A geological disaster prevention and control early warning intelligent decision method and system and a storage medium

Abstract

The application discloses a geological disaster prevention and control early warning intelligent decision method and system and a storage medium, relates to the technical field of geological disaster prevention and control, and aims to solve the technical problem that in the prior art, a traditional geological disaster prediction method often depends on limited data and experience judgment, it is difficult to accurately capture the complex changes of a geological environment, and a prediction result has a large error, specifically, by dynamically monitoring regional environment and geological data, accurately identifying hidden danger points and constructing a disaster prediction path, the system detects a path triggering state in real time, captures a disaster trajectory and an extension trend, and then generates a targeted prevention and control deployment, meanwhile, a prevention and control efficiency dynamic evaluation mechanism is introduced, the disaster expansion speed and the prevention and control effect are quantitatively analyzed, adaptive optimization of the deployment strategy is realized, the accuracy of hidden danger identification and the early warning timeliness are significantly improved, the change from passive response to active and accurate prevention and control is realized, and effective technical support is provided for regional safety.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geological disaster prevention and control, in particular to a geological disaster prevention and control early warning intelligent decision-making method and system and a storage medium. BACKGROUND

[0002] As a phenomenon with extremely destructive power in nature, geological disasters threaten the safety of human life and property and the stability of the ecological environment at all times. Common geological disasters such as mountain collapse, landslide, debris flow and ground subsidence often have suddenness and strong destructive power. In the face of such a serious geological disaster situation, the traditional geological disaster prevention and control means gradually show their limitations and have been difficult to meet the current needs of efficient and precise disaster prevention and reduction.

[0003] However, in the prior art, the traditional geological disaster prediction method often relies on limited data and experience judgment, which is difficult to accurately capture the complex changes of the geological environment, resulting in large errors in the prediction results, and the prevention and control measures for geological disasters often lack pertinence and cannot meet the needs of different disaster scenes. Therefore, a solution is proposed. SUMMARY

[0004] The purpose of the present application is to solve the problems mentioned above, and a geological disaster prevention and control early warning intelligent decision-making method, system and storage medium are proposed.

[0005] The purpose of the present application can be achieved by the following technical solutions: a geological disaster prevention and control early warning intelligent decision-making method, the specific steps are as follows:

[0006] Step S1: monitoring and analyzing the distribution of the protection area;

[0007] S11: hidden point analysis, point division is performed on the protection area, and geological monitoring is performed according to the divided points, the floating of the protection area point environment data is inferred through monitoring, to obtain hidden points;

[0008] S12: prediction path determination, according to the real-time risk parameters and the floating of the corresponding influence data under the current environment, the same type of risk parameter is determined, and the disaster path of the protection area is determined in combination with the corresponding hidden points;

[0009] Step S2: path triggering detection;

[0010] According to the monitoring of the real-time risk parameters and the geological parameter monitoring of the area where the hidden points are located, the path triggering detection is performed, so as to perform disaster prediction, and targeted deployment is performed according to the prediction result;

[0011] Step S3: prevention and control efficiency evaluation;

[0012] S31: prevention and control deployment, according to the comparison between the predicted disaster path and the generated disaster trajectory, the real-time disaster route is determined for targeted deployment;

[0013] S32: prevention and control efficiency evaluation according to the prevention and control deployment.

[0014] Further, the hidden point analysis process in S11 is as follows:

[0015] The protection area is divided into points according to the coverage range of the equipped sensors, and the boundaries of the divided points are connected. Real-time environmental data of the divided points are monitored, wherein the real-time environmental data represents rainfall, wind power value; the monitoring period is determined and the historical protection stage is obtained according to the monitoring period length; the time period in which the environmental data of the divided points in the current protection stage continuously shows an adverse trend is obtained, and the time length ratio is collected according to the progress time of the current protection stage, and is marked as an adverse influence ratio; according to the adverse influence ratio, the current protection stage is divided, that is, the time ratio except the adverse influence ratio is marked as a normal influence ratio; the peak alternation rising frequency of the adverse trend corresponding to the environmental data in the time period corresponding to the adverse influence ratio is obtained.

[0016] Further, if the adverse influence ratio and the normal influence ratio corresponding numerical weight ratio shows an increasing trend, and the peak alternation rising frequency of the adverse trend corresponding to the environmental data in the time period corresponding to the adverse influence ratio exceeds the set alternation rising frequency threshold, the corresponding divided point in the current protection stage is marked as a hidden point;

[0017] If the adverse influence ratio and the normal influence ratio corresponding numerical weight ratio does not show an increasing trend, or the peak alternation rising frequency of the adverse trend corresponding to the environmental data in the time period corresponding to the adverse influence ratio does not exceed the set alternation rising frequency threshold, the corresponding divided point in the current protection stage is marked as a non-hidden point.

