Control method of drainage system

Abstract

The invention discloses a control method of a drainage system. The method comprises the following steps: collecting a historical meteorological data set of a target area where power equipment is located in a historical period of time; predicting the plurality of historical meteorological data sets to obtain a predicted meteorological data set of the target area in a future time period; based on the predicted meteorological data set, determining a risk assessment result of the target area, the risk assessment result being used for representing a probability of occurrence of a risk event associated with the predicted meteorological data set in the local area; based on the risk assessment result, determining control data of a drainage system in the target area; and controlling a drainage system to drain accumulated water in the target area according to the control data. The technical problem that the efficiency of processing accumulated water in the transformer substation is low is solved.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a control method for a drainage system. Background Art

[0002] At present, key power equipment such as substations are extremely vulnerable to waterlogging when faced with natural disasters such as heavy rains and floods. Substations are the hub of power transmission and distribution, and waterlogging may cause damage to electrical equipment, and in severe cases, cause large-scale power outages, affecting the stable operation of the power system.

[0003] In the related art, the forecast of regional local weather for power facilities is usually determined based on large-scale meteorological conditions. However, large-scale meteorological data is difficult to reflect local rainfall and water level changes, and there may be a large deviation between meteorological data and actual conditions. Therefore, the above method has the technical problem of low efficiency in dealing with water accumulation in substations.

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

[0005] The embodiment of the present invention provides a control method for a drainage system, so as to at least solve the technical problem of low efficiency in processing water accumulation in a substation.

[0006] According to one aspect of an embodiment of the present invention, a method for controlling a drainage system is provided, which may include: collecting a historical meteorological data set of a target area where power equipment is located within a historical period; predicting multiple historical meteorological data sets to obtain a predicted meteorological data set for the target area in a future period; determining a risk assessment result for the target area based on the predicted meteorological data set, wherein the risk assessment result is used to characterize the probability of a risk event associated with the predicted meteorological data set occurring in a local area; determining control data of a drainage system in the target area based on the risk assessment result; and controlling the drainage system to drain the accumulated water in the target area according to the control data.

[0007] Optionally, during a historical period, a historical meteorological data set of a target area where the power equipment is located is collected, including: obtaining an initial meteorological data set collected by multiple sensors in the target area; determining an adjustment coefficient corresponding to the sensor; and using the adjustment coefficient to adjust the initial meteorological data set collected by the corresponding sensor to obtain a historical meteorological data set.

[0008] Optionally, determining an adjustment coefficient corresponding to the sensor includes: determining a deployment position of the sensor; and determining an adjustment coefficient corresponding to the deployment position.

[0009] Optionally, the method may also include: in response to a sensor abnormality, determining at least one adjacent sensor adjacent to the sensor with the abnormality among multiple sensors; and adjusting the initial meteorological data set collected by the sensor with the abnormality based on the initial meteorological data set collected by the at least one adjacent sensor.

[0010] Optionally, multiple historical meteorological data sets are predicted to obtain a predicted meteorological data set for the target area in a future period, including: determining the terrain characteristics of the target area; determining a weight coefficient that matches the terrain characteristics; using the weight coefficient to perform weighted summation on the historical meteorological data sets to obtain input data; inputting the input data into a micro-meteorological model to predict a predicted meteorological data set, wherein the micro-meteorological model is trained based on terrain characteristics and a meteorological data sample set; the method also includes: adjusting model parameters of the micro-meteorological model based on the historical meteorological data set and the predicted meteorological data set collected in the future period.

[0011] Optionally, based on the predicted meteorological data set, determining the risk assessment result of the target area includes: determining the amount of water accumulation in the target area in a future period based on the predicted meteorological data set; and determining the risk assessment result based on the amount of water accumulation.

[0012] Optionally, based on the predicted meteorological data set, the amount of water accumulation in the target area in a future period is determined, including: constructing a water flow model based on the terrain characteristics of the target area; and inputting the predicted meteorological data set into the water flow model to obtain the amount of water accumulation.

[0013] Optionally, based on the amount of accumulated water, a risk assessment result is determined, including: converting the amount of accumulated water to obtain a drainage pressure index, wherein the drainage pressure index is used to characterize the working state of the drainage system; using a pressure threshold to judge the drainage pressure index to obtain a judgment result; and determining a risk assessment result that matches the judgment result.

[0014] Optionally, based on the risk assessment results, control data of the drainage system in the target area is determined, including: determining the control data based on the risk assessment results, the drainage pressure index and the amount of accumulated water.

[0015] Optionally, the method may further include: determining a working state of the drainage system and a predicted amount of water accumulation in the target area under the working state; and adjusting a working power of the drainage system based on the predicted amount of water accumulation.

[0016] According to another aspect of an embodiment of the present invention, a control device for a drainage system is also provided, and the device may include: a collection unit, used to collect historical meteorological data sets of a target area where the power equipment is located within a historical period; a prediction unit, used to predict multiple historical meteorological data sets to obtain a predicted meteorological data set of the target area in a future period; a first determination unit, used to determine a risk assessment result of the target area based on the predicted meteorological data set, wherein the risk assessment result is used to characterize the probability of a risk event associated with the predicted meteorological data set occurring in a local area; a second determination unit, used to determine control data of the drainage system in the target area based on the risk assessment result; and a control unit, used to control the drainage system to discharge the accumulated water in the target area according to the control data.

[0017] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the control method of the drainage system of an embodiment of the present invention.

[0018] According to another aspect of an embodiment of the present invention, a processor is further provided. The processor is used to run a program, wherein the program, when run by the processor, executes the control method of the drainage system of an embodiment of the present invention.

[0019] According to another aspect of an embodiment of the present invention, a program product is further provided. The program product includes computer instructions. When the computer instructions are executed by a processor, the control method of the drainage system according to the embodiment of the present invention is implemented.

[0020] In an embodiment of the present invention, during a historical period, a historical meteorological data set of a target area where the power equipment is located is collected; a plurality of historical meteorological data sets are predicted to obtain a predicted meteorological data set of the target area in a future period; based on the predicted meteorological data set, a risk assessment result of the target area is determined, wherein the risk assessment result is used to characterize the probability of a risk event associated with the predicted meteorological data set occurring in a local area; based on the risk assessment result, control data of a drainage system in the target area is determined; and according to the control data, the drainage system is controlled to discharge the accumulated water in the target area. That is, in an embodiment of the present invention, a historical meteorological data set is predicted to obtain a predicted meteorological data set, based on the predicted meteorological data set, a risk assessment result of the target area (e.g., a local area) can be determined, based on the risk assessment result, control data of the drainage system can be determined, and the drainage system is controlled according to the control data to prevent waterlogging in the substation, thereby solving the technical problem of low efficiency in handling accumulated water in the substation, and achieving the technical effect of improving the efficiency of handling accumulated water in the substation. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. 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:

[0022] Figure 1 is a flow chart of a method for controlling a drainage system according to an embodiment of the present invention;

[0023] Figure 2 is a flow chart of a method for optimizing substation waterlogging prevention operation and maintenance in extreme weather environments according to an embodiment of the present invention;

[0024] Figure 3 is a schematic diagram of a substation flood prevention operation and maintenance optimization system facing extreme weather environments according to an embodiment of the present invention;

[0025] Figure 4 is a schematic diagram of a control device for a drainage system according to an embodiment of the present invention;

[0026] Figure 5 is a structural block diagram of a computer terminal according to an embodiment of the present invention;

[0027] Figure 6 It is a block diagram of an electronic device of a drainage system control method according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] 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.

[0029] 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.

[0030] According to an embodiment of the present invention, an embodiment of a control method for a drainage system is provided, and the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and, although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0031] In this embodiment, a control method for a drainage system is proposed. The method can predict a historical meteorological data set to obtain a predicted meteorological data set. Based on the predicted meteorological data set, a risk assessment result of a target area (e.g., a local area) can be determined. Based on the risk assessment result, control data of the drainage system can be determined. The drainage system is controlled according to the control data to prevent waterlogging in the substation, thereby solving the technical problem of low efficiency in handling water accumulation in the substation and achieving the technical effect of improving the efficiency of handling water accumulation in the substation.

[0032] Figure 1 FIG. 1 is a flow chart of a method for controlling a drainage system according to an embodiment of the present invention. Figure 1 As shown, the method may include the following steps:

[0033] Step S102: collecting historical meteorological data sets of the target area where the power equipment is located during the historical period.

[0034] In the technical solution provided in the above step S102 of the present invention, the above-mentioned power equipment may be key power equipment such as substations, generators, transformers, switchgear, cables, etc., and the type of power equipment is not specifically limited here. The above-mentioned target area may be a local area in or around the substation, such as a low-lying area around the substation, an entrance or exit of a drainage channel, a corner prone to water accumulation, etc. Each target area may have different terrain characteristics, hydrological conditions and drainage capabilities. The above-mentioned historical meteorological data set may include historical meteorological data of the target area at multiple times, and may include data such as rainfall, surface water level, drainage channel flow velocity and surface permeability corresponding to multiple times. It should be noted that this is only for example, and there is no specific limitation on the data type in the historical meteorological data set.

[0035] Optionally, historical meteorological data of the target area where the power equipment is located in a historical period can be collected by sensors and other equipment to obtain a historical meteorological data set. The above historical meteorological data can be local meteorological data, so the historical meteorological data set can be a local meteorological data set.

[0036] For example, in the local meteorological data collection, we can focus on the variables that are crucial to the prediction of waterlogging. Therefore, we can collect the following historical meteorological data: rainfall R(t) that directly determines the source of water accumulation in the substation area, surface water level L(t) that monitors the water level changes inside and outside the substation and is used to assess the water accumulation in the area, flow velocity V(t) in the drainage channel that ensures the smoothness of the drainage system and warns of the risk of insufficient drainage capacity in advance, and surface permeability P(t) that is used to monitor the water absorption capacity of the soil and the ground and judge the water flow path after rainfall. It should be noted that the types of historical meteorological data mentioned above are only for illustration and are not specifically limited here.

