Risk early warning method, device and equipment for power grid assets under natural disasters and medium

Abstract

The embodiment of the invention relates to a risk early warning method, device and equipment for power grid assets under natural disasters and a medium, and the method comprises the steps: determining an index score corresponding to a plurality of early warning indexes according to the index data of a target region matched with a preset early warning index, the early warning indexes at least comprise a first-level index of a disaster-inducing factor, and the index score is a second-level index corresponding to the first-level index of the disaster-inducing factor; the disaster-inducing factors comprise secondary indexes corresponding to a plurality of different natural disaster types, obtaining weights corresponding to the indexes of each level, performing weighted summation based on the index scores and the weights to obtain a target risk index corresponding to the power grid assets of the target area, and further querying a preset mapping relation table of different risk indexes and risk levels, and determining a target risk level corresponding to the target risk index, and outputting the target risk level and a risk processing strategy matched with the target risk level. By adopting the technical scheme, various early warning indexes such as various natural disasters are comprehensively considered, and objective, comprehensive and accurate risk early warning is realized.

Description

Technical Field

[0001] The present disclosure relates to the technical field of power grid asset risk warning, and particularly to a risk warning method, device, equipment and medium for power grid assets under natural disasters. Background Art

[0002] In recent years, the threat of natural disasters to power grid assets in some regions has become increasingly prominent. Extreme weather events such as heavy rain, floods, lightning strikes, and blizzards occur frequently, which not only directly affect the physical structure and operation efficiency of the power grid, but also pose a severe challenge to the safety of power grid assets by damaging infrastructure and affecting energy supply.

[0003] In the context of frequent occurrence of extreme natural disasters, the risk events and loss degrees faced by power grid assets are constantly escalating. On the one hand, traditional risk assessments often only describe the risk situation based on historical meteorological data and relevant cases. At present, a complete set of risk assessment index systems has not been formed, which affects the efficiency and effectiveness of the assessment work. In addition, the assessment methods are relatively single, and the multiple risk factors and their intertwined effects are not fully considered, resulting in certain subjectivity and one-sidedness in risk assessment and making it difficult to achieve objective, comprehensive, and accurate assessments. On the other hand, traditional methods often focus on the analysis of single disaster factors and are difficult to comprehensively reflect the comprehensive impact of multiple disaster superposition effects on the power grid. This not only hinders the risk management of power grid assets, but also increases the uncertainty and potential risks in the decision-making process.

[0004] Therefore, for power grid assets under natural disasters, it is urgent to establish a set of scientific, reasonable, and practical risk assessment, warning schemes and tools. Summary of the Invention

[0005] In order to solve the above technical problems or at least partially solve the above technical problems, the present disclosure provides a risk warning method, device, equipment and medium for power grid assets under natural disasters.

[0006] In a first aspect, an embodiment of the present disclosure provides a risk warning method for power grid assets under natural disasters, including:

[0007] Determine the index score corresponding to the warning index according to the index data of the target area that matches the preset warning index, where the warning index is multiple, and at least includes a first-level index of disaster-causing factors, and the disaster-causing factors include second-level indexes corresponding to multiple different natural disaster types;

[0008] Obtain the weight corresponding to each level of index;

[0009] Perform weighted summation based on the index score and the weight to obtain the target risk index corresponding to the power grid assets in the target area;

[0010] Query the preset mapping relationship table between different risk indexes and risk levels, and determine the target risk level corresponding to the target risk index;

[0011] Output the target risk level and the risk handling strategy matching the target risk level.

[0012] In a second aspect, an embodiment of the present disclosure provides a risk early warning device for power grid assets under natural disasters, including:

[0013] A first acquisition module, configured to determine the index score corresponding to the early warning index according to the index data of the target area matching the preset early warning index, where there are multiple early warning indexes, at least including a first-level index of disaster-causing factors, and the disaster-causing factors include second-level indexes corresponding to multiple different natural disaster types;

[0014] A second acquisition module, configured to acquire the weights corresponding to the indexes at each level;

[0015] A first determination module, configured to perform weighted summation based on the index score and the weight to obtain the target risk index corresponding to the power grid assets in the target area;

[0016] A second determination module, configured to query the preset mapping relationship table between different risk indexes and risk levels, and determine the target risk level corresponding to the target risk index;

[0017] An early warning output module, configured to output the target risk level and the risk handling strategy matching the target risk level.

[0018] In a third aspect, an embodiment of the present disclosure provides an electronic device, where the electronic device includes: a processor; a memory for storing executable instructions of the processor; the processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the risk early warning method for power grid assets under natural disasters as described in the first aspect.

[0019] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, where the storage medium stores a computer program, and the computer program is used to implement the risk early warning method for power grid assets under natural disasters as described in the first aspect.

[0020] The technical solutions provided by the embodiments of the present disclosure have the following advantages compared with the prior art:

[0021] The risk warning scheme for power grid assets under natural disasters provided by the embodiments of the present disclosure determines the index scores corresponding to the warning indicators according to the index data of the target area that matches the preset warning indicators. Among them, there are multiple warning indicators, including at least the first-level indicator of disaster-causing factors. The disaster-causing factors include second-level indicators corresponding to multiple different natural disaster types, and the weights corresponding to the indicators at each level are obtained. Then, based on the index scores and weights, a weighted sum is performed to obtain the target risk index corresponding to the power grid assets in the target area. Furthermore, by querying the preset mapping relationship table between different risk indexes and risk levels, the target risk level corresponding to the target risk index is determined, and the target risk level and the risk handling strategy matching the target risk level are output. By adopting the scheme of the present disclosure, through setting multiple warning indicators and determining the target risk level of the power grid assets in the target area according to the index scores and weights of each determined warning indicator, the risk warning of the power grid assets in the target area is realized, which is beneficial to reducing the risk loss probability, maximizing the level of the power grid in the target area to resist natural disasters, and effectively ensuring the safe operation of the power grid in the target area; various warning indicators such as multiple natural disasters are comprehensively considered, realizing objective, comprehensive, and accurate risk warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In combination with the accompanying drawings and with reference to the following specific embodiments, the above and other features, advantages, and aspects of the embodiments of the present disclosure will become more obvious. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic, and the original components and elements are not necessarily drawn to scale.