[0018] Further, the prediction path determination process in S12 is as follows:

[0019] The geological parameters of hidden points and non-hidden points are monitored, wherein the geological parameters represent surface displacement, deep displacement, and crack opening degree; the adverse trend stage of the environmental parameters corresponding to the hidden point is determined, and the floating span of the geological parameters in the corresponding stage is obtained; at the same time, according to the current protection stage, the geological parameter floating span of the non-hidden point in the non-adverse trend stage is obtained; and is marked as current interference floating data and current non-interference floating data, respectively;

[0020] According to the historical protection stage, the geological parameter floating span range of the division point position in the adverse trend stage and the corresponding non-adverse trend stage is obtained, and the disaster production result corresponding to the stage is obtained according to the historical protection result; if disaster occurs, it is marked as an adverse trend disaster range and a non-adverse trend disaster range respectively; if no disaster occurs, it is marked as an adverse trend safety range and a non-adverse trend safety range respectively.

[0021] Further, according to data comparison, if the current disturbance floating data is in the adverse trend disaster range, the current hidden danger point position is marked as a predicted disaster point position; if the current disturbance floating data is in the adverse trend safety range, the current hidden danger point position is marked as a low-risk disaster point position; if the current non-interference floating data is in the non-adverse trend disaster range, the current non-hidden danger point is marked as a predicted extension point position; if the current non-interference floating data is in the non-adverse trend safety range, the current non-hidden danger point is marked as a monitoring no-odd point position.

[0022] According to the connection of the predicted disaster point position, a predicted disaster path is constructed, and the predicted extension point position is connected with the predicted disaster path to construct a predicted extension path; at the same time, the adjacent monitoring no-odd point positions of the predicted disaster path and the predicted extension path are marked, wherein both the positions in the path and the positions adjacent to the path need to be marked.

[0023] Further, the path trigger detection process in S2 is as follows:

[0024] Trigger monitoring is performed on the predicted disaster path and the predicted extension path, the geological data floating span growth rate of the predicted disaster point position in the predicted disaster path is collected, and the time when the geological data floating span grows is collected synchronously, if the floating span growth rate exceeds the set growth rate threshold, or the time when the geological data floating span grows exceeds the set time quantity, it is inferred that the path is triggered, and the corresponding predicted disaster path is intercepted, the road section of the path detection trigger disaster is marked as a disaster trajectory; according to the geographical position of the predicted disaster path, the high terrain section of the disaster trajectory is marked as a to-be-produced section;

[0025] At the same time, the geological parameter floating process of the corresponding adjacent division point positions of the predicted extension path and the predicted disaster path is obtained, if the corresponding interval value of the predicted extension path floating parameter and the predicted disaster path floating parameter continuously shortens, the corresponding predicted extension path is marked as a real-time extension path; otherwise, if the corresponding interval value of the predicted extension path floating parameter and the predicted disaster path floating parameter does not shorten, the corresponding predicted extension path is marked as a temporary extension path; wherein the floating parameter represents the floating speed or the floating span of the geological parameter; at the same time, the monitoring no-odd point position is monitored in real time, and the corresponding point position is merged into the disaster trajectory when the geological parameter is abnormal, otherwise, it is continuously monitored.

[0026] Further, the prevention and control deployment process in S31 is as follows:

[0027] The generated disaster trajectory and real-time extension path are determined, and the generated disaster trajectory and real-time extension path are compared with the predicted disaster path and predicted extension path respectively. If the number of overlapping points continues to rise, or the number of non-generated disaster points in the predicted path continues to decrease, the trajectory of the predicted path is used for prevention and deployment while real-time disaster prevention is performed; the predicted path is the general term of the predicted disaster path and the predicted extension path;

[0028] If the number of overlapping points does not continue to rise, and the number of non-generated disaster points in the predicted path does not continue to decrease, disaster blocking deployment is performed, and the real-time disaster trajectory is prevented while the disaster trajectory is prevented from moving to the predicted path.

[0029] Further, the prevention and control efficiency evaluation process in S32 is as follows:

[0030] The increase speed of the number of division points corresponding to the generated disaster trajectory after the current prevention and deployment is obtained, and the increase speed of the number of division points corresponding to the generated disaster trajectory in the to-be-generated section after the current prevention and deployment is obtained; the sum of the increase speeds is obtained according to the speed superposition, and is marked as the prevention and control speed characteristic information;

[0031] The constant value of the number of non-generated disaster points in the predicted path after the current prevention and deployment and the number of remaining division points in the predicted path are obtained, and the point number ratio is calculated according to the ratio;

[0032] If the prevention and control speed characteristic information exceeds the set speed threshold, or the point number ratio does not exceed the set number ratio threshold, it is inferred that the prevention and control deployment efficiency cannot adapt to the current disaster, and the real-time generated disaster trajectory is combined for re-deployment; if the prevention and control speed characteristic information does not exceed the set speed threshold, and the point number ratio exceeds the set number ratio threshold, it is inferred that the prevention and control deployment efficiency adapts to the current disaster, and the disaster is prevented according to the current prevention and control deployment.

