CN117032972A - Slope monitoring system based on cloud network side - Google Patents
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Abstract
The invention discloses a side slope monitoring system based on cloud network side ends, which belongs to the technical field of monitoring systems and comprises the following components: the receiving module is used for receiving the request task of the manager through the cloud; the building module is used for building an acquisition resource model and a calculation resource model; the first calculation module is used for calculating the resource requirement of the request task by utilizing the acquisition resource model and the calculation resource model; the integration module is used for integrating to obtain a resource matrix of each edge server; the distribution module is used for distributing the request task to the target edge server; the issuing module is used for introducing an ant colony algorithm to determine an optimal issuing strategy, and issuing the request task distributed to the target edge server to the target acquisition equipment according to the optimal issuing strategy; the second calculation module is used for completing monitoring of a request task by utilizing the target edge server and the target acquisition equipment and calculating a landslide risk value; and the feedback module is used for determining early warning information according to the landslide risk value and feeding the early warning information back to the manager.
Description
Technical Field
The invention belongs to the technical field of monitoring systems, and particularly relates to a slope monitoring system based on a cloud network side end.
Background
The slope detection system is a technical system for monitoring and evaluating the slope stability. The side slope refers to an inclined surface of a mountain, a road or other earth surface, and unstable side slope can cause disaster events such as soil landslide, rock collapse and the like, so that personnel safety and property are threatened. The cloud network edge refers to a complete system formed by combining cloud, network and edge nodes in a cloud computing, network communication and edge computing framework. The cloud computing system relates to a central cloud of cloud computing, a communication network of network transmission and edge nodes of edge computing. Slope monitoring systems have begun to attempt to use this architecture for the processing of monitoring data due to the rapid response capability of cloud-side computing resources down to the edge side.
The improvement of the existing cloud network side is often only based on the improvement of the slope monitoring system structure, the improvement of the system performance is limited, how to unload high concurrency tasks to the edge side and the acquisition terminal is not considered, and due to long-distance signal transmission loss, even if the cloud network side structure is adopted, the response rate of the slope monitoring system is still very slow, the requirements of real-time monitoring tasks cannot be met, in addition, due to the fact that resources of the cloud network side have no reasonable allocation strategy, overload of some edge servers occurs, and the situation that some edge servers are idle, the task processing efficiency is low, and a large amount of calculation resources are wasted.
Disclosure of Invention
In order to solve the technical problems that how to unload high concurrency tasks to an edge side and an acquisition terminal is not considered in the prior art, and even if a cloud network edge structure is adopted due to long-distance signal transmission loss, the system response speed is still very slow, and the requirements of real-time monitoring tasks cannot be met.
The invention provides a side slope monitoring system based on a cloud network side end, which comprises a cloud end, edge ends and acquisition terminals, wherein the acquisition terminals are arranged on each side slope along a road at intervals, and the acquisition terminals are connected with the cloud end through the edge ends;
side slope monitoring system based on cloud network side still includes:
the receiving module is used for receiving a request task of a manager through the cloud, wherein the request task comprises a plurality of calculation tasks and a plurality of acquisition tasks;
the first establishing module is used for establishing an acquisition resource model by taking equipment attributes of the acquisition equipment as evaluation indexes, wherein the equipment attributes comprise acquisition pixels of the image acquisition equipment and response frequencies of the audio acquisition equipment;
the second building module is used for building a computing resource model by combining computing resources of the edge server and corresponding acquisition equipment, wherein the computing resources comprise processor frequency, the number of processor cores, display card throughput, running memory residual quantity and storage memory residual quantity;
the first computing module is used for computing to obtain the resource requirement of the request task by utilizing the acquisition resource model and the computing resource model, wherein the resource requirement comprises the acquisition resource requirement and the computing resource requirement;
the integration module is used for integrating resources of the edge servers and the corresponding acquisition equipment to obtain a resource matrix of each edge server;
the allocation module is used for allocating the request task to the target edge server by taking the resource occupancy rate as an allocation rule in combination with the resource requirement of the request task and the resource matrix, and updating the resource load of the target server, wherein the calculation resource of the target edge server and the acquisition resource of the corresponding acquisition equipment are both larger than the resource requirement of the request task;
the issuing module is used for combining the resource load, introducing an ant colony algorithm to determine an optimal issuing strategy, and issuing the request task distributed to the target edge server to the target acquisition equipment according to the optimal issuing strategy;
the second calculation module is used for completing monitoring of a request task by utilizing the target edge server and the target acquisition equipment and calculating landslide risk values of all slopes;
and the feedback module is used for determining early warning information according to the landslide risk value and feeding the early warning information back to the manager, wherein the early warning information comprises common early warning and dangerous early warning.