[0023] Figure 1 It is a schematic flowchart of a risk warning method for power grid assets under natural disasters provided by an exemplary embodiment of the present disclosure;

[0024] Figure 2 It shows a schematic diagram of a warning index system for risk assessment of power grid assets under natural disasters according to an exemplary embodiment of the present disclosure;

[0025] FIG. 3(a) shows a schematic diagram of the classification standard of disaster-causing factors according to an exemplary embodiment of the present disclosure;

[0026] FIG. 3(b) shows a schematic diagram of the classification standard of disaster-bearing environments according to an exemplary embodiment of the present disclosure;

[0027] FIG. 3(c) shows a schematic diagram of the classification standard of disaster-affected bodies according to an exemplary embodiment of the present disclosure;

[0028] FIG. 3(d) shows a schematic diagram of the first classification standard of risk management capabilities according to an exemplary embodiment of the present disclosure;

[0029] FIG. 3(e) shows a schematic diagram of the second classification standard of risk management capabilities according to an exemplary embodiment of the present disclosure;

[0030] Figure 4 Schematic flowchart of the risk warning method for power grid assets under natural disasters provided by another exemplary embodiment of the present disclosure;

[0031] Figure 5 Schematic diagram showing the warning level division criteria corresponding to different natural disaster types in an exemplary embodiment of the present disclosure;

[0032] Fig. 6(a) shows a schematic diagram of the index score table corresponding to the plain terrain in an exemplary embodiment of the present disclosure;

[0033] Fig. 6(b) shows a schematic diagram of the index score table corresponding to the mountain terrain in an exemplary embodiment of the present disclosure;

[0034] Fig. 7(a) shows a risk level diagram of different power grid equipment under different natural disaster types in the plain terrain of the target area in an exemplary embodiment of the present disclosure;

[0035] Fig. 7(b) shows a risk level diagram of different power grid equipment under different natural disaster types in the mountain terrain of the target area in an exemplary embodiment of the present disclosure;

[0036] Figure 8 Schematic structural diagram of the risk warning device for power grid assets under natural disasters provided by an embodiment of the present disclosure. Detailed implementation manners

[0037] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0038] It should be understood that the various steps recited in the method embodiments of the present disclosure can be executed in a different order and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this regard.

[0039] The term "including" and its variants used herein are open-ended, i.e., "including but not limited to". The term "based on" is "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0040] It should be noted that concepts such as "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependent relationships.

[0041] It should be noted that the modification of "one" and "multiple" mentioned in this disclosure is illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly specified in the context, it should be understood as "one or more".

[0042] The names of the messages or information exchanged between multiple devices in the embodiments of this disclosure are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0043] The risk warning method, device, equipment and medium of power grid assets under natural disasters provided by this disclosure will be explained in detail below with reference to the accompanying drawings.

[0044] Figure 1 It is a schematic flowchart of the risk warning method of power grid assets under natural disasters provided by an exemplary embodiment of this disclosure. This method can be executed by the risk warning device of power grid assets under natural disasters provided by the embodiments of this disclosure. The device can be implemented in software and / or hardware and can be integrated in an electronic device, such as a computer.

[0045] As Figure 1 shown, the risk warning method of power grid assets under natural disasters may include the following steps:

[0046] Step 101, determine the index score corresponding to the warning index according to the index data of the target area that matches the preset warning index. Among them, there are multiple warning indexes, including at least one first-level index of disaster-causing factors, and the disaster-causing factors include multiple second-level indexes corresponding to different natural disaster types.

[0047] Among them, the warning indexes are pre-established, including multiple first-level indexes and at least one second-level index corresponding to each first-level index. The second-level index may further include at least one third-level index, and the third-level index may further include at least one fourth-level index. All the indexes constitute a complete index system. As Figure 2 shown, the warning indexes under this index system include five first-level indexes, namely disaster-causing factors, disaster-bearing environment, disaster-affected bodies, risk management capabilities, and risk adjustment factors.

[0048] As Figure 2As shown, a disaster-causing factor refers to a variant factor that may cause casualties, property losses, socio-economic losses, and ecological environment degradation, including natural disaster-causing factors such as typhoons, heavy rains, floods, landslides, debris flows, etc., and also including environmental and human-induced disaster-causing factors such as environmental pollution, traffic accidents, chemical accidents, etc. In the embodiments of the present disclosure, natural disasters of different natural disaster types such as storms, heavy rains, heavy snows, floods, lightning strikes, rain and snow icing, etc. are selected as the secondary indicators of the disaster-causing factors, and other secondary indicators can also be expanded according to actual situations. The disaster-bearing environment refers to the earth's surface system composed of the atmosphere, hydrosphere, lithosphere, and human society, including the natural environment and the human environment. Any natural disaster must occur in a certain disaster-bearing environment. In the embodiments of the present disclosure, the sensitivity influence coefficient of the terrain and geomorphic environment (simply represented by the terrain and geomorphic due to the limitation of the attached drawing size, Figure 2 is simply represented by the terrain and geomorphic in Figure 2 ), the influence coefficient of the river or lake water system environment (simply represented by the river and lake due to the limitation of the attached drawing size,

[0049] is simply represented by the river and lake in Figure 2 ), and the vegetation coverage rate are selected as the secondary indicators of the disaster-bearing environment, and other secondary indicators can also be expanded according to actual situations. The disaster-affected body refers to the object of action of various disaster-causing factors, which is the material and cultural environment directly affected and damaged by disasters. Generally, it can be divided into three categories: humans, property, and natural resources. The damage degree of the disaster-affected body is not only related to the disaster-causing factors, but also depends on the vulnerability or fragility of the disaster-affected body itself. In the embodiments of the present disclosure, the vulnerability of the power grid assets as the disaster-affected body refers to the degree of damage suffered by the power grid assets when they are subjected to external interference. The degree of damage suffered by the power grid assets is determined by the degree of exposure of the power grid assets to the disaster-causing factors and the intensity of the power grid's resistance to the influence of the disaster-causing factors. Therefore, the asset distribution structure and asset disaster resistance ability of the power grid asset disaster-affected body are selected as the secondary indicators of the disaster-affected body, and other secondary indicators can also be expanded according to actual situations. Among them, the asset distribution structure is defined as the degree of exposure of the power grid assets vulnerable to damage under the influence of natural disaster factors to the disaster-causing factors, including cable density, overhead distribution line density, pole density, distribution equipment density, and main equipment density as its tertiary indicators; the asset disaster resistance ability is defined as the self-disaster resistance ability of the power grid assets vulnerable to damage under the influence of natural disaster factors, including voltage level, proportion of intelligent substations, proportion of over-aged assets, and operation and maintenance cost per ten thousand yuan of power grid assets as its tertiary indicators. The risk adjustment factors include five secondary indicators: insurance payout ratio, proportion of underground cable assets, special fund reserve situation, emergency scientific research investment, and advanced technology usage situation.