[0037] Alternatively, historical meteorological data can directly affect the drainage capacity and waterlogging situation of substations, which can provide the most useful input for micrometeorological models and support subsequent waterlogging risk assessment and drainage scheduling strategies.

[0038] Optionally, during a historical period, historical meteorological data of a local area where the substation is located is collected to obtain historical meteorological data sets corresponding to multiple historical moments.

[0039] Step S104, predicting multiple historical meteorological data sets to obtain a predicted meteorological data set for the target area in the future period.

[0040] In the technical solution provided in the above step S104 of the present invention, multiple historical meteorological data sets are obtained, and multiple historical meteorological data sets are predicted to obtain a predicted meteorological data set for the target area in the future period. The above predicted meteorological data set can be used to determine the amount of water accumulation in the target area corresponding to each future moment. For example, the predicted meteorological data can be used to characterize the amount of water accumulation, or can be used to characterize water level information, and can include information such as rainfall, permeability, drainage flow rate, and soil saturation state in the target area at the future moment. It should be noted that this is only for example, and there is no specific limitation on the characterization content of the predicted meteorological data.

[0041] Step S106, determining a risk assessment result of the target area based on the forecast meteorological data set, wherein the risk assessment result is used to characterize the probability of a risk event associated with the forecast meteorological data set occurring in the local area.

[0042] In the technical solution provided in step S106 of the present invention, the risk assessment result of the target area can be determined based on the predicted meteorological data set. The risk assessment result can be used to characterize the probability of a risk event associated with the predicted meteorological data set occurring in the local area, that is, the risk assessment result can be used to determine the probability that the amount of water accumulated in the target area is greater than the water volume threshold. The risk event can be a waterlogging event occurring in the substation area.

[0043] Optionally, based on the amount of accumulated water, the probability of waterlogging in the target area can be determined, and based on the probability, the risk assessment result can be determined. For example, the greater the probability of waterlogging, the higher the risk level. The risk assessment result can be a waterlogging risk assessment result, which can be in the form of numbers, Chinese characters, etc., and the form of expression of the risk assessment result is not specifically limited here.

[0044] For example, a local meteorological data set is collected, which may include rainfall, surface water level, drainage channel flow rate, and surface permeability. The local meteorological data is preprocessed, and the preprocessed data is input into the micro-meteorological model to predict the amount of accumulated water. The waterlogging risk can be predicted based on the amount of accumulated water, and the waterlogging risk can be assessed based on the result of the waterlogging risk prediction to obtain a risk assessment result. At the same time, the waterlogging risk assessment result can be dynamically updated based on the real-time amount of accumulated water.

[0045] Step S108: determining control data of the drainage system in the target area based on the risk assessment result.

[0046] In the technical solution provided in the above step S108 of the present invention, the control data of the drainage system in the target area can be determined based on the risk assessment results. Among them, the above control data can be used to determine the number of drainage systems to be scheduled, the power of the drainage system and other data, and can be used to control the operation of the drainage system. There is no specific restriction on the type of control data here. The above drainage system may include at least one drainage device, which can be used to drain the accumulated water in the target area, for example, it can be a drainage pump. It should be noted that as long as it is a device used for drainage, it should be within the scope of protection of this application, and no specific restriction is made here.

[0047] For example, based on the results of the waterlogging risk assessment, combined with the real-time collected meteorological data and the real-time feedback of the working status of the drainage system, the control data of the drainage system can be determined; for example, the working power of the water pump can be dynamically adjusted according to the adjusted working status of the drainage system and the predicted amount of water accumulation. The dynamic adjustment of the working power of the water pump can include local scheduling and global scheduling.

[0048] Step S110, controlling the drainage system to drain the accumulated water in the target area according to the control data.

[0049] In the technical solution provided in the above step S110 of the present invention, after the control data is determined, the drainage system can be controlled to work according to the control data to achieve the purpose of draining the accumulated water in the target area. Furthermore, the feedback drainage results after the operation of the drainage equipment (such as a water pump) can be monitored and adjusted in real time, and the feedback drainage results can be combined with the real-time updated prediction results (that is, the predicted meteorological data) to perform long-term global optimization.

[0050] Through the above-mentioned steps S102 and S110 of the present invention, in the historical period, the historical meteorological data set of the target area where the power equipment is located is collected; multiple historical meteorological data sets are predicted to obtain the predicted meteorological data set of the target area in the future period; based on the predicted meteorological data set, the risk assessment result of the target area is determined, wherein the risk assessment result is used to characterize the probability of a risk event associated with the predicted meteorological data set occurring in the local area; based on the risk assessment result, the control data of the drainage system in the target area is determined; according to the control data, the drainage system is controlled to discharge the accumulated water in the target area. That is to say, in the embodiment of the present invention, the historical meteorological data set is predicted to obtain the predicted meteorological data set, based on the predicted meteorological data set, the risk assessment result of the target area (for example, the local area) can be determined, based on the risk assessment result, the control data of the drainage system can be determined, and the drainage system is controlled according to the control data to prevent the problem of waterlogging in the substation, thereby solving the technical problem of low efficiency in handling accumulated water in the substation, and achieving the technical effect of improving the efficiency of handling accumulated water in the substation.

[0051] The above method of this embodiment is further introduced below.

[0052] As an optional implementation, step S102 collects historical meteorological data sets of the target area where the power equipment is located during a historical period, including: obtaining an initial meteorological data set collected by multiple sensors in the target area; determining an adjustment coefficient corresponding to the sensor; and using the adjustment coefficient to adjust the initial meteorological data set collected by the corresponding sensor to obtain a historical meteorological data set.

[0053] In this embodiment, in order to obtain the initial meteorological data set in the target area, sensors can be reasonably arranged in the target area, and the acquisition strategy of the sensors can be determined. The initial meteorological data set in the target area can be acquired by using the sensors. Furthermore, considering that different sensors may be affected by the terrain, resulting in data deviation, the initial meteorological data set can be adjusted using an adjustment coefficient to obtain a historical meteorological data set. The adjustment coefficient can be the weight of the sensor, and the adjustment coefficient can be dynamically adjusted according to the importance and reliability of the sensor location.

[0054] For example, for the collection of rainfall R(t) in the initial meteorological data set, rainfall sensors can be arranged at multiple points inside and outside the substation area. The rainfall collected by each sensor can be used to dynamically calculate the local rainfall distribution. The average value of multiple sensors can reflect the overall rainfall change in the area. The rainfall can be determined by the following steps:

[0055]

[0056] Among them, R i (t) can be used to represent the rainfall collected by the i-th sensor at time t, and n can be the number of sensors.

[0057] For the collection of surface water level L(t) in the initial meteorological data set, water level sensors can be placed at key locations such as low-lying areas of the substation, the entrance and exit of the drainage ditch, etc. Through the water level sensors, the water level changes can be monitored in real time. The layout of the water level sensors can follow the changes in the terrain in the target area. The water level sensors need to cover all areas in the target area where water may accumulate. The surface water level corresponding to the historical moment can be determined by the following formula:

[0058] L(t)=max(L1(t),L2(t),…,L m (t))

[0059] Among them, L i (t) is the reading of the i-th water level sensor, and m is the total number of water level sensors. By selecting the maximum value, the most serious waterlogging point can be captured.

[0060] For monitoring the flow velocity V(t) of the drainage channel in the initial meteorological data set, a flow velocity sensor can be installed in the drainage channel, which can be used to monitor the speed of water flow and drainage smoothness in the target area. By collecting drainage velocity data, the load state of the drainage system can be predicted and possible blockage risks can be detected. The flow velocity of the drainage channel corresponding to the historical moment can be determined by the following formula:

[0061]

[0062] Among them, V(t) can be used to characterize the water flow velocity in the drainage channel calculated based on the water level change rate. If V(t) is lower than the preset value, it means that the drainage channel may be blocked or the rainfall exceeds the processing capacity of the drainage system.

[0063] For the monitoring of the surface permeability P(t) in the initial meteorological data set, permeability sensors can be arranged in the soil and pavement area of ​​the substation to measure the soil's water absorption capacity. The surface permeability can help the system determine whether the accumulated water after rainfall will quickly drain or infiltrate into the ground. The surface permeability can be calculated using the following formula:

[0064]

[0065] Among them, V in (t) can be used to characterize the amount of water absorbed by the soil per unit time, and A can be used to characterize the area of ​​the soil. Areas with lower permeability are more likely to accumulate water.

[0066] It should be noted that the types of initial meteorological data, types of sensors, and the methods of sensor data collection are merely examples and are not specifically limited here.

[0067] Optionally, initial meteorological data can be obtained through the above-mentioned sensor, and the initial meteorological data can be adjusted using the adjustment coefficient corresponding to the sensor to obtain a historical meteorological data set. The following further explains the method of determining the adjustment coefficient and adjusting the initial meteorological data set based on the adjustment coefficient to obtain a historical meteorological data set.

[0068] As an optional implementation manner, determining an adjustment coefficient corresponding to a sensor includes: determining a deployment position of the sensor; and determining an adjustment coefficient corresponding to the deployment position.

[0069] In this embodiment, considering that different sensors may be affected by the terrain, resulting in deviations in the collected initial meteorological data, this embodiment uses an adjustment coefficient to preprocess (eg, calibrate and fuse) the initial meteorological data set to obtain a historical meteorological data set.

[0070] Optionally, a normalization function S(t) is defined, which can be used to perform weighted averaging on multi-point data:

[0071]

[0072] Among them, D i (t) can be used to represent the initial meteorological data collected by sensor i at time t, w i It can be used to characterize the weight (ie, adjustment coefficient) corresponding to the sensor. The weight w i It can be dynamically adjusted based on the importance and reliability of the sensor location to ensure that the merged data is more accurate.

[0073] Optionally, adjustment coefficients corresponding to sensors at different deployment locations may be pre-set. After initial meteorological data is acquired using the sensor, the initial meteorological data may be processed using an adjustment system corresponding to the sensor to obtain final historical meteorological data.