Compared with the prior art, the invention has at least the following beneficial technical effects:
(1) According to the invention, a cloud network side system structure is introduced into the side slope monitoring system, and the processing resources are sunk to the side slope, so that the short-distance processing resources can bring rapid data transmission capability, the load of a cloud server is reduced, the response speed is improved, and the reliability and stability of the system are maintained under the condition of unstable network connection or higher delay.
(2) In the invention, the hardware facilities of the edge servers and the acquisition equipment are combined, the task processing capacity of each edge server and the acquisition equipment controlled by the edge servers is evaluated, the actual processing capacity of each edge server is obtained practically and reliably, the task queuing condition caused by the overload of the distributed tasks is avoided, and the processing efficiency and the response capacity of the monitoring system are improved. In addition, the cloud end sends the request task to the edge server, and further sends the subtasks to the acquisition equipment through the edge server, the multi-stage sending is performed, the task allocation strategy is refined, the resource utilization rate of the monitoring system is improved, the balanced monitoring of the monitoring system is maintained, the running stability and the response speed of the monitoring system are further improved, and real-time monitoring and early warning can be provided for side slope disasters.
(3) According to the invention, the slope characteristics are collected in real time through the arranged slope monitoring system, the landslide risk value of the slope is calculated, and the early warning is carried out in a layered manner according to the landslide risk value, so that a manager is reminded of timely taking corresponding measures, the cloud network side end structure has rapid system response capability, the early warning accuracy and timeliness are improved, and the occurrence probability of landslide disasters is reduced.
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The above features, technical features, advantages and implementation of the present invention will be further described in the following description of preferred embodiments with reference to the accompanying drawings in a clear and easily understood manner.
FIG. 1 is a schematic structural diagram of a slope monitoring system based on a cloud network edge;
FIG. 2 is a schematic structural diagram of another slope monitoring system based on a cloud network edge provided by the invention;
fig. 3 is a schematic structural diagram of an edge server and an acquisition device according to the present invention.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will explain the specific embodiments of the present invention with reference to the accompanying drawings. It is evident that the drawings in the following description are only examples of the invention, from which other drawings and other embodiments can be obtained by a person skilled in the art without inventive effort.
For simplicity of the drawing, only the parts relevant to the invention are schematically shown in each drawing, and they do not represent the actual structure thereof as a product. Additionally, in order to simplify the drawing for ease of understanding, components having the same structure or function in some of the drawings are shown schematically with only one of them, or only one of them is labeled. Herein, "a" means not only "only this one" but also "more than one" case.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
In this context, it should be noted that the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected, unless otherwise explicitly stated and defined. Either mechanically or electrically. Can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
In addition, in the description of the present invention, the terms "first," "second," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
In one embodiment, referring to fig. 1, a schematic structural diagram of a slope monitoring system based on a cloud network edge is shown. Referring to fig. 2, a schematic structural diagram of another slope monitoring system based on a cloud network edge is shown. Referring to fig. 3, a schematic structural diagram of an edge server and an acquisition device provided by the present invention is shown.
The invention provides a side slope monitoring system based on a cloud network side, which comprises a cloud end, edge ends and acquisition terminals, wherein the acquisition terminals are arranged on each side slope along a road at intervals, the acquisition terminals are connected with the cloud end through the edge ends, the acquisition terminals comprise a plurality of acquisition devices, the edge ends comprise a plurality of edge servers, and each edge server is connected with the plurality of acquisition devices.
It should be noted that the components of the system include a cloud end, an edge end and an acquisition terminal. The acquisition terminals are arranged at intervals at each side slope position along the road and are used for acquiring physical parameter data of the side slopes. The acquisition terminal is connected with the cloud end through the edge end to form a node network for edge calculation. In the acquisition terminal, a plurality of acquisition devices are included, and the devices can be various sensors, such as a displacement sensor, an inclination sensor, a pressure sensor and the like, and are used for monitoring the change of physical parameters of the slope in real time. The edge end consists of a plurality of edge servers, each edge server is connected with a plurality of acquisition devices, the edge servers are responsible for receiving data from the acquisition devices and processing and analyzing the data, the existence of the edge end can lighten the load of a cloud end, improve the speed of data processing and response and simultaneously keep real-time connection with the acquisition terminal.