[0049] The risk management ability refers to the ability of a society or region to effectively prevent and reduce disaster risks through means such as technology, education, management, materials, and social mobilization when facing natural disasters. In the embodiments of the present disclosure, the pre-disaster prevention risk factors, pre-disaster preparation risk factors, in-disaster response risk factors, and post-disaster recovery risk factors are used as secondary indicators. Among them, the pre-disaster prevention risk factors include five tertiary indicators: monitoring and early warning, daily patrol, asset protection, security publicity, and emergency management. The monitoring and early warning include two quaternary indicators: natural disaster early warning and early warning timeliness. The daily patrol includes three quaternary indicators: standard of patrol personnel, daily patrol efficiency, and intelligent level of daily patrol. The asset protection includes one quaternary indicator: asset protection measures. The security publicity includes two quaternary indicators: compliance publicity and publicity frequency. The emergency management includes two quaternary indicators: compilation of emergency plan and emergency plan management. The pre-disaster preparation risk factors include three tertiary indicators: emergency organization, emergency response, and emergency material guarantee. The emergency organization includes two quaternary indicators: construction situation of repair team and personnel reserve quantity of repair team. The emergency response includes four quaternary indicators: emergency response process, emergency training, theme of emergency drill, and emergency drill frequency. The emergency material guarantee includes two quaternary indicators: reserve quantity of emergency vehicles and storage of emergency materials. The in-disaster response risk factors include one tertiary indicator: response and rescue ability. The response and rescue ability includes one quaternary indicator: arrival time of emergency personnel at the scene. The post-disaster recovery risk factors include two tertiary indicators: power restoration ability and continuous improvement ability. The power restoration ability includes two quaternary indicators: power restoration time and user power supply rate. The continuous improvement ability includes one quaternary indicator: continuous improvement situation.

[0050] It should be noted that Figure 2 the shown index system is only used as an example to explain the present disclosure and cannot be used as a limitation to the present disclosure. In actual applications, an index system that conforms to the actual situation of different regions can be formulated according to the actual situation of the regions.

[0051] Through the establishment of the above-mentioned index system, comprehensively considering factors such as the occurrence probability, intensity, and duration of natural disasters, as well as the geographical location, structural characteristics, and protection measures of power grid assets, and combining factors such as the characteristics of power grid assets and the ability to prevent and resist disasters, the risk assessment framework and index system for power grid assets are proposed for the first time. This index system includes five first-level indicators, namely disaster-causing factors, disaster-forming environments, disaster-bearing bodies, risk management capabilities, and risk adjustment factors. According to different evaluation dimensions, it is subdivided into 21 secondary indicators, 20 tertiary indicators, and 22 quaternary indicators, effectively solving the pain point that the natural disaster risk has been unable to be quantitatively evaluated for a long time, providing a set of replicable and popularizable risk assessment tools, realizing a comprehensive and accurate assessment of the risk of power grid assets, helping to identify potential risks in advance, formulate effective preventive measures, and being able to respond quickly when risks occur to reduce asset losses. At the same time, this index system further broadens the content and scope of risk assessment from natural objective factors to cover fields such as system construction, emergency management, and scientific and technological research and development, providing an opportunity for carrying out risk management consulting services that integrate risk consulting and management consulting. By systematically evaluating the risk of natural disasters to power grid assets, potential safety hazards can be discovered in time, and effective preventive and response measures can be formulated, thus significantly reducing the impact of natural disasters on power supply and ensuring the continuity and stability of urban power supply.

[0052] In the embodiments of the present disclosure, the index data of the target area matching each warning index is obtained, and the index scores corresponding to each warning index are determined according to the obtained index data. It can be understood that the sources of the index data of different indicators are different. For the flood index, the historical flood data released by the target area can be obtained as the index data. For other secondary indicators in the disaster-causing factors, the historical meteorological data released by the meteorological data network can be obtained as the index data. For each tertiary indicator under the disaster-bearing body and each quaternary indicator under the risk management ability, the relevant data provided by the power enterprises in the target area can be obtained as the index data. For the insurance loss ratio, a secondary indicator under the risk adjustment factor, the historical loss ratio of all risks insurance for power grid property under different natural disasters in a specific time period in the target area can be obtained as the index data. For the other several secondary indicators under the risk adjustment factor, the relevant data provided by the power enterprises in the target area can be obtained as the index data.

[0053] Among them, the index data of the target area matching the preset warning index refers to the index data of the target area matching each maximum-level index in the first-level indicators. For the disaster-causing factors, disaster-forming environments, and risk adjustment factors, the maximum-level index is each secondary indicator; for the disaster-bearing body, the maximum-level index is each tertiary indicator; for the risk management ability, the maximum-level index is each quaternary indicator. When determining the index scores corresponding to each indicator according to the index data of each indicator, the index scores corresponding to each maximum-level index are determined according to the index data of the target area matching each maximum-level index.

[0054] It can be understood that different indicators can be used to determine the corresponding indicator scores in different ways. Specifically, for each secondary indicator under the disaster-causing factor, the corresponding indicator score can be determined by querying the disaster-causing factor grading standard shown in Figure 3(a) according to the corresponding indicator data. For example, for heavy rain, assuming that the obtained indicator data is the average rainfall intensity, which is 65 mm / h, then by querying the heavy rain disaster grading standard shown in Figure 3(a), it can be determined that the indicator score corresponding to heavy rain is 0.7. For each secondary indicator under the disaster-bearing environment, the corresponding indicator score can be determined by querying the disaster-bearing environment grading standard shown in Figure 3(b) according to the corresponding indicator data. For example, for the vegetation coverage rate, assuming that the vegetation coverage rate of the target area is 23%, then by querying the vegetation coverage level table shown in Figure 3(b), it can be determined that the indicator score corresponding to the vegetation coverage rate is 0.8. For each tertiary indicator under the disaster-affected body, the corresponding indicator score can be determined by querying the disaster-affected body grading standard shown in Figure 3(c) according to the corresponding indicator data. For example, for the cable density, assuming that the cable density of the target area is 8.9 km / km², then by querying the cable density grading standard shown in Figure 3(c), it can be determined that the indicator score corresponding to the cable density is 0.7. For the following four-level indicators of the risk management ability: daily inspection efficiency, intelligent level of daily inspection, construction of emergency repair teams, number of personnel reserves in emergency repair teams, emergency response process, and emergency training, the corresponding indicator scores can be determined by querying the risk management ability grading standard 1 shown in Figure 3(d) according to the corresponding indicator data; for the following four-level indicators of the risk management ability: time for emergency personnel to arrive at the scene, power restoration time, and user power supply rate, the corresponding indicator scores can be determined by querying the risk management ability grading standard 2 shown in Figure 3(e) according to the corresponding indicator data. Among them, the risk management ability grading standards shown in Figure 3(d) and Figure 3(e) also give the calculation methods for each four-level indicator. For example, the power restoration time = (actual power restoration time / standard power restoration time) × 100%; for the remaining four-level indicators of the risk management ability, the corresponding indicator scores of 0 or 1 are given according to whether the indicator data of the corresponding indicator is yes or no. If the indicator data is "yes", the corresponding indicator score is 0, and if the indicator data is "no", the corresponding indicator score is 1. For example, for the four-level indicator of early warning timeliness, if the obtained indicator data is "yes", then it is determined that the indicator score corresponding to early warning timeliness is 0.For each secondary indicator under the risk adjustment factor, the corresponding indicator data (historical loss ratio value) of the historical loss ratio is the corresponding indicator score; the corresponding indicator data (ratio of the proportion of underground cable assets) of the proportion of underground cable assets is the corresponding indicator score; for the corresponding indicator data of the special fund reserve situation, if the special fund is reserved, the indicator score is 0, and if not reserved, the indicator score is 1; for the corresponding indicator data of the emergency scientific research investment, if the investment funds are available, the indicator score is 0, and if not invested, the indicator score is 1; for the corresponding indicator data of the use of advanced technologies, if advanced technologies are used, the indicator score is 0, and if not used, the indicator score is 1.