[0033] A geological disaster prevention and control early warning intelligent decision system, comprising an intelligent decision platform, wherein the intelligent decision platform is communicatively connected with a hidden danger point analysis unit, a predicted path determination unit, a path trigger detection unit, a prevention and control deployment unit, and an efficiency evaluation unit;

[0034] The hidden danger point analysis unit divides the points in the protection area, and performs geological monitoring according to the division points, and infers the floating of the point environment data in the protection area through monitoring to obtain the hidden danger point;

[0035] The predicted path determination unit determines the same type of risk parameter according to the real-time risk parameter and the floating of the corresponding influence data under the current environment, determines the disaster path of the protection area in combination with the corresponding hidden danger point;

[0036] The path triggering detection unit performs path triggering detection according to the monitoring of the real-time risk parameter and the monitoring of the geological parameter of the area where the hidden danger point is located, thereby performing disaster prediction, and performing targeted deployment according to the prediction result.

[0037] The prevention and control deployment unit compares the predicted disaster path with the generated disaster trajectory, determines the real-time disaster route, and performs targeted deployment.

[0038] The efficiency evaluation unit evaluates the prevention and control efficiency according to the prevention and control deployment.

[0039] A computer storage medium has a computer program stored thereon, and the program is executed by a processor to implement the geological disaster prevention and control early warning intelligent decision-making method.

[0040] Compared with the prior art, the beneficial effects of the present application are:

[0041] 1. Through the hidden danger point analysis in step S1, the hidden danger points and non-hidden danger points are dynamically marked in combination with the adverse trend and peak value alternation frequency of environmental data (such as rainfall and wind power value). This data-driven analysis can effectively distinguish high-risk areas and avoid the problems of missed reports or false reports caused by slow changes in environmental parameters in traditional methods. At the same time, the hidden danger point type is updated in real time with the progress of the protection stage and pushed to the administrator terminal, ensuring the timeliness of the monitoring information and providing a reliable basis for early warning.

[0042] 2. In the prediction path determination S12, not only the geological parameter floating of the hidden danger point is considered, but also the data of the non-hidden danger point is integrated to construct the predicted disaster path and the predicted extension path. Through the comparison of historical data and real-time parameters, the point type (such as the predicted disaster point, the low-risk disaster point, etc.) is accurately classified, and a multi-dimensional disaster propagation network is formed. This dynamic path prediction can adapt to the changes of complex geological environment and improve the predictability of disaster development trend, thereby providing a scientific basis for the preposition of prevention and control resources.

[0043] 3. Step S2 monitors the floating span and growth rate of the geological parameter (such as surface displacement and crack opening degree) in real time, judges the path triggering condition in time, sets the growth rate threshold and time quantity threshold, ensures that the disaster can be identified at the germination stage, and cuts the path into a generated disaster trajectory. At the same time, by analyzing the parameter interval change between the predicted extension path and the disaster path, the real-time extension path and the temporary non-extension path are distinguished, the real-time evaluation of disaster diffusion risk is realized, and the response delay is reduced.

[0044] 4、In the prevention and control deployment, according to the overlap of the actual disaster track and the predicted path, the deployment strategy is dynamically adjusted, if the overlapping points increase, the protection of the predicted path is strengthened, otherwise, the disaster blocking deployment is carried out to prevent the disaster from spreading to the predicted area, and the flexibility ensures that the prevention and control measures are highly matched with the actual development of the disaster, avoids waste of resources, and reduces the risk of secondary disasters caused by chain reaction.

[0045] 5、Through the prevention and control efficiency evaluation, based on the prevention and control speed characteristic information and the point quantity ratio, the deployment effect is quantitatively evaluated, if the efficiency is insufficient, the system automatically prompts to redeploy or adjust the facility specification, forms a closed loop control, and the continuous optimization mechanism ensures that the prevention and control strategy is always adapted to the current disaster situation, improves the resilience and reliability of the overall disaster prevention system. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to facilitate those skilled in the art to understand, the present application will be further described below with reference to the accompanying drawings.

