional model.

[0096] In an optional embodiment, the intelligent risk warning system determines a first type of risk factor based on the target data set and the target three-dimensional model, wherein the first type of risk factor is used to characterize the abnormal behavior factors of the substation equipment and the abnormal behavior factors of the operators when operating the substation, and then determines a second type of risk factor based on the association rules and the target three-dimensional model, wherein the second type of risk factor is used to characterize the potential risk factors of preset risk events occurring at the operating site, and finally the first type of risk factor and the second type of risk factor are used as substation operation risk information.

[0097] Optionally, the intelligent risk warning system uses a long short-term memory network (LSTM) to identify the first type of risk factors, and uses the target data set and the target three-dimensional point cloud data as the input data X of the model, as shown in formula (16):

[0098] X=[P 3D new , x t ′] (16)

[0099] The calculation formula of LSTM unit is shown in (17)-(21):

[0100] f t =σ(W f x′ t +U f h t-1 +b f )(17)

[0101] i t =σ(W i x′ t +U i h t-1 +b i )(18)

[0102] o t =σ(W o x t ′+U o h t-1 +b o )(19)

[0103] c t =f t ⊙c t-1 +i t ⊙tanh(W c x′ t +U c h t-1 +b c )(20)

[0104] h t =o t⊙tanh(c t )(twenty one)

[0105] Among them, f t 、i t , o t They are forget gate, input gate and output gate respectively, c t is the unit state, h t is the hidden state, W f , W i , W o , W c , W y is the LSTM weight matrix, U f , U i , U o , U c is the LSTM hidden state weight matrix, b f , b i , b o , b c , b y is the LSTM bias vector.

[0106] Optionally, the intelligent risk warning system trains the LSTM model through historical substation data, historical risk events, and the association between historical substations and historical risk events to obtain a target model. The input data X is input into the target model for analysis to identify abnormal behaviors of operators and abnormal states of equipment. In the LSTM model, the hidden state h of the last layer is t The behavior classification result y is output through the fully connected layer and the Softmax function. The classification result y is the first type of risk factor, as shown in formula (22):

[0107] y=softmax(W y h t +b y )(twenty two)

[0108] Among them, W y and b y are the weight matrix and bias vector respectively.

[0109] Optionally, the intelligent risk warning system can determine the second type of risk factors based on the generated association rules and the three-dimensional target model. The second type of risk factors represent the potential risk factors of the substation. Finally, the first type of risk factors and the second type of risk factors together constitute the operational risk information of the substation.

[0110] From the above content, it can be seen that based on the acquisition of the first and second risk factors, the risk information of the entire substation operation can be obtained, and corresponding solutions can be taken for the substation based on the acquired risk information, which also provides a basis for subsequent risk warnings.

[0111] In an optional embodiment, the intelligent risk warning system obtains the weights and impact values ​​corresponding to the first and second risk factors, respectively, wherein the weights and impact values ​​change with the substation data, and the impact values ​​are used to characterize the degree of influence of the first and second risk factors on abnormal events occurring in the substation. The risk levels corresponding to the first and second risk factors are determined based on the weights and impact values, and finally, warning information for the substation is generated based on the risk level and the preset warning threshold.

[0112] Optionally, the intelligent risk early warning system establishes a risk assessment model, quantifies the risk level of each risk factor, and defines the weight of each risk factor. i The weight is determined by personnel in the relevant technical field or machine learning models, and the impact value b of each risk factor is defined i , the impact value is obtained through actual data measurement. The risk level E is determined according to the weight and impact value of the risk factor, and the risk is divided into five levels: very high, high, medium, low, and acceptable. Different response measures are taken according to different risk levels. The formula of risk level E is shown in (23):

[0113]

[0114] Here, n is the number of risk factors.