[0074] For example, the water level information corresponding to sensor 1 is 5, the water level information corresponding to sensor 2 is 6, and the water level information corresponding to sensor 3 is 4. Based on the deployment positions of the above three sensors, it can be determined that the adjustment coefficients corresponding to the three sensors are 0.2, 0.5, and 0.5 respectively. The water level information collected by the sensors is adjusted based on the adjustment coefficients, so that the final water level information corresponding to the target area is: 5*0.2+6*0.5+4*0.5=6.

[0075] As an optional implementation, the method may also include: in response to a sensor abnormality, determining at least one adjacent sensor adjacent to the sensor with the abnormality among multiple sensors; and adjusting the initial meteorological data set collected by the sensor with the abnormality based on the initial meteorological data set collected by the at least one adjacent sensor.

[0076] In this embodiment, in order to improve the accuracy of the collected initial meteorological data, when a sensor is abnormal, the initial meteorological data collected by the abnormal sensor can be corrected using the data of adjacent sensors.

[0077] Optionally, if it is determined that a sensor has an abnormality, at least one adjacent sensor to the abnormal sensor can be determined among multiple sensors, and the initial meteorological data set collected by the abnormal sensor can be adjusted based on the initial meteorological data set collected by the adjacent sensor.

[0078] For example, under extreme weather conditions (such as heavy rain, heavy precipitation), some sensors may fail or the data may be abnormal. In order to ensure the reliability of the data, this embodiment can automatically judge the state of the sensor, and can adjust the data through a redundant mechanism. For example, when a rainfall sensor (or rain sensor) detects abnormally high rainfall data (that is, rainfall data), the initial meteorological data collected by the abnormal sensor can be corrected by using the initial meteorological data set collected by the neighboring sensor (that is, the adjacent sensor). Among them, the adjustment formula for dynamic data can be:

[0079] If R i (t) Abnormal

[0080] Among them, R adjusted (t) can be used to characterize the adjusted sensor data, and R_i(t) can be used to represent the current sensor (that is, the sensor with an abnormality).

[0081] Optionally, the above adjustment process can ensure that even if some sensors fail, the system can still provide reliable rainfall and water level information.

[0082] As an optional implementation, step S104 predicts multiple historical meteorological data sets to obtain a predicted meteorological data set for the target area in the future period, including: determining the terrain characteristics of the target area; determining a weight coefficient that matches the terrain characteristics; using the weight coefficient to perform weighted summation on the historical meteorological data sets to obtain input data; inputting the input data into a micro-meteorological model to predict a predicted meteorological data set, wherein the micro-meteorological model is trained based on terrain characteristics and a meteorological data sample set; the method also includes: adjusting the model parameters of the micro-meteorological model based on the historical meteorological data set and the predicted meteorological data set collected in the future period.

[0083] In this embodiment, the micro-meteorological model can be trained in advance based on different terrain features and meteorological data sample sets. When the meteorological data of the target area needs to be predicted, the terrain features of the target area can be determined, and the weight coefficients matching the terrain features can be determined. The weight coefficients can be used to perform weighted summation on the historical meteorological data set to obtain input data. Further, the input data can be input into the micro-meteorological model to predict the predicted meteorological data set. Among them, the above-mentioned terrain features can include low-lying areas and the vicinity of drainage channels.

[0084] Optionally, the historical meteorological data in the historical meteorological data set are integrated to obtain input data, for example, the real-time rainfall R final (t), surface water level L final (t), drainage flow rate V final (t) and surface permeability P final The integrated input data can be input into the micro-meteorological model as the basis for waterlogging risk prediction, thus ensuring real-time response to weather changes.

[0085] In this embodiment, a micro-meteorological model can be obtained based on terrain features and meteorological data sample set training. Among them, the main task of constructing the above-mentioned micro-meteorological model can be to integrate core variables such as rainfall R(t), surface water level L(t), drainage flow rate V(t), and surface permeability P(t). The input data obtained after integration can be directly used as the basic input of the micro-meteorological model, and different regions and facilities have different weighted coefficients for weighting these data. Among them, the above-mentioned weight coefficients can be used to characterize the importance of different historical meteorological data under the terrain characteristics.

[0086] Optionally, multiple historical meteorological data are integrated in a weighted manner to obtain input data, and the input data is processed to ensure that the model can fully reflect the actual impact of different regions and terrains (ie, terrain features).

[0087] For example, the historical meteorological data collected by various regions (such as low-lying areas, drainage channels, etc.) and sensors can be combined into the model in a weighted manner to form a local environmental model M(t). Assume that n sensors collect data:

[0088]

[0089] Among them, w i Can be the weight coefficient, D i (t) can be the measurement value of sensor i at time t (such as rainfall, water level, drainage flow rate, etc.), and n can be the number of sensors. M(t) can be used to characterize the input data obtained by calculation.

[0090] Optionally, the local environment model can be used to perform local adaptive optimization.

[0091] Optionally, in order to combine the special scenario of the substation, a regional impact factor (i.e., weight coefficient) is designed. By using the weight coefficient, the weight of low-lying areas or areas with difficult drainage is higher, while the weight of highlands or areas with smooth drainage is lower. i It can represent the drainage pressure coefficient or historical waterlogging frequency of a certain area, which makes the model more localized. The above weight coefficient can be determined based on testing or experience, and the method for determining the weight coefficient is not specifically limited here.

[0092] Optionally, in order to make the micro-meteorological model adaptive, an adaptive feedback mechanism is introduced in this embodiment to cope with the dynamic changes of meteorological and drainage conditions. When the historical meteorological data set is obtained, the model can be adaptively adjusted according to the current waterlogging situation and drainage bottleneck effect to obtain input data, and the input data can be predicted to improve the future prediction accuracy.

[0093] Furthermore, the model parameters can be dynamically adjusted by comparing the newly collected data with the model prediction results (ie, the predicted meteorological data set). The adaptive feedback formula is as follows:

[0094] M(t+Δt)=M(t)+α·(D new (t)-D pred (t))

[0095] Among them, D new (t) can be the latest meteorological data collected in real time, D pred (t) can be the model prediction result, and α can be the update coefficient. This adaptive mechanism can update the model in real time, and adjust the model parameters of the micrometeorological model through the above adaptive formula to ensure accurate prediction under different weather conditions.

[0096] As an optional implementation, step S106, based on the predicted meteorological data set, determines the risk assessment result of the target area, including: based on the predicted meteorological data set, determining the amount of water accumulation in the target area in a future period; based on the amount of water accumulation, determining the risk assessment result.

[0097] In this embodiment, the amount of water accumulation in the target area in a future period can be determined based on the predicted meteorological data set, and the risk assessment result can be determined based on the amount of water accumulation.

[0098] For example, by predicting multiple historical meteorological data sets, a predicted meteorological data set can be obtained, which may include data such as rainfall, permeability, drainage flow rate, and soil saturation state. Based on the predicted meteorological data set, the amount of water accumulation in the target area can be determined. Furthermore, based on the amount of water accumulation, a risk assessment result corresponding to the amount of water accumulation can be determined. For example, the more water accumulation, the higher the risk level can be determined, and the less water accumulation, the lower the risk level can be determined.

[0099] As an optional implementation, based on the predicted meteorological data set, the amount of water accumulation in the target area in a future period is determined, including: constructing a water flow model based on the terrain characteristics of the target area; inputting the predicted meteorological data set into the water flow model to obtain the amount of water accumulation.

[0100] In this embodiment, the prevention and control of waterlogging in the substation cannot be separated from the simulation of water flow characteristics in different terrains. The accumulation and discharge of water flow in low-lying areas and highlands are completely different. Therefore, in order to build a more accurate model for these differences, the terrain characteristics can be incorporated into the design of the water flow model to construct a water flow model. Furthermore, the predicted meteorological data set can be input into the water flow model to obtain the amount of accumulated water. Among them, the above-mentioned terrain characteristics may include characteristics such as terrain slope and the geometric structure of the drainage channel. It should be noted that this is only for example, and there is no specific restriction on the type of terrain characteristics.

[0101] For example, considering that the drainage velocity V(t) is affected by the terrain slope and the geometric structure of the drainage channel, a terrain correction term σ(z i ) to reflect the effects of water flow acceleration or deceleration in different terrains:

[0102] V(t)=V0(t)·(1+σ(z i ))

[0103] Where V0(t) is the initial drainage velocity, σ(z i ) can be a terrain correction term, z i It can be used to represent different terrain units within the substation, and the σ(z i) can be positive, indicating an acceleration of water flow, while in areas with good drainage, the terrain correction term can be negative, indicating a slowdown of water flow.

[0104] Furthermore, a dynamic waterlogging model can be designed to predict the changes in waterlogging in the substation. To better meet the actual situation of the substation, two key factors, drainage bottleneck and soil saturation, can be added to the original model. These two factors greatly affect the evolution of waterlogging. The amount of waterlogging S(t) depends on rainfall, permeability, drainage flow rate, and soil saturation. The new dynamic waterlogging model (i.e., water flow model) is defined as:

[0105] S(t+Δt)=S(t)+(R(t)·(1-P(t)))-(V(t)-B(t))

[0106] Among them, R(t) is rainfall, P(t) is permeability, V(t) is drainage velocity, and B(t) is drainage bottleneck effect. When the drainage pipe is blocked or the flow is overloaded, B(t) increases and slows down the drainage speed. B(t) can be defined as:

[0107] B(t)=λ·max(0,S(t)-S crit )

[0108] Among them, λ is the bottleneck coefficient, S crit is the carrying capacity threshold of the drainage system, S(t)>S crit When the drainage capacity is limited, there is a risk of waterlogging. By introducing the drainage bottleneck term B(t), the model can automatically identify the reduced efficiency of the drainage system under overload conditions, accurately predict the critical point of substation drainage, and improve the accuracy of waterlogging prediction.

[0109] In this embodiment, the micro-meteorological model may include a local environment model and a dynamic water flow model, and the local environment model may be used to perform weighted summation of historical meteorological data using a weight coefficient to obtain input data. The input data may be predicted to obtain predicted meteorological data, and further, the predicted meteorological data may be input into the dynamic water flow model to predict the amount of accumulated water.

