Power substation geological disaster risk assessment and early warning method, system and device based on multi-source data evolution direction discrimination and medium

Abstract

The present application relates to the technical field of geological disaster monitoring and early warning, and particularly to a substation geological disaster risk assessment and early warning method, system, device and medium based on multi-source data evolution direction discrimination. Monitoring equipment is arranged at the substation to collect surface water level, ground deformation and underground seepage state; the monitoring points with coordinated abnormal changes are screened according to the change trend of the above states, the potential geological disturbance area is determined, and one of the influence items is extracted; the surface expansion trend and the underground extension trend are respectively determined according to the consistency relationship of the change direction between the parameters of the one influence item; high and low risk areas are divided according to the regional stability performance, the spatial distribution position and the area proportion are counted, the high risk concentrated distribution belt and the extension direction are identified, and the risk assessment result is formed. The present application can distinguish the surface and underground dominant types of geological activities, and reveal the concentrated distribution and expansion trend of the risk, thereby improving the pertinence of substation geological disaster prevention and control.

Description

Technical Field

[0001] This invention relates to the field of geological disaster monitoring and early warning technology, and in particular to a method, system, equipment and medium for geological disaster risk assessment and early warning of substations based on the determination of the evolution direction of multi-source data. Background Technology

[0002] The field of substation safety protection technology involves the monitoring and early warning of natural environmental disasters and man-made safety threats. Currently, this field generally relies on sensor networks, remote sensing images, and intelligent analysis algorithms to build a monitoring system inside and around the substation. By collecting various monitoring data such as surface water level, ground deformation, and underground seepage, and combining threshold judgment or general identification algorithms, the risk status in the area is comprehensively assessed, forming a processing flow from data collection and hazard identification to early warning, in order to achieve early detection and prevention of potential geological hazards in the substation operating environment.

[0003] However, existing methods have the following shortcomings: First, after acquiring monitoring data such as water level, deformation, and seepage, existing technologies typically incorporate different risk sources into the same assessment system for comprehensive judgment, making it difficult to conduct further detailed analysis of specific geological processes. Second, existing judgment methods mainly rely on threshold comparisons or general grading rules, lacking the exploration of the correlation between the changing directions of multiple monitoring parameters. When both subsidence and water level anomalies are observed in the monitoring data, it is impossible to further distinguish whether the phenomenon is caused by surface expansion or underground seepage. Third, due to the lack of dynamic extrapolation of the direction and evolution of geological activities, existing technologies cannot accurately predict the concentrated distribution location and expansion direction of risks, resulting in insufficient revelation of the subsequent evolution trend of geological hazards in the assessment results, affecting the accuracy and timeliness of proactive prevention and control of geological disasters at substations. Summary of the Invention

[0004] In view of the problems existing in the prior art, the present invention is proposed.

[0005] Therefore, the problem that this invention aims to solve is the technical problem that existing technologies are unable to distinguish the dominant types of geological activities and cannot accurately predict the evolution trend of risks.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide a method for assessing and warning of geological disaster risks in substations based on the determination of the evolution direction of multi-source data, which includes deploying monitoring equipment in the substation to collect data on surface water level, ground deformation and underground seepage status. Based on the changing trends of surface water level, ground deformation and underground seepage, the processing terminal screens monitoring points that simultaneously show synergistic abnormal changes, identifies potential geological disturbance areas, and extracts the monitoring quantity with the strongest persistence as one of the influencing factors. The processing terminal determines the surface expansion trend and the underground extension trend based on the consistency of the change direction among one of the parameters of the influencing items. The processing terminal divides high-risk and low-risk areas based on regional stability performance, statistically analyzes spatial distribution locations and area proportions, identifies high-risk concentrated distribution zones and extension directions, and generates substation geological disaster risk assessment results.

[0007] As a preferred embodiment of the substation geological disaster risk assessment and early warning method based on multi-source data evolution direction discrimination according to the present invention, wherein: the processing terminal acquires the surface water level change records, ground deformation records and underground seepage change records collected by the monitoring equipment within the substation monitoring area; The processing terminal determines the surface expansion trend based on the consistency of the change direction between the surface water level change record and the ground deformation record, and determines the underground extension trend based on the consistency of the change direction between the underground seepage change record and the ground deformation record. The processing terminal assesses regional stability performance and classifies high-risk and low-risk areas based on the surface expansion trend and the underground extension trend. The processing terminal statistically analyzes the spatial distribution and area ratio of the high-risk and low-risk areas, identifies the concentrated distribution zones with spatial continuity and their extension directions in the high-risk areas, and forms the substation geological disaster risk assessment results.

[0008] As a preferred embodiment of the substation geological disaster risk assessment and early warning method based on multi-source data evolution direction discrimination described in this invention, the screening of monitoring points that simultaneously exhibit synergistic abnormal changes and the determination of potential geological disturbance areas includes marking monitoring points that simultaneously exhibit rising, sinking and enhanced changes in surface water level, ground deformation and underground seepage status as abnormal monitoring points, and determining the area where the abnormal monitoring points are located as potential geological disturbance areas.