The working flow of the whole system is as follows: the cloud receives the request tasks issued by the manager, then the cloud issues the responsive request tasks to the edge end according to the resource reserves of the terminals, the edge server of the edge end further completes the task issuing from the edge server to the acquisition equipment according to the resource reserves of the acquisition equipment, the reasonable allocation of the request tasks is completed, the request tasks are processed in parallel, and if the side slope is found to be abnormal or at risk, the system can generate early warning information and timely inform the manager through the edge end. The connection and cooperation among the acquisition terminal, the edge end and the cloud end realize the whole process of slope monitoring.
The side slope monitoring system based on the cloud network side end can provide real-time and comprehensive side slope monitoring and analysis, help discover the change and potential risk of the side slope in time, and take corresponding measures to ensure traffic safety and personnel and property safety. Meanwhile, the application of edge calculation can reduce the dependence on cloud resources and improve the efficiency and response speed of data processing.
Side slope monitoring system based on cloud network side still includes:
the receiving module 1 is configured to receive a request task of a manager through a cloud, where the request task includes a plurality of computing tasks and a plurality of collecting tasks.
Wherein the requested task is a task request sent to the system by the administrator. It includes a plurality of computing tasks and a plurality of acquisition tasks. The computing task is a task that requires computation or processing in the system, and may involve analysis of side slope data, risk assessment, and the like. The acquisition task is a task that requires the system to perform data acquisition or monitoring, and may include acquisition of physical parameters of the slope, image or audio data, etc. by a sensor. The manager informs the slope monitoring system which computing tasks and acquisition tasks need to be performed by sending the request tasks.
The first establishing module 2 is configured to establish an acquisition resource model by using an equipment attribute of an acquisition device as an evaluation index, where the equipment attribute includes an acquisition pixel of the image acquisition device and a response frequency of the audio acquisition device.
The acquisition resources mainly comprise the data acquisition capacity of the acquisition equipment, and in the data acquisition process, the acquisition resources mainly comprise the response frequency of the acquisition pixels of the image acquisition equipment and the response frequency of the audio acquisition equipment, so that the quality of the image, the video and the audio data collected by the participants can be directly influenced. The acquisition resources of the acquisition equipment mainly comprise cameras and microphones of the acquisition equipment, the advantages and disadvantages of the acquisition resources can be measured through frequency responses of the camera pixels and the microphones, and the data acquisition capacity of the acquisition equipment is calculated by establishing an acquisition resource model so as to provide allocation standards for subsequent task allocation, so that the problem of overload of task allocation is avoided, and the monitoring capacity of a side slope monitoring system is improved.
In a possible implementation manner, the first establishing module is specifically configured to perform the following steps:
s1021: taking the difference between the self noise of the audio acquisition equipment and the maximum sound pressure level as a response frequency, the calculation mode of the response frequency is as follows:
wherein the sound pressure level representing self-noise, the maximum sound pressure level of the audio acquisition device,representing edge servers s k The lower m acquisition devices, w n Representing an nth request task;
s1022: taking the collection pixels of the image collection equipment and the response frequency of the audio collection equipment as evaluation indexes, and establishing a collection resource model:
where α represents a weight coefficient, p (a) represents an acquisition pixel, M (α) represents a response frequency, and F (a) represents an acquisition resource evaluation result.
Where self-noise refers to the noise level generated by the audio acquisition device itself without external acoustic interference. It is usually caused by the circuitry and sensors of the device and can be obtained by measuring the output sound level of the device in a mute environment. The maximum sound pressure level refers to the maximum sound intensity that the audio acquisition device can record. It reflects the upper limit of sound that a device can receive, typically expressed in decibels (dB). The response frequency refers to the degree of sensitivity of the audio acquisition device to sounds of different frequencies. The response frequency is calculated by measuring the difference between the self-noise and the maximum sound pressure level. The smaller the difference, the more balanced the response of the device across the frequencies, the larger the difference, the more or less sensitive the device to sound at certain specific frequencies. By establishing the acquisition resource model, the response frequency of the pixels of the image acquisition equipment and the response frequency of the audio acquisition equipment can be comprehensively considered, so that the resource quality and performance of different acquisition equipment can be evaluated and compared. This helps to select acquisition devices that fit the needs and provides a reference for subsequent resource allocation and task scheduling.
The second building module 3 is configured to build a computing resource model by combining computing resources of the edge server and the corresponding acquisition device, where the computing resources include processor frequency, number of processor cores, graphics card throughput, running memory remaining and storage memory remaining.
