Method for determining anti-waterlogging strategy of transformer substation based on weather environment and storage medium

Abstract

The invention discloses a method for determining a transformer substation waterlogging prevention strategy based on a weather environment and a storage medium. The method comprises the following steps: preprocessing initial weather information collected by a sensor network to obtain first weather information; inputting the first weather information into a dynamic water flow model, and predicting a ponding area set; in response to a fault of a sensor corresponding to the ponding area set, updating the first weather information by using the fault sensor node and a neighbor node set of the fault sensor node to obtain second weather information; performing data adjustment processing on the second weather information based on the node adjustment coefficient to obtain target weather information; and determining an anti-waterlogging strategy of the transformer substation based on the ponding area set, the target weather information and the early warning trigger threshold. According to the invention, the technical problems of low substation waterlogging prevention strategy making accuracy and low waterlogging prevention measure execution efficiency caused by low early warning accuracy of a substation waterlogging monitoring and early warning system in a complex weather environment in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the field of determining a strategy for preventing waterlogging in a substation, and in particular to a method for determining a strategy for preventing waterlogging in a substation based on a weather environment and a storage medium. Background Art

[0002] Substations are an important part of the power system, and their equipment is very sensitive to the external environment. In particular, waterlogging problems will directly affect the normal operation of substation equipment, causing power outages and even equipment damage. The current waterlogging monitoring and early warning system mainly relies on sensors such as water level and rainfall, and determines the risk of waterlogging by monitoring the data collected by the sensors. However, the existing waterlogging monitoring and early warning system has the following problems when facing complex weather environments:

[0003] (1) The sensor network is affected by bad weather, resulting in signal transmission interruption or data loss, which affects the continuity of data collection;

[0004] (2) The waterlogging monitoring and early warning system is unable to adjust the warning threshold in real time, resulting in warning delays and errors.

[0005] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0006] An embodiment of the present invention provides a method for determining a substation anti-flooding strategy based on the weather environment and a storage medium, so as to at least solve the technical problems in the related art of low accuracy in formulating substation anti-flooding strategies and low efficiency in executing anti-flooding measures due to low warning accuracy of substation anti-flooding monitoring and early warning systems in complex weather environments.

[0007] According to one embodiment of the present invention, there is provided a method for determining a substation flood prevention strategy based on the weather environment, comprising: pre-processing initial weather information collected by a sensor network of the substation to obtain first weather information, wherein the sensor network is constructed based on sensor nodes and communication links between nodes; inputting the first weather information into a dynamic water flow model to predict a set of waterlogging areas, wherein the dynamic water flow model is constructed based on shallow water equations and water level gradient information; in response to a failure of a sensor corresponding to the set of waterlogging areas, updating the first weather information using the faulty sensor node and a set of neighboring nodes of the faulty sensor node to obtain second weather information; performing data adjustment processing on the second weather information based on a node adjustment coefficient to obtain target weather information; determining the substation flood prevention strategy based on the set of waterlogging areas, the target weather information and an early warning trigger threshold, wherein the substation flood prevention strategy is used to determine flood prevention measures.

[0008] Optionally, the initial weather information includes at least: first water level information, first water flow velocity information, first soil moisture information and first precipitation information. The initial weather information collected by the sensor network of the substation is input into the dynamic water flow model. Predicting the set of waterlogged areas includes: obtaining terrain height information of the area covered by the sensor network; performing gradient operation on the first water level information to obtain water level gradient information; inputting the first water level information, first water flow velocity information, first soil moisture information, first precipitation information and terrain height information collected by the sensor network into the shallow water equation to obtain candidate solutions of the shallow water equation, wherein the drainage term of the shallow water equation is determined based on the terrain feedback coefficient and the soil moisture information; in response to the candidate solution, the drainage term of the shallow water equation obtains a minimum value, and the water level gradient value is determined to be zero based on the water level gradient, and the area corresponding to the sensor node is determined to be a waterlogged area.

[0009] Optionally, in response to a failure of a sensor corresponding to a set of waterlogged areas, the initial weather information is updated using the faulty sensor node and the set of neighboring nodes of the faulty sensor node to obtain the second weather information, including: determining a distance weight coefficient between nodes based on the attenuation coefficient and the distance information between the faulty sensor node and each node in the set of neighboring nodes; compensating the initial weather information of the faulty sensor node using the distance weight coefficient and the initial weather information corresponding to the sensor node in the set of neighboring nodes to obtain the second weather information.

[0010] Optionally, the target weather information includes at least: target water level information, target water flow velocity information, target soil moisture and target precipitation information, and determining the substation anti-flooding strategy based on the waterlogging area set, target weather information and warning trigger threshold includes: in response to the target water level information and target water flow velocity information being greater than the warning trigger threshold, determining a waterlogging risk score based on the target water level information, target water flow velocity information, target soil moisture and target precipitation information; determining warning level information based on the waterlogging risk score; and determining anti-flooding measures based on the warning level information.

[0011] Optionally, the method for determining the substation flood prevention strategy based on the weather environment also includes: obtaining the state information of the sensor nodes in the sensor network and the compensation coefficient of the sensor nodes; determining the node adjustment coefficient based on the state information and the compensation coefficient; determining the warning level information according to the flood risk score; and determining the flood prevention measures according to the warning level information.

[0012] Optionally, the method for determining the substation flood prevention strategy based on the weather environment also includes: obtaining the initial collection frequency, frequency adjustment coefficient, change rate and rate threshold of the water level information in the initial weather information of the sensor network; in response to the change rate being greater than the rate threshold, determining the target collection frequency based on the initial collection frequency and the frequency adjustment coefficient; and controlling the sensor network to collect the initial weather information according to the target collection frequency.

[0013] Optionally, the method for determining the substation flood prevention strategy based on the weather environment also includes: in response to a failure of a sensor corresponding to a set of waterlogged areas, obtaining a terrain correction factor, load information of the communication link, and delay information between nodes; determining initial link weight information based on the load information and delay information; adjusting the initial link weight information using the terrain correction factor to obtain target link weight information; and determining the data transmission path of the sensor using the target link weight information.

[0014] According to another aspect of an embodiment of the present invention, there is also provided a device for determining a substation anti-flooding strategy based on the weather environment, comprising: a first processing module, used to pre-process initial weather information collected by a sensor network of the substation to obtain first weather information, wherein the sensor network is constructed based on sensor nodes and communication links between nodes; a prediction module, used to input the first weather information into a dynamic water flow model to predict a set of waterlogging areas, wherein the dynamic water flow model is constructed based on shallow water equations and water level gradient information; a second processing module, used to update the first weather information using a faulty sensor node and a set of neighboring nodes of the faulty sensor node in response to a failure of a sensor corresponding to the set of waterlogging areas to obtain second weather information; a first determination module, used to perform data adjustment processing on the second weather information based on a node adjustment coefficient to obtain target weather information; a second determination module, used to determine the substation anti-flooding strategy based on the set of waterlogging areas, the target weather information and the warning trigger threshold, wherein the substation anti-flooding strategy is used to determine anti-flooding measures.

[0015] Optionally, the device for determining the substation flood prevention strategy based on the weather environment also includes an acquisition module for acquiring terrain height information of the area covered by the sensor network; the prediction module is also used to: perform gradient operation on the first water level information to obtain water level gradient information; input the first water level information, first water flow velocity information, first soil moisture information, first precipitation information and terrain height information collected by the sensor network into the shallow water equation to obtain candidate solutions of the shallow water equation, wherein the drainage term of the shallow water equation is determined based on the terrain feedback coefficient and the soil moisture information; in response to the candidate solution, the drainage term of the shallow water equation obtains a minimum value, and based on the water level gradient, the water level gradient value is determined to be zero, and the area corresponding to the sensor node is determined to be a waterlogged area.