[0047] Figure 1 The method flowchart of the present application;

[0048] Figure 2 The system principle block diagram of the present application. DETAILED DESCRIPTION

[0049] In order to make those skilled in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0050] In this paper, the phrase "embodiment" means that the specific features, structures or characteristics described in conjunction with the embodiment can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily mean the same embodiment, nor is it independent or alternative to other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0051] Please refer to Figure 1 As shown in the figure, an intelligent decision-making method for geological disaster prevention and control warning, the specific steps are as follows:

[0052] Step S1: monitoring and analyzing the distribution of protection area;

[0053] S11: Hidden point analysis, point position division is performed on the protection area, and geological monitoring is performed according to the divided point position, the floating of the environmental data of the protection area point position is inferred through monitoring, to obtain hidden points;

[0054] S12: Prediction path determination, according to the real-time risk parameter combined with the floating of the corresponding influence data under the current environment, the same type of risk parameter is determined, and the disaster path of the protection area is determined combined with the corresponding hidden point;

[0055] Step S2: Path trigger detection;

[0056] According to the monitoring of the real-time risk parameter and the geological parameter monitoring of the area where the hidden point is located, the path trigger detection is performed, so as to perform disaster prediction, and targeted deployment is performed according to the prediction result;

[0057] Step S3: Prevention and control efficiency evaluation;

[0058] S31: Prevention and control deployment, according to the comparison between the predicted disaster path and the disaster trajectory, the real-time disaster route is determined for targeted deployment;

[0059] S32: Prevention and control efficiency evaluation according to the prevention and control deployment.

[0060] In S11, the hidden point analysis process is as follows:

[0061] The protection area is divided into point positions according to the coverage range of the equipped sensors, and the boundaries of the divided point positions are connected, and real-time environmental data monitoring is performed on the divided point positions, wherein the real-time environmental data is represented by rainfall, wind value, etc.

[0062] The monitoring period is determined and the historical protection stage is obtained according to the monitoring period length, the time period in which the environmental data of the divided point position in the current protection stage continuously shows an adverse trend is obtained, and the time length ratio is collected according to the progress time of the current protection stage, and is marked as an adverse influence ratio, wherein the adverse trend is represented by the influence of each type of environmental data on the corresponding geology. If the increase of the environmental parameter will lead to the occurrence of mountain disaster, such as continuous increase of rainfall which will increase the disaster probability, then the increase is an adverse trend, on the contrary, if the decrease of the environmental parameter will lead to the occurrence of mountain disaster, such as continuous decrease of temperature which will increase soil freeze cracking, then the decrease is an adverse trend, therefore, according to different types of environmental parameters, the adverse trend is different;

[0063] In order to adapt to various environmental data scenarios, a unified expression of adverse trends is made; for example, for mountain disasters, continuous increase in rainfall will saturate the soil moisture content of the mountain, increasing the probability of landslides and debris flows, so the increase in rainfall is an adverse trend; in cold regions, continuous temperature drop may cause soil frost heaving, which may trigger geological disasters such as ground subsidence, so the temperature drop is an adverse trend; through in-depth analysis of the influence of various types of environmental data on geology, the adverse trend of environmental data can be accurately identified.

[0064] According to the adverse impact ratio, the current protection stage is divided, that is, the time ratio other than the adverse impact ratio is marked as the normal impact ratio; for example, in a month of protection stage, if the adverse trend of the environmental data of a point is 10 days, then the adverse impact ratio is 10 / 30≈0.33, and the normal impact ratio is 1-0.33=0.67; these two ratios are like a "scale" to measure the risk of geological disasters, and through their changes, the stability of the current protection stage of the geological environment can be intuitively understood;

[0065] Obtain the peak alternation rising frequency of the adverse trend of the environmental data in the adverse impact ratio corresponding period;

[0066] If the adverse impact ratio and the normal impact ratio corresponding numerical weight ratio shows an increasing trend, and the peak alternation rising frequency of the adverse trend of the environmental data in the adverse impact ratio corresponding period exceeds the set alternation rising frequency threshold, then the corresponding division point in the current protection stage is marked as a hidden danger point;

[0067] If the adverse impact ratio and the normal impact ratio corresponding numerical weight ratio does not show an increasing trend, or the peak alternation rising frequency of the adverse trend of the environmental data in the adverse impact ratio corresponding period does not exceed the set alternation rising frequency threshold, then the corresponding division point in the current protection stage is marked as a non-hidden danger point;

[0068] It needs to be explained that the environmental data corresponding to different mountain positions in the protection area is not consistent, if there is no high-span gap in the environmental data of adjacent areas, the analysis result of the environmental data of any area is used as the criterion, in addition, in the process of marking hidden danger points, with the change of environmental data and the increase of protection stage, hidden danger points and corresponding non-hidden danger points will change alternately, and the latest point type is continuously sent to the communication terminal of the administrator;

[0069] For example, in the geological disaster monitoring of a mountainous area, if the threshold is set to every 3 hours, when the peak rainfall frequency of the area reaches or exceeds every 3 hours within the corresponding period, it indicates that the rainfall changes abnormally violently, the stability of the geological environment is seriously threatened, and the risk of geological disasters in the area significantly increases. At this time, combined with the change trend of the weight proportion of the corresponding value of the adverse impact ratio and the normal impact ratio, if the weight proportion also shows an increasing trend, the corresponding division point of the area is most likely to be marked as a hidden danger point. The setting of this threshold can help us capture the abnormal changes of environmental data in time, identify potential geological disaster hazards in advance, and provide an important basis for subsequent prevention and control measures.