It should be noted that, the computing resource model is established to evaluate the computing power, that is, the data processing power, of the existing edge server or the acquisition device connected to the edge server. By considering the indexes such as the processor frequency, the number of processor cores, the throughput of the display card, the running memory residual quantity, the storage memory residual quantity and the like, the demand of computing resources can be quantified, and accurate resource demand information is provided for the subsequent resource integration and distribution modules, so that the system can meet the demand of computing tasks. The model is also beneficial to optimizing resource allocation and task scheduling of the slope monitoring system, reasonably arranging edge end equipment and improving the efficiency and performance of the system.
In a possible implementation manner, the second establishing module is specifically configured to perform the following steps:
s1031: establishing a decision matrix of the computing resources:
wherein B represents a decision matrix, each column of elements in the decision matrix corresponding to a processor frequency, a number of processor cores, a graphics card throughput, an operating memory residual amount, and a storage memory residual amount;
s1032: carrying out standardization processing on the decision matrix to obtain a standardization matrix:
wherein Z represents a standardized matrix, Z ij Representing elements in the standardized matrix;
s1033: defining maximum and minimum values of the standardized matrix, and calculating distance values between each computing resource and the maximum and minimum values:
wherein,a distance value representing the computing resource from the maximum value, +.>A distance value, θ, representing the distance between the computing resource and the minimum value j Representing a weight coefficient assigned to each index in the computing resource according to an entropy weight method;
s1034: according to the distance value, calculating to obtain calculation resource evaluation results of each edge server and corresponding acquisition equipment:
where G (i) represents the computing resource evaluation result.
It should be noted that, by establishing a decision matrix, a normalization process, a calculated distance value, and a calculation resource evaluation result, the calculation resources of the edge server and the corresponding acquisition device may be quantized and evaluated. This facilitates the system in comparing and selecting computing power and resource utilization for different devices, thereby enabling more efficient resource management and task allocation.
The first computing module 4 is configured to calculate a resource requirement of the requested task using the acquisition resource model and the computing resource model, where the resource requirement includes an acquisition resource requirement and a computing resource requirement.
Where acquisition resource requirement refers to the resource requirement of a requesting task for an acquisition device. The method calculates the requirements of the request task on the acquisition equipment based on equipment attributes defined in the acquisition resource model, such as acquisition pixels of the image acquisition equipment and response frequency of the audio acquisition equipment. For example, if the requested task requires high resolution image data, then the acquisition resource requirements will involve an image acquisition device with high pixel acquisition capabilities. The computing resource demand refers to the amount of demand for computing resources by a requesting task. The method calculates the requirement of the request task on the computing resource based on indexes defined in the computing resource model, such as processor frequency, the number of processor cores, graphics card throughput, running memory residual quantity, storage memory residual quantity and the like. For example, if a requesting task requires complex computing and data processing operations, then the computing resource requirements will involve an edge server with high processor frequency and large memory capacity.
In the actual application process, the system can calculate the demand of the request task on the acquisition resources and the calculation resources according to the type, the attribute and the requirement of the request task sent by the manager by utilizing the acquisition resource model and the calculation resource model. Such resource demand information may provide accurate guidance for subsequent resource integration and allocation modules to ensure that the system is able to provide adequate resource support for the requested task and meet the task's demands. By accurately calculating the resource requirements, the system can effectively allocate and utilize resources, and the efficiency and performance of the system are improved.
And the integration module 5 is used for integrating resources of the edge servers and the corresponding acquisition equipment to obtain a resource matrix of each edge server.
It should be noted that, the resource matrix reflects the computing resources and the corresponding acquisition resources of each edge server. The method is beneficial to subsequent resource allocation and task scheduling, and the integration module obtains the resource matrix of each edge server by integrating and correlating the resources of the edge server and the acquisition equipment so as to meet the requirements of a slope monitoring system on calculation and acquisition resources and provide accurate resource information for subsequent resource allocation and task scheduling. Before the task allocation is carried out, the acquisition capacity and the calculation capacity of the edge server and the acquisition equipment connected with the edge server need to be determined so as to ensure that the situation of overload queuing of the allocated tasks does not occur, optimize the task allocation strategy to the greatest extent and improve the task processing efficiency and speed of the slope monitoring system.
In one possible implementation, the resource matrix is:
wherein,representing a plurality of acquisition devices connected to an edge server, CR s (s k )＝G(s k ) Representing edge servers s k Is->Representation and edge server s k Connected multiple acquisition devices->Is->Representing acquisition resources of a plurality of acquisition devices connected to an edge server.
And the allocation module 6 is used for allocating the request task to the target edge server by taking the resource occupancy rate as an allocation rule in combination with the resource requirement of the request task and the resource matrix, and updating the resource load of the target server.
The computing resources of the target edge server and the acquisition resources of the corresponding acquisition equipment are both larger than the resource requirements of the request task.