[0055] Step 102: Obtain the weights corresponding to the indicators at each level.

[0056] As an example, professionals can pre-set and store the weights corresponding to the indicators at each level according to experience and requirements in advance, so as to obtain the weights of the indicators at each level when performing calculations.

[0057] As an example, the commonly used weighting methods can be pre-adopted to determine and store the weights corresponding to the indicators at each level in advance, so as to obtain the weights of the indicators at each level when performing calculations. Exemplarily, weighting methods such as the Analytic Hierarchy Process (AHP), Decision-making Trial and Evaluation Laboratory (DEMATEL), frequency estimation method, Bayesian estimation method, and entropy weight method can be used to determine the weights corresponding to the indicators at each level. The ways of determining the weights of different indicators can be the same or different. For example, the frequency estimation method can be used to determine the weights corresponding to various natural disaster indicators, and the analytic hierarchy process can be used to determine the weights of the remaining indicators.

[0058] Step 103: Perform weighted summation based on the indicator scores and weights to obtain the target risk index corresponding to the power grid assets in the target area.

[0059] In this embodiment, after obtaining the indicator scores and the corresponding weights of each indicator, weighted summation can be performed based on the indicator scores and weights of the corresponding indicators to obtain the target risk index corresponding to the power grid assets in the target area.

[0060] Specifically, when determining the target risk index, for each first-level indicator, the index scores corresponding to each maximum-level indicator under each first-level indicator are weighted and summed with the corresponding weights of the indicators to obtain the index scores of the upper-level indicators of each maximum-level indicator; then, based on the index scores of the upper-level indicators and the corresponding weights, weighted summation is performed and the operation is carried out level by level to the upper level until the index scores corresponding to each first-level indicator are obtained. Finally, based on the index scores corresponding to each first-level indicator and the weights, weighted summation is performed to obtain the target risk index corresponding to the power grid assets in the target area.

[0061] That is to say, for a first-level indicator, calculations are carried out level by level from its maximum-level indicator upwards until the index score of this first-level indicator is obtained. Taking the indicator of risk management ability as an example, the index scores of each fourth-level indicator under this indicator are obtained. The index scores of each fourth-level indicator belonging to the same third-level indicator are weighted and summed with the corresponding weights of the indicators, and the result obtained is the index score of the third-level indicator to which it belongs; then, the index scores of each third-level indicator belonging to the same second-level indicator are weighted and summed with the corresponding weights of the third-level indicators, and the result obtained is the index score of the second-level indicator to which it belongs; after obtaining the index scores of each second-level indicator, the index scores of each second-level indicator are weighted and summed with the corresponding weights of the indicators, and the result obtained is the index score of risk management ability.

[0062] Step 104: Query the preset mapping relationship table between different risk indexes and risk levels, and determine the target risk level corresponding to the target risk index.

[0063] Among them, the mapping relationship table between different risk indexes and risk levels can be preset according to requirements and experience. For example, the mapping relationship table between different risk indexes and risk levels is shown in Table 1.

[0064] Table 1

[0065] Risk Index Risk Level 0.85 - 1 (including 1) Extra - large Risk (Level 4) 0.65 - 0.85 (including 0.85) Major Risk (Level 3) 0.35 - 0.65 (including 0.65) Relatively Large Risk (Level 2) 0 (including 0) - 0.35 (including 0.35) General Risk (Level 1)

[0066] In this embodiment, after determining the target risk index corresponding to the power grid assets in the target area, the preset mapping relationship table between different risk indexes and risk levels can be queried to determine the target risk level corresponding to the target risk index, so as to obtain the target risk level corresponding to the power grid assets in the target area.

[0067] For example, assuming that the determined target risk index is 0.876, then by querying Table 1, it can be determined that the target risk level of the power grid assets in the target area is an extremely high risk (Level 4).

[0068] Step 105: Output the target risk level and the risk handling strategy matching the target risk level.

[0069] Among them, the risk handling strategies corresponding to different risk levels can be preset. The risk handling strategies are used to help users take appropriate handling measures to cope with risks when power grid assets encounter natural disasters, so as to minimize losses as much as possible.

[0070] In this embodiment, after determining the target risk level, the risk handling strategy matching the target risk level can be determined according to the target risk level, and then the target risk level and the matching risk handling strategy are output to inform the user of the risk level of the power grid assets in the target area under natural disasters and the risk handling measures that can be taken when the disaster occurs.

[0071] The risk warning method for power grid assets under natural disasters provided by the embodiments of the present disclosure determines the index scores corresponding to the warning indicators according to the index data of the target area matching the preset warning indicators. Among them, there are multiple warning indicators, including at least the first-level indicator of disaster-causing factors. The disaster-causing factors include second-level indicators corresponding to multiple different natural disaster types, and the weights corresponding to each level of indicators are obtained. Then, based on the index scores and weights, weighted summation is performed to obtain the target risk index corresponding to the power grid assets in the target area. Furthermore, by querying the preset mapping relationship table between different risk indexes and risk levels, the target risk level corresponding to the target risk index is determined, and the target risk level and the risk handling strategy matching the target risk level are output. By adopting the solution of the present disclosure, by setting multiple warning indicators and determining the target risk level of the power grid assets in the target area according to the index scores and weights of each determined warning indicator, the risk warning of the power grid assets in the target area is realized, which is beneficial to reducing the probability of risk losses, maximizing the level of the power grid in the target area to resist natural disasters, and effectively ensuring the safe operation of the power grid in the target area; various warning indicators such as multiple natural disasters are comprehensively considered, and objective, comprehensive, and accurate risk warning is realized.

[0072] In an alternative embodiment of the present disclosure, the risk assessment and warning of power grid assets in the target area under a single natural disaster can also be performed. In this scenario, for a single natural disaster, it is necessary to first determine the weights corresponding to different disaster levels under the natural disaster. It can be understood that there can be multiple index data of the corresponding second-level indicators obtained for a type of natural disaster, and thus multiple index scores can be obtained. For example, for the natural disaster of heavy rain, there can be multiple index data of the second-level indicator of rainfall intensity, and each index data corresponds to an index score. Therefore, under a single natural disaster, the index scores of the second-level indicators of the power grid assets in the target area (when there are multiple index scores corresponding to the same disaster level, the average value of the index scores of the same disaster level is used as the index score of the second-level indicator of the disaster level) are multiplied by the weights of the corresponding disaster levels, and then accumulated to obtain the risk factor h of the second-level indicator, then:

[0073] h = ∑W l P l 。

[0074] Wherein, l is the disaster level of a single natural disaster, and W l represents the average value of the index scores of the secondary indicators of the disaster level of level l, and P l represents the weight corresponding to the disaster level of level l.