[0009] As a preferred embodiment of the substation geological disaster risk assessment and early warning method based on multi-source data evolution direction discrimination described in this invention, the step of extracting the monitoring quantity with the strongest persistence as one of the influencing factors includes comparing the change duration of surface water level, ground deformation and underground seepage state in the potential geological disturbance area, and determining the monitoring quantity with the longest change duration as one of the influencing factors.

[0010] As a preferred embodiment of the substation geological disaster risk assessment and early warning method based on multi-source data evolution direction discrimination described in this invention, the consistency relationship of the change direction includes: the surface water level change direction is consistent with the ground deformation direction, corresponding to the surface expansion trend; the underground seepage change direction is consistent with the ground deformation direction, corresponding to the underground extension trend.

[0011] As a preferred embodiment of the substation geological disaster risk assessment and early warning method based on multi-source data evolution direction discrimination described in this invention, the stability performance of the assessment area is divided into high-risk areas and low-risk areas, including classifying areas that show continuous changes and consistent deformation trends as high-risk areas, and classifying areas with short-term changes and insignificant fluctuations as low-risk areas.

[0012] As a preferred embodiment of the substation geological disaster risk assessment and early warning method based on multi-source data evolution direction discrimination described in this invention, the step of identifying the spatially continuous concentrated distribution zone and extension direction in the high-risk area includes comparing the spatial positional relationship between the high-risk areas, merging adjacent high-risk areas with the same risk level into a concentrated distribution zone, and determining the extension direction based on the spatial distribution characteristics of the concentrated distribution zone.

[0013] Secondly, embodiments of the present invention provide a substation geological disaster risk assessment and early warning system based on multi-source data evolution direction discrimination, which includes a monitoring module for deploying monitoring equipment in the substation and collecting surface water level, ground deformation and underground seepage status through the monitoring equipment; The risk source identification module is used to process the terminal to screen monitoring points that show coordinated abnormal changes based on the changing trends of surface water level, ground deformation and underground seepage status, identify potential geological disturbance areas, and extract the monitoring quantity with the strongest persistence as one of the influencing factors. The direction determination module is used to determine the surface expansion trend or the underground extension trend based on the directional correlation between the parameters involved in the main influencing features. The risk assessment module is used to divide high-risk and low-risk areas based on the regional stability performance, and to statistically analyze the spatial distribution and area proportion of each risk area, identify high-risk concentrated distribution zones and their extension directions, and generate substation geological disaster risk assessment results.

[0014] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of the substation geological disaster risk assessment and early warning method for multi-source data evolution direction discrimination as described in the first aspect of the present invention.

[0015] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of the substation geological disaster risk assessment and early warning method for multi-source data evolution direction discrimination as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: In the first stage, by deploying monitoring equipment and screening monitoring points with coordinated abnormal changes, the analysis scope is narrowed to potential disturbance areas with anomalies, and one of the dominant influencing factors is extracted, avoiding indiscriminate comprehensive processing of all data; in the middle stage, by judging the consistency of the change direction between parameters of one of the influencing factors, it is possible to distinguish whether the geological activity in the area is an expansion trend driven by surface water level or an extension trend driven by underground seepage, thus overcoming the shortcomings of existing technologies in distinguishing between the dominant surface and underground types; in the last stage, based on the regional stability performance, high- and low-risk areas are divided, and the spatial distribution and area proportion are statistically analyzed to identify high-risk concentrated distribution zones and extension directions, integrating scattered risk points into concentrated areas with spatial continuity and expansion trends. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of a substation geological hazard risk assessment and early warning method for determining the evolution direction of multi-source data; Figure 2 A computer equipment diagram for a substation geological hazard risk assessment and early warning method for determining the evolution direction of multi-source data; Figure 3 Another flowchart for substation geological hazard risk assessment and early warning method for determining the evolution direction of multi-source data; Figure 4 A flowchart illustrating the process of obtaining geological risk source identification results for a substation geological hazard risk assessment and early warning method that uses multi-source data to determine the evolution direction of geological disasters. Figure 5 A flowchart illustrating the acquisition of distribution information, one of the geological impact items in a substation geological hazard risk assessment and early warning method for determining the evolution direction of multi-source data; Figure 6 A flowchart for obtaining the geological disaster evolution distribution results for multi-source data evolution direction discrimination; Figure 7 A flowchart for obtaining the geological hazard risk level classification map of a substation geological hazard risk assessment and early warning method for determining the evolution direction of multi-source data; Figure 8 A flowchart illustrating the process of obtaining geological hazard risk assessment results for a substation geological hazard risk assessment and early warning method that uses multi-source data to determine the evolution direction. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0022] Example 1 Reference Figure 1 This is the first embodiment of the present invention, which provides a substation geological hazard risk assessment and early warning method based on multi-source data evolution direction discrimination, including: S100: Install monitoring equipment in the substation to collect data on surface water level, ground deformation, and underground seepage.