Wherein the resource occupancy is used to evaluate the ratio between the currently used resources of the edge server and the total resource capacity. As an allocation rule, the resource occupancy is used to determine whether the target edge server has sufficient available resources to receive a new requested task. Typically, edge servers with low resource occupancy are selected to receive tasks to balance the resource utilization of the system. Once the requested task is assigned to the target edge server, the integration module updates the resource load of the target server. This includes updating the computing resources and collecting the usage of the resources, ensuring that the resource load of the edge servers remains consistent with the actual task demands. So as to ensure the accuracy of the real-time monitoring of the slope monitoring system.
It should be noted that, the task allocation of the request to the target edge server is the task allocation of the first stage, and the cloud end ensures reasonable task allocation and stable operation of the slope monitoring system through the resource matrix of each edge server.
In a possible embodiment, the allocation module is specifically configured to perform the following steps:
s1061: calculating the resource occupancy rate of the resource requirements of all the request tasks to all the edge calculation server resources:
wherein V(s) k ) Representing the occupancy rate of resources, A w Representing the total amount of resource demand for all requested tasks, A s Representing the total amount of resources of all edge servers, A u Representing the total amount of resources of all the acquisition devices;
s1062: according to the resource occupancy rate, calculating the maximum task processing amount of each edge server:
wherein,indicating maximum task throughput, A k Representing edge servers and connected acquisitions obtained from a resource matrixThe total amount of resources of the device;
s1063: and distributing the corresponding request task to the target edge server according to the maximum task processing amount, and updating the resource load of the edge server.
And calculating the maximum task processing amount, and calculating the total amount of resources of each edge server and the connected acquisition equipment according to the resource matrix. The total amount of resources is then used to calculate the maximum amount of tasks that each edge server can handle. The maximum task processing amount represents the upper limit of the number of tasks which can be processed by the edge server under the current resource configuration, and the allocation of the request task and the update of the resource load are carried out by calculating the resource occupancy rate and the maximum task processing amount under the condition of considering the resource limitations of the edge server and the acquisition equipment. Therefore, the resource management and task scheduling of the edge computing system can be realized, and the performance and efficiency of the system are improved.
And the issuing module 7 is used for combining the resource load, introducing an ant colony algorithm to determine an optimal issuing strategy, and issuing the request task distributed to the target edge server to the target acquisition equipment according to the optimal issuing strategy.
The resource load refers to the current used resource condition of the edge server, and the resource load comprises the use condition of computing resources and acquisition resources. The information can reflect the busyness of the server and the resource utilization condition, and the larger the resource load is, the lower the probability of selecting the corresponding node is, so as to ensure the balanced task allocation of the system. The ant colony algorithm is a heuristic optimization algorithm, is proposed by the action heuristic of ants in the process of searching food, simulates the actions of the ants in the process of exploring and selecting paths, and performs path selection through deposition and volatilization of pheromones. By introducing an ant colony algorithm to determine an optimal task issuing strategy, the system can fully utilize the resources of the edge server and the acquisition equipment and optimize the effects of task allocation and data acquisition. The ant colony algorithm can find an optimal task distribution path according to the current resource load and task demands, and the performance and efficiency of the system are improved.
The request task distributed to the target edge server is distributed to the target acquisition equipment according to an optimal distribution strategy, the distribution process is a second-stage distribution process, and the high-efficiency operation of the slope monitoring system is ensured through finer task distribution.
In a possible implementation manner, the issuing module is specifically configured to perform the following steps:
s1071: and taking the resource utilization rate of the ants passing through the path as an exit rule, taking the resource load as a node screening condition of the ants, and carrying out path selection, wherein the node is a cross node of acquisition equipment connected with the destination edge server and a subtask of the request task.
In one possible implementation, the resource utilization is calculated by:
where J (t) represents the resource utilization, n represents the number of tasks allocated to the acquisition device,representing the sum of the resource demands of the n tasks assigned to the acquisition device, +.>And the acquired resource evaluation result and the calculated resource evaluation result of the acquisition equipment obtained by calculation according to the acquired resource model and the calculated resource model are represented.
S1072: calculating the residual pheromone of the path travelled by the ants:
wherein ρ represents a pheromone volatilization factor, 0 < ρ < 1, J (t) represents a resource utilization rate, t represents a t-th ant,z represents the total number of ants, (1- ρ) τ i，j Representing the attenuation values of the pheromones,represents the pheromone increment value, and Q represents the pheromone constant.
S1073: calculating a heuristic value of the ant selection designated node:
wherein eta i，j Representing the heuristic.