[0110] As an optional implementation, a risk assessment result is determined based on the amount of accumulated water, including: converting the amount of accumulated water to obtain a drainage pressure index, wherein the drainage pressure index is used to characterize the working state of the drainage system; using a pressure threshold to judge the drainage pressure index to obtain a judgment result; and determining a risk assessment result that matches the judgment result.

[0111] In this embodiment, the amount of accumulated water may be converted to obtain a drainage pressure index, and further, the drainage pressure index may be judged using a pressure threshold to obtain a judgment result.

[0112] Optionally, the drainage pressure index P is designed by combining the dynamic water accumulation model and the response capacity of the drainage system. stress (t), this index can be used to measure the drainage load of the substation at a certain moment, and then infer the risk of waterlogging. This index is directly related to factors such as water accumulation, drainage bottleneck, and drainage flow rate. Drainage pressure index formula:

[0113]

[0114] When P stress When (t)>1, it means that the system is in an overloaded state, the drainage cannot keep up with the growth rate of water accumulation, and the risk of waterlogging increases. stress (t), we can calculate the flood risk level at a certain moment in the future in real time. risk Set alarms to trigger waterlogging warnings when pressure index exceeds thresholds.

[0115] As an optional implementation, step S108, based on the risk assessment result, determines the control data of the drainage system in the target area, including: determining the control data based on the risk assessment result, the drainage pressure index and the amount of accumulated water.

[0116] In this embodiment, the control data may be determined based on the risk assessment result, the drainage pressure index, and the amount of accumulated water.

[0117] Optionally, combine the waterlogging prediction S(t) and the drainage pressure index P stress (t), the water accumulation situation and future waterlogging risk in different areas of the substation can be output. With these data, the drainage system of the substation can adjust the drainage strategy in advance to prevent waterlogging. The final output formula is:

[0118]

[0119] Among them, S(t) is the amount of water accumulation, P stress (t) is the drainage pressure index, is the flood risk assessment value for each area. This output can guide the operation and maintenance decision of the substation to avoid equipment damage. Output(t) can be used to determine the drainage strategy (i.e., control data).

[0120] In this embodiment, waterlogging risk assessment can be performed based on the results of waterlogging risk prediction, and the waterlogging risk assessment can be dynamically updated based on the real-time amount of accumulated water.

[0121] Optionally, to ensure detailed processing of waterlogging risk assessment, key areas of each substation can be iIndividual assessments are conducted instead of simply conducting a unified assessment on a global scale. Regionalization can reflect the different impacts of different terrains on waterlogging and drainage efficiency. The waterlogging risk assessment formula for each region can be defined as:

[0122]

[0123] Among them, α1 and α2 are adjustment parameters to control the impact of water accumulation and drainage pressure index on risk; For region z i The amount of water accumulation; is the critical value of regional waterlogging; is the drainage pressure index, which reflects the pressure status of the drainage system. The above risk assessment formula can closely combine the amount of water accumulation and the drainage system status obtained from the micro-meteorological model to assess the risk of each area in real time.

[0124] Alternatively, the flood risk of each area does not exist independently, and the mutual influence between areas must be considered. The accumulation of water in a low-lying area may affect other areas through the drainage system. Therefore, an influence coefficient κ between regions can be designed ij , which is used to simulate the water flow interaction between regions. Optionally, the global risk assessment formula can be:

[0125]

[0126] Among them, κ ij For region z i For adjacent regions z j The influence coefficient, R global (t) is the global waterlogging risk. This formula comprehensively evaluates the global risk by considering the water conduction and drainage pressure between regions.

[0127] Furthermore, in order to ensure the accuracy of waterlogging risk assessment, new meteorological data can be received in real time to dynamically adjust the risk assessment results of each area. This step needs to be combined with an adaptive update mechanism.

[0128] Define the real-time update formula:

[0129]

[0130] Among them, S new (t) is the latest water accumulation, S pred (t) is the predicted waterlogging volume, and γ is the adjustment coefficient. Through adaptive updating, the model can adjust the risk assessment in a timely manner to ensure that the risk is constantly corrected with new meteorological changes. global (t) exceeds the set threshold T riskWhen the system fails, it will automatically trigger an early warning signal and notify maintenance personnel to initiate emergency drainage measures.

[0131] Among them, S pred (t) is the water accumulation prediction at the current time t0 for the future time t. In order to calculate the amount of water accumulation at the future time, we start from the current time t0 and gradually predict the amount of water accumulation S at the future time through short time steps Δt pred (t). That is, the calculation formula using S(t+Δt) in step S1 is as follows:

[0132]

[0133] Among them, S pred (t): predicted amount of water accumulation at future time t; S(t0): amount of water accumulation at current time t0; N: number of time steps, calculated as (t-t0) / Δt; Δt: length of time of each time step.

[0134] Optionally, S(t0) is the amount of water accumulation at the current moment; starting from t0, the amount of water accumulation at each time step Δt is gradually accumulated to the future moment t (according to the formula S(t0+Δt)), that is, S pred (t). In this way, we start from the current water volume and gradually calculate the predicted water volume at future moments.

[0135] In this embodiment, the working status of the drainage system can be fed back in real time based on the results of the waterlogging risk assessment combined with real-time meteorological data. Once it is detected that the working status of the drainage system is lower than a certain critical value and the risk of waterlogging in the future is found to increase in combination with the results of the waterlogging risk assessment, preventive adjustment measures can be taken in advance.

[0136] Optionally, a model is designed to comprehensively evaluate the state of the drainage system. This model not only relies on real-time physical parameters, but also takes into account the possible bottleneck effect and overload risk of the drainage channel. To this end, this embodiment introduces a drainage system comprehensive index E drain (t) is used to reflect the working status of the drainage system in real time. The comprehensive index formula of the drainage system can be:

[0137]

[0138] Among them, V drain (t) is the real-time flow rate of the drainage channel; C capacity (t) is the current remaining capacity of the drainage system; σ(t) is the drainage bottleneck correction term. When the drainage system encounters blockage or local bottleneck, σ(t) increases, reducing the drainage efficiency; P pump (t) is the current pump power, P maxis the maximum power of the water pump. By introducing the correction term σ(t), we take into account the decrease in efficiency of the drainage system when it encounters physical obstacles or pipe blockages. This formula can evaluate the overall operating status of the drainage system in real time and reflect whether it can cope with the challenges of future waterlogging.

[0139] Optionally, once the system detects the drainage comprehensive index E drain (t) is lower than a certain critical value E crit , and combined with waterlogging risk assessment When it is found that the risk of waterlogging will increase in the future, preventive adjustment measures need to be taken in advance to ensure that the drainage system is in an efficient working state. The preventive adjustment formula can be:

[0140]

[0141] Among them, P adjust (t) is the adjusted water pump power; · is the adjustment coefficient, which controls the adjustment strength; is the result of waterlogging risk assessment; E crit is the critical efficiency value of the drainage system.

[0142] Optionally, when the waterlogging risk assessment result is higher than the current drainage efficiency, the preventive adjustment formula can automatically increase the pump power to improve the efficiency of the drainage system and ensure that it can cope with the upcoming rainfall and waterlogging.

[0143] Furthermore, the drainage system is susceptible to local pipe blockage, drainage path obstruction and other factors under extreme weather conditions, so it is necessary to dynamically detect and adjust these potential bottlenecks. We introduce the bottleneck effect correction term σ(t), whose value is dynamically adjusted according to the real-time monitored flow rate, pressure changes and historical water accumulation data. Bottleneck effect correction formula:

[0144]

[0145] Among them, V normal is the normal flow rate of the drainage system; λ is the correction factor, which controls the impact of the bottleneck effect on drainage efficiency. This correction term means that when the flow rate is lower than the normal value, the system considers that there may be a physical bottleneck in the drainage channel and reduces the drainage efficiency accordingly. By detecting the bottleneck effect, the system can take measures in advance, such as changing the drainage path or adjusting the working mode of the water pump, to ensure efficient operation of the system.

[0146] Finally, to ensure that the system can adapt to changing meteorological and drainage conditions, an adaptive feedback mechanism can be introduced to dynamically optimize pump scheduling. This mechanism continuously adjusts the system's operating parameters by monitoring the difference between the actual power of the pump and the predicted power in real time. The adaptive feedback formula can be:

[0147] P final (t) = P adjust (t)+β·(P real (t)-P pred (t))

[0148] Among them, P final (t) can adjust the power of the final water pump; P real (t) can be the actual power currently monitored in real time; P pred (t) can be the predicted water pump power; β can be the feedback adjustment coefficient.

[0149] Through the above feedback mechanism, the system can continuously optimize the power distribution and start-up sequence of the water pump according to the actual operating status to avoid overload or long-term inefficient operation.

[0150] As an optional implementation, the method may further include: determining the working state of the drainage system and the predicted amount of water accumulation in the target area under the working state; and adjusting the working power of the drainage system based on the predicted amount of water accumulation.

[0151] In this embodiment, the working state of the drainage system can be determined, and the working state can be used to determine the working power, working current or working voltage of each drainage device in the drainage system, and can include a normal working state or an overload working state, and the type of working state is not specifically limited here. Further, based on the working state and the predicted amount of accumulated water, the working efficiency of the drainage system can be adjusted. The drainage system can include at least one drainage device, which can be a water pump.

[0152] For example, the working state of each drainage device in the drainage system is determined, and the predicted water accumulation in the target area under the working state is used. If the predicted water accumulation exceeds the water accumulation threshold, the working power of the drainage system can be adjusted and / or the number of drainage devices working in the drainage system can be increased to speed up the drainage of the accumulated water. If the predicted water accumulation does not exceed the water accumulation threshold, the working power of the drainage system can be reduced and / or the number of drainage devices working in the drainage system can be reduced. It should be noted that there is no specific limitation on the adjustment method here.

[0153] In this embodiment, the working power of the water pump can be dynamically adjusted according to the adjusted working state of the drainage system and the predicted amount of water accumulation. The dynamic adjustment of the working power of the water pump can be divided into local scheduling and global scheduling.