[0070] In the prediction path determination process in S12, the following steps are performed:

[0071] The geological parameters of the hidden danger points and non-hidden danger points are monitored, where the geological parameters include surface displacement, deep displacement, crack opening degree, etc.

[0072] The adverse trend stage of the environmental parameters corresponding to the hidden danger points is determined, and the floating span of the geological parameters in the corresponding stage is obtained. At the same time, according to the current protection stage, the floating span of the geological parameters of the non-hidden danger points in the non-adverse trend stage is obtained, and is marked as current interference floating data and current non-interference floating data, respectively.

[0073] According to the historical protection stage, the geological parameter floating span range of the division points in the adverse trend stage and the geological parameter floating span range of the division points in the non-adverse trend stage are obtained, and according to the historical protection result, the disaster production result in the corresponding stage is obtained. If a disaster occurs, the adverse trend disaster range and the non-adverse trend disaster range are marked, respectively. If no disaster occurs, the adverse trend safety range and the non-adverse trend safety range are marked, respectively.

[0074] According to the data comparison, if the current interference floating data is in the adverse trend disaster range, the current hidden danger point is marked as a predicted disaster point. If the current interference floating data is in the adverse trend safety range, the current hidden danger point is marked as a low-risk disaster point. If the current non-interference floating data is in the non-adverse trend disaster range, the current non-hidden danger point is marked as a predicted extension point. If the current non-interference floating data is in the non-adverse trend safety range, the current non-hidden danger point is marked as a monitoring non-anomaly point.

[0075] The predicted disaster path is constructed by connecting the predicted disaster points, and the predicted extension path is constructed by connecting the predicted extension points with the predicted disaster path. The adjacent monitoring non-anomaly points of the predicted disaster path and the predicted extension path are marked, where both the points in the path and the points adjacent to the path need to be marked. According to different point types, targeted prediction and protection are performed.

[0076] In S2, the path trigger detection process is as follows:

[0077] The predicted disaster path and the predicted extension path are monitored for triggering, the growth rate of the floating span of the geological data corresponding to the predicted disaster point in the predicted disaster path is collected, the time when the floating span of the geological data grows is synchronously collected, if the growth rate of the floating span exceeds the set growth rate threshold, or the time when the floating span of the geological data grows exceeds the set time quantity, it is inferred that the path triggers, the corresponding predicted disaster path is intercepted, the road section where the path triggers the disaster is marked as a disaster trajectory, and then the high-elevation road section where the disaster trajectory is generated is marked as a to-be-generated road section according to the geographical position of the predicted disaster path.

[0078] For the setting of the threshold value, specific scenarios are, for example, the growth rate threshold of the ground displacement is set to 5 mm per hour, when the ground displacement growth rate of a certain predicted disaster point is monitored to reach 8 mm per hour, or the time when the geological data floating span grows is monitored multiple times in a short time and exceeds the set time quantity, such as being set to more than 3 times per hour, the system will determine that the path triggers at this time; once the path triggers, the system will quickly intercept the corresponding predicted disaster path, and mark the road section where the path triggers the disaster as a disaster trajectory, which is like a "road map" of the disaster occurrence, and clearly shows the possible development direction of the disaster.

[0079] It needs to be explained that the set time quantity is also an important parameter in the path trigger detection, which refers to the time quantity limit of the growth of the geological data floating span in the monitoring process; continuing the above mountain landslide disaster monitoring as an example, the set time quantity is set to 5 times per hour, if the time when the geological data (such as ground displacement, crack opening degree, etc.) floating span of a certain predicted disaster point grows is monitored to reach or exceed 5 times in an hour, even if the growth rate of each time does not exceed the geological data floating span growth rate threshold, the system will infer that the path triggers, because the frequent geological data growth time indicates that the state of the geological body is extremely unstable and may cause a geological disaster at any time, through the monitoring of the set time quantity, the signs of the occurrence of the geological disaster can be captured from another angle, further improving the accuracy and reliability of the path trigger detection;

[0080] Meanwhile, the geological parameter floating process of the adjacent division point corresponding to the predicted extension path and the predicted disaster path is obtained, if the corresponding interval value of the predicted extension path floating parameter and the predicted disaster path floating parameter continuously shortens, the corresponding predicted extension path is marked as a real-time extension path, otherwise, if the corresponding interval value of the predicted extension path floating parameter and the predicted disaster path floating parameter does not shorten, the corresponding predicted extension path is marked as a temporary extension path; wherein, the floating parameter is represented by the floating speed, floating span and other parameters of the geological parameter, any one parameter is used as a comparison standard, the monitoring non- abnormal point is monitored in real time, and the corresponding point is merged into a disaster trajectory when the geological parameter is abnormal, otherwise, continuous monitoring is performed.