S1074: combining the pheromone and the heuristic value, calculating the probability of selecting the j acquisition equipment by the ith task of the ant:
where α represents a pheromone factor and β represents a heuristic function factor.
S1075: and according to the probability that the ith task of the ant selects the jth acquisition equipment, determining an optimal issuing strategy, and issuing the request task distributed to the target edge server to the target acquisition equipment according to the optimal issuing strategy.
It should be noted that, by calculating the resource utilization rate, updating the pheromone, calculating the heuristic value and calculating the probability, the ant can select the optimal path and issue the request task to the target acquisition device according to the optimal issuing strategy. Therefore, the effective allocation of tasks and the reasonable utilization of resources can be realized, and the performance and the efficiency of the system are improved.
And the second calculation module 8 is used for completing monitoring of the request task by utilizing the target edge server and the target acquisition equipment and calculating landslide risk values of the slopes.
It should be noted that, with the obtained side slope data and the calculation task performed, the target edge server calculates the landslide risk value of each side slope using a corresponding algorithm and model. The landslide risk value is an evaluation index of the potential risk degree of landslide occurrence on the side slope, and can be calculated and analyzed through various factors such as topography, soil state, rainfall condition and the like. The method is beneficial to judging the stability of the side slope and predicting the potential landslide risk, and provides timely early warning and monitoring reports.
In a possible implementation manner, the second computing module is specifically configured to perform the following steps:
s1081: the method comprises the steps that weather big data of all positions along a road are obtained through a national weather bureau to form a weather characteristic sequence, wherein the weather big data comprise rainfall, wind intensity, rainfall duration and rainfall predicted duration;
s1082: normalizing the weather feature sequence to obtain hundred differentiation;
s1083: giving different weights to each weather feature, wherein the weight of rainfall Q is lambda, the weight of wind power W is mu, and the weight of rainfall duration T is psi;
s1084: according to the meteorological feature sequence, calculating landslide risk values of slopes along the road:
F i ＝λ·Q i +μ·W i +ψ·T i 。
it can be understood that by acquiring meteorological data, normalizing, weighting and calculating landslide risk values, the landslide risk of the side slope can be evaluated and analyzed, which is helpful for predicting and pre-warning the potential risk of the side slope landslide and taking corresponding measures to ensure the safety of the road.
And the feedback module 9 is used for determining early warning information according to the landslide risk value and feeding the early warning information back to the manager.
The early warning information comprises common early warning and danger early warning.
In a possible implementation manner, the feedback module is specifically configured to perform the following steps:
s1091: and when the landslide risk value is larger than the preset risk value, ordinary early warning is carried out.
The preset risk value is a threshold value or standard set in the slope monitoring system and is used for judging the degree of slope landslide risk, and the preset risk value is set according to specific conditions, environments and requirements. The preset risk value can be adjusted according to specific application scenes and requirements so as to adapt to different monitoring and early warning requirements. It should be noted that, the size of the preset risk value can be set by a person skilled in the art according to actual needs, and the present invention is not limited herein.
S1092: and calculating the estimated occurrence time of the landslide according to the characteristics of the side slope, the rainfall and the wind intensity.
S1093: and carrying out danger early warning under the condition that the expected occurrence time of landslide is earlier than the expected duration time of rainfall.
Under the condition that the expected occurrence time of landslide is earlier than the expected duration time of rainfall, the landslide probability is extremely high, and the landslide risk is further increased along with the increase of the expected duration time of rainfall, in the condition, the early warning level is improved, a manager is reminded to take corresponding precautionary measures in advance, major accidents are avoided, common early warning and danger early warning information is determined through landslide risk values, and the information is fed back to related managers, so that the manager can be timely warned, the manager can take corresponding measures to deal with potential landslide risk, and the effectiveness and safety of slope monitoring are guaranteed.
Compared with the prior art, the invention has at least the following beneficial technical effects:
(1) According to the invention, a cloud network side system structure is introduced into the side slope monitoring system, and the processing resources are sunk to the side slope, so that the short-distance processing resources can bring rapid data transmission capability, the load of a cloud server is reduced, the response speed is improved, and the reliability and stability of the system are maintained under the condition of unstable network connection or higher delay.
(2) In the invention, the hardware facilities of the edge servers and the acquisition equipment are combined, the task processing capacity of each edge server and the acquisition equipment controlled by the edge servers is evaluated, the actual processing capacity of each edge server is obtained practically and reliably, the task queuing condition caused by the overload of the distributed tasks is avoided, and the processing efficiency and the response capacity of the monitoring system are improved. In addition, the cloud end sends the request task to the edge server, and further sends the subtasks to the acquisition equipment through the edge server, the multi-stage sending is performed, the task allocation strategy is refined, the resource utilization rate of the monitoring system is improved, the balanced monitoring of the monitoring system is maintained, the running stability and the response speed of the monitoring system are further improved, and real-time monitoring and early warning can be provided for side slope disasters.