[0154] Optionally, the core of intelligent scheduling is to match the power of the water pump with the current predicted water volume. By evaluating the predicted water volume and the current state of the drainage system, the working power of the water pump is dynamically adjusted. The dynamic power scheduling formula can be:

[0155]

[0156] Among them, P adjust (t) represents the pump power adjusted according to the current water accumulation situation; S pred (t) represents the future predicted water volume; V drain (t) represents the flow rate of the current drainage channel; C capacity (t) represents the remaining capacity of the drainage system; Indicates the adjustment coefficient, which is used to balance the water accumulation and power adjustment range.

[0157] Optionally, if the predicted water accumulation S pred (t) is high, while the drainage flow rate V drain (t) is low, the system can automatically increase the pump power P adjust (t) to ensure that the accumulated water can be drained quickly. When the drainage system is close to full capacity (i.e. C capacity (t) is small), power regulation will become more aggressive to avoid overload operation.

[0158] Optionally, the system can calculate P in real time based on each water accumulation prediction result. adjust (t). The pump power is adjusted automatically according to the formula and no manual intervention is required.

[0159] In this embodiment, the drainage system of the substation may be distributed in different areas, and the terrain and drainage requirements of these areas are different. For example, the waterlogging in low-lying areas is more serious, so the scheduling of water pumps needs to give priority to the needs of these areas. The regional power allocation formula can be:

[0160]

[0161] in, Indicates allocation to region z i The pump power; Represents area z i The amount of water accumulation; represents the critical value of water accumulation in the area; γ represents the adjustment coefficient, which is used to control the regional power allocation ratio.

[0162] Optionally, when a region z i The amount of water accumulation is close to its critical value When the system will use more pump power allocated to the area. Low-lying areas usually have larger Therefore, their power allocation ratio is higher to ensure that the accumulated water in these areas can be drained quickly.

[0163] In this embodiment, the system can monitor the amount of water accumulation in all areas in real time through sensors and dynamically allocate power to different areas. Areas with serious water accumulation can be given priority so that power allocation can maximize the matching of drainage needs.

[0164] Optionally, to avoid excessive or inefficient operation of the water pump, the system can optimize the start-up and shutdown strategy of the water pump in real time according to the actual drainage situation. The optimization formula for starting and shutting down the water pump is:

[0165]

[0166] Among them, P final (t+Δt) represents the adjusted water pump power; S actual (t) represents the real-time monitored water volume; β represents the feedback adjustment coefficient, which is used to adjust the power according to the actual situation. This formula automatically adjusts the power of the water pump by comparing the real-time monitored water volume with the predicted water volume. If the actual water volume is much larger than the predicted value, the system will automatically increase the water pump power to cope with it; conversely, if the water volume is small, the system will reduce the power to reduce excessive work of the water pump.

[0167] Optionally, the system can detect water accumulation at fixed time intervals (such as every minute) and compare the water accumulation prediction results. The system can automatically adjust the start and stop of the water pump according to the feedback formula to ensure maximum drainage efficiency while avoiding water pump overload.

[0168] Furthermore, in order to ensure that the system can cope with future changes in rainfall and waterlogging, the system must not only handle current waterlogging, but also perform global intelligent scheduling based on future weather and waterlogging forecasts. The global scheduling formula can be:

[0169] P global (t) = P final (t)+δ·(S future (t)-S pred (t))

[0170] Among them, P global (t) represents the total power of global scheduling; S future (t) represents the future predicted water accumulation; S pred (t) represents the current waterlogging prediction; δ represents the global adjustment coefficient, which controls the weight of future waterlogging.

[0171] In this embodiment, the feedback drainage results after the operation of the water pump can be monitored in real time, and long-term global optimization can be performed based on the feedback results combined with the real-time updated prediction results.

[0172] Optionally, in order to allow the system to make adaptive adjustments based on the actual drainage effect, a feedback adjustment formula is also designed. This formula can be used to monitor the amount of accumulated water S in real time.actual (t) and waterlogging prediction S pred The system adjusts the power of the water pump by the difference between (t). The system optimizes the power output of the water pump through difference feedback to form a closed-loop control. The feedback adjustment formula can be:

[0173] P feedback (t) = P global (t)+β·(S actual (t)-S pred (t))

[0174] Among them, P feedback (t) is the pump power adjusted according to the feedback; β is the feedback adjustment coefficient, which is used to control the intensity of the feedback response; S actual (t) is the amount of water accumulated monitored in real time, S pred (t) is the previous predicted value of water accumulation.

[0175] In this embodiment, the drainage efficiency E drain (t), and further adjust the drainage capacity of the water pump. The higher the drainage efficiency, the more flexible the water pump scheduling. The water pump power can be dynamically optimized according to the efficiency in each time period. The efficiency optimization formula can be:

[0176]

[0177] Among them, P final (t+Δt) is the final adjusted water pump power; E drain (t) is the current drainage efficiency; E normal This is the drainage efficiency under normal circumstances.

[0178] In this embodiment, in order to further enhance the robustness of the system, a dynamic feedback and closed-loop control mechanism can also be introduced. This mechanism performs comprehensive feedback and adjustment through real-time detection of multiple source data such as pump power, water accumulation changes, and drainage efficiency, ensuring that the system's adjustment behavior is always consistent with the actual environment. Closed-loop feedback formula:

[0179]

[0180] Among them, P closed-loop (t) can be the power after closed-loop feedback adjustment; S crit It can be the critical value of water accumulation set for the system; ∈ is the closed-loop adjustment coefficient.

[0181] In this embodiment, combined with the future water accumulation prediction S future (t), while performing real-time feedback adjustment, global optimization can also be performed through long-term water accumulation prediction to ensure that the system not only handles the current situation, but also can respond to future weather changes in advance. The global optimization formula can be:

[0182] P global-opt (t) = P closed-loop (t)+δ·(S future (t)-S pred (t))

[0183] Among them, P global-opt (t) can be the final pump power after global optimization; μ can be the adjustment coefficient used to balance future predictions.

[0184] In an embodiment of the present invention, a historical meteorological data set is predicted to obtain a predicted meteorological data set. Based on the predicted meteorological data set, a risk assessment result of a target area (e.g., a local area) can be determined. Based on the risk assessment result, control data of a drainage system can be determined. The drainage system is controlled according to the control data to prevent waterlogging in the substation, thereby solving the technical problem of low efficiency in handling water accumulation in the substation and achieving the technical effect of improving the efficiency of handling water accumulation in the substation.

[0185] The following is a further supplementary explanation of the technical solution of the present application using a satellite-based power line area vegetation detection and analysis method.

[0186] At present, with the frequent occurrence of extreme weather events caused by global climate change, key power facilities such as substations are extremely vulnerable to waterlogging when facing natural disasters such as heavy rains and floods. Substations are the hubs of power transmission and distribution. Waterlogging may cause damage to electrical equipment, and in severe cases, cause large-scale power outages, affecting the stable operation of the power system. To address this problem, existing substation waterlogging prevention technologies usually rely on macro-meteorological data and preset drainage systems for emergency treatment. Most of these technical solutions use regional weather forecasts to eliminate the risk of rising water levels in advance through drainage equipment such as water pumps to prevent waterlogging.

[0187] However, these existing technologies have significant disadvantages. First, traditional weather forecasts rely on macro-meteorological data and lack high-precision predictions of local weather in the substation area. Regional weather forecasts are usually based on large-scale meteorological conditions, which makes it difficult to reflect the local rainfall and hydrological changes of the substation, especially in complex terrain or special geological environments, where there may be large deviations between meteorological data and actual conditions. Secondly, existing drainage systems usually operate based on preset thresholds or rules, and have poor flexibility. For example, when the water level or rainfall reaches the set standard, the system will start the drainage equipment, but if the rainfall exceeds expectations or the equipment fails, the system cannot respond flexibly, resulting in an increase in the risk of waterlogging. In addition, the current drainage strategies are mostly single-device control, lacking the ability to respond to the environment and equipment status in real time, which makes the existing system show great limitations in dealing with extreme weather. The existing substation waterlogging prevention system lacks accurate weather forecasts and intelligent drainage control methods, which makes it unable to effectively respond to sudden rainstorms or extreme precipitation, and is prone to equipment failures and even endangers the safety of the entire power system.

[0188] The existing substation waterlogging prevention system relies on a wide range of regional meteorological data, lacks accurate prediction of the local environment of the substation, and has the technical problem of low efficiency in handling waterlogging in the substation. In this embodiment, in order to solve this problem, micro-meteorological modeling technology is used to collect local meteorological data around the substation in real time (for example, historical meteorological data sets such as rainfall, wind speed, air humidity, soil moisture, etc.), and a high-precision micro-meteorological model is constructed in combination with the geographical and hydrological characteristics of the substation. Compared with traditional macro-meteorological forecasts, micro-meteorological models can more finely reflect local rainfall dynamics, especially in areas with complex terrain or changeable climates, and can significantly improve the prediction accuracy of rainfall and water level changes. Through this model, the system can make accurate predictions of future rainfall intensity, precipitation flow path and possible waterlogging before extreme weather occurs. This method can not only warn of possible waterlogging risks earlier and more accurately, but also effectively avoid the errors caused by traditional large-scale meteorological forecasts, thereby winning valuable time for the deployment of waterlogging prevention measures.

[0189] Existing drainage systems mostly use preset water levels or rainfall thresholds for operation, which lack flexibility. In view of this shortcoming, in this embodiment, a substation anti-waterlogging operation and maintenance optimization method and system facing extreme weather environments are proposed. The method designs an intelligent drainage scheduling mechanism based on meteorological forecasts. Through the prediction results of the micro-meteorological model, the system can dynamically adjust the working mode of the drainage system. For example, before the arrival of a rainstorm, the system can start the drainage pump in advance according to the predicted rainfall intensity to reduce the water level around the substation, thereby avoiding the occurrence of waterlogging. If it is predicted that the rainfall will continue to intensify, the system can also dynamically adjust the drainage path to ensure the smooth flow of each drainage channel. In addition, when the working state of the drainage equipment is detected to be abnormal, the system will automatically adjust the drainage task allocation according to the meteorological forecast information, such as switching to backup drainage equipment or reconfiguring the drainage strategy. This intelligent drainage scheduling mechanism greatly improves the flexibility and emergency handling capabilities of the system, ensuring that the substation can efficiently respond to sudden waterlogging risks in extreme weather.