[0016] Optionally, the second processing module is also used to: determine the distance weight coefficient between nodes based on the attenuation coefficient and the distance information between the faulty sensor node and each node in the neighbor node set; use the distance weight coefficient and the initial weather information corresponding to the sensor node in the neighbor node set to compensate for the initial weather information of the faulty sensor node to obtain the second weather information.

[0017] Optionally, the second determination module is also used to: in response to the target water level information and the target water flow velocity information being greater than the warning trigger threshold, determine the waterlogging risk score based on the target water level information, the target water flow velocity information, the target soil moisture and the target precipitation information; determine the warning level information according to the waterlogging risk score; and determine waterlogging prevention measures according to the warning level information.

[0018] Optionally, the acquisition module is further used to acquire state information of sensor nodes in the sensor network and compensation coefficients of the sensor nodes; the first determination module is further used to determine the node adjustment coefficient based on the state information and the compensation coefficient.

[0019] Optionally, the acquisition module is also used to acquire the initial acquisition frequency, frequency adjustment coefficient, change rate and rate threshold of the water level information in the initial weather information of the sensor network; the first processing module is also used to determine the target acquisition frequency based on the initial acquisition frequency and the frequency adjustment coefficient in response to the change rate being greater than the rate threshold; the device for determining the substation flood prevention strategy based on the weather environment also includes a control module for controlling the sensor network to collect the initial weather information according to the target acquisition frequency.

[0020] Optionally, the acquisition module is also used to obtain a terrain correction factor, load information of the communication link and delay information between nodes in response to a failure of a sensor corresponding to a set of waterlogged areas; the first determination module is also used to: determine initial link weight information based on the load information and delay information; adjust the initial link weight information using the terrain correction factor to obtain target link weight information; and determine the data transmission path of the sensor using the target link weight information.

[0021] According to another aspect of an embodiment of the present invention, a substation waterlogging warning system is also provided, comprising: a memory storing an executable program; a processor for running the program, wherein the program executes the above-mentioned method for determining a substation waterlogging prevention strategy based on the weather environment when running.

[0022] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned method for determining a substation flood prevention strategy based on weather environment when running.

[0023] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program, which implements the above-mentioned method for determining a substation flood prevention strategy based on weather environment when executed by a processor.

[0024] In an embodiment of the present invention, the initial weather information collected by the sensor network of the substation is preprocessed to obtain the first weather information, and the first weather information is input into the dynamic water flow model to predict a set of waterlogged areas. In response to a failure of the sensor corresponding to the set of waterlogged areas, the first weather information is updated using the faulty sensor node and the set of neighboring nodes of the faulty sensor node to obtain the second weather information, and the second weather information is data adjusted based on the node adjustment coefficient to obtain the target weather information. Finally, the substation anti-waterlogging strategy is determined based on the set of waterlogged areas, the target weather information and the warning trigger threshold, thereby achieving the purpose of accurately predicting the set of waterlogged areas and reporting waterlogging warnings in a timely and accurate manner, thereby achieving the technical effect of improving the accuracy of formulating anti-waterlogging strategies for substations and the efficiency of executing anti-waterlogging measures, and further solving the technical problem in the related art that the accuracy of anti-waterlogging strategies for substations is low and the efficiency of executing anti-waterlogging measures is low due to the low warning accuracy of the substation waterlogging monitoring and early warning system in complex weather environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0026] Figure 1 It is a hardware structure block diagram of a method for determining a substation flood prevention strategy based on weather environment according to one embodiment of the present invention;

[0027] Figure 2 is a flow chart of a method for determining a substation flood prevention strategy based on weather environment according to one embodiment of the present invention;

[0028] Figure 3 is a flow chart of another method for determining a substation flood prevention strategy based on weather environment according to one embodiment of the present invention;

[0029] Figure 4 It is a structural block diagram of a device for determining a substation flood prevention strategy based on weather environment according to one embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention 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 interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] The method for determining a substation flood prevention strategy based on weather conditions provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 FIG. 1 is a hardware structure block diagram of a method for determining a substation flood prevention strategy based on weather environment according to one embodiment of the present invention. Figure 1 As shown, the computer terminal 10 (or electronic device 10) may include one or more (102a, 102b, ..., 102n are used to illustrate) processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0033] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10 (or electronic device). As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0034] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method for determining the anti-flooding strategy of the substation based on the weather environment in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, the above-mentioned method for determining the anti-flooding strategy of the substation based on the weather environment is realized. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0035] The transmission module 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission module 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission module 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0036] The display may be, for example, a touch screen type liquid crystal display (LCD), which may enable a user to interact with a user interface of the computer terminal 10 (or electronic device).

[0037] It should be noted that, in some optional embodiments, the above Figure 1 The computer device (or electronic device) shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. It should be noted that Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the above-described computer device (or electronic device).

[0038] According to an embodiment of the present invention, a method embodiment of a method for determining a substation flood prevention strategy based on weather environment 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 may be executed in an order different from that shown here.

[0039] Figure 2 is a flow chart of a method for determining a substation flood prevention strategy based on weather environment according to one embodiment of the present invention, such as Figure 2 As shown, the method comprises the following steps:

[0040] Step S21, preprocessing the initial weather information collected by the sensor network of the substation to obtain first weather information, wherein the sensor network is constructed based on the communication links between sensor nodes and nodes;

[0041] In step S21, the sensor network is constructed based on sensor nodes and communication links between nodes to collect weather information of the substation and its surrounding areas in real time. The sensor network includes several sensor nodes, each of which includes a water level sensor, a flow rate sensor, a soil moisture sensor and a precipitation sensor to collect weather information. The layout of the sensor grid needs to take into account the needs of multi-level perception, that is, to cover regional water flow and local waterlogging at the same time. The sensor nodes are connected through a wireless network, and all sensor nodes can report data in real time.

[0042] Specifically, multiple sensor nodes are deployed in the substation and its surrounding areas according to the terrain, drainage system, climate conditions, etc., covering all potential waterlogging risk areas. Each sensor node includes a water level sensor, a flow rate sensor, a soil moisture sensor, and a precipitation sensor, and the layout of the sensor nodes follows the following rules: (1) Water level sensor: deployed at drainage outlets, low-lying areas, and water flow convergence points. (2) Flow rate sensor: deployed in drainage channels, ramps, and other locations with faster water flow to monitor the direction of water flow. (3) Soil moisture sensor: deployed on the surface and near the drainage system around the substation to evaluate the water absorption capacity of the soil. (4) Precipitation sensor: installed at a high place around the substation to avoid interference from accumulated water that affects the accuracy of rainfall monitoring.

[0043] The above initial weather information includes but is not limited to: initial water level W i , initial water flow velocity V i , initial soil moisture H i and initial precipitation R i The sensor network collects initial weather information synchronously every Δt to ensure that each sensor node provides initial weather information in the same time period. In order to ensure the timing consistency of the data stream, the GPS time synchronization mechanism is used to make the timestamps of all sensor nodes consistent. The initial weather information is recorded as: D(t) = {W i (t),V i (t),H i (t),R i(t)}, i = 1, 2, …, n, where D(t) represents the original data set collected from all n sensor nodes at time t.