[0075] Next, the result of multiplying the index factor of the secondary indicator of the natural disaster by the weight of the corresponding primary indicator, and the result of multiplying the index scores of other primary indicators by the corresponding weights of the primary indicators are accumulated, that is, the risk index of the power grid assets in the target area under the single natural disaster is obtained. Furthermore, the corresponding safety level can be determined, and the safety level and the risk treatment strategy matching the safety level are output. Thus, the risk early warning of the power grid assets in the target area under different types of single natural disasters is realized, which helps to adopt appropriate treatment strategies for different natural disasters for risk prevention and response.

[0076] In an alternative embodiment of the present disclosure, risk assessment and early warning can also be performed separately for different power grid equipment (such as switches, disconnecting switches, cables, overhead distribution lines, joints, etc.) in the target area. Thus, as Figure 4 shown, on the basis of the foregoing embodiment, the method for risk early warning of power grid assets under natural disasters in the present disclosure may further include the following steps:

[0077] Step 201, determine the disaster early warning levels corresponding to different natural disaster types in the target area according to the index data of the secondary indicators corresponding to different natural disaster types in the target area and the early warning level division criteria corresponding to different natural disaster types.

[0078] It can be understood that the secondary indicators corresponding to different natural disaster types are different. For the natural disaster of heavy rain, the corresponding secondary indicator is rainfall intensity; for the natural disaster of flood, the corresponding secondary indicator is flood intensity; for the natural disaster of strong wind, the corresponding secondary indicator is maximum wind speed, and so on. The early warning levels corresponding to different secondary indicators are different. Among them, the early warning signals for heavy rain, strong wind, heavy snow, rain and snow freezing, and flood are divided into four levels, which are represented by red, orange, yellow, and blue respectively; the lightning strike early warning signal is divided into three levels, which are represented by red, orange, and yellow respectively. Exemplarily, the early warning level division criteria corresponding to different natural disaster types are as Figure 5 shown.

[0079] In this embodiment, after obtaining the index data of the secondary indexes corresponding to different natural disaster types in the target area, the disaster warning levels corresponding to different natural disaster types in the target area can be further determined according to the warning level division criteria corresponding to different natural disaster types. For example, assuming that the obtained index data of the rainfall intensity is that the rainfall within 3 hours exceeds 50 millimeters but is less than 100 millimeters, then the rainstorm warning level is determined to be orange.

[0080] Step 202: According to the disaster warning levels corresponding to different natural disaster types in the target area, query the index score table of different power grid equipment in the target area under natural disasters to obtain the index scores of the secondary indexes of different natural disaster types corresponding to different power grid equipment in the target area.

[0081] In this embodiment, after determining the disaster warning levels corresponding to different natural disaster types in the target area, the index score table of different power grid equipment in the target area under natural disasters can be further queried to determine the index scores of the secondary indexes of different natural disaster types corresponding to different power grid equipment in the target area.

[0082] As an example, without distinguishing the terrain characteristics of the target area, only an index score table of different power grid equipment in the target area under natural disasters needs to be determined in advance, and the index scores of the secondary indexes of different natural disaster types corresponding to different power grid equipment in the target area can be determined by querying this table.

[0083] As another example, topographic features can be divided into two types: plain topography and mountainous topography. Among them, the relative altitude between 20 and 60 (including 60) meters is plain topography, and the relative altitude between 60 and 1500 meters is mountainous topography. The topographic features of the target area include at least one of mountainous topography and plain topography. The index score tables of different power grid devices corresponding to the target area under natural disasters include the index score table corresponding to mountainous topography and the index score table of plain topography. Exemplarily, the index score table corresponding to plain topography is shown in Figure 6(a), and the index score table corresponding to mountainous topography is shown in Figure 6(b). Thus, when determining the index scores of the secondary indicators of different natural disaster types corresponding to different power grid devices in the target area, according to the target topographic features of the target area and the disaster warning levels corresponding to different natural disaster types in the target area, query the index score table corresponding to the target topographic features to obtain the index scores of the secondary indicators of different natural disaster types corresponding to different power grid devices with the target topographic features in the target area, and the target topographic features are at least one of mountainous topography and plain topography. For example, assuming that the target topographic feature of the target area is mountainous topography and the rainstorm warning level is orange, by querying the index score table corresponding to mountainous topography shown in Figure 6(b), the index score of the rainstorm secondary indicator (rainfall intensity) corresponding to each power grid device with mountainous topography in the target area can be determined. For example, the index score of the rainfall intensity corresponding to the switch is 0.84. It should be noted that in this embodiment, when the target area includes both mountainous topography and plain topography, the index scores of the secondary indicators of the same natural disaster in different topographies can be respectively queried from Figure 6(a) and Figure 6(b), and then the average value of the two is calculated as the index score of the secondary indicator of this natural disaster.

[0084] Step 203: For different power grid devices, determine the index scores of the disaster-causing factors corresponding to different power grid devices according to the index scores of the secondary indicators of different natural disaster types and the weights of the corresponding indicators.

[0085] In this embodiment, after determining the index scores of the secondary indicators of different natural disaster types corresponding to different power grid devices in the target area, for each power grid device, weighted summation can be performed according to the index scores of the secondary indicators of different natural disaster types corresponding to the power grid device and the weights of the corresponding indicators to obtain the index score of the primary indicator (i.e., the disaster-causing factor) corresponding to the power grid device, so as to obtain the index scores of the disaster-causing factors corresponding to all power grid devices respectively.

[0086] Step 204: For different power grid devices, determine the device risk index of each power grid device in the target area according to the index score of the disaster-causing factor corresponding to each power grid device, the index scores of other primary indicators in the warning indicators, and the weights of the corresponding indicators.

[0087] In this embodiment, after determining the index scores of the disaster-causing factors corresponding to different power grid devices, for each power grid device, the weighted value of the index score of the disaster-causing factor and the weight of the disaster-causing factor can be calculated, and the weighted value of the index score of other first-level indicators in the early warning indicators and the weight of the corresponding indicators can be calculated. Then, the accumulated weighted values are obtained to get the device risk index corresponding to each power grid device in the target area.

[0088] It should be noted that in this embodiment, the determination of the index scores of other first-level indicators in the early warning indicators is the same as the method for determining the index scores of each first-level indicator in the foregoing embodiment, and both are obtained by performing level-by-level operations upward from the maximum-level index scores and weights. To avoid repetition, it will not be elaborated here.

[0089] Step 205: According to the device risk index of each power grid device in the target area, determine the device risk level of each power grid device in the target area and output the device risk level of each power grid device.

[0090] In this embodiment, after obtaining the device risk index of each power grid device in the target area, the device risk level of each power grid device in the target area can be determined according to the device risk index, and the device risk levels of each power grid device can be output.