[0081] In S31, the prevention and control deployment process is as follows:

[0082] The disaster trajectory and the real-time extension path are determined, and the disaster trajectory and the real-time extension path are compared with the predicted disaster path and the predicted extension path respectively, if the number of overlapping points continuously rises, or the number of non-disaster points in the predicted path continuously decreases, the trajectory of the predicted path is used for prevention and control deployment while the real-time disaster is prevented, the disaster prevention and control deployment is improved in pertinence, and it needs to be explained that the predicted path is the general term of the predicted disaster path and the predicted extension path;

[0083] If the number of overlapping points does not continuously rise, and the number of non-disaster points in the predicted path does not continuously decrease, disaster blocking deployment is performed, the real-time disaster trajectory is prevented, and the disaster trajectory is prevented from moving to the predicted path, so as to reduce unnecessary disaster influence caused by point chain reaction.

[0084] In S32, the prevention and control efficiency evaluation process is as follows:

[0085] The increasing speed of the number of division points corresponding to the disaster trajectory generated after the current prevention and control deployment is obtained, and the increasing speed of the number of division points corresponding to the disaster trajectory generated in the to-be-generated section after the current prevention and control deployment is obtained, the sum of the increasing speeds is obtained according to the speed superposition, and is marked as the prevention and control speed characteristic information;

[0086] The constant value of the number of non-disaster points in the predicted path after the current prevention and control deployment and the number of remaining division points in the predicted path are obtained, and the point number ratio is calculated according to the ratio;

[0087] If the prevention and control speed characteristic information exceeds the set speed threshold, or the point number ratio does not exceed the set number ratio threshold, it is inferred that the prevention and control deployment efficiency cannot adapt to the current disaster, and the real-time disaster trajectory is combined to perform re-deployment, and the facility specification is replaced, the area of the set region is increased, and other parameters are increased when the trajectory is guided according to the current deployment;

[0088] If the prevention and control speed characteristic information does not exceed the set speed threshold, and the point number ratio exceeds the set number ratio threshold, it is inferred that the prevention and control deployment efficiency is adapted to the current disaster, and disaster protection is carried out according to the current prevention and control deployment;

[0089] It needs to be explained that the set speed threshold is a key index in the prevention and control efficiency evaluation, which is used to measure the limit of the prevention and control speed characteristic information. For example, in the prevention and control of debris flow disasters in a certain area, the set speed threshold is 8 points per hour. If the actual calculated prevention and control speed characteristic information exceeds 8 points per hour, it indicates that under the current prevention and control deployment, the spread speed of the disaster is too fast, and the current prevention and control measures have not effectively contained the development of the disaster, and the prevention and control deployment needs to be adjusted and optimized. The setting of this threshold can directly reflect the control effect of the current prevention and control measures on the disaster, and provide a clear basis for the adjustment of the prevention and control strategy.

[0090] The set number ratio threshold is also an important reference in the prevention and control efficiency evaluation, which is used to measure the limit of the point number ratio. For example, in the prevention and control of geological disasters in a certain mountainous area, the set number ratio threshold is 0.6. If the actual calculated point number ratio does not exceed 0.6, it means that the proportion of non-disaster points on the predicted path is low, and the potential disaster risk is large. The current prevention and control deployment has not been able to fully reduce the disaster risk, and the prevention and control strategy needs to be reexamined and adjusted. On the contrary, if the point number ratio exceeds 0.6, it indicates that the current prevention and control deployment has achieved certain results in reducing the disaster risk, and the disaster protection can continue according to the current prevention and control deployment. The setting of this threshold can help us accurately evaluate the control ability of the prevention and control deployment on the potential disaster risk on the predicted path, and timely adjust the prevention and control strategy to ensure the effectiveness of the prevention and control work.

[0091] Please refer to Figure 2 As shown in the figure, the present application also proposes a geological disaster prevention and control early warning intelligent decision system, which comprises an intelligent decision platform, wherein the intelligent decision platform is communicatively connected with a hidden danger point analysis unit, a predicted path determination unit, a path trigger detection unit, a prevention and control deployment unit and an efficiency evaluation unit;

[0092] The hidden danger point analysis unit divides the protection area into points, and conducts geological monitoring according to the divided points, and infers the floating of the protection area point environment data through monitoring, to obtain the hidden danger point.