[0044] Specifically, since the data measured by different sensors have different dimensions and ranges, the collected data are standardized to adjust all the data to a uniform scale. For each data item, a standardization formula as shown in expression (1) is used.

[0045]

[0046] Among them, X i represents the original data, μ X represents the mean value of the variable in the past period of time, σ X is the standard deviation of the variable.

[0047] At the same time, the data filtering process is based on local anomaly detection to remove sensor abnormal values ​​that exceed the threshold range. The set threshold is based on the historical data performance of the sensor and the reasonable range in the physical scene. For example, for a water level sensor, the abnormal water level is set to exceed W max , then the condition W is satisfied i (t)>W max The data points will be marked as outliers and further compensated by the neighboring sensor data.

[0048] Furthermore, due to the influence of extreme weather, some sensor nodes may fail or lose data. In this case, a local interpolation algorithm based on spatial proximity is used to compensate for missing values ​​based on the data of adjacent sensor nodes. Reasonable inference is made through the data of adjacent sensors to maintain the continuity of the data stream. Assume that the water level data of node i is missing, and the water level data of its adjacent node j is W j (t), the missing data are interpolated using the weighted average formula shown in expression (2).

[0049]

[0050] in, is the neighbor set of sensor node i; w ij is the distance-based weighting factor, d ij is the distance between nodes i and j.

[0051] Furthermore, after completing data collection and standardization, a structured data set D will be generated at time t. ′(t) , including standardized water level, flow rate, soil moisture and rainfall data, namely the first weather information dataset D ′(t) , D ′(t)=D′(t)={W i ′(t),V i ′(t),H i ′(t),R i ′(t)},i=1,2,…,n, where, Indicates the first water level, V i ′(t) represents the first water flow velocity, H i ′(t) represents the first soil moisture and R i ′(t) represents the first precipitation.

[0052] Step S22, inputting the first weather information into a dynamic water flow model to predict a set of waterlogged areas, wherein the dynamic water flow model is constructed based on a shallow water equation and water level gradient information;

[0053] In step S22, the above dynamic water flow model includes shallow water equations (SWE) and water level gradient information. SWE is used to simulate the change of water level and the movement of water flow in the terrain. This equation is suitable for modeling shallow water flow in the terrain, such as waterlogging. The shallow water equation is shown in expression (3).

[0054]

[0055] Where h = W i ′(t) represents the water depth, which is obtained from the water level information provided by the sensor; u, v represent the velocity of the water flow along the x and y directions, which are obtained from the V provided by the flow velocity sensor i ′(t) adjustment; R i ′(t) represents rainfall input, which indicates the impact of precipitation on the water level at time t; P(h, Z(x, y), t) represents the drainage term, which takes into account the terrain and the capacity of the drainage system, and dynamically controls the discharge rate of water in different areas according to the terrain height Z(x, y) and the design of the drainage system.

[0056] Specifically, based on the shallow water equation, according to the water level gradient information Further optimize the dynamic adjustment of the water flow path to ensure that the possible future waterlogging area set can be accurately predicted. The water flow movement direction and flow velocity change trend are calculated through the water level gradient information. The calculation process of the water level gradient information is shown in expression (4).

[0057]

[0058] in, Indicates water level gradient information; h = W i ′(t) represents the water depth, which is obtained from the water level information given by the sensor.

[0059] The above-mentioned waterlogging area set is used to represent the predicted area where waterlogging may occur, denoted as A(t).

[0060] Specifically, the shallow water equation is used to simulate the change of water level and the movement of water flow in the terrain, and the water level gradient information is calculated to predict possible waterlogging points in the future, and the predicted waterlogging points are marked as potential waterlogging areas. Based on the shallow water equation and water level gradient information, real-time water level distribution, flow rate and waterlogging area set predictions are generated. The output at time t includes: water level distribution h(x,y,t), which reflects the water level distribution in the area covered by the sensor network at the current time and in the future. The flow velocity distribution u(x,y,t),v(x,y,t) represents the output of the water flow speed at different locations through the analysis of the flow velocity sensor data. The waterlogging area set A(t) represents the marked potential waterlogging area.

[0061] Step S23, in response to a sensor corresponding to the set of waterlogged areas failing, the first weather information is updated using the failed sensor node and the set of neighboring nodes of the failed sensor node to obtain the second weather information;

[0062] Specifically, it is determined whether the sensor corresponding to the set of waterlogged areas is faulty. If the sensor network fails due to sensor failure, the self-healing mechanism is activated to ensure the normal operation of the sensor network. The triggering condition of the self-healing mechanism is based on expression (5).

[0063]

[0064] Among them, δ(t) = 1 indicates that the self-healing mechanism needs to be started, which is the case when the sensor node is located at a potential water accumulation point and a failure occurs; s i (t) represents the working state of sensor node i, where s i (t) = 1 means sensor node i works normally, s i (t) = 0 indicates that sensor node i fails; A(t) indicates the water accumulation area.

[0065] Furthermore, when the sensor corresponding to the set of waterlogged areas fails, a self-healing demand signal is issued. At this time, the substation waterlogging warning system will obtain the current faulty sensor node and the neighbor node set of the faulty sensor node, use the neighbor node set to undertake the compensation task, and adjust the communication path in combination with the terrain correction factor to rebuild the topology of the sensor network. After the sensor network completes self-healing, a new network state will be generated, where the new network state includes the second weather information of each sensor node, data compensation status, and the communication path after topology reconstruction. The substation waterlogging warning system does not have to deal with the failure of all sensor nodes, but gives priority to nodes in key areas to ensure that limited self-healing resources are reasonably allocated. In this way, resources can be concentrated on the most critical part when the threat of waterlogging in the substation is the most serious.

[0066] Step S24, performing data adjustment processing on the second weather information based on the node adjustment coefficient to obtain target weather information;

[0067] Specifically, due to the different reliability of different sensor nodes, the substation waterlogging warning system performs weighted fusion on the second weather information. Therefore, the node adjustment coefficient β is introduced i (t).

[0068] Specifically, the node adjustment coefficient β i (t) The second weather information is adjusted and weighted according to the reliability of the sensor data. The data of each sensor is weighted fused, as shown in expression (6).

[0069]

[0070] Among them, β i (t) represents the compensation coefficient of sensor node i; D i ″(t) represents the second weather information corresponding to sensor node i.

[0071] Furthermore, after data fusion, the substation waterlogging warning system needs to dynamically adjust the data to ensure consistency in time and space. Due to the wide distribution of sensor networks, the time points of data collection may not be completely synchronized, and the delay problem of data compensation may cause slight differences in time between data of different nodes. Therefore, the substation waterlogging warning system ensures the consistency of data in time and space through time interpolation and spatial proximity adjustment. The specific adjustment scheme is to smooth the data difference through linear time interpolation method according to the time difference Δt of adjacent sensor nodes. For two adjacent sensor nodes i and j, the calculation process of the acquisition time difference is shown in expression (7).

[0072] Δt ij =|t i -t j| (7)

[0073] Among them, t i represents the acquisition timestamp of sensor node i; t j Represents the acquisition timestamp of sensor node j.

[0074] If Δt ij When the set threshold is exceeded, the substation waterlogging warning system uses a linear interpolation formula to adjust the data of the two to eliminate the time deviation. The specific adjustment process is shown in expression (8).