(3) According to the invention, the slope characteristics are collected in real time through the arranged slope monitoring system, the landslide risk value of the slope is calculated, and the early warning is carried out in a layered manner according to the landslide risk value, so that a manager is reminded of timely taking corresponding measures, the cloud network side end structure has rapid system response capability, the early warning accuracy and timeliness are improved, and the occurrence probability of landslide disasters is reduced.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (10)
1. The side slope monitoring system based on the cloud network side end is characterized by comprising a cloud end, an edge end and an acquisition terminal, wherein the acquisition terminal is arranged on each side slope along a road at intervals, and is connected with the cloud end through the edge end, the acquisition terminal comprises a plurality of acquisition devices, the edge end comprises a plurality of edge servers, and each edge server is connected with the plurality of acquisition devices; side slope monitoring system based on cloud net limit still includes:
the receiving module is used for receiving a request task of a manager through the cloud, wherein the request task comprises a plurality of calculation tasks and a plurality of acquisition tasks;
the first establishing module is used for establishing an acquisition resource model by taking equipment attributes of the acquisition equipment as evaluation indexes, wherein the equipment attributes comprise acquisition pixels of the image acquisition equipment and response frequencies of the audio acquisition equipment;
the second building module is used for building a computing resource model by combining computing resources of the edge server and corresponding acquisition equipment, wherein the computing resources comprise processor frequency, the number of processor cores, display card throughput, running memory residual quantity and storage memory residual quantity;
the first computing module is used for computing the resource requirement of the request task by utilizing the acquisition resource model and the computing resource model, wherein the resource requirement comprises the acquisition resource requirement and the computing resource requirement;
the integration module is used for integrating resources of the edge servers and the corresponding acquisition equipment to obtain a resource matrix of each edge server;
the allocation module is used for combining the resource requirement of the request task and the resource matrix, allocating the request task to a target edge server by taking the resource occupancy rate as an allocation rule, and updating the resource load of the target server, wherein the computing resource of the target edge server and the acquisition resource of the corresponding acquisition equipment are both larger than the resource requirement of the request task;
the issuing module is used for combining the resource matrix, introducing an ant colony algorithm to determine an optimal issuing strategy, and issuing a request task distributed to the target edge server to target acquisition equipment according to the optimal issuing strategy;
the second calculation module is used for completing monitoring of the request task by utilizing the target edge server and the target acquisition equipment and calculating landslide risk values of the slopes;
and the feedback module is used for determining early warning information according to the landslide risk value and feeding the early warning information back to the manager, wherein the early warning information comprises common early warning and dangerous early warning.
2. The cloud network edge based slope monitoring system of claim 1, wherein the first establishing module is specifically configured to perform the following steps:
s1021: taking the difference between the self noise of the audio acquisition equipment and the maximum sound pressure level as the response frequency, wherein the calculation mode of the response frequency is as follows:
wherein the sound pressure level of the self-noise is represented, the maximum sound pressure level of the audio acquisition device is represented,representing edge servers s k The lower m acquisition devices, w n Representing an nth request task;
s1022: taking the collection pixels of the image collection equipment and the response frequency of the audio collection equipment as evaluation indexes, and establishing a collection resource model:
wherein α represents a weight coefficient, p (a) represents the acquisition pixel, M (α) represents the response frequency, and F (a) represents an acquisition resource evaluation result.
3. The cloud network edge-based slope monitoring system of claim 1, wherein the second establishing module is specifically configured to perform the following steps:
s1031: establishing a decision matrix of the computing resource:
wherein B represents the decision matrix, and each column of elements in the decision matrix corresponds to the processor frequency, the number of processor cores, the graphics card throughput, the running memory residual and the storage memory residual;
s1032: carrying out standardization processing on the decision matrix to obtain a standardized matrix:
wherein Z represents the normalization matrix, Z ij Representing elements in the normalized matrix;
s1033: defining maximum and minimum values of the standardized matrix, and calculating distance values between each computing resource and the maximum and minimum values:
wherein,a distance value representing said computing resource from said maximum value,/or->Representing the computing resourcesA distance value from the minimum value, θ j Representing a weight coefficient assigned to each index in the computing resource according to an entropy weight method;
s1034: according to the distance values, calculating to obtain calculation resource evaluation results of the edge servers and the corresponding acquisition equipment:
wherein G (i) represents the computing resource evaluation result.