[0091] For example, a risk level classification standard for different power grid devices can be established in advance. The risk level classification standard records the mapping relationship between the numerical range of the device risk index and the device risk level. Therefore, according to the device risk index of each power grid device, querying the risk level classification standard can determine the device risk level corresponding to each power grid device.

[0092] The risk early warning method for power grid assets under natural disasters in this embodiment realizes the risk assessment and early warning of different types of power grid devices by determining the corresponding device risk index for different power grid devices in the target area, and then determining the device risk level of each power grid device and outputting it, which helps users take appropriate preventive and emergency measures according to the device risk levels of different power grid devices.

[0093] Further, in an alternative embodiment of the present disclosure, when the terrain feature of the target area is the target terrain feature, and the target terrain feature includes at least one of mountain terrain and plain terrain, risk warnings for different power grid devices under different natural disasters can also be carried out for the target mountain terrain. Specifically, according to the target terrain feature of the target area and the disaster warning levels corresponding to different natural disaster types in the target area, an index score table corresponding to the target terrain feature is queried to obtain the index scores of the secondary indicators of different natural disaster types corresponding to different power grid devices with the target terrain feature of the target area. Then, according to the index scores of the secondary indicators of different natural disaster types corresponding to different power grid devices with the target terrain feature of the target area and the weights of the corresponding indicators, the weighted values of the secondary indicators of different natural disaster types corresponding to different power grid devices with the target terrain feature of the target area are determined; then, for different power grid devices, according to the weighted values of the secondary indicators of different natural disaster types corresponding to different power grid devices with the target terrain feature of the target area, the weights of the disaster-causing factors, as well as the index scores of other primary indicators in the warning indicators and the weights of the corresponding indicators, the equipment risk indices of different power grid devices with the target terrain feature of the target area under different natural disasters are determined, where the equipment risk index for each power grid device with the target terrain feature of the target area is calculated separately for each natural disaster type, and the equipment risk index is obtained by accumulating the product of the weighted value of the secondary indicator of this natural disaster type and the weight of the disaster-causing factor and the product of the index scores of other primary indicators and the corresponding weights; then, according to the equipment risk indices of different power grid devices with the target terrain feature of the target area under different natural disasters, the equipment risk levels of each power grid device with the target terrain feature of the target area under different natural disasters are determined; finally, according to the equipment risk levels of each power grid device with the target terrain feature of the target area under different natural disasters, a risk level map under the target terrain feature of the target area is drawn according to a preset drawing template.

[0094] It can be understood that when the target area includes two terrain features, namely plain terrain and mountain terrain, risk level maps are drawn respectively for each terrain feature.

[0095] Exemplarily, the drawing template may be a radar chart. Fig. 7(a) shows the risk level chart of different power grid devices under different natural disaster types in the plain terrain of the target area in an exemplary embodiment of the present disclosure, and Fig. 7(b) shows the risk level chart of different power grid devices under different natural disaster types in the mountain terrain of the target area in an exemplary embodiment of the present disclosure. As shown in Fig. 7(a), in the plain terrain, for the switch, the risk level is 4 under the rainstorm disaster, 3 under the flood disaster, 2 under the lightning strike disaster, and 1 under the storm, blizzard, and rain-snow-ice disaster. For the disconnecting switch, the risk level is 4 under the rainstorm and lightning strike disasters, 2 under the rain-snow-ice disaster, and 1 under the flood, storm, and blizzard disasters. For the joint, the risk level is 3 under the rainstorm and flood disasters, 2 under the storm, lightning strike, and rain-snow-ice disasters, and 1 under the blizzard disaster. For the cable, the risk level is 4 under the flood disaster, 3 under the blizzard, storm, lightning strike, and rain-snow-ice disasters, and 1 under the rainstorm disaster. For the overhead distribution line, the risk level is 4 under the flood, storm, and rain-snow-ice disasters, 3 under the blizzard and lightning strike disasters, and 2 under the rainstorm disaster. For the low-voltage switchgear, the risk level is 3 under the storm and blizzard disasters, 2 under the lightning strike, rainstorm, and rain-snow-ice disasters, and 1 under the flood disaster. For the distribution box, the risk level is 3 under the lightning strike and rain-snow-ice disasters, 2 under the blizzard, storm, and rainstorm disasters, and 1 under the flood disaster. For the electric pole, the risk level is 4 under the blizzard, storm, and lightning strike disasters, 3 under the flood and rainstorm, and 2 under the rain-snow-ice disaster. For the transformer, the risk level is 4 under the rainstorm, blizzard, flood, and lightning strike disasters, and 3 under the storm and rain-snow-ice disasters.

[0096] As shown in Figure 7(b), in mountainous terrain, for switches, the risk level is 4 under rainstorm disasters, 3 under flood and lightning disasters, 2 under storm disasters, and 1 under heavy snow and rain / snow / ice disasters. For disconnect switches, the risk level is 4 under lightning disasters, 3 under rainstorm and rain / snow / ice disasters, and 2 under flood, storm, and heavy snow disasters. For joints, the risk level is 4 under flood disasters, 3 under rainstorm disasters, 2 under storm, lightning, and rain / snow / ice disasters, and 1 under heavy snow disasters. For cables, the risk level is 4 under rain / snow / ice disasters, 3 under heavy snow, storm, lightning, and flood disasters, and 2 under rainstorm disasters. For overhead distribution lines, the risk level is 4 under storm and rain / snow / ice disasters, and 3 under rainstorm, flood, heavy snow, and lightning disasters. For low-voltage cabinets, the risk level is 3 under lightning, flood, rainstorm, storm, and heavy snow disasters, and 2 under rain / snow / ice disasters. For distribution boxes, the risk level is 3 under lightning and heavy snow disasters, 2 under flood disasters, and 1 under rainstorm, rain / snow / ice, and storm disasters. For electric poles, the risk level is 4 under rainstorm, heavy snow, flood, storm, and lightning disasters, and 2 under rain / snow / ice disasters. For transformers, the risk level is 4 under rainstorm, heavy snow, flood, and lightning disasters, 3 under storm disasters, and 2 under rain / snow / ice disasters.

[0097] It can be seen that the risk levels of different types of power grid equipment vary under different regions and natural disasters. By separately drawing the risk level diagrams of different power grid equipment under different natural disasters for different terrain features, the risk levels of different power grid equipment in different terrains under each type of natural disaster can be intuitively and clearly displayed, facilitating users to intuitively understand the risks of each power grid equipment under different natural disasters, and then taking appropriate preventive and emergency measures.

[0098] To implement the above embodiments, the present disclosure also provides a risk warning device for power grid assets under natural disasters.

[0099] Figure 8 It is a schematic structural diagram of a risk warning device for power grid assets under natural disasters provided by an embodiment of the present disclosure. The device is implemented in a software and / or hardware manner and can be integrated in an electronic device.