[0075]

[0076] Among them, D adjusted (t) represents the adjusted data, t i represents the acquisition timestamp of sensor node i; t j represents the acquisition timestamp of sensor node j; D i ″(t) represents the second weather information corresponding to sensor node i; D j ″(t) represents the second weather information corresponding to sensor node j.

[0077] Furthermore, after completing data fusion and dynamic adjustment, the substation waterlogging warning system generates a consistent target weather information dataset D final (t), the data set contains the target water level, target water flow velocity, target soil moisture and target precipitation after node adjustment coefficient processing and time interpolation processing: D final (t) = {W final (t),V final (t),H final (t),R final (t)}.

[0078] Step S25, determining the substation anti-flooding strategy based on the waterlogging area set, target weather information and warning trigger threshold, wherein the substation anti-flooding strategy is used to determine anti-flooding measures.

[0079] Specifically, according to the target weather information dataset D final The target water level W in (t) final (t), target water flow velocity V final (t), target soil moisture H final (t) and target precipitation R final (t) Dynamically adjust the warning trigger threshold, as shown in expression (9).

[0080] W thresh (t) = W 0 +γ 1 ·R final (t)-γ2 ·Z(x,y) (9)

[0081] Among them, W thresh (t) represents the dynamic warning triggering threshold, which is used to determine whether to trigger a warning; W 0 Represents the basic water level threshold, set as a constant, in meters (m); γ 1 It represents the rainfall impact coefficient, in meters / millimeter (m / mm), indicating how many meters the water level threshold increases for every 1 mm increase in precipitation; R final (t) represents the target precipitation, in millimeters (mm); γ 2 It represents the terrain influence coefficient, and its unit is meter / meter (m / m), which means the reduction of water level threshold for every 1 meter decrease of terrain height. Z(x,y) represents the terrain height, and its unit is meter (m), which means the height of terrain.

[0082] Furthermore, when the substation waterlogging warning system detects that the target water level and target water flow velocity exceed the warning trigger threshold, the waterlogging risk score Γ(t) is calculated, the warning level is determined according to the score, and corresponding waterlogging prevention measures are generated.

[0083] Based on the above steps S21 to S25, the initial weather information collected by the sensor network of the substation is preprocessed to obtain the first weather information, and the first weather information is input into the dynamic water flow model to predict the set of waterlogging areas. In response to the failure of the sensor corresponding to the set of waterlogging areas, the first weather information is updated using the faulty sensor node and the set of neighboring nodes of the faulty sensor node to obtain the second weather information, and the second weather information is data adjusted based on the node adjustment coefficient to obtain the target weather information. Finally, the substation anti-waterlogging strategy is determined based on the set of waterlogging areas, the target weather information and the warning trigger threshold, so as to achieve the purpose of accurately predicting the set of waterlogging areas and reporting waterlogging warnings in a timely and accurate manner, thereby achieving the technical effect of improving the accuracy of formulating anti-waterlogging strategies for substations and the efficiency of implementing anti-waterlogging measures, thereby solving the technical problem in the related technology that the accuracy of anti-waterlogging strategies for substations is low and the efficiency of implementing anti-waterlogging measures is low due to the low warning accuracy of substation waterlogging monitoring and early warning systems in complex weather environments.

[0084] Optionally, in step S22, the first weather information at least includes: first water level information, first water flow velocity information, initial soil moisture and first precipitation information. The first weather information collected by the sensor network of the substation is input into the dynamic water flow model, and the predicted waterlogging area set includes:

[0085] Step S221, obtaining terrain height information of the sensor network coverage area;

[0086] In step S221, the terrain height information is used to indicate the height of the terrain, which is recorded as Z(x, y).

[0087] Step S222, performing a gradient operation on the first water level information to obtain water level gradient information;

[0088] Specifically, a gradient operation is performed on the first water level information according to expression (2) to obtain water level gradient information.

[0089] Step S223, inputting the first water level information, the first water flow velocity information, the first soil moisture information, the first precipitation information and the terrain height information collected by the sensor network into the shallow water equation to obtain a candidate solution of the shallow water equation, wherein the drainage term of the shallow water equation is determined based on the terrain feedback coefficient and the soil moisture information;

[0090] Specifically, according to expression (1), P(h, Z(x, y), t) represents the drainage term. Taking into account the terrain and the capacity of the drainage system, the discharge rate of water flow in different areas is dynamically controlled according to the terrain height Z(x, y) and the design of the drainage system. Considering the complex terrain structure of the substation and its surrounding area, the soil moisture correction term λ(H) is introduced into P(h, Z(x, y), t). i ′(t)), dynamically adjusts the simulation accuracy of water flow. This soil moisture correction term is combined with soil moisture H i ′(t) and drainage capacity change, describing the impact of soil water absorption on the drainage system. This soil moisture correction term enables the substation waterlogging warning system to flexibly respond to different soil moisture conditions. By adjusting the drainage term to reflect the soil water absorption capacity and terrain differences, especially in specific areas of the substation, the accuracy of the water flow model is enhanced. The drainage term P(h, Z(x, y), t) is shown in expression (10).

[0091] P(h,Z(x,y),t)=βZ(x,y)+λ(H i ′(t)) (10)

[0092] Among them, β represents the terrain feedback coefficient, which controls the intensity of the influence of terrain on water flow; λ(H i ′(t)) represents the soil moisture correction term, which is used to describe the effect of soil on water level rise when it is saturated with water, that is, the greater the soil moisture, the worse the drainage capacity.

[0093] Step S224, in response to the candidate solution making the drainage term of the shallow water equation obtain a minimum value, and based on the water level gradient, determining that the water level gradient value is zero, determining that the area corresponding to the sensor node is a waterlogged area.

[0094] Specifically, the first water level information is subjected to gradient calculation according to expression (3) to obtain water level gradient information, and the first water level information, first water flow velocity information, first soil moisture information, first precipitation information and terrain height information collected by the sensor network are input into expression (1) to obtain a candidate solution of the shallow water equation. When the candidate solution makes the drainage term of the shallow water equation obtain a minimum value, and the water level gradient value is determined to be zero based on the water level gradient, the area corresponding to the sensor node is determined to be the water accumulation area A(t). The constraint conditions of the water accumulation area A(t) are shown in expression (11).

[0095]

[0096] As shown in expression (11), in the area where the water flow gradient is zero and the drainage capacity is insufficient, the substation waterlogging warning system will mark the area as a waterlogging area, and all waterlogging areas are combined into a waterlogging area set.

[0097] Based on the above steps S221 to S224, the terrain height information of the area covered by the sensor network is obtained, and the first water level information is gradient operated to obtain the water level gradient information, and the first water level information, the first water flow velocity information, the first soil moisture information, the first precipitation information and the terrain height information collected by the sensor network are input into the shallow water equation to obtain the candidate solution of the shallow water equation. Finally, in response to the candidate solution, the drainage term of the shallow water equation obtains a minimum value, and the water level gradient value is determined to be zero based on the water level gradient, and the area corresponding to the sensor node is determined to be a waterlogged area. The drainage term is adjusted to reflect the water absorption capacity of the soil and the terrain differences, thereby enhancing the accuracy of the dynamic water flow model, thereby improving the effectiveness and accuracy of the substation's anti-waterlogging strategy.