[0023] S200: The processing terminal screens monitoring points that show coordinated abnormal changes based on the changing trends of surface water level, ground deformation and underground seepage, identifies potential geological disturbance areas, and extracts the monitoring quantity with the strongest persistence as one of the influencing factors.

[0024] S300: The processing terminal determines the surface expansion trend and the underground extension trend based on the consistency of the change direction among the parameters of one of the influencing factors.

[0025] S400: The processing terminal divides high-risk and low-risk areas based on regional stability performance, statistically analyzes spatial distribution locations and area proportions, identifies high-risk concentrated distribution zones and extension directions, and generates substation geological disaster risk assessment results.

[0026] It should be noted that the geological conditions of the substation site are complex, and the changes in parameters such as surface water level, ground deformation, and underground seepage during abnormal periods vary in magnitude, duration, and direction. If judgment is made based solely on a single parameter or a uniform threshold, it is difficult to determine the specific triggers and subsequent trends of geological activity. When monitoring data simultaneously show anomalies in water level, deformation, or seepage, if it is not possible to further distinguish whether the phenomenon is an expansion caused by surface runoff erosion or an extension dominated by underground seepage and piping, the effectiveness of subsequent risk level classification and control measures will be affected. Therefore, it is necessary to combine the correspondence between the changing directions of various monitoring parameters for comprehensive judgment, and based on this, determine the concentrated area and extension direction of the risk to form a complete assessment result.

[0027] Monitoring equipment is deployed at the substation to obtain the original state of surface water level, ground deformation, and underground seepage; coordinated abnormal changes are screened from multiple monitoring points to identify potential geological disturbance areas and extract one of the influencing factors to narrow the analysis scope; based on the consistency of the change direction among the parameters of one of the influencing factors, the surface expansion trend and the underground extension trend are distinguished to clarify the type of geological activity; risk areas are divided in combination with regional stability performance, and the spatial distribution location and area proportion are statistically analyzed to identify high-risk concentrated distribution zones and extension directions, forming the substation geological hazard risk assessment results, realizing a complete process from data collection to risk assessment.

[0028] Example 2 Reference Figure 1 - Figure 8 This is the second embodiment of the present invention.

[0029] In this embodiment, step S100 involves deploying monitoring equipment at the substation to collect data on surface water level, ground deformation, and underground seepage status, including the following step A1: A1: Install monitoring equipment in the substation to collect data on surface water level, ground deformation, and underground seepage.

[0030] Specifically, refer to Figure 3 Water level monitoring equipment is deployed at the drainage ditch of the substation to form monitoring point D1, settlement monitoring equipment is deployed in the foundation area to form monitoring points F1 and F2, and seepage monitoring equipment is deployed in the slope area to form monitoring points S1 and S2. The above monitoring equipment is used to continuously acquire surface water level readings, ground deformation readings and underground seepage readings at the corresponding locations, and send the readings to the processing terminal in a unified timestamp format.

[0031] The processing terminal continuously collects surface water level, ground deformation and underground seepage status through the aforementioned monitoring equipment according to a preset cycle. For example, it triggers data collection once every 4 hours at 00:00, 04:00, 08:00, 12:00, 16:00 and 20:00 each day. The raw data collected from each monitoring point within multiple consecutive observation cycles (denoted as T1 to T7, i.e., 7 consecutive days) are summarized and recorded to form a preliminary monitoring record covering 5 monitoring points in drainage ditches, foundation areas and slope areas for 7 consecutive observation cycles.

[0032] As shown in Table 1 below, the data update frequency was determined to be once every 4 hours, and the continuous monitoring range was all 5 monitoring points, thus obtaining preliminary monitoring records.

[0033] Table 1. Weekly (T1-T7) Data Recording Table for Monitoring Points

[0034] The processing terminal determines the data update frequency to be once every 4 hours based on actual monitoring needs, and determines the continuous monitoring range to cover all the above monitoring points. The preliminary monitoring records are archived and stored according to the monitoring point number and the observation period number.

[0035] The number, location, and collection cycle of the monitoring points mentioned above are only examples. In practical applications, the number and location of monitoring points can be increased or adjusted according to the actual area and geological conditions of the substation site to cover different key areas within the site.

[0036] Among them, the monitoring equipment used to collect surface water level can be a water level sensor or a pressure water level gauge, the monitoring equipment used to collect ground deformation can be a hydrostatic level or an inclination sensor, and the monitoring equipment used to collect underground seepage status can be a piezometer. All of the above monitoring equipment are connected to the processing terminal through wired or wireless communication, and transmit the collected data to the processing terminal in real time or according to a preset cycle for subsequent steps.

[0037] In this embodiment, in step S200, the processing terminal screens monitoring points that simultaneously exhibit synergistic abnormal changes based on the changing trends of surface water level, ground deformation, and underground seepage status, identifies potential geological disturbance areas, and extracts the monitoring quantity with the strongest persistence as one of the influencing factors, including the following steps B1-B2: B1: Screening monitoring points that simultaneously exhibit synergistic abnormal changes to identify potential geological disturbance areas, including marking monitoring points where surface water level, ground deformation, and underground seepage status simultaneously show rising, sinking, and enhanced changes as abnormal monitoring points, and identifying the areas where abnormal monitoring points are located as potential geological disturbance areas.