[0100] As Figure 8 shown, the risk warning device 50 for power grid assets under natural disasters may include: a first acquisition module 510, a second acquisition module 520, a first determination module 530, a second determination module 540, and a warning output module 550.

[0101] Among them, the first acquisition module 510 is used to determine the index score corresponding to the early warning index according to the index data of the target area that matches the preset early warning index. Among them, there are multiple early warning indexes, including at least the first-level index of disaster-causing factors, and the disaster-causing factors include second-level indexes corresponding to multiple different natural disaster types;

[0102] The second acquisition module 520 is used to acquire the weights corresponding to the indexes at each level;

[0103] The first determination module 530 is used to perform weighted summation based on the index score and the weight to obtain the target risk index corresponding to the power grid assets in the target area;

[0104] The second determination module 540 is used to query the preset mapping relationship table between different risk indexes and risk levels, and determine the target risk level corresponding to the target risk index;

[0105] The early warning output module 550 is used to output the target risk level and the risk handling strategy matching the target risk level.

[0106] Optionally, the early warning indexes further include the following first-level indexes: disaster-bearing environment, hazard-exposed elements, risk management ability, and risk adjustment factor;

[0107] Among them, the disaster-bearing environment includes multiple second-level indexes;

[0108] The hazard-exposed elements include multiple second-level indexes, and each second-level index includes multiple third-level indexes;

[0109] The risk management ability includes multiple second-level indexes, each second-level index includes multiple third-level indexes, and each third-level index includes at least one fourth-level index;

[0110] The risk adjustment factor includes multiple second-level indexes.

[0111] Further optionally, the index data of the target area that matches the preset early warning index refers to the index data of the target area that matches each maximum-level index in the first-level indexes; the first acquisition module 510 is further used for:

[0112] According to the index data of the target area that matches each maximum-level index, determine the index score corresponding to each maximum-level index.

[0113] Further optionally, the first determination module 530 is further used for:

[0114] For each first-level index, perform weighted summation on the index score corresponding to each maximum-level index under each first-level index and the weight of the corresponding index to obtain the index score of the upper-level index of each maximum-level index;

[0115] Perform weighted summation based on the index scores of the upper-level indicators and the weights of the corresponding indicators, and perform the operation level by level up to the upper level until the index scores corresponding to each first-level indicator are obtained;

[0116] Perform weighted summation based on the index scores and weights corresponding to each first-level indicator to obtain the target risk index corresponding to the power grid assets in the target area.

[0117] Optionally, the risk warning device 50 for power grid assets under natural disasters further includes:

[0118] A third determination module, configured to determine the disaster warning levels corresponding to different natural disaster types in the target area according to the index data of the secondary indicators corresponding to different natural disaster types in the target area and the warning level division criteria corresponding to different natural disaster types;

[0119] A fourth determination module, configured to query the index score table of different power grid devices in the target area under natural disasters according to the disaster warning levels corresponding to different natural disaster types in the target area, and obtain the index scores of the secondary indicators of different natural disaster types corresponding to different power grid devices in the target area;

[0120] A fifth determination module, configured to, for different power grid devices, determine the index scores of the disaster-causing factors corresponding to different power grid devices according to the index scores of the secondary indicators of different natural disaster types and the weights of the corresponding indicators;

[0121] A sixth determination module, configured to, for different power grid devices, determine the device risk index of each power grid device in the target area according to the index scores of the disaster-causing factors corresponding to each power grid device, the index scores of other first-level indicators in the warning indicators, and the weights of the corresponding indicators;

[0122] A seventh determination module, configured to determine the device risk levels of each power grid device in the target area according to the device risk index of each power grid device in the target area and output the device risk levels of each power grid device.

[0123] Further optionally, the terrain features of the target area include at least one of mountainous terrain and plain terrain, and the index score table of different power grid devices in the target area corresponding to natural disasters includes the index score table corresponding to mountainous terrain and the index score table of plain terrain; the fourth determination module is further configured to:

[0124] Query the index score table corresponding to the target terrain feature according to the target terrain feature of the target area and the disaster warning levels corresponding to different natural disaster types in the target area, and obtain the index scores of the secondary indicators of different natural disaster types corresponding to different power grid devices with the target terrain feature in the target area, where the target terrain feature is at least one of mountainous terrain and plain terrain.

[0125] Further optionally, the risk warning device 50 for power grid assets under natural disasters further includes:

[0126] A risk assessment module, configured to determine the weighted values of the secondary indicators of different natural disaster types corresponding to different power grid devices with different target terrain features in the target area according to the index scores of the secondary indicators of different natural disaster types corresponding to different power grid devices with different target terrain features in the target area and the weights of the corresponding indicators; for different power grid devices, determine the equipment risk index of different power grid devices with different target terrain features in the target area under different natural disasters according to the weighted values of the secondary indicators of different natural disaster types corresponding to different power grid devices with different target terrain features in the target area, the weights of the disaster-causing factors, as well as the index scores of other primary indicators in the warning indicators and the weights of the corresponding indicators; determine the equipment risk level of each power grid device with different target terrain features in the target area under different natural disasters according to the equipment risk index of different power grid devices with different target terrain features in the target area under different natural disasters;

[0127] A drawing module, configured to draw a risk level map of the target terrain feature in the target area according to the equipment risk levels of each power grid device with different target terrain features in the target area under different natural disasters according to a preset drawing template.

[0128] The risk warning device for power grid assets under natural disasters provided by the embodiments of the present disclosure can execute the risk warning method for power grid assets under natural disasters provided by the embodiments of the present disclosure, and has corresponding functional modules and beneficial effects for executing the method. The content not described in detail in the device embodiments of the present disclosure can be referred to the description in any method embodiment of the present disclosure.

[0129] The embodiments of the present disclosure also provide a computer program product, including computer programs / instructions, which when executed by a processor implement the risk warning method for power grid assets under natural disasters provided by any embodiment of the present disclosure.

[0130] According to one or more embodiments of the present disclosure, the present disclosure provides an electronic device, including:

[0131] A processor;

[0132] A memory for storing executable instructions of the processor;

[0133] The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the risk warning method for power grid assets under natural disasters provided by any embodiment of the present disclosure.

[0134] According to one or more embodiments of the present disclosure, the present disclosure provides a computer-readable storage medium storing a computer program for implementing the risk warning method of power grid assets under natural disasters provided in any embodiment of the present disclosure.

[0135] It should be noted that the computer-readable medium in the present disclosure may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, 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 above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0136] The above computer-readable medium may be included in the above electronic device; or it may exist separately and not be assembled into the electronic device.

[0137] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in a different order than noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or by a combination of dedicated hardware and computer instructions.

[0138] The units involved in the embodiments described in the present disclosure can be implemented in software or in hardware. In some cases, the name of the unit does not constitute a limitation on the unit itself.