[0098] Optionally, in step S23, in response to a sensor corresponding to the set of waterlogged areas failing, the first weather information is updated using the failed sensor node and the set of neighboring nodes of the failed sensor node to obtain the second weather information, including:

[0099] Step S231, determining a distance weight coefficient between nodes based on the attenuation coefficient and the distance information between the faulty sensor node and each node in the neighbor node set;

[0100] Specifically, when the self-healing demand signal δ(t) = 1 is detected, the substation waterlogging warning system will obtain the neighbor node set of the fault sensor node i Among them, the neighboring nodes will undertake the compensation task. The first weather information of the faulty sensor node i needs to be compensated by the weighted data of the neighboring nodes. In this process, the attenuation coefficient and the distance information between the faulty sensor node and each node in the neighboring node set are introduced to determine the distance weight coefficient between nodes.

[0101] The distance weight coefficient between nodes is shown in expression (12).

[0102]

[0103] Among them, d ij is the distance between the faulty sensor node i and the adjacent sensor node j, where the adjacent sensor node j is the set of neighboring nodes Any sensor node in the set; α is the attenuation coefficient, which is adjusted by the distance information between the faulty sensor node and each node in the neighbor node set; d ik is the distance between node i and node k, node k is the set of neighbor nodes Nodes in .

[0104] Step S232, using the distance weight coefficient and the first weather information corresponding to the sensor node in the neighbor node set, the first weather information of the faulty sensor node is compensated to obtain the second weather information.

[0105] Specifically, the first weather information of the faulty sensor node is compensated by using the distance weight coefficient and the first weather information corresponding to the sensor node in the neighbor node set to obtain the second weather information. The specific process is shown in expression (13).

[0106]

[0107] Among them, w ij (t) represents the distance weight coefficient between node i and node k; D j ′ (t) represents the first weather information corresponding to sensor node j; D i ″(t) represents the second weather information corresponding to sensor node i.

[0108] Based on the above steps S231 to S232, the distance weight coefficient between nodes is determined based on the attenuation coefficient and the distance information between the faulty sensor node and each node in the neighbor node set, and the first weather information corresponding to the sensor node in the neighbor node set is compensated for by using the distance weight coefficient and the first weather information corresponding to the sensor node in the neighbor node set to obtain the second weather information, thereby effectively reducing the large-scale data loss caused by the failure of a sensor node, ensuring the continuity and accuracy of weather information, and improving the accuracy of formulating anti-flooding strategies for substations and the efficiency of implementing anti-flooding measures.

[0109] Optionally, in step S25, the target weather information at least includes: target water level information, target water flow velocity information, target soil moisture and target precipitation information, and determining the substation waterlogging prevention strategy based on the waterlogging area set, target weather information and warning trigger threshold includes:

[0110] Step S251, in response to the target water level information and the target water flow velocity information being greater than the warning trigger threshold, determining a waterlogging risk score based on the target water level information, the target water flow velocity information, the target soil moisture and the target precipitation information;

[0111] Specifically, when it is detected that the target water level and the target water flow velocity are greater than the warning trigger threshold, the waterlogging risk score Γ(t) is determined based on the target water level information, the target water flow velocity information, the target soil moisture and the target precipitation information, wherein the specific calculation process of the waterlogging risk score Γ(t) is shown in expression (14).

[0112] Γ(t)=α 1 W final (t)+α 2 V final (t)+α 3 R final (t)+α 4 H final (t) (14)

[0113] Among them, Γ(t) represents the waterlogging risk score; α 1 ,α 2 ,α 3 ,α 4 It represents the risk weight coefficient, which determines the influence of water level, flow rate, precipitation and soil moisture on the overall risk. It is usually set to α 1 ,α 2 ,α 3 ,α 4 The sum of is 1; W final (t) represents the target water level; V final (t) represents the target water flow velocity; R final (t) represents the target precipitation; H final (t) represents the target soil moisture.

[0114] Step S252, determining warning level information according to the waterlogging risk score;

[0115] Step S253, determining flood prevention measures according to the warning level information.

[0116] Specifically, by calculating the value of Γ(t), the waterlogging risk is divided into different warning levels, and corresponding waterlogging prevention measures are given.

[0117] Specifically, the warning signal S is calculated based on the risk score Γ(t), the warning level, the set of waterlogging areas, and the anti-waterlogging measures. warn (t), where S warn(t) = {Γ(t), warning level, A(t), E(t)}. This warning signal not only indicates the existence of risk, but also provides specific emergency operation suggestions based on terrain and sensor data, thereby effectively guiding the management and disposal of substations.

[0118] Furthermore, S warn (t) represents a real-time warning signal, which includes the following: Γ(t) is the current risk score, which indicates the severity of the current waterlogging risk. Warning level represents the warning level determined based on Γ(t), where the warning levels include but are not limited to yellow, orange and red warnings, corresponding to low, medium and high risks respectively. A(t) represents the waterlogging area, such as the "southeast corner of the substation" or the "west drainage ditch", which helps staff identify key risk areas. E(t) represents waterlogging prevention measures, which provide corresponding operation guidance in combination with the warning level. For example, yellow warning: it is recommended to strengthen monitoring and no major action is required for the time being. Orange warning: it is recommended to start some drainage equipment and move some important power equipment to a higher place to ensure that the waterlogging will not affect the operation of key areas. Red warning: it is recommended to start the comprehensive drainage system, shut down key power equipment, start the emergency power supply, and prepare for personnel evacuation. If the waterlogging in a certain area is more serious, the drainage and equipment protection measures in that area are recommended first to prevent the escalation of risks. This automated response suggestion mechanism helps to reduce human intervention and improve the accuracy of emergency operations.

[0119] Furthermore, after the warning signal is issued, the substation waterlogging warning system will enter the dynamic monitoring stage and continuously track the data changes of the sensor network. In order to avoid frequent warning fluctuations or false alarms, the duration T of the warning signal is monitored. warn To judge the stability of the risk. If the sensor data continues to show high risk, the warning state will be maintained; if the risk score decreases, the warning will be gradually lifted. The duration of the warning signal T warn The calculation process of is shown in expression (15).

[0120]

[0121] Among them, T warn Indicates the duration of the warning, in hours or minutes, indicating the time period from when the warning is triggered to when it is released; t 0 Indicates the warning trigger time, that is, the time when the warning is issued; t 1 Indicates the warning release time, that is, the moment when the risk is judged to have been released; δ warn (t) represents the warning status indicator function, 1 means the warning is activated, and 0 means the warning is lifted.

[0122] Specifically, the anti-flooding strategy is dynamically adjusted according to the progress of the anti-flooding measures. As the sensor data is updated in real time, the risk score Γ(t) is dynamically adjusted, and the warning is upgraded or lifted when necessary. For example, if the water level is detected to continue to rise and the flow rate increases, the yellow warning may be upgraded to orange, prompting the activation of drainage equipment and transfer equipment. If the data shows that the risk is reduced and the water level and flow rate return to normal, the warning will be gradually downgraded until it is lifted.

[0123] Furthermore, after each warning event, the detailed process of the entire event will be recorded, including triggering conditions, countermeasures, duration, etc. These historical data will be used for self-optimization of the substation waterlogging warning system. Through machine learning to analyze past warning data, the thresholds and risk scoring models for future warnings are automatically adjusted. For example, if a certain warning level is frequently triggered but the risk does not ultimately occur, the threshold of the water level or flow rate will be appropriately raised in the next similar event to avoid unnecessary false alarms. After experiencing several orange warnings, it was found that the rising trend of water levels in these events was not completely related to the actual waterlogging risk. The weight of the water level threshold can be automatically reduced, and the impact of flow rate or rainfall intensity can be increased to ensure a more accurate risk assessment next time.