[0038] Specifically, refer to Figure 4Based on the preliminary monitoring records, the processing terminal calculates the water level change, settlement rate, and seepage intensity of each monitoring point within the observation period for the water level, ground deformation, and underground seepage data. The water level change is the difference between the last and first observation readings of the monitoring point, the settlement rate is the daily average change of the monitoring point within the observation period, and the seepage intensity is the instantaneous value of the last observation reading of the monitoring point.

[0039] For example, the water level change at monitoring point D1 is 0.20 meters, the settlement rate at monitoring point F1 is 0.633 mm / day, the settlement rate at monitoring point F2 is 0.167 mm / day, and the seepage intensity at monitoring point S1 is 0.70 liters / second. The processing terminal sets a judgment threshold based on the historical monitoring data of the substation for the same period in the past three years, which is used to compare the above calculation results.

[0040] Water level threshold Calculate using the following formula: Right now It equals the historical maximum water level for the same period multiplied by a preset multiple. Taking the historical maximum water level for the same period as 1.05 meters and the preset multiple as 1.1, we substitute them into Formula 1 to obtain... It equals 1.05 multiplied by 1.1, which is 1.155 meters.

[0041] Settlement rate threshold Calculate using the following formula: Right now This equals the historical average settlement rate for the same period plus the standard deviation of a preset multiple. Taking the historical average rate of 0.15 mm / day, the standard deviation of 0.20 mm / day, and the preset multiple of 2, we obtain... This equals 0.15 plus 2 multiplied by 0.20, which is 0.55 mm / day.

[0042] seepage intensity threshold Calculate using the following formula: Right now It equals the maximum historical seepage intensity multiplied by a preset multiple. Taking the maximum historical seepage intensity of 0.65 L / s and the preset multiple of 1.2, we obtain... It equals 0.65 multiplied by 1.2, which is 0.78 liters per second.

[0043] The processing terminal compares the water level change, settlement rate, and seepage intensity calculated from each monitoring point with the water level threshold. Settlement rate threshold and seepage intensity threshold A point-by-point comparison is performed, and monitoring points exceeding the corresponding threshold are marked as abnormal monitoring points. The processing terminal further calculates the collaborative anomaly intensity index for the area based on the ratio of each monitoring quantity to its corresponding threshold. This index is used to comprehensively measure the degree to which multiple monitoring quantities in the area deviate from the normal state, rather than relying solely on whether a single monitoring quantity exceeds the limit. in, , , These are water level change, settlement rate, and seepage intensity, respectively. , , For the corresponding weight and satisfying . The larger the value, the greater the degree to which multiple monitoring quantities in the area deviate from the normal state. The processing terminal can use this value to sort the disturbance levels of potential geological disturbance areas, providing a priority basis for subsequent resource allocation.

[0044] For example, the settlement rate at monitoring point F1 is 0.633 mm / day, which is greater than the settlement rate threshold. The water level was 0.55 mm / day, therefore it was marked as an abnormal monitoring point, while the water level reading corresponding to monitoring point D1 did not exceed the water level threshold. The seepage intensity at monitoring point S1 was 0.70 liters per second, which did not exceed the seepage intensity threshold. Therefore, it was not marked.

[0045] Monitoring point F2, adjacent to monitoring point F1, had a settlement rate of 0.167 mm / day, which did not exceed the settlement rate threshold. The rate was 0.55 mm / day, so monitoring point F2 was not marked as an abnormal monitoring point. This shows that the processing terminal performs threshold comparisons on multiple monitoring points in the same area separately, and only marks the monitoring points that actually exceed the threshold as abnormal, thus avoiding a general judgment on the entire area.

[0046] The coordinated anomaly here refers to a situation where one or more of the monitored parameters, such as water level, ground deformation, and underground seepage, exceed the corresponding threshold at the same time. The processing terminal retrieves data from other monitoring points within a preset range around the anomaly monitoring point based on the on-site location marker of the monitoring point, performs correlation analysis, and assesses the geological disturbance potential of the area.

[0047] For example, to determine whether there are other monitoring points around the abnormal monitoring point whose seepage intensity or water level changes are close to the threshold, the area within a preset radius centered on the abnormal monitoring point is marked as a potential geological disturbance area, generating a substation geological risk source identification result. The geological risk source identification result is used to record the spatial location of the potential geological disturbance area and its corresponding abnormal monitoring point information, providing an analysis range for the subsequent extraction of one of the influencing factors.

[0048] B2: Extract the monitoring quantity with the strongest persistence as one of the influencing factors, including comparing the duration of changes in surface water level, ground deformation and underground seepage status in the potential geological disturbance area, and determining the monitoring quantity with the longest change duration as one of the influencing factors.