[0190] Optionally, this embodiment overcomes the problems of low prediction accuracy and lack of flexibility in drainage scheduling in the prior art, and realizes refined management of substation waterlogging protection. The system (that is, the substation waterlogging prevention operation and maintenance optimization system facing extreme weather environments) can not only provide accurate waterlogging warnings based on high-precision local meteorological modeling, but also ensure the safe operation of substations under extreme weather conditions through an intelligent drainage scheduling mechanism, thereby effectively reducing the risk of waterlogging.

[0191] Figure 2 is a flow chart of a substation anti-flooding operation and maintenance optimization method facing extreme weather environment according to an embodiment of the present invention. Figure 2 As shown, the method may include the following steps:

[0192] Step S202, collect local meteorological data, determine the amount of accumulated water based on the local meteorological data, and make a waterlogging risk prediction based on the amount of accumulated water.

[0193] In this embodiment, local meteorological data (i.e., historical meteorological data) is collected, and the local meteorological data may include data such as rainfall, surface water level, drainage channel flow rate, and surface permeability. The local meteorological data may be preprocessed, and the amount of accumulated water may be determined using a micro-meteorological model based on the preprocessed data, and the risk of waterlogging may be predicted based on the amount of accumulated water.

[0194] Optionally, when an abnormality occurs in the current sensor, correction may be performed using data from adjacent sensors.

[0195] In this embodiment, the above preprocessing may include:

[0196] The standardized function s(t) of rainfall and water level data is defined to represent the current water accumulation state, and the weighted average of multi-point data is expressed as follows:

[0197]

[0198] Among them, D i (t) represents the meteorological data collected by sensor i at time t, ω is the weight of the sensor, and n represents the number of sensors;

[0199] When the current sensor is abnormal, correction is performed through the data of the adjacent sensors, as shown below:

[0200] If R i (t) Abnormal

[0201] Among them, R adjusted (t) represents the corrected sensor data, R i (t) represents the current sensor.

[0202] Furthermore, a micro-meteorological model is constructed based on the pre-processed data to calculate the amount of accumulated water, and the risk of waterlogging is predicted based on the amount of accumulated water, which may include:

[0203] The outputs of each area and sensor are combined into the model through weighted coefficients to form a local environment model M(t). Assume that n sensors collect data, which can be expressed as follows:

[0204]

[0205] Among them, w i is the weight coefficient, D i (t) is the measurement value of sensor i at time t, n is the number of sensors;

[0206] Constructing a dynamic water flow model to calculate water accumulation can include:

[0207] Introducing the terrain correction term σ(z i ), reflecting the water flow acceleration or deceleration effect of different terrains, is expressed as follows:

[0208] V(t)=V0(t)·(1+σ(z i ))

[0209] Where V0(t) is the initial drainage velocity, σ(z i ) is the terrain correction term, z i Represents different terrain units within the substation;

[0210] The rainfall, infiltration rate, drainage velocity, and soil saturation state are added to the current water volume to form a dynamic water flow model, which is expressed as follows:

[0211] S pred (t)=S(t+Δt)=S(t)+(R(t)·(1-P(t)))-(V(t)-B(t))

[0212] Among them, S pred (t) = S(t+Δt) is the predicted water accumulation, S(t) is the current water accumulation, R(t) is the rainfall; P(t) is the permeability, V(t) is the drainage flow rate, and B(t) is the drainage bottleneck effect. When the drainage pipe is blocked or the flow is overloaded, B(t) increases, slowing down the drainage speed;

[0213] The local environment model is dynamically adjusted based on the newly collected data and model prediction results, as shown below:

[0214] M(t+Δt)=M(t)+α·(D new (t)-D pred (t))

[0215] Among them, D new (t) is the latest data collected in real time, D pred (t) is the predicted value of the model, and α is the update coefficient;

[0216] Calculate the drainage pressure index P based on the current water volume stress (t), expressed as follows:

[0217]

[0218] Among them, when P stress When (t)>1, it means that the system is in an overloaded state, the drainage cannot keep up with the growth rate of water accumulation, and the risk of waterlogging increases.

[0219] Step S204: Perform waterlogging risk assessment based on the result of waterlogging risk prediction.

[0220] In this embodiment, waterlogging risk assessment is performed based on the waterlogging risk prediction result to obtain a risk assessment result. At the same time, the waterlogging risk assessment result (ie, risk assessment result) is dynamically updated based on the real-time water accumulation amount.

[0221] Optionally, for each substation key area z i Conduct individual assessments and define the waterlogging risk assessment formula for each area:

[0222]

[0223] in, is the waterlogging risk index for each region, α1 and α2 are adjustment parameters to control the impact of water accumulation and drainage pressure index on the risk; For region z i The amount of water accumulation; is the critical value of regional waterlogging; It is the drainage pressure index, which reflects the pressure status of the drainage system;

[0224] The global risk assessment formula is designed based on the waterlogging risk assessment formula for each region, as shown below:

[0225]

[0226] Among them, κ ij For region z i For adjacent regions z j The influence coefficient, R global (t) is the overall waterlogging risk;

[0227] The waterlogging risk assessment is dynamically updated based on the real-time water accumulation, as shown below:

[0228]

[0229] in, is the dynamically updated waterlogging risk index of each region, S new (t) is the latest water accumulation, S pred (t) is the predicted water accumulation, and γ is the adjustment coefficient.

[0230] Furthermore, according to the results of waterlogging risk assessment, the working status of the drainage system is reflected in real time based on the comprehensive index of the drainage system, which is combined with real-time meteorological data. drain (t) is expressed as follows:

[0231]

[0232] Among them, V drain (t) is the real-time flow rate of the drainage channel; C capacity (t) is the current remaining capacity of the drainage system; σ(t) is the drainage bottleneck correction term. When the drainage system encounters blockage or local bottleneck, σ(t) increases, reducing the drainage efficiency; P pump (t) is the current pump power, P max is the maximum power of the water pump;

[0233] Once it is detected that the working status of the drainage system is lower than a certain critical value and combined with the results of the waterlogging risk assessment, it is found that the risk of future waterlogging will increase, preventive adjustment measures will be taken in advance, as shown below:

[0234]

[0235] Among them, P adjust(t) is the adjusted water pump power; θ is the adjustment coefficient, which controls the adjustment strength; is the result of waterlogging risk assessment; E crit is the critical efficiency value of the drainage system.

[0236] Step S206, determining preventive adjustment measures based on the waterlogging risk assessment results.

[0237] In this embodiment, the working status of the drainage system can be determined in real time based on the waterlogging risk assessment results combined with real-time meteorological data. Once it is detected that the working status of the drainage system is lower than a certain critical value, it can be found in combination with the waterlogging risk assessment results that the risk of waterlogging will increase in the future, and preventive adjustment measures can be taken in advance. That is, the control data of the drainage system in and around the target area can be determined.

[0238] Optionally, when the drainage system encounters a bottleneck period under extreme weather conditions, the bottleneck effect correction term σ(t) is designed and dynamically adjusted according to the real-time monitored flow rate, pressure changes and historical water accumulation data, as shown below:

[0239]

[0240] Among them, V normal is the normal flow rate of the drainage system; λ is the correction coefficient, which controls the impact of the bottleneck effect on the drainage efficiency.

[0241] Furthermore, the working power of the water pump is dynamically adjusted according to the adjusted working state of the drainage system and the predicted amount of water accumulation. The dynamic adjustment of the working power of the water pump can be divided into local scheduling and global scheduling, which may include:

[0242] By evaluating the predicted value of water accumulation and the current status of the drainage system, the working power of the water pump is dynamically adjusted, as shown below:

[0243]

[0244] Among them, P adjust (t) represents the adjusted water pump power; S pred (t) represents the predicted water accumulation; V drain (t) represents the flow rate of the current drainage channel; C capacity (t) represents the remaining capacity of the drainage system; Indicates the adjustment coefficient, which is used to balance the water accumulation and power adjustment range;

[0245] The local schedule is expressed as follows:

[0246]

[0247] in, Indicates allocation to region zi The pump power; Represents area z i The amount of water accumulation; represents the critical value of water accumulation in the area; γ represents the adjustment coefficient, which is used to control the regional power allocation ratio;

[0248] The global schedule is expressed as follows:

[0249] P global (t) = P final (t)+δ·(S future (t)-S pred (t))

[0250] Among them, P global (t) represents the total power of global scheduling; S future (t) represents the future predicted water accumulation; S pred (t) represents the current waterlogging prediction; δ represents the global adjustment coefficient, which controls the weight of future waterlogging.

[0251] Step S208: dynamically adjust the control data based on the control result.

[0252] In this embodiment, the working power of the water pump can be dynamically adjusted according to the adjusted working status of the drainage system and the predicted amount of water accumulation, wherein the above-mentioned dynamic adjustment of the working power of the water pump can be divided into local scheduling and global scheduling.

[0253] Step S210: In response to an abnormality in the abnormal sub-region, output prompt information.

[0254] In this embodiment, the feedback drainage result after the operation of the water pump is monitored and adjusted in real time, and long-term global optimization is performed based on the feedback result combined with the real-time updated prediction result.

[0255] Optionally, the amount of accumulated water S monitored in real time actual (t) and waterlogging prediction S pred (t) to adjust the pump power, expressed as follows:

[0256] P feedback (t) = P global (t)+β·(S actual (t)-S pred (t))

[0257] Among them, P feedback (t) is the pump power adjusted according to the feedback; β is the feedback adjustment coefficient, which is used to control the intensity of the feedback response; S actual (t) is the amount of water accumulated monitored in real time, S pred (t) is the previous water accumulation prediction value;

[0258] According to the drainage efficiency E drain (t), adjust the drainage capacity of the water pump; the drainage efficiency is the highest and the water pump scheduling is the most flexible;

[0259] Design closed-loop optimization, expressed as follows:

[0260]

[0261] Among them, P closed-loop (t) is the power after closed-loop feedback regulation; S crit is the set water accumulation critical value; ∈ is the closed-loop adjustment coefficient.