4. The cloud network edge based slope monitoring system of claim 1, wherein the resource matrix is:
wherein,representing a plurality of acquisition devices, CR, connected to said edge server s (s k )＝G(s k ) Representing the edge server s k Is->Representation and the edge server s k Connected multiple acquisition devices->Is->Representing acquisition resources of a plurality of acquisition devices connected to the edge server.
5. The cloud-network-edge-based slope monitoring system of claim 1, wherein the distribution module is specifically configured to perform the following steps:
s1061: calculating the resource occupancy rate of the resource requirements of all the request tasks to all the edge calculation server resources:
wherein V(s) k ) Representing the resource occupancy rate, A w Representing the total amount of resource demand for all requested tasks, A s Representing the total amount of resources of all edge servers, A u Representing the total amount of resources of all the acquisition devices;
s1062: calculating the maximum task processing amount of each edge server according to the resource occupancy rate:
wherein,representing the maximum task throughput, A k Representing the total amount of resources of the edge server and the connected acquisition equipment obtained according to the resource matrix;
s1063: and distributing the corresponding request task to the target edge server according to the maximum task processing amount, and updating the resource load of the edge server.
6. The cloud network edge-based slope monitoring system of claim 1, wherein the issuing module is specifically configured to perform the following steps:
s1071: taking the resource utilization rate of ants passing through a path as an exit rule, taking the resource load as a node screening condition of the ants, and carrying out path selection, wherein the node is a cross node of acquisition equipment connected with the destination edge server and a subtask of the request task:
s1072: calculating the pheromone of the path residues of the ants:
wherein ρ represents pheromone volatilization factor, 0 < ρ < 1, J (t) represents the resource utilization rate, t represents the t-th ant, z represents the total number of ants, (1- ρ) τ i，j Representing the attenuation values of the pheromones,representing the pheromone increment value, and Q represents the pheromone constant;
s1073: calculating a heuristic value of the ant selection designated node:
wherein eta i，j Representing the heuristic value;
s1074: and calculating the probability of selecting the j-th acquisition device by the ith task of the ant by combining the pheromone and the heuristic value:
wherein alpha represents a pheromone factor, and beta represents a heuristic function factor;
s1075: and determining the optimal issuing strategy according to the probability of selecting the jth acquisition equipment from the ith task of the ant, and issuing the request task distributed to the target edge server to the target acquisition equipment according to the optimal issuing strategy.
7. The cloud network edge based slope monitoring system of claim 6, wherein the resource utilization rate is calculated by:
wherein J (t) represents the resource utilization, n represents the number of tasks allocated to the acquisition device,representing the sum of the resource demands of n tasks allocated to the acquisition device,/>And representing the acquisition resource evaluation result and the calculation resource evaluation result of the acquisition equipment obtained by calculation according to the acquisition resource model and the calculation resource model.
8. The cloud-network-edge-based slope monitoring system of claim 1, wherein the second computing module is specifically configured to perform the following steps:
s1081: the method comprises the steps of acquiring weather big data of all places along a road through a national weather bureau to form a weather characteristic sequence, wherein the weather big data comprise rainfall, wind intensity, rainfall duration and rainfall predicted duration;
s1082: normalizing the weather feature sequence to obtain a hundred differentiation;
s1083: giving different weights to each weather feature, wherein the weight of the rainfall Q is lambda, the weight of the wind power W is mu, and the weight of the rainfall lasting time T is psi;
s1084: according to the meteorological feature sequence, calculating landslide risk values of the slopes at all positions along the road:
F i ＝λ·Q i +μ·W i +ψ·T i 。
9. the cloud network edge based slope monitoring system of claim 8, wherein the feedback module is specifically configured to perform the following steps:
s1091: when the landslide risk value is larger than a preset risk value, ordinary early warning is carried out;
s1092: calculating the estimated occurrence time of landslide according to the slope characteristics, the rainfall and the wind intensity;
s1093: and carrying out danger early warning under the condition that the expected occurrence time of the landslide is earlier than the expected duration time of rainfall.
10. The cloud network edge based slope monitoring system of claim 9, wherein the calculating method of the estimated landslide occurrence time is as follows:
wherein m represents the slope quality, g represents the gravitational acceleration, t represents the time, W represents the wind power, Q represents the rainfall, S represents the horizontal plane area of the rainfall acting on the slope, and sigma and a respectively represent the slope anti-slip degree and the slope acceleration limit value obtained according to the slope characteristics.
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