[0115] Optionally, the weight of the risk factor a i and impact value b i It can be dynamically adjusted according to the real-time substation data X and the substation historical data H, where the weight a i and impact value b i The calculation formulas are shown in formulas (24) and (25) respectively:

[0116] a i (t+1)=m·a i (t)+(1-m)·f(X,H) (24)

[0117] Where m is the adjustment coefficient and f(·) is the prediction function based on the machine learning model.

[0118]

[0119] in, is the impact value based on historical data, is the impact value based on real-time data, is the impact value based on the scores of people in the relevant technical field, is the influence value predicted by the machine learning model, α, β, γ, δ are weight coefficients, α+β+γ+δ=1.

[0120] Among them, substation historical data includes past operation records, equipment and environmental status, sensor data, risk assessment and early warning records. Operation records include historical action records, operation steps, equipment usage, historical abnormal events, fault records, accident reports, etc. of operators. Equipment and environmental status include historical equipment operation data, maintenance records, fault logs, environmental parameters such as temperature, humidity, electromagnetic interference, etc. Sensor data include data collected from various sensors (such as visual sensors, inertial sensors, lidar, etc.) and historical location information, such as location data of operators and equipment collected through Beidou high-precision positioning technology. Risk assessment and early warning records include historical risk assessment reports and assessment results, including risk levels, risk factors, historical early warning records, conditions for early warning triggering, and subsequent measures.

[0121] Optionally, the intelligent risk warning system sets different warning thresholds according to the risk level. When the risk level is greater than the warning threshold, a warning will be issued in a timely manner, and a warning strategy will be formulated, including the type of warning information, sending method and recipient, to remind substation managers or operators in real time, and will adopt a variety of methods to release warning information, including SMS, email, mobile phone applications and other channels to ensure that substation managers or operators can receive warning information in a timely manner. At the same time, a visualization page will be constructed to display risk distribution and warning information through a graphical interface to assist decision-making.

[0122] Optionally, the intelligent risk warning system updates the data of the work site in real time to ensure the continuity of risk monitoring, collects feedback from operators and managers, analyzes the effectiveness and accuracy of the warning, and then continuously optimizes the risk monitoring and warning system based on the feedback information.

[0123] From the above content, it can be seen that by obtaining the weight and impact value of each risk factor, the risk factors can be quantified, the risk level can be clearly identified, and then an early warning can be made based on the risk level, helping substation managers or operators to quickly identify the risk location, risk category, risk level, etc., and continuously monitor the operation site, update the risk assessment results and early warning information in real time, collect on-site feedback information, and continuously optimize the risk monitoring algorithm and early warning system to improve the accuracy and reliability of the system.

[0124] According to an embodiment of the present application, an embodiment of an early warning device for substation operation risks is also provided. Figure 2 is a schematic diagram of an optional early warning device for substation operation risks according to an embodiment of the present application, such as Figure 2As shown, the early warning device for substation operation risk includes: a first acquisition unit 201, a first determination unit 202, a second determination unit 203, and a first generation unit 204.

[0125] Optionally, a first acquisition unit 201 is used to obtain N substation data, wherein N is an integer greater than 1, and the N substation data at least include location information and behavior information of the substation operators, location information and status information of the substation equipment, and location information and usage status information of the operators' operating tools; a first determination unit 202 is used to determine a target three-dimensional model of the substation based on the substation data, wherein the target three-dimensional model of the substation changes with changes in the substation data, and the target three-dimensional model is used to characterize the three-dimensional scene of the operators when performing construction work on the substation; a second determination unit 203 is used to determine the operation risk information of the substation based on the target three-dimensional model; a first generation unit 204 is used to generate early warning information for the substation based on the operation risk information of the substation.

[0126] Optionally, the first determination unit 202 includes: a first processing subunit and a first determination subunit. The first processing subunit is used to adopt a time synchronization strategy, a space synchronization strategy and an information complement strategy to perform data fusion on N substation data to obtain operation site information, wherein the time synchronization strategy is used to time align the N substation data according to the timestamp information of each substation data; the space synchronization strategy is used to convert the N substation data into the same spatial coordinate system; the information complement strategy is used to integrate the substation data with a preset logical relationship; the first determination subunit is used to determine the target three-dimensional model of the substation according to the operation site information.