[0262] Furthermore, the global optimization is performed based on the feedback results combined with the real-time updated prediction results, as shown below:

[0263] P global-opt (t) = P closed-loop (t)+δ·(S future (t)-S pred (t))

[0264] Among them, P global-opt (t) is the final pump power after global optimization; μ is the adjustment coefficient used to balance future predictions.

[0265] In this embodiment, a substation anti-flooding operation and maintenance optimization system facing extreme weather environments is also disclosed. Figure 3 is a schematic diagram of a substation anti-flooding operation and maintenance optimization system facing extreme weather environments according to an embodiment of the present invention, such as Figure 3 As shown, the system may include: a data collection module 302 , a risk assessment module 304 , a prevention execution module 306 , a drainage scheduling module 308 and a global optimization module 310 .

[0266] The data acquisition module 302 is used to collect local meteorological data, including rainfall, surface water level, drainage channel flow rate and surface permeability, and pre-process the local meteorological data, build a micro-meteorological model based on the pre-processed data to calculate the amount of accumulated water, and predict the risk of waterlogging based on the amount of accumulated water; when the current sensor is abnormal, it is corrected through the data of the adjacent sensors;

[0267] The risk assessment module 304 is used to perform waterlogging risk assessment based on the waterlogging risk prediction results, and dynamically update the waterlogging risk assessment based on the real-time water accumulation amount;

[0268] The prevention execution module 306 is used to provide real-time feedback on the working status of the drainage system based on the results of the waterlogging risk assessment and in combination with real-time meteorological data. Once it is detected that the working status of the drainage system is lower than a certain critical value and the risk of future waterlogging is found to increase in combination with the results of the waterlogging risk assessment, preventive adjustment measures are taken in advance;

[0269] The drainage scheduling module 308 is used to dynamically adjust the working power of the water pump according to the adjusted working state of the drainage system and the predicted amount of accumulated water. The dynamic adjustment of the working power of the water pump can be divided into local scheduling and global scheduling;

[0270] The global optimization module 310 is used to monitor and adjust the feedback drainage results of the water pump in real time, and perform long-term global optimization based on the feedback results combined with the real-time updated prediction results.

[0271] In this embodiment, a historical meteorological data set is predicted to obtain a predicted meteorological data set. Based on the predicted meteorological data set, a risk assessment result of a target area (e.g., a local area) can be determined. Based on the risk assessment result, control data of a drainage system can be determined. The drainage system is controlled according to the control data to prevent waterlogging in the substation, thereby solving the technical problem of low efficiency in handling water accumulation in the substation and achieving the technical effect of improving the efficiency of handling water accumulation in the substation.

[0272] According to an embodiment of the present invention, a control device for a drainage system is also provided. It should be noted that the control device for a drainage system of this embodiment can be used to execute the control method for a drainage system of embodiment 1 of the present invention.

[0273] Figure 4 Schematic diagram of a control device for a drainage system according to an embodiment of the present invention. Figure 4 As shown, the control device 40 of the drainage system may include: a collection unit 402 , a prediction unit 404 , a first determination unit 406 , a second determination unit 408 and a control unit 410 .

[0274] The collection unit 402 is used to collect historical meteorological data sets of the target area where the power equipment is located within a historical period.

[0275] The prediction unit 404 is used to predict multiple historical meteorological data sets to obtain a predicted meteorological data set for the target area in the future period.

[0276] The first determining unit 406 is used to determine a risk assessment result of the target area based on the forecast meteorological data set, wherein the risk assessment result is used to characterize the probability of a risk event associated with the forecast meteorological data set occurring in the local area.

[0277] The second determining unit 408 is used to determine control data of the drainage system in the target area based on the risk assessment result.

[0278] The control unit 410 is used to control the drainage system to drain the accumulated water in the target area according to the control data.

[0279] The control device of the drainage system of this embodiment collects historical meteorological data sets of the target area where the power equipment is located within the historical time period through the collection unit; predicts multiple historical meteorological data sets through the prediction unit to obtain the predicted meteorological data set of the target area in the future time period; determines the risk assessment result of the target area based on the predicted meteorological data set through the first determination unit, wherein the risk assessment result is used to characterize the probability of a risk event associated with the predicted meteorological data set occurring in the local area; determines the control data of the drainage system in the target area based on the risk assessment result through the second determination unit; and controls the drainage system to discharge the accumulated water in the target area according to the control data through the control unit, thereby solving the technical problem of low efficiency in handling accumulated water in the substation and achieving the technical effect of improving the efficiency in handling accumulated water in the substation.

[0280] The embodiment of the present invention can provide a computer terminal, which can be any computer terminal device in a computer terminal group. Optionally, in this embodiment, the computer terminal can also be replaced by a terminal device such as a mobile terminal.

[0281] Optionally, in this embodiment, the computer terminal may be located in at least one network device among a plurality of network devices of the computer network.

[0282] In this embodiment, the above-mentioned computer terminal can execute the program code of the following steps in the control method of the drainage system: within a historical period, collect historical meteorological data sets of the target area where the power equipment is located; predict multiple historical meteorological data sets to obtain a predicted meteorological data set for the target area in a future period; based on the predicted meteorological data set, determine the risk assessment result of the target area, wherein the risk assessment result is used to characterize the probability of a risk event associated with the predicted meteorological data set occurring in the local area; based on the risk assessment result, determine the control data of the drainage system in the target area; and according to the control data, control the drainage system to drain the accumulated water in the target area.

[0283] Optionally, Figure 5 is a structural block diagram of a computer terminal according to an embodiment of the present invention. Figure 5 As shown, the computer terminal 508 may include: one or more (only one is shown in the figure) processors 502 , a memory 504 and a transmission device 506 .

[0284] The memory can be used to store software programs and modules, such as program instructions / modules corresponding to the control method and device of the drainage system in the embodiment of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, the control method of the drainage system described above is realized. The memory 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 may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal 508 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 combinations thereof.

[0285] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: collect historical meteorological data sets in the target area where the power equipment is located within a historical period; predict multiple historical meteorological data sets to obtain a predicted meteorological data set for the target area in a future period; determine a risk assessment result for the target area based on the predicted meteorological data set, wherein the risk assessment result is used to characterize the probability of a risk event associated with the predicted meteorological data set occurring in a local area; determine control data for a drainage system in the target area based on the risk assessment result; and control the drainage system to drain the accumulated water in the target area according to the control data.

[0286] It can be understood by those skilled in the art that Figure 5 The structure shown is for illustration only, and the computer terminal 508 may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (MID for short), a PAD, or other terminal devices. Figure 5 The structure of the computer terminal 508 is not limited. For example, the computer terminal 508 may also include Figure 5 More or fewer components (such as network interfaces, display devices, etc.) shown in, or having Figure 5 Different configurations shown.

[0287] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0288] According to an embodiment of the present invention, a computer-readable storage medium is further provided. The computer-readable storage medium includes a stored program, wherein the program executes the control method of the drainage system in the above embodiment.

[0289] Optionally, in this embodiment, the computer-readable storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the mobile terminals in a mobile terminal group.

[0290] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: collecting historical meteorological data sets of the target area where the power equipment is located within a historical period; predicting multiple historical meteorological data sets to obtain a predicted meteorological data set for the target area in a future period; determining a risk assessment result for the target area based on the predicted meteorological data set, wherein the risk assessment result is used to characterize the probability of a risk event associated with the predicted meteorological data set occurring in the local area; determining control data for the drainage system in the target area based on the risk assessment result; and controlling the drainage system to drain the accumulated water in the target area according to the control data.

[0291] Optionally, the computer-readable storage medium may also execute program codes of the following steps: obtaining an initial meteorological data set collected by multiple sensors in the target area; determining an adjustment coefficient corresponding to the sensor; and using the adjustment coefficient to adjust the initial meteorological data set collected by the corresponding sensor to obtain a historical meteorological data set.

[0292] Optionally, the computer-readable storage medium may also execute program codes for the following steps: determining a deployment position of the sensor; and determining an adjustment coefficient corresponding to the deployment position.

[0293] Optionally, the computer-readable storage medium may also execute program code for the following steps: in response to a sensor abnormality, determining, among multiple sensors, at least one adjacent sensor adjacent to the sensor with the abnormality; and adjusting, based on an initial meteorological data set collected by at least one adjacent sensor, an initial meteorological data set collected by the sensor with the abnormality.

[0294] Optionally, the computer-readable storage medium may also execute program codes for the following steps: determining the terrain features of the target area; determining a weight coefficient that matches the terrain features; using the weight coefficient to perform weighted summation on the historical meteorological data set to obtain input data; inputting the input data into a micro-meteorological model to predict a predicted meteorological data set, wherein the micro-meteorological model is trained based on the terrain features and a meteorological data sample set; the method further includes: adjusting the model parameters of the micro-meteorological model based on the historical meteorological data set and the predicted meteorological data set collected in the future period.

[0295] Optionally, the computer-readable storage medium may also execute program codes for the following steps: determining the amount of water accumulation in the target area in a future period based on a predicted meteorological data set; and determining a risk assessment result based on the amount of water accumulation.

[0296] Optionally, the computer-readable storage medium may also execute program codes of the following steps: constructing a water flow model based on the topographic features of the target area; and inputting the predicted meteorological data set into the water flow model to obtain the amount of accumulated water.

[0297] Optionally, the computer-readable storage medium may also execute program codes of the following steps: converting the amount of accumulated water to obtain a drainage pressure index, wherein the drainage pressure index is used to characterize the working state of the drainage system; judging the drainage pressure index using a pressure threshold to obtain a judgment result; and determining a risk assessment result that matches the judgment result.