[0124] Based on the above steps S251 to S253, in response to the target water level information and the target water flow velocity information being greater than the warning trigger threshold, a waterlogging risk score is determined based on the target water level information, the target water flow velocity information, the target soil moisture and the target precipitation information; the warning level information is determined according to the waterlogging risk score; and the waterlogging prevention measures are determined according to the warning level information, thereby achieving the purpose of timely and accurate reporting of waterlogging warnings, thereby achieving the technical effect of improving the accuracy of formulating waterlogging prevention strategies and the efficiency of implementing waterlogging prevention measures in substations, and further solving the technical problem in related technologies of low accuracy of waterlogging prevention strategy formulation and low efficiency of implementing waterlogging prevention measures in substations due to the low warning accuracy of waterlogging monitoring and early warning systems in complex weather environments.

[0125] Optionally, the method for determining a substation flood prevention strategy based on weather conditions further includes:

[0126] Step S261, obtaining state information of sensor nodes and compensation coefficients of sensor nodes in the sensor network;

[0127] Step S262: determining the node adjustment coefficient based on the state information and the compensation coefficient.

[0128] Specifically, set the node adjustment coefficient β i The formula of (t) is shown in expression (16).

[0129]

[0130] Among them, si (t) represents the state of sensor node i (s i (t) = 0 means node failure, s i (t) = 1 means normal operation); λ i (t) represents the compensation coefficient of sensor node i, which depends on the compensation times or the uncertainty of the interpolation process.

[0131] Based on the above steps S261 to S263, the status information of the sensor nodes in the sensor network and the compensation coefficients of the sensor nodes are obtained; the node adjustment coefficients are determined based on the status information and the compensation coefficients, ensuring that the most reliable information can be extracted from different sensor data sources to form a robust overall data set, thereby ensuring the accuracy of the early warning strategy.

[0132] Optionally, the method for determining a substation flood prevention strategy based on weather conditions further includes:

[0133] Step S271, obtaining the initial acquisition frequency, frequency adjustment coefficient, change rate and rate threshold of the water level information in the first weather information of the sensor network;

[0134] In step S271, the initial acquisition frequency is used to represent the acquisition frequency initially set for the sensor network.

[0135] Step S272, in response to the change rate being greater than the rate threshold, determining a target acquisition frequency based on the initial acquisition frequency and the frequency adjustment coefficient;

[0136] Specifically, when the change rate of the first water level in the first weather information is greater than the rate threshold, the initial acquisition frequency is dynamically adjusted to the target acquisition frequency, wherein the calculation process of the target acquisition frequency is shown in expression (17).

[0137]

[0138] in, α>1 indicates the frequency adjustment coefficient; represents the changing rate of the water level in the first weather information; κ represents the rate threshold.

[0139] Step S273, controlling the sensor network to collect initial weather information according to the target collection frequency.

[0140] Specifically, based on the analysis of the changing rate of the water level information in the first weather information, the initial collection samples are adaptively adjusted so that when the water level or flow rate changes drastically (such as rapid water accumulation caused by heavy rain), the sensor can adaptively adjust the collection frequency to improve the accuracy of the early warning signal.

[0141] Based on the above steps S271 to S273, the initial collection frequency, frequency adjustment coefficient, change rate and rate threshold of the first water level information in the first weather information of the sensor network are obtained; in response to the change rate being greater than the rate threshold, the target collection frequency is determined based on the initial collection frequency and the frequency adjustment coefficient, and the sensor network is controlled to collect the initial weather information at the target collection frequency, so that the weather information can be updated in a timely and accurate manner, thereby improving the accuracy of the dynamic model prediction.

[0142] Optionally, the method for determining a substation flood prevention strategy based on weather conditions further includes:

[0143] Step S281, in response to a sensor corresponding to the set of waterlogged areas failing, obtaining a terrain correction factor, load information of a communication link, and delay information between nodes;

[0144] Step S282, determining initial link weight information based on load information and delay information;

[0145] Specifically, the sensor network is represented as a graph G(t) = (V, E), where V is the set of sensor nodes and E is the communication link between nodes. The initial link weight W ij (t) is determined by the delay between nodes and the load of the communication link.

[0146] The calculation process of the initial link weight information is shown in expression (18).

[0147]

[0148] Among them, L ij (t) is the communication delay between sensor node i and its neighboring sensor node j; ρ ij (t) represents the load between sensor node i and its adjacent sensor node j. The higher the load, the smaller the link weight.

[0149] Step S283, adjusting the initial link weight information using the terrain correction factor to obtain target link weight information;

[0150] Specifically, in the complex environment around the substation, terrain and water flow will seriously affect the communication performance of the sensor network. Therefore, considering the dynamic impact of the physical environment on the sensor network, the initial link weight information is adjusted by the terrain correction factor θ(Z(x,y),h(x,y,t)). The target link weight is shown in expression (19).

[0151]

[0152] Among them, when h(x,y,t) (water level) is high and Z(x,y) (terrain) is low, the value of θ increases, indicating that the communication link of the node is more affected by the water level; γ is the adjustment coefficient, which is used to control the degree of correction of the communication by the water level and terrain. This correction factor enables the system to flexibly adjust the communication path to avoid large amounts of data transmission in low-lying areas with severe waterlogging. By considering the dynamic changes in terrain and water level, nodes with better communication quality can be selected first to improve the stability of data transmission. For example, when the water level in a certain area rises rapidly, the weight of the link in that area is automatically increased, and its communication priority is reduced to ensure that the data of important nodes can be transmitted through other channels, thereby enhancing the robustness of the entire system.

[0153] Step S284: determine the data transmission path of the sensor using the target link weight information.

[0154] Specifically, after the self-healing process is completed, a new network state is generated. The communication path is dynamically adjusted through the minimum spanning tree algorithm, and paths with low load and low communication delay are given priority to ensure the reliability of data transmission. It can ensure that when a sensor fails, the remaining sensors can continue to transmit data normally through topology reconstruction. Especially in the case of high load and high delay, the transmission path can be automatically optimized to reduce the impact of link bottlenecks on data transmission. This enables the sensor network to maintain efficient data flow in the face of extreme weather, and the entire substation waterlogging warning system will not collapse due to the failure of a single sensor or link.

[0155] Specifically, the substation waterlogging warning system will record information such as bottleneck nodes and link loads that appear during the self-healing process, and make corresponding adjustments in advance in similar situations in the future by analyzing historical data. For example, if node failures often occur in a certain area, redundant nodes can be reserved to reduce the burden of self-healing. The self-healing state feedback mechanism enables the system to have self-learning and self-optimization capabilities. The experience of each self-healing will be reflected in the future adjustments of the substation waterlogging warning system, so that the substation waterlogging warning system can gradually adapt to environmental changes and continuously enhance its robustness. This is particularly important for future extreme weather events, and can help the substation waterlogging warning system respond more flexibly to complex waterlogging scenarios.

[0156] Based on the above steps S281 to S284, in response to the failure of the sensor corresponding to the set of waterlogged areas, the terrain correction factor, the load information of the communication link and the delay information between nodes are obtained; the initial link weight information is determined based on the load information and the delay information; the initial link weight information is adjusted using the terrain correction factor to obtain the target link weight information; the data transmission path of the sensor is determined using the target link weight information, and the entire sensor network structure is systematically reconstructed by combining the water flow dynamics, terrain characteristics, sensor status and the load of the communication path. Ensure that the efficiency and stability of data collection and communication can still be maintained in complex environments.