[0049] Specifically, refer to Figure 5 The processing terminal acquires records of water level changes, ground deformation, and active periods of underground seepage within the potential geological disturbance area. It extracts corresponding data for the area over multiple past observation periods (e.g., the past 30 days, denoted as T1 to T30) and calculates the changing trends and fluctuation cycles of water level, ground deformation, and underground seepage. For example, the water level at monitoring point D1 shows a fluctuation cycle of approximately 7 days within 30 days. The ground deformation at monitoring points F1 and F2 both show a continuously increasing subsidence trend within 30 days, with a fluctuation cycle of 30 days. The seepage at monitoring point S1 experiences multiple peaks with rainfall events within 30 days, with each peak lasting 3 to 5 days.

[0050] The processing terminal compares the fluctuation periods of various monitoring quantities and selects the monitoring quantity with the longest duration and most significant changes as one of the influencing factors. Here, "one of the influencing factors" refers to the monitoring quantity that, within the same potential geological disturbance area, has stronger persistence and a more dominant impact on regional stability compared to other monitoring quantities. A persistence index is calculated to replace a qualitative comparison based solely on the length of the fluctuation period. in, For the i-th type of monitoring quantity within the observation window Duration of continuous change within, For its range of change, This represents the maximum value of the change in each monitored quantity within this area. Processing terminal selection. Using the largest monitored quantity as the main influencing factor, the determination of "strongest persistence" takes into account both the time and magnitude dimensions, avoiding misjudgments caused by comparing only the fluctuation cycle.

[0051] For example, by comparing water level (7-day cycle), ground deformation (30-day duration), and seepage (every 3 to 5 days), ground deformation is identified as one of the most persistent influencing factors in the region. The processing terminal further calculates the magnitude and rate of change of this influencing factor in the region. For example, the settlement change of monitoring point F1 in 30 days is 9.7 mm, with an average daily change rate of 0.323 mm / day, and the settlement change of monitoring point F2 in 30 days is 2.0 mm, with an average daily change rate of 0.067 mm / day. The average rate of change of each monitoring point in the region is taken as the overall rate of change of this influencing factor in the region.

[0052] The processing terminal combines the location and extent information of the potential geological disturbance area to further delineate the spatial distribution range of one of the influencing factors, and records its location, extent, and direction of continuous change. For example, the location of one of the influencing factors is recorded as one side of the substation foundation area, the extent is recorded as a preset radius area centered on the abnormal monitoring point, and the direction of continuous change is recorded as vertically downward. This generates the distribution information of one of the geological influencing factors of the substation. This information is used to characterize the dominant monitoring quantity and its spatial distribution characteristics within the potential geological disturbance area.

[0053] In this embodiment, in step S300, the processing terminal determines the surface expansion trend and the underground extension trend based on the consistency relationship of the change direction between one of the influencing parameters, including the following C1 step: C1: The consistency of change direction includes the following: the consistency between the change direction of surface water level and the change direction of ground deformation corresponds to the trend of surface expansion, and the consistency between the change direction of underground seepage and the change direction of ground deformation corresponds to the trend of underground extension.

[0054] Specifically, refer to Figure 6 The processing terminal combines the location, direction of change and distribution range of one of the geological impact items recorded in the distribution information of one of the geological impact items of the substation, and divides the substation into two types of analysis areas: the base area and the slope area. Directional comparisons are then performed on the water level changes, ground deformation and underground seepage changes within the same analysis area.

[0055] The consistency relationship of change direction here refers to whether the change direction of two types of monitoring quantities in the same analysis area is the same. If they are the same, they are judged to be consistent; if they are different, they are judged to be inconsistent. This consistency relationship is the basis for distinguishing between surface expansion trend and underground extension trend.

[0056] The directional consistency judgment is changed from a binary comparison to continuous quantification: the processing terminal represents the change process of each monitored quantity as a directional angle, and calculates the consistency coefficient between the directional angles of the two types of monitored quantities. in, The angle representing the direction of water level (or seepage) change. This represents the ground deformation direction angle. When C is greater than a preset consistency threshold... At that time, the two directions are determined to be consistent. Based on this, the surface spread intensity and underground extension intensity are further calculated to quantify the degree of manifestation of each of the two trends: in, The water level-deformation consistency coefficient. The seepage-deformation consistency coefficient. This represents the amount of deformation change in the region. When two types of trend indicators appear simultaneously in the same region, the processing terminal is compared. and The size of the value is used to determine the dominant type of the region, thus solving the technical problem that existing technologies cannot determine the dominant type when both water level and seepage are abnormal.

[0057] The processing terminal determines whether the direction of water level change in the region is consistent with the direction of ground deformation. When the direction of water level change is the same as the direction of ground deformation and the duration of this consistency exceeds the preset time, the analysis region is determined to have a corresponding surface expansion trend. Here, the surface expansion trend refers to the trend in which geological activity is mainly driven by changes in surface water level and develops along the surface direction.