[0139] The functions described above herein can be performed, at least in part, by one or more hardware logic components. By way of example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system on a chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0140] In the context of the present disclosure, a machine-readable medium may be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. 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, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0141] The above description is only a preferred embodiment of the present disclosure and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the disclosure involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosure concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions disclosed in the present disclosure.

[0142] In addition, although the operations are depicted in a particular order, this should not be construed as requiring that the operations be performed in the particular order shown or in sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features described in the context of separate embodiments may also be implemented combinatorially in a single embodiment. Conversely, the various features described in the context of a single embodiment may also be implemented separately or in any suitable sub-combination in multiple embodiments.

[0143] Although the subject matter has been described in language specific to structural features and / or methodological logical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms for implementing the claims.

Claims

1. A risk warning method for power grid assets under natural disasters, characterized in that: The method comprises: Determine the index score corresponding to the early warning index according to the index data of the target area matching the preset early warning index, wherein the early warning index is multiple, including at least a primary index of a disaster-causing factor, and the disaster-causing factor includes secondary indicators corresponding to multiple different natural disaster types; Get the weights corresponding to the indicators at each level; Performing a weighted sum based on the indicator score and the weight to obtain a target risk index corresponding to the power grid assets in the target area; Querying a preset mapping relationship table of different risk indices and risk levels to determine a target risk level corresponding to the target risk index; The target risk level and a risk handling strategy matching the target risk level are output.

2. The method according to claim 1, characterized in that The early warning indicators also include the following primary indicators: disaster-prone environment, disaster-bearing body, risk management capacity and risk adjustment factor; The disaster-prone environment includes multiple secondary indicators; The disaster-bearing body includes a plurality of secondary indicators, and each secondary indicator includes a plurality of tertiary indicators; The risk management capability includes a plurality of second-level indicators, each second-level indicator includes a plurality of third-level indicators, and each third-level indicator includes at least one fourth-level indicator; The risk adjustment factor includes multiple secondary indicators.

3. The method according to claim 2, characterized in that The indicator data of the target area matching the preset early warning indicator refers to the indicator data of the target area matching each maximum level indicator in the first level indicator; The step of determining the indicator score corresponding to the early warning indicator according to the indicator data of the target area matching the preset early warning indicator comprises: The indicator score corresponding to each maximum level indicator is determined according to the indicator data of the target area matching each maximum level indicator.

4. The method according to claim 3, characterized in that The step of performing weighted summation based on the indicator score and the weight to obtain a target risk index corresponding to the power grid assets in the target area includes: For each first-level indicator, the indicator score corresponding to each maximum level indicator under each first-level indicator is weighted and summed with the weight of the corresponding indicator to obtain the indicator score of the previous level indicator of each maximum level indicator; Perform weighted summation based on the indicator score of the previous level indicator and the weight of the corresponding indicator, and perform calculations on the previous level one by one until the indicator score corresponding to each first-level indicator is obtained; A weighted sum is performed based on the indicator score and weight corresponding to each primary indicator to obtain a target risk index corresponding to the power grid assets in the target area.

5. The method according to claim 1, characterized in that The method further comprises: Determine the disaster warning level corresponding to the different natural disaster types in the target area according to the indicator data of the secondary indicators corresponding to the different natural disaster types in the target area and the warning level classification standards corresponding to the different natural disaster types; According to the disaster warning levels corresponding to the different natural disaster types in the target area, query the index score table of different power grid equipment corresponding to the target area under natural disasters to obtain the index scores of the secondary indicators of different natural disaster types corresponding to different power grid equipment in the target area; For the different power grid devices, according to the index scores of the secondary indicators of the different natural disaster types and the weights of the corresponding indicators, determine the index scores of the disaster-causing factors corresponding to the different power grid devices; For the different power grid equipment, determine the equipment risk index of each power grid equipment in the target area according to the index score of the disaster-causing factor corresponding to each power grid equipment, the index scores of other first-level indicators in the early warning index, and the weights of the corresponding indicators; According to the equipment risk index of each power grid equipment in the target area, the equipment risk level of each power grid equipment in the target area is determined and the equipment risk level of each power grid equipment is output.

6. The method according to claim 5, characterized in that The terrain feature of the target area includes at least one of mountainous terrain and plain terrain, and the index score table of different power grid equipment corresponding to the target area under the natural disaster includes an index score table corresponding to mountainous terrain and an index score table corresponding to plain terrain; According to the disaster warning levels corresponding to the different natural disaster types in the target area, querying the index score table of different power grid equipment corresponding to the target area under natural disasters, and obtaining the index scores of the secondary indicators of different natural disaster types corresponding to different power grid equipment in the target area, includes: According to the target terrain features of the target area and the disaster warning levels corresponding to the different natural disaster types in the target area, the indicator score table corresponding to the target terrain features is queried to obtain the indicator scores of the secondary indicators of different natural disaster types corresponding to different power grid equipment of the target terrain features in the target area, wherein the target terrain features are at least one of mountainous terrain and plain terrain.

7. The method according to claim 6, characterized in that The method further comprises: Determine the weighted values ​​of the secondary indicators of different natural disaster types corresponding to different power grid equipment of the target terrain features in the target area according to the index scores of the secondary indicators of different natural disaster types corresponding to different power grid equipment of the target terrain features in the target area and the weights of the corresponding indicators; For the different power grid equipment, according to the weighted values ​​of the secondary indicators of different natural disaster types corresponding to the different power grid equipment of the target terrain features in the target area and the weights of the disaster-causing factors, as well as the indicator scores of other primary indicators in the early warning indicators and the weights of the corresponding indicators, determine the equipment risk index of the different power grid equipment of the target terrain features in the target area under different natural disasters; Determining the equipment risk level of each power grid equipment of the target terrain feature of the target area under different natural disasters according to the equipment risk index of different power grid equipment of the target terrain feature of the target area under different natural disasters; According to the equipment risk level of each power grid equipment of the target terrain feature in the target area under different natural disasters, a risk level map of the target terrain feature in the target area is drawn according to a preset drawing template.

8. A risk warning device for power grid assets under natural disasters, characterized in that: The device comprises: A first acquisition module is used to determine the index score corresponding to the early warning index according to the index data of the target area matching the preset early warning index, wherein the early warning index is multiple, including at least a primary index of a disaster-causing factor, and the disaster-causing factor includes secondary indicators corresponding to multiple different natural disaster types; The second acquisition module is used to obtain the weights corresponding to the indicators at each level; A first determination module is used to perform weighted summation based on the indicator score and the weight to obtain a target risk index corresponding to the power grid assets in the target area; A second determination module is used to query a preset mapping relationship table of different risk indices and risk levels to determine a target risk level corresponding to the target risk index; The early warning output module is used to output the target risk level and the risk handling strategy matching the target risk level.

9. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the executable instructions to implement the risk warning method for power grid assets under natural disasters as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to implement the risk warning method for power grid assets under natural disasters as described in any one of claims 1 to 7.

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