[0127] Optionally, the first determination subunit includes: a first acquisition module, a first processing module, and a first determination module. The first acquisition module is used to acquire the device postures of multiple data acquisition devices, wherein the data acquisition devices are used to collect substation data, and the multiple data acquisition devices include laser radars, cameras, and inertial sensors; the first processing module is used to fuse the device postures of the multiple data acquisition devices to obtain the target posture; the first determination module is used to determine the target three-dimensional model of the substation according to the work site information and the target posture.

[0128] Optionally, the first determination module includes: a first determination submodule, a second determination submodule, a first processing submodule, and a third determination submodule. Among them, the first determination submodule is used to determine the depth map of the substation's operation site based on the operation site information and the target posture; the second determination submodule is used to determine the three-dimensional point cloud data of the operation site based on the depth map and the image of the operation site collected by the camera; the first processing submodule is used to perform resolution enhancement processing on the three-dimensional point cloud data to obtain target three-dimensional point cloud data; the third determination submodule is used to determine the target three-dimensional model based on the target three-dimensional point cloud data.

[0129] Optionally, the second determination unit 203 includes: a second determination subunit, a first acquisition subunit, and a third determination subunit. The second determination subunit is used to determine the target data set according to N substation data, target posture, environmental parameters of the operation site, and operation data of the operator, wherein the operation data represents the operation behavior of the operator; the first acquisition subunit is used to obtain association rules, wherein the association features between the preset risk event and the N substation data are determined; the third determination subunit is used to determine the substation operation risk information according to the target data set, the association rules, and the target three-dimensional model.

[0130] Optionally, the third determination subunit includes: a second determination module, a third determination module, and a fourth determination module. The second determination module is used to determine the first type of risk factors according to the target data set and the target three-dimensional model, wherein the first type of risk factors are used to characterize the abnormal behavior factors of the equipment in the substation and the abnormal behavior factors of the operators when operating the substation; the third determination module is used to determine the second type of risk factors according to the association rules and the target three-dimensional model, wherein the second type of risk factors are used to characterize the potential risk factors of the preset risk events occurring at the operation site; the fourth determination module is used to use the first type of risk factors and the second type of risk factors as the substation operation risk information.

[0131] Optionally, the first generation unit 204 includes: a second acquisition subunit, a fourth determination subunit, and a first generation subunit. The second acquisition subunit is used to obtain the weights and impact values ​​corresponding to the first risk factor and the second risk factor, respectively, wherein the weights and impact values ​​change with the change of the substation data, and the impact values ​​are used to characterize the degree of influence of the first risk factor or the second risk factor on the abnormal event occurring in the substation; the fourth determination subunit is used to determine the risk level corresponding to the first risk factor and the second risk factor, respectively, according to the weights and impact values; the first generation subunit is used to generate warning information for the substation according to the risk level and the preset warning threshold.

[0132] According to another aspect of the present application, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed, the device where the computer-readable storage medium is located executes the above-mentioned early warning method for substation operation risks.

[0133] According to another aspect of the present application, an electronic device is also provided, wherein the electronic device includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above-mentioned early warning method for substation operation risks.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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, Random Access Memory), mobile hard disk, disk or CD-ROM and other media that can store program codes.

[0140] 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 substation operation risks, characterized in that: include: Acquire N substation data, where N is an integer greater than 1, and the N substation data at least include location information and behavior information of substation operators, location information and status information of equipment in the substation, and location information and usage status information of operating tools of the operators; Determining a target three-dimensional model of the substation according to the substation data, wherein the target three-dimensional model of the substation changes as the substation data changes, and the target three-dimensional model is used to represent a three-dimensional scene when the operator performs construction work on the substation; Determining operational risk information of the substation according to the target three-dimensional model; Early warning information for the substation is generated according to the operation risk information of the substation.