[0058] For example, when the water level monitoring point on one side of the base area shows a continuous rise, and the deformation monitoring point in the same area shows a synchronous acceleration of subsidence, and the two maintain the same direction in multiple adjacent observation periods, it is determined that there is a surface expansion trend on one side of the base area. The processing terminal further determines whether the direction of underground seepage change in the area is consistent with the direction of surface deformation. When the direction of underground seepage change is the same as the direction of surface deformation, it is determined that the analysis area corresponds to an underground extension trend. Here, the underground extension trend refers to the trend that geological activity is mainly driven by changes in underground seepage and develops along the underground direction.

[0059] For example, when the seepage flow at a seepage monitoring point on one side of a slope increases significantly, and adjacent deformation monitoring points simultaneously show horizontal displacement in the same direction, it is determined that there is an underground extension trend on one side of the slope. Within the analysis area of ​​the foundation zone and the slope zone, the processing terminal draws the direction and extent of geological activity based on the above determination results, and marks and associates the area corresponding to the surface extension trend with the area corresponding to the underground extension trend. For example, the surface extension trend area on the foundation zone side and the underground extension trend area on the slope zone side are marked as two adjacent areas on the same evolution chain, forming the geological hazard evolution distribution result of the substation. This result is used to characterize the directional distribution and extent of geological activity within the substation area.

[0060] In this embodiment, in step S400, the processing terminal divides high-risk and low-risk areas based on regional stability performance, statistically analyzes spatial distribution locations and area proportions, identifies high-risk concentrated distribution zones and their extension directions, and forms a substation geological hazard risk assessment result, including the following steps D1-D3: D1: Processing terminal acquires records of surface water level changes, ground deformation records, and underground seepage changes within the substation monitoring area collected by monitoring equipment; The processing terminal determines the surface expansion trend based on the consistency of the change direction between surface water level change records and surface deformation records, and determines the underground extension trend based on the consistency of the change direction between underground seepage change records and surface deformation records. The processing terminal assesses regional stability based on surface expansion and underground extension trends, and classifies areas into high-risk and low-risk zones. The processing terminal statistically analyzes the spatial distribution and area ratio of high-risk and low-risk areas, identifies spatially continuous concentrated distribution zones and extension directions within high-risk areas, and generates geological hazard risk assessment results for substations.

[0061] Specifically, refer to Figure 7 The processing terminal acquires the surface deformation characteristics and underground seepage characteristics of the areas corresponding to the surface expansion trend and underground extension trend in the geological disaster evolution and distribution results of substations. This includes information such as surface deformation, seepage rate and seepage depth of each area. Based on the deformation trend and seepage change characteristics of each area, a preliminary classification is made. For example, the side of the foundation area showing continuous settlement is classified as one type of zoning characteristic, and the side of the slope area with a sudden increase in seepage flow and accompanied by horizontal displacement is classified as another type of zoning characteristic.

[0062] The processing terminal performs comparative analysis on the stability performance of each region based on the classification results. The stability performance of a region refers to whether the change in ground deformation and underground seepage within the region continues to increase and whether the change trend remains consistent during the observation period. Based on this, the processing terminal judges whether each region shows a state of continuous change and consistent trend.

[0063] D2: Assess regional stability performance and classify areas into high-risk and low-risk areas. This includes classifying areas that show continuous changes and consistent deformation trends as high-risk areas, and areas that show short-term changes and insignificant fluctuations as low-risk areas.

[0064] Specifically, the processing terminal, in conjunction with the definitions of deformation rate and displacement rate in relevant geological hazard risk assessment standards, designates areas exhibiting continuous changes and consistent deformation trends as high-risk areas, and areas with short-term changes and insignificant fluctuations as low-risk areas. For example, if the settlement rate on one side of the foundation area and the horizontal displacement rate on one side of the slope area both continuously increase and exhibit consistent trends, then both of these areas are designated as high-risk areas. If the rate of change in other areas remains at a low level and no continuous aggravation trend is observed, then that part of the area is designated as a low-risk area.

[0065] By introducing a stability decay index, the basis for dividing regions into high- and low-risk areas is transformed from a qualitative description of "whether the changes are continuous and the trend is consistent" to a quantitative calculation: in, , These are the weighting coefficients. This represents the historical average stability decay index for the same period. Let be the standard deviation, and k be a preset multiple. When A region is designated as high-risk if it is in a high-risk state, and as low-risk otherwise. This threshold setting method is the same as in B1. , , The statistical construction method is consistent, maintaining the coherence of the internal logic of the technical solution.

[0066] The processing terminal further combines the aforementioned determination results of the direction and extent of geological activity to revise the location of the boundaries between high-risk and low-risk areas. For example, if a high-risk area on one side of the base zone and a high-risk area on the adjacent slope zone are spatially adjacent and belong to the same evolutionary chain, then the boundaries of the two are merged and adjusted into a continuous high-risk area, forming a geological hazard risk level classification map of the substation. This classification map is used to record the boundary location of high-risk and low-risk areas and their respective stability levels.