[0157] Figure 3 FIG. 1 is a flow chart of another method for determining a substation flood prevention strategy based on weather environment according to one embodiment of the present invention. Figure 3 As shown, the method comprises the following steps:

[0158] Step S301, preprocessing the initial weather information collected by the sensor network of the substation to obtain first weather information;

[0159] Step S302, obtaining terrain height information of the area covered by the sensor network in the predicted waterlogging area;

[0160] Step S303, performing a gradient operation on the first water level information of the predicted waterlogging area set to obtain water level gradient information;

[0161] Step S304, inputting the first water level information, the first water flow velocity information, the first soil moisture information, the first precipitation information and the terrain height information collected by the sensor network into the shallow water equation to obtain a candidate solution of the shallow water equation;

[0162] Step S305, in response to the candidate solution making the drainage term of the shallow water equation obtain a minimum value, and based on the predicted collective water level gradient of the waterlogged area, determining that the water level gradient value is zero, determining that the area corresponding to the sensor node is a waterlogged area;

[0163] Step S306, in response to a sensor corresponding to the set of waterlogged areas failing, determining a distance weight coefficient between nodes based on the attenuation coefficient and distance information between the failed sensor node and each node in the set of neighboring nodes;

[0164] Step S307, using the distance weight coefficient and the initial weather information corresponding to the sensor node in the neighbor node set to compensate the initial weather information of the faulty sensor node to obtain the second weather information;

[0165] Step S308, performing data adjustment processing on the second weather information based on the node adjustment coefficient to obtain target weather information;

[0166] Step S309, in response to the target water level information and the target water flow velocity information being greater than the warning trigger threshold, determining a waterlogging risk score based on the target water level information, the target water flow velocity information, the target soil moisture and the target precipitation information;

[0167] Step S310, determining warning level information according to the waterlogging risk score;

[0168] Step S311, determining flood prevention measures according to the warning level information.

[0169] Based on the above steps S301 to S311, the initial weather information collected by the sensor network of the substation is preprocessed to obtain the first weather information, and the first weather information is input into the dynamic water flow model to predict the set of waterlogging areas. In response to the failure of the sensor corresponding to the set of waterlogging areas, the first weather information is updated using the faulty sensor node and the set of neighboring nodes of the faulty sensor node to obtain the second weather information, and the second weather information is data adjusted based on the node adjustment coefficient to obtain the target weather information. Finally, the substation anti-waterlogging strategy is determined based on the set of waterlogging areas, the target weather information and the warning trigger threshold, so as to achieve the purpose of accurately predicting the set of waterlogging areas and reporting waterlogging warnings in a timely and accurate manner, thereby achieving the technical effect of improving the accuracy of formulating anti-waterlogging strategies for substations and the efficiency of executing anti-waterlogging measures, thereby solving the technical problem in the related technology that the accuracy of anti-waterlogging strategies for substations is low and the efficiency of executing anti-waterlogging measures is low due to the low warning accuracy of substation waterlogging monitoring and early warning systems in complex weather environments.

[0170] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present invention.

[0171] In an embodiment of the present invention, a device for determining a substation flood prevention strategy based on a weather environment is also provided, and the device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0172] Figure 4 is a structural block diagram of a device for determining a substation flood prevention strategy based on weather environment according to one embodiment of the present invention, such as Figure 4 As shown, the device comprises:

[0173] The first processing module 401 is used to pre-process the initial weather information collected by the sensor network of the substation to obtain the first weather information, wherein the sensor network is constructed based on the communication links between the sensor nodes and the nodes;

[0174] A prediction module 402 is used to input the first weather information into a dynamic water flow model to predict a set of waterlogged areas, wherein the dynamic water flow model is constructed based on a shallow water equation and water level gradient information;

[0175] The second processing module 403 is used to update the first weather information using the faulty sensor node and the neighboring node set of the faulty sensor node in response to the sensor corresponding to the set of waterlogged areas failing, so as to obtain the second weather information;

[0176] A first determination module 404 is used to perform data adjustment processing on the second weather information based on the node adjustment coefficient to obtain target weather information;

[0177] The second determination module 405 is used to determine the substation anti-flooding strategy based on the waterlogging area set, target weather information and warning trigger threshold, wherein the substation anti-flooding strategy is used to determine anti-flooding measures.

[0178] Optionally, the device for determining the substation flood prevention strategy based on the weather environment also includes an acquisition module 406, which is used to obtain terrain height information of the area covered by the sensor network; the prediction module 402 is also used to: perform gradient operation on the first water level information to obtain water level gradient information; input the first water level information, first water flow velocity information, first soil moisture information, first precipitation information and terrain height information collected by the sensor network into the shallow water equation to obtain candidate solutions of the shallow water equation, wherein the drainage term of the shallow water equation is determined based on the terrain feedback coefficient and the soil moisture information; in response to the candidate solution, the drainage term of the shallow water equation obtains a minimum value, and based on the water level gradient, the water level gradient value is determined to be zero, and the area corresponding to the sensor node is determined to be a waterlogged area.

[0179] Optionally, the second processing module is also used for 403: determining the distance weight coefficient between nodes based on the attenuation coefficient and the distance information between the faulty sensor node and each node in the neighbor node set; using the distance weight coefficient and the initial weather information corresponding to the sensor node in the neighbor node set to compensate for the initial weather information of the faulty sensor node to obtain the second weather information.

[0180] Optionally, the second determination module is also used for 405: in response to the target water level information and the target water flow velocity information being greater than the warning trigger threshold, determining the waterlogging risk score based on the target water level information, the target water flow velocity information, the target soil moisture and the target precipitation information; determining the warning level information according to the waterlogging risk score; and determining waterlogging prevention measures according to the warning level information.

[0181] Optionally, the acquisition module 406 is further used to acquire state information of sensor nodes in the sensor network and compensation coefficients of the sensor nodes; the first determination module 404 is further used to determine the node adjustment coefficient based on the state information and the compensation coefficient.

[0182] Optionally, the acquisition module 406 is also used to obtain the initial acquisition frequency, frequency adjustment coefficient, change rate and rate threshold of the water level information in the initial weather information of the sensor network; the first processing module 401 is also used to determine the target acquisition frequency based on the initial acquisition frequency and the frequency adjustment coefficient in response to the change rate being greater than the rate threshold; the device for determining the substation flood prevention strategy based on the weather environment also includes a control module for controlling the sensor network to collect the initial weather information according to the target acquisition frequency.

[0183] Optionally, the acquisition module 406 is also used to obtain the terrain correction factor, load information of the communication link and delay information between nodes in response to a failure of the sensor corresponding to the set of waterlogged areas; the first determination module 404 is also used to: determine the initial link weight information based on the load information and the delay information; adjust the initial link weight information using the terrain correction factor to obtain the target link weight information; and determine the data transmission path of the sensor using the target link weight information.

[0184] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0185] According to another aspect of an embodiment of the present invention, a substation waterlogging warning system is also provided, comprising: a memory storing an executable program; a processor for running the program, wherein the program executes the above-mentioned method for determining a substation waterlogging prevention strategy based on the weather environment when running.