[0298] Optionally, the computer-readable storage medium may also execute program code of the following steps: determining control data based on risk assessment results, drainage pressure index and water accumulation amount.

[0299] Optionally, the computer-readable storage medium may also execute program codes for the following steps: determining the working state of the drainage system and the predicted amount of water accumulated in the target area under the working state; and adjusting the working power of the drainage system based on the predicted amount of water accumulated.

[0300] In this embodiment, a historical meteorological data set is predicted to obtain a predicted meteorological data set. Based on the predicted meteorological data set, a risk assessment result of a target area (e.g., a local area) can be determined. Based on the risk assessment result, control data of a drainage system can be determined. The drainage system is controlled according to the control data to prevent waterlogging in the substation, thereby solving the technical problem of low efficiency in handling water accumulation in the substation and achieving the technical effect of improving the efficiency of handling water accumulation in the substation.

[0301] According to an embodiment of the present invention, a processor is further provided, and the processor is used to run a program, wherein when the program is run by the processor, the control method of the drainage system in the above embodiment is executed.

[0302] Optionally, in this embodiment, the computer terminal may be located in at least one network device among a plurality of network devices of the computer network.

[0303] In this embodiment, the above-mentioned computer terminal can execute the program code of the following steps in the control method of the drainage system: within a historical period, collect historical meteorological data sets of the target area where the power equipment is located; predict multiple historical meteorological data sets to obtain a predicted meteorological data set for the target area in a future period; based on the predicted meteorological data set, determine the risk assessment result of the target area, wherein the risk assessment result is used to characterize the probability of a risk event associated with the predicted meteorological data set occurring in the local area; based on the risk assessment result, determine the control data of the drainage system in the target area; and according to the control data, control the drainage system to drain the accumulated water in the target area.

[0304] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the control method and device of the drainage system in the embodiment of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the control method of the drainage system mentioned above. The memory 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 may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal 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 combinations thereof.

[0305] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: collect historical meteorological data sets in the target area where the power equipment is located within a historical period; predict multiple historical meteorological data sets to obtain a predicted meteorological data set for the target area in a future period; determine a risk assessment result for the target area based on the predicted meteorological data set, wherein the risk assessment result is used to characterize the probability of a risk event associated with the predicted meteorological data set occurring in a local area; determine control data for a drainage system in the target area based on the risk assessment result; and control the drainage system to drain the accumulated water in the target area according to the control data.

[0306] Optionally, the processor may also execute program code of the following steps: obtaining an initial meteorological data set collected by multiple sensors in the target area; determining an adjustment coefficient corresponding to the sensor; and using the adjustment coefficient to adjust the initial meteorological data set collected by the corresponding sensor to obtain a historical meteorological data set.

[0307] Optionally, the processor may also execute program codes of the following steps: determining a deployment position of the sensor; and determining an adjustment coefficient corresponding to the deployment position.

[0308] Optionally, the processor may also execute program code for the following steps: in response to a sensor abnormality, determining, among multiple sensors, at least one adjacent sensor adjacent to the sensor with the abnormality; and adjusting, based on an initial meteorological data set collected by at least one adjacent sensor, an initial meteorological data set collected by the sensor with the abnormality.

[0309] Optionally, the processor may also execute the following program code: determining the terrain features of the target area; determining a weight coefficient that matches the terrain features; using the weight coefficient to perform weighted summation on the historical meteorological data set to obtain input data; inputting the input data into a micro-meteorological model to predict a predicted meteorological data set, wherein the micro-meteorological model is trained based on the terrain features and a meteorological data sample set; the method also includes: adjusting the model parameters of the micro-meteorological model based on the historical meteorological data set and the predicted meteorological data set collected in the future period.

[0310] Optionally, the processor may also execute program codes of the following steps: determining the amount of water accumulation in the target area in a future period based on a predicted meteorological data set; and determining a risk assessment result based on the amount of water accumulation.

[0311] Optionally, the processor may also execute program codes of the following steps: constructing a water flow model based on the topographic features of the target area; and inputting the predicted meteorological data set into the water flow model to obtain the amount of accumulated water.

[0312] Optionally, the processor may also execute the program code of the following steps: convert the amount of accumulated water to obtain a drainage pressure index, wherein the drainage pressure index is used to characterize the working state of the drainage system; use the pressure threshold to judge the drainage pressure index to obtain a judgment result; and determine a risk assessment result that matches the judgment result.

[0313] Optionally, the processor may also execute program code of the following steps: determining control data based on risk assessment results, drainage pressure index and water accumulation amount.

[0314] Optionally, the processor may also execute program codes of the following steps: determining the working state of the drainage system and the predicted amount of water accumulation in the target area under the working state; and adjusting the working power of the drainage system based on the predicted amount of water accumulation.

[0315] By adopting the embodiment of the present invention, a historical meteorological data set is predicted to obtain a predicted meteorological data set. Based on the predicted meteorological data set, a risk assessment result of a target area (e.g., a local area) can be determined. Based on the risk assessment result, control data of a drainage system can be determined. The drainage system is controlled according to the control data to prevent waterlogging in the substation, thereby solving the technical problem of low efficiency in handling water accumulation in the substation and achieving the technical effect of improving the efficiency of handling water accumulation in the substation.

[0316] According to an embodiment of the present invention, a computer program product is further provided. The computer program product includes computer instructions, wherein the computer instructions implement the control method of the drainage system in the above embodiment when executed by a processor.

[0317] An embodiment of the present application may provide an electronic device, which may include a memory and a processor.

[0318] Figure 6 1 is a block diagram of an electronic device for a method for controlling a drainage system according to an embodiment of the present application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0319] like Figure 6 As shown, the device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 to a random access memory (RAM) 603. In RAM 603, various programs and data required for the operation of the device 600 can also be stored. The computing unit 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0320] A number of components in the device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 604, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0321] The computing unit 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as a verification method for data. For example, in some embodiments, the verification method for data may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the verification method for data described above may be executed. Alternatively, in other embodiments, the computing unit 601 may be configured to execute the data verification method in any other appropriate manner (for example, by means of firmware).

[0322] According to an embodiment of the present application, a method for controlling a drainage system is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0323] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a dedicated or general purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0324] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0325] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0326] To provide interaction with a user, the systems and techniques described herein may be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or an LCD (liquid crystal display, monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a path ball) through which the user can provide input to the computer. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0327] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a Local Area Network (LAN), a Wide Area Network (WAN), and the Internet.

[0328] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0329] 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.

[0330] 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.

[0331] In the several embodiments provided by the present invention, 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 units can be a logical function division, and there can be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, 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.

[0332] 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 over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0333] 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.

[0334] 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 methods of 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.

[0335] The above are only preferred embodiments 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 principles 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 controlling a drainage system, characterized in that: include: Collect historical meteorological data sets of the target area where the power equipment is located during the historical period; Predicting the plurality of historical meteorological data sets to obtain a predicted meteorological data set for the target area in a future period; Based on the forecast meteorological data set, determining a risk assessment result of the target area, wherein the risk assessment result is used to characterize the probability of a risk event associated with the forecast meteorological data set occurring in the local area; Based on the risk assessment results, determining control data of the drainage system in the target area; According to the control data, the drainage system is controlled to drain the accumulated water in the target area.

2. The method according to claim 1, characterized in that The historical meteorological data set of the target area where the power equipment is located is collected during the historical period, including: Acquire an initial meteorological data set collected by multiple sensors in the target area; Determining an adjustment coefficient corresponding to the sensor; The adjustment coefficient is used to adjust the initial meteorological data set collected by the corresponding sensor to obtain the historical meteorological data set.

3. The method according to claim 2, characterized in that The determining the adjustment coefficient corresponding to the sensor includes: Determining a deployment location of the sensor; The adjustment coefficient corresponding to the deployment position is determined.

4. The method according to claim 2, characterized in that: The method further comprises: In response to the sensor being abnormal, determining at least one adjacent sensor adjacent to the sensor having the abnormality among the plurality of sensors; Based on the initial meteorological data set collected by the at least one adjacent sensor, the initial meteorological data set collected by the sensor with the abnormality is adjusted.

5. The method according to claim 1, characterized in that The step of predicting the plurality of historical meteorological data sets to obtain a predicted meteorological data set for the target area in a future period includes: determining topographical features of the target area; Determining a weight coefficient matching the terrain feature; Using the weight coefficient, weighted summing is performed on the historical meteorological data set to obtain input data; Inputting the input data into a micro-meteorological model to predict the predicted meteorological data set, wherein the micro-meteorological model is trained based on the terrain features and the meteorological data sample set; The method further includes: adjusting the model parameters of the micro-meteorological model based on the historical meteorological data set and the predicted meteorological data set collected in the future period.

6. The method according to claim 1, characterized in that Determining the risk assessment result of the target area based on the predicted meteorological data set includes: Determining the amount of water accumulation in the target area within the future period based on the predicted meteorological data set; Based on the amount of accumulated water, the risk assessment result is determined.

7. The method according to claim 6, characterized in that The step of determining the amount of water accumulation in the target area within the future period based on the predicted meteorological data set includes: Based on the topographical features of the target area, a water flow model is constructed; The predicted meteorological data set is input into the water flow model to obtain the amount of accumulated water.

8. The method according to claim 6, characterized in that The step of determining the risk assessment result based on the amount of accumulated water includes: Converting the accumulated water volume to obtain a drainage pressure index, wherein the drainage pressure index is used to characterize the working state of the drainage system; Using the pressure threshold to judge the drainage pressure index, and obtain a judgment result; Determine the risk assessment result that matches the judgment result.

9. The method according to claim 1, characterized in that: Determining control data of the drainage system in the target area based on the risk assessment result includes: The control data is determined based on the risk assessment result, the drainage pressure index and the amount of accumulated water.

10. The method according to claim 1, characterized in that The method further comprises: Determining the working state of the drainage system and the predicted amount of water accumulation in the target area under the working state; Based on the predicted amount of water accumulation, the operating power of the drainage system is adjusted.

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