[0067] Obtain the geological hazard risk level classification map of the substation, and preliminarily identify the high-risk area (HR-01) and low-risk area (LR-01, i.e., all areas except HR-01) marked on the map. Record the spatial location of HR-01 (northeast corner and adjacent slope) and its corresponding risk level (high), and record the spatial location of LR-01 (the remaining areas) and its corresponding risk level (low). Using spatial coordinate data, determine the boundary point set [(E:114.3051°,N:30.5822°),…,(E:114.3055°,N:30.5820°)] and risk level attribute (high) of the HR-01 area, and determine the boundary point set and risk level attribute (low) of the LR-01 area. Based on these data, mark the distribution of high-risk and low-risk areas, and the output results are summarized in Table 2. Table 2. Regional Distribution of Risk Levels

[0068] D3: Identify spatially continuous concentrated distribution zones and extension directions within high-risk areas, including comparing the spatial relationships between high-risk areas, merging adjacent high-risk areas with the same risk level into concentrated distribution zones, and determining the extension direction based on the spatial distribution characteristics of the concentrated distribution zones.

[0069] Specifically, refer to Figure 8 The processing terminal records the spatial location of each area and its corresponding risk level according to the high-risk and low-risk areas marked on the substation geological disaster risk level classification map, and calculates the area proportion of each risk level area in the monitoring area. For example, it calculates the ratio of the area of ​​the merged high-risk area to the total area of ​​the monitoring area to obtain the area proportion of the high-risk area in the entire monitoring area.

[0070] The processing terminal determines whether there is spatial continuity between high-risk areas based on the spatial location and area proportion of each risk level area. Here, spatial continuity refers to whether adjacent high-risk areas are connected end to end in spatial location and whether their risk levels are consistent. The processing terminal identifies adjacent high-risk areas with the same risk level as concentrated distribution zones, and determines the extension direction of the concentrated distribution zone by combining the previously determined surface expansion trend and underground extension trend direction. For example, if the high-risk area after the aforementioned base area side and slope area side are merged shows a continuous distribution, and its extension direction is consistent with the previously determined underground extension trend direction, then the area is marked as a high-risk concentrated distribution zone, and its extension direction is recorded.

[0071] We introduce weighted calculations of spatial continuity index and extension direction angle to replace the simple rule of "merging adjacent areas with the same risk level": in, This represents the number of adjacent high-risk areas. This represents the total number of high-risk areas. Let i be the area of ​​the i-th high-risk area. This represents the evolution direction angle of the region. When SC exceeds a preset proportion, it is determined to be a concentrated distribution zone. The overall extension direction is obtained by weighting by area, so that the determination of the extension direction no longer depends on the direction label of a single area, but comprehensively considers the quantitative results of the area weight of each area within the concentrated zone.

[0072] The processing terminal integrates the above-mentioned risk level, spatial location, area proportion, and concentrated distribution zone information to form a geological disaster risk assessment result for the overall substation site status. This result includes the location of the concentrated distribution zone of high-risk areas and its extension direction, which can be used as a reference for subsequent early warning and prevention and control decisions.

[0073] Example 3 Reference Figure 2 This is the third embodiment of the present invention.

[0074] The above is a schematic scheme for a substation geological hazard risk assessment and early warning method based on multi-source data evolution direction discrimination. It should be noted that the technical solution of this multi-source data evolution direction discrimination substation geological hazard risk assessment and early warning system belongs to the same concept as the aforementioned multi-source data evolution direction discrimination substation geological hazard risk assessment and early warning method. Details not described in detail in this embodiment can be found in the description of the aforementioned multi-source data evolution direction discrimination substation geological hazard risk assessment and early warning method.

[0075] This embodiment also provides a substation geological disaster risk assessment and early warning system based on the evolution direction discrimination of multi-source data, including: a monitoring module, used to deploy monitoring equipment in the substation and collect surface water level, ground deformation and underground seepage status through the monitoring equipment; The risk source identification module is used to process the terminal to screen monitoring points that show coordinated abnormal changes based on the changing trends of surface water level, ground deformation and underground seepage status, identify potential geological disturbance areas, and extract the monitoring quantity with the strongest persistence as one of the influencing factors. The direction discrimination module is used to determine the surface expansion trend and the underground extension trend based on the consistency relationship of the change direction among the parameters involved in one of the influencing items.

[0076] The processing terminal performs a comparative analysis of the stability performance of each region based on the classification results. Regional stability performance refers to whether the magnitude of changes in surface deformation and subsurface seepage within the region continues to increase and whether the changing trends remain consistent during the observation period. The processing terminal uses this to determine whether each region exhibits continuous change and a consistent trend. The directional correlation between parameters involved in the influencing characteristics is also analyzed to determine whether there is a surface expansion trend or a subsurface extension trend. The risk assessment module is used to divide high-risk and low-risk areas based on the regional stability performance, and to statistically analyze the spatial distribution and area proportion of each risk area, identify high-risk concentrated distribution zones and their extension directions, and generate substation geological disaster risk assessment results.

[0077] This embodiment also provides an electronic device applicable to substation geological disaster risk assessment and early warning based on multi-source data evolution direction discrimination, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the substation geological disaster risk assessment and early warning method based on multi-source data evolution direction discrimination as proposed in the above embodiment.

[0078] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the substation geological disaster risk assessment and early warning method for determining the evolution direction of multi-source data as proposed in the above embodiment.