[0093] The predicted path determination unit determines the same type of risk parameter according to the real-time risk parameter combined with the floating of the corresponding influence data under the current environment, determines the disaster path of the protection area combined with the corresponding hidden danger point;

[0094] The path trigger detection unit performs path trigger detection according to the monitoring of real-time risk parameters and the geological parameter monitoring of the region where the hidden danger point is located, so as to perform disaster prediction, and performs targeted deployment according to the prediction result.

[0095] The prevention and control deployment unit compares the predicted disaster path with the generated disaster trajectory, determines the real-time disaster route, and performs targeted deployment;

[0096] The efficiency evaluation unit evaluates the prevention and control efficiency according to the prevention and control deployment.

[0097] A computer storage medium stores a computer program, which is executed by a processor to implement the geological disaster prevention and control early warning intelligent decision-making method. Those skilled in the art can understand that all or part of the steps of the above method embodiments can be completed by a computer program related hardware. The foregoing computer program can be stored in a computer readable storage medium. When the program is executed, the steps of the above method embodiments are executed. The foregoing storage medium includes ROM, RAM, a magnetic disc, an optical disc, and various media that can store program codes.

[0098] The preferred embodiments of the application disclosed above are only used to help explain the application. The preferred embodiments do not describe all the details and limit the application to the specific embodiments. Obviously, many modifications and variations can be made according to the content of the specification. The specification selects and describes these embodiments in order to better explain the principles and practical applications of the application, so that those skilled in the art can well understand and use the application. The application is limited by the claims and their entire scope and equivalents.

Claims

1. A smart decision-making method for geological disaster prevention and early warning, characterized in that, The specific steps are as follows: Step S1: Monitoring and analysis of the distribution of protected areas; S11: Hazard point analysis, dividing the protection area into points, and conducting geological monitoring based on the points. By monitoring, the fluctuation of environmental data at the points in the protection area is inferred to identify hazard points. S12: Predicted Path Determination. Based on real-time risk parameters and the fluctuation of corresponding impact data under the current environment, risk parameters of the same type are determined, and the disaster path of the protected area is determined in conjunction with the corresponding hazard points. The predicted path determination process in S12 is as follows: Geological parameters of both hazardous and non-hazardous points are monitored, with geological parameters represented as surface displacement, deep displacement, and crack opening / closing degree. The unfavorable trend stage of environmental parameters corresponding to the hazardous point is determined, and the fluctuation range of geological parameters within the corresponding stage is obtained. At the same time, based on the current protection stage, the fluctuation range of geological parameters of non-hazardous points within the non-unfavorable trend stage is obtained, and these are respectively marked as the current disturbed fluctuation data and the current undisturbed fluctuation data. Based on the historical protection stages, the range of geological parameter fluctuations for the points within the unfavorable trend stage and the range of geological parameter fluctuations for the points within the corresponding non-unfavorable trend stage are obtained. The disaster occurrence results for the corresponding stage are obtained based on the historical protection results. If a disaster occurs, it is marked as the unfavorable trend disaster range and the non-unfavorable trend disaster range, respectively. If no disaster occurs, they are marked as the safe range of adverse trends and the safe range of non-adverse trends, respectively. Based on data comparison, if the current disturbance fluctuation data is within the unfavorable trend disaster range, then the current potential hazard location will be marked as a predicted disaster location; if the current disturbance fluctuation data is within the unfavorable trend safe range, then the current potential hazard location will be marked as a low-risk disaster location. If the current undisturbed floating data is within the non-adverse trend disaster range, then mark the current non-hazard point as the predicted extension point; If the current undisturbed floating data is within the safe range of a non-adverse trend, then mark the current non-hazardous point as a monitoring point with no abnormalities. A predicted disaster path is constructed by connecting the predicted disaster points, and a predicted extension path is constructed by connecting the predicted extension points with the predicted disaster path; at the same time, adjacent monitoring points with no difference between the predicted disaster path and the predicted extension path are marked, wherein points that are in the path or adjacent to the path need to be marked. Step S2: Path trigger detection; Based on real-time risk parameter monitoring and geological parameter monitoring of the area where the potential hazard point is located, path trigger detection is performed to predict disasters and make targeted deployments based on the prediction results. Trigger monitoring is performed on the predicted disaster path and the predicted extension path. The growth rate of the floating span of the geological data corresponding to the predicted disaster point in the predicted disaster path is collected, and the time when the floating span of the geological data increases is collected simultaneously. If the growth rate of the floating span exceeds the set growth rate threshold, or the number of times the floating span of the geological data increases exceeds the set number of times, the path is inferred to be triggered, and the corresponding predicted disaster path is intercepted. The road segment that triggers the disaster is marked as the disaster trajectory. Step S3: Assessment of prevention and control efficiency; S31: Prevention and control deployment: Based on the comparison between the predicted disaster path and the actual disaster trajectory, determine the real-time disaster route and carry out targeted deployment; S32: Conduct an assessment of the effectiveness of prevention and control measures based on the deployment of prevention and control measures.