2. The early warning method for substation operation risk according to claim 1 is characterized in that: Determining a target three-dimensional model of the substation according to the substation data includes: The time synchronization strategy, the space synchronization strategy and the information complement strategy are adopted to fuse the N substation data to obtain the operation site information, wherein the time synchronization strategy is used to time align the N substation data according to the timestamp information of each substation data; the space synchronization strategy is used to convert the N substation data into the same space coordinate system; the information complement strategy is used to integrate the substation data with a preset logical relationship; A target three-dimensional model of the substation is determined according to the work site information.

3. The early warning method for substation operation risk according to claim 2 is characterized in that: Determining a target three-dimensional model of the substation according to the work site information includes: Acquire device positions of a plurality of data acquisition devices, wherein the data acquisition devices are used to collect the substation data, and the plurality of data acquisition devices include a laser radar, a camera, and an inertial sensor; Fusing the device postures of the multiple data acquisition devices to obtain a target posture; A target three-dimensional model of the substation is determined according to the work site information and the target posture.

4. The early warning method for substation operation risk according to claim 3 is characterized in that: Determining a target three-dimensional model of the substation according to the work site information and the target posture includes: Determine a depth map of the operation site of the substation according to the operation site information and the target posture; Determining three-dimensional point cloud data of the work site according to the depth map and the image of the work site captured by the camera; Performing resolution enhancement processing on the three-dimensional point cloud data to obtain target three-dimensional point cloud data; The target three-dimensional model is determined according to the target three-dimensional point cloud data.

5. The early warning method for substation operation risk according to claim 3 is characterized in that: Determining substation operation risk information according to the target three-dimensional model, the early warning method for substation operation risk also includes: Determine a target data set according to the N substation data, the target position, the environmental parameters of the work site, and the operation data of the operator, wherein the operation data represents the operation behavior of the operator; Obtaining association rules, wherein the association rules are used to determine the association characteristics between the preset risk events and the N substation data; The substation operation risk information is determined according to the target data set, the association rules and the target three-dimensional model.

6. The early warning method for substation operation risk according to claim 5 is characterized in that: Determining the substation operation risk information according to the target data set, the association rule and the target three-dimensional model also includes: Determine a first type of risk factor according to the target data set and the target three-dimensional model, wherein the first type of risk factor is used to characterize an abnormal behavior factor of equipment in the substation and an abnormal behavior factor of the operator when operating the substation; Determining a second type of risk factor according to the association rule and the target three-dimensional model, wherein the second type of risk factor is used to characterize a potential risk factor for the occurrence of the preset risk event at the operation site; The first risk factor and the second risk factor are used as the substation operation risk information.

7. The early warning method for substation operation risk according to claim 6 is characterized in that: Generating early warning information for the substation according to the substation operation risk information, including: Obtaining weights and impact values ​​corresponding to the first risk factor and the second risk factor, respectively, wherein the weights and the impact values ​​change with the change of the substation data, and the impact values ​​are used to characterize the degree of influence of the first risk factor or the second risk factor on the abnormal event occurring in the substation; Determine the risk levels corresponding to the first risk factor and the second risk factor respectively according to the weight and the impact value; Early warning information for the substation is generated according to the risk level and a preset early warning threshold.

8. An early warning device for substation operation risks, characterized in that: include: A first acquisition unit acquires N substation data, wherein N is an integer greater than 1, and the N substation data at least include location information and behavior information of substation operators, location information and status information of equipment in the substation, and location information and usage status information of operating tools of the operators; A first determination unit determines a target three-dimensional model of the substation according to the substation data, wherein the target three-dimensional model of the substation changes with changes in the substation data, and the target three-dimensional model is used to represent a three-dimensional scene when the operator performs construction work on the substation; A second determining unit, determining the operation risk information of the substation according to the target three-dimensional model; The first generating unit generates early warning information for the substation according to the operation risk information of the substation.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located executes the early warning method for substation operation risks as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the early warning method for substation operation risks as described in any one of claims 1 to 7.

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