[0079] The storage medium proposed in this embodiment and the substation geological disaster risk assessment and early warning method for realizing multi-source data evolution direction discrimination proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0080] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0081] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A substation geological hazard risk assessment and early warning method based on multi-source data evolution direction discrimination, characterized in that: This includes deploying monitoring equipment in substations to collect data on surface water levels, ground deformation, and underground seepage. Based on the changing trends of surface water level, ground deformation and underground seepage, the processing terminal screens monitoring points that simultaneously show synergistic abnormal changes, identifies potential geological disturbance areas, and extracts the monitoring quantity with the strongest persistence as one of the influencing factors. The processing terminal determines the surface expansion trend and the underground extension trend based on the consistency of the change direction among the influencing parameters. The processing terminal divides high-risk and low-risk areas based on regional stability performance, statistically analyzes spatial distribution locations and area proportions, identifies high-risk concentrated distribution zones and extension directions, and generates substation geological disaster risk assessment results.

2. The substation geological hazard risk assessment and early warning method based on multi-source data evolution direction discrimination as described in claim 1, characterized in that: The processing terminal acquires surface water level change records, ground deformation records, and underground seepage change records collected by the monitoring equipment within the substation monitoring area; the processing terminal determines the surface expansion trend based on the consistency relationship of the change direction between the surface water level change records and the ground deformation records, and determines the underground extension trend based on the consistency relationship of the change direction between the underground seepage change records and the ground deformation records. The processing terminal assesses regional stability performance and classifies high-risk and low-risk areas based on the surface expansion trend and the underground extension trend. The processing terminal statistically analyzes the spatial distribution and area ratio of the high-risk and low-risk areas, identifies the concentrated distribution zones with spatial continuity and their extension directions in the high-risk areas, and forms the substation geological disaster risk assessment results.

3. The substation geological hazard risk assessment and early warning method based on multi-source data evolution direction discrimination as described in claim 2, characterized in that: The screening process identifies potential geological disturbance areas by monitoring points that simultaneously exhibit synergistic abnormal changes. This includes marking monitoring points where surface water level, ground deformation, and underground seepage simultaneously show rising, sinking, and enhanced changes as abnormal monitoring points, and identifying the areas where these abnormal monitoring points are located as potential geological disturbance areas.

4. The substation geological hazard risk assessment and early warning method based on multi-source data evolution direction discrimination as described in claim 3, characterized in that: The extraction of the monitoring quantity with the strongest persistence as one of the influencing factors includes comparing the duration of changes in surface water level, ground deformation and underground seepage state within the potential geological disturbance area, and determining the monitoring quantity with the longest duration of change as one of the influencing factors.

5. The substation geological hazard risk assessment and early warning method based on multi-source data evolution direction discrimination as described in claim 4, characterized in that: The consistency relationship of the change direction includes that the change direction of surface water level is consistent with the change direction of ground deformation, which corresponds to the trend of surface expansion, and the change direction of underground seepage is consistent with the change direction of ground deformation, which corresponds to the trend of underground extension.

6. The substation geological hazard risk assessment and early warning method based on multi-source data evolution direction discrimination as described in claim 5, characterized in that: The stability performance of the assessed area was divided into high-risk and low-risk areas. This includes classifying areas that show continuous changes and consistent deformation trends as high-risk areas, and areas with short-term changes and insignificant fluctuations as low-risk areas.

7. The substation geological hazard risk assessment and early warning method based on multi-source data evolution direction discrimination as described in claim 6, characterized in that: The identification of spatially continuous concentrated distribution zones and extension directions in high-risk areas includes comparing the spatial positional relationships between the high-risk areas, merging adjacent high-risk areas with the same risk level into concentrated distribution zones, and determining the extension direction based on the spatial distribution characteristics of the concentrated distribution zones.

8. A substation geological hazard risk assessment and early warning system based on multi-source data evolution direction discrimination, employing the substation geological hazard risk assessment and early warning method based on multi-source data evolution direction discrimination as described in any one of claims 1-7, characterized in that: Includes a monitoring module for deploying monitoring equipment in substations to collect data on surface water level, ground deformation, and underground seepage status. The risk source identification module is used to process the terminal to screen monitoring points that show coordinated abnormal changes based on the changing trends of surface water level, ground deformation and underground seepage status, identify potential geological disturbance areas, and extract the monitoring quantity with the strongest persistence as one of the influencing factors. The direction determination module is used to determine the surface expansion trend and the underground extension trend based on the directional correlation between the parameters involved in the main influencing features. The risk assessment module is used to divide high-risk and low-risk areas based on the regional stability performance, and to statistically analyze the spatial distribution and area proportion of each risk area, identify high-risk concentrated distribution zones and their extension directions, and generate substation geological disaster risk assessment results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the substation geological disaster risk assessment and early warning method according to any one of claims 1-7 for determining the multi-source data evolution direction.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the substation geological disaster risk assessment and early warning method according to any one of claims 1-7 for determining the multi-source data evolution direction.

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