2. The intelligent decision-making method for geological disaster prevention and early warning according to claim 1, characterized in that, The process of analyzing potential hazards in S11 is as follows: The protected area is divided into points based on the coverage of the equipped sensors, and the boundaries of the points are connected. Real-time environmental data is monitored at the points, where real-time environmental data is represented by rainfall and wind speed. The monitoring period is determined, and historical protection stages are extracted based on the duration of the monitoring period to obtain the historical protection stages. The time periods in the current protection stage where the environmental data of the points continuously shows an adverse trend are obtained, and the duration percentage is collected based on the progress time of the current protection stage and marked as the adverse impact ratio. The current protection stage is divided according to the adverse impact ratio, that is, the percentage of time other than the adverse impact ratio is marked as the normal impact ratio. The frequency of peak value changes in the adverse trend of the environmental data within the corresponding time period of the adverse impact ratio is obtained.

3. The intelligent decision-making method for geological disaster prevention and early warning according to claim 2, characterized in that, If the weighting of the adverse impact ratio and the normal impact ratio increases, and the frequency of peak changes in the adverse trend of environmental data within the corresponding period exceeds the set threshold for the frequency of changes, then the corresponding points in the current protection phase will be marked as potential hazards. If the weighting of the adverse impact ratio and the normal impact ratio does not increase, or if the frequency of peak changes in the adverse trend of environmental data within the corresponding period of the adverse impact ratio does not exceed the set threshold for the frequency of changes, then the corresponding points in the current protection phase will be marked as non-hazard points.

4. The intelligent decision-making method for geological disaster prevention and early warning according to claim 1, characterized in that, The path trigger detection process in S2 is as follows: Based on the geographical location of the predicted disaster path, high-altitude road sections that will generate disaster trajectories are marked as road sections to be generated; Simultaneously, the geological parameter fluctuation process of adjacent points corresponding to the predicted extension path and the predicted disaster path is acquired. If the interval between the floating parameters of the predicted extension path and the floating parameters of the predicted disaster path continuously shortens, the corresponding predicted extension path is marked as a real-time extension path; otherwise, if the interval between the floating parameters of the predicted extension path and the floating parameters of the predicted disaster path does not shorten, the corresponding predicted extension path is marked as a path that has not yet been extended. Here, the floating parameter represents the floating speed or floating span of the geological parameter. At the same time, points with no abnormalities are monitored in real time, and when the geological parameters are abnormal, the corresponding points are merged into the disaster trajectory; otherwise, monitoring continues.

5. The intelligent decision-making method for geological disaster prevention and early warning according to claim 1, characterized in that, The prevention and control deployment process in S31 is as follows: The generated disaster trajectory and real-time extension path are determined. The generated disaster trajectory and real-time extension path are compared with the predicted disaster path and predicted extension path, respectively. If the number of overlapping points continues to increase, or the number of points in the predicted path that do not generate disasters continues to decrease, then protection deployment is carried out based on the trajectory of the predicted path while implementing real-time disaster protection. The predicted path is a general term for the predicted disaster path and the predicted extension path. If the number of overlapping points does not continue to rise, and the number of points in the predicted path that do not generate disasters does not continue to decrease, then disaster blocking deployment will be carried out to protect the real-time disaster trajectory while preventing the disaster trajectory from moving into the predicted path.

6. The intelligent decision-making method for geological disaster prevention and early warning according to claim 5, characterized in that, The process for evaluating the effectiveness of prevention and control measures in S32 is as follows: The system obtains the rate of increase in the number of disaster-related points corresponding to the disaster trajectory after the current protection deployment. Simultaneously, after the current protection deployment, it obtains the rate of increase in the number of disaster-related points in the road segment to be affected. The sum of the increase rates is obtained by superimposing the rates and marked as the prevention and control rate feature information. The constant value of the number of disaster-free points in the predicted path after the current protection deployment is obtained, along with the remaining number of points in the predicted path. The point number ratio is then calculated based on this ratio. If the speed characteristic information of prevention and control exceeds the set speed threshold, or the number of points does not exceed the set number ratio threshold, it is inferred that the efficiency of prevention and control deployment cannot adapt to the current disaster, and redeployment is carried out in combination with the disaster trajectory generated in real time; if the speed characteristic information of prevention and control does not exceed the set speed threshold, and the number of points exceeds the set number ratio threshold, it is inferred that the efficiency of prevention and control deployment adapts to the current disaster, and disaster protection is carried out according to the current prevention and control deployment.

7. A geological disaster prevention and early warning intelligent decision-making system, characterized in that, It is applied to the intelligent decision-making method for geological disaster prevention and early warning as described in any one of claims 1-6.

8. A computer storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements a smart decision-making method for geological disaster prevention and early warning as described in any one of claims 1-6.

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