[0186] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0187] Step S1, preprocessing initial weather information collected by a sensor network of a substation to obtain first weather information, wherein the sensor network is constructed based on sensor nodes and communication links between nodes;

[0188] Step S2, inputting the first weather information into a dynamic water flow model to predict a set of waterlogged areas, wherein the dynamic water flow model is constructed based on a shallow water equation and water level gradient information;

[0189] Step S3, in response to a sensor corresponding to the set of waterlogged areas failing, the first weather information is updated using the failed sensor node and the set of neighboring nodes of the failed sensor node to obtain the second weather information;

[0190] Step S4, performing data adjustment processing on the second weather information based on the node adjustment coefficient to obtain target weather information;

[0191] Step S5, determining the substation anti-flooding strategy based on the waterlogging area set, target weather information and warning trigger threshold, wherein the substation anti-flooding strategy is used to determine anti-flooding measures.

[0192] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned method for determining a substation flood prevention strategy based on weather environment when running.

[0193] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0194] Step S1, preprocessing initial weather information collected by a sensor network of a substation to obtain first weather information, wherein the sensor network is constructed based on sensor nodes and communication links between nodes;

[0195] Step S2, inputting the first weather information into a dynamic water flow model to predict a set of waterlogged areas, wherein the dynamic water flow model is constructed based on a shallow water equation and water level gradient information;

[0196] Step S3, in response to a sensor corresponding to the set of waterlogged areas failing, the first weather information is updated using the failed sensor node and the set of neighboring nodes of the failed sensor node to obtain the second weather information;

[0197] Step S4, performing data adjustment processing on the second weather information based on the node adjustment coefficient to obtain target weather information;

[0198] Step S5, determining the substation anti-flooding strategy based on the waterlogging area set, target weather information and warning trigger threshold, wherein the substation anti-flooding strategy is used to determine anti-flooding measures.

[0199] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0200] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program, which implements the above-mentioned method for determining a substation flood prevention strategy based on weather environment when executed by a processor.

[0201] Optionally, in this embodiment, the computer program product may be configured to store computer program instructions for performing the following steps:

[0202] Step S1, preprocessing initial weather information collected by a sensor network of a substation to obtain first weather information, wherein the sensor network is constructed based on sensor nodes and communication links between nodes;

[0203] Step S2, inputting the first weather information into a dynamic water flow model to predict a set of waterlogged areas, wherein the dynamic water flow model is constructed based on a shallow water equation and water level gradient information;

[0204] Step S3, in response to a sensor corresponding to the set of waterlogged areas failing, the first weather information is updated using the failed sensor node and the set of neighboring nodes of the failed sensor node to obtain the second weather information;

[0205] Step S4, performing data adjustment processing on the second weather information based on the node adjustment coefficient to obtain target weather information;

[0206] Step S5, determining the substation anti-flooding strategy based on the waterlogging area set, target weather information and warning trigger threshold, wherein the substation anti-flooding strategy is used to determine anti-flooding measures.

[0207] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0208] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0209] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0210] 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.

[0211] 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.

[0212] In addition, each functional unit in each embodiment of the present invention 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.

[0213] 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 invention, in essence, 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 for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0214] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining a substation flood prevention strategy based on weather conditions, characterized in that: include: Preprocessing initial weather information collected by a sensor network of the substation to obtain first weather information, wherein the sensor network is constructed based on sensor nodes and communication links between nodes; Inputting the first weather information into a dynamic water flow model to predict a set of waterlogged areas, wherein the dynamic water flow model is constructed based on a shallow water equation and water level gradient information; In response to a failure of a sensor corresponding to the set of waterlogged areas, the first weather information is updated using a faulty sensor node and a set of neighboring nodes of the faulty sensor node to obtain second weather information; Performing data adjustment processing on the second weather information based on the node adjustment coefficient to obtain target weather information; A substation anti-flooding strategy is determined based on the set of waterlogging areas, the target weather information and an early warning trigger threshold, wherein the substation anti-flooding strategy is used to determine anti-flooding measures.

2. The method for determining a substation flood prevention strategy based on weather conditions according to claim 1, characterized in that: The first weather information at least includes: first water level information, first water flow velocity information, first soil moisture information and first precipitation information. The first weather information collected by the sensor network of the substation is input into the dynamic water flow model. The predicted waterlogging area set includes: Acquiring terrain height information of the area covered by the sensor network; Performing a gradient operation on the first water level information to obtain water level gradient information; Inputting the first water level information, the first water flow velocity information, the first soil moisture information, the first precipitation information and the terrain height information collected by the sensor network into the shallow water equation to obtain a candidate solution of the shallow water equation, wherein the drainage term of the shallow water equation is determined based on a terrain feedback coefficient and the soil moisture information; In response to the candidate solution making the drainage term of the shallow water equation obtain a minimum value, and based on the water level gradient determining that the water level gradient value is zero, determining that the area corresponding to the sensor node is a waterlogged area.

3. The method for determining a substation flood prevention strategy based on weather conditions according to claim 1, characterized in that: In response to a sensor corresponding to the set of waterlogged areas failing, the first weather information is updated using a failed sensor node and a set of neighboring nodes of the failed sensor node to obtain the second weather information, including: Determine a distance weight coefficient between nodes based on an attenuation coefficient and distance information between the faulty sensor node and each node in the neighbor node set; The first weather information of the faulty sensor node is compensated by using the distance weight coefficient and the first weather information corresponding to the sensor node in the neighbor node set to obtain the second weather information.

4. The method for determining a substation flood prevention strategy based on weather conditions according to claim 1, characterized in that: The target weather information at least includes: target water level information, target water flow velocity information, target soil moisture and target precipitation information. Determining the substation flood prevention strategy based on the waterlogging area set, the target weather information and the warning trigger threshold includes: In response to the target water level information and the target water flow velocity information being greater than the warning trigger threshold, determining a waterlogging risk score based on the target water level information, the target water flow velocity information, the target soil moisture, and the target precipitation information; determining warning level information according to the waterlogging risk score; The flood prevention measures are determined according to the warning level information.

5. The method for determining a substation flood prevention strategy based on weather conditions according to claim 1, characterized in that: The method further comprises: Acquiring state information of sensor nodes and compensation coefficients of sensor nodes in the sensor network; The node adjustment coefficient is determined based on the state information and the compensation coefficient.

6. The method for determining a substation flood prevention strategy based on weather conditions according to claim 2, characterized in that: The method further comprises: Acquire an initial acquisition frequency, a frequency adjustment coefficient, a change rate and a rate threshold of the sensor network; In response to the change rate being greater than the rate threshold, determining a target acquisition frequency based on the initial acquisition frequency and the frequency adjustment coefficient; The sensor network is controlled to collect the initial weather information according to the target collection frequency.

7. The method for determining a substation flood prevention strategy based on weather conditions according to claim 1, characterized in that: The method further comprises: In response to a failure of a sensor corresponding to the set of waterlogged areas, obtaining a terrain correction factor, load information of the communication link, and delay information between nodes; Determine initial link weight information based on the load information and the delay information; Using the terrain correction factor to adjust the initial link weight information to obtain target link weight information; The target link weight information is used to determine the data transmission path of the sensor.

8. A substation waterlogging early warning system, characterized in that: include: A memory storing an executable program; A processor is used to run the program, wherein when the program is running, the method for determining the substation flood prevention strategy based on the weather environment as described in any one of claims 1 to 7 is executed.

9. A non-volatile storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method for determining a substation flood prevention strategy based on weather environment as described in any one of claims 1 to 7 when running.

10. A computer program product, characterized in that It comprises a computer program, which, when executed by a processor, implements the method for determining a substation flood prevention strategy based on weather environment according to any one of claims 1 to 7.

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