Urban monitoring system based on edge computing and control method thereof

Abstract

The embodiment of the invention provides a city monitoring system based on edge computing and a control method thereof, and the method comprises the steps: obtaining an emergency task flow in the city monitoring system and a maximum computing energy group of edge computing equipment through monitoring the edge computing equipment in the city monitoring system; mapping the emergency task flow into a mutual mapping data field between the edge computing device and the emergency task flow by the maximum computing energy group; determining limit time delay mutual entropies corresponding to the emergency task flows, and determining time delay indexes of the edge computing devices according to the mutual entropies and the limit time delay mutual entropies; determining a balance response component corresponding to the mutual mapping data field according to all edge emergency mutual mapping data in the mutual mapping data field, and obtaining a balance mapping group of the mutual mapping data field through the time delay index and the balance response component of the emergency task; edge computing equipment allocation is performed on the emergency task flow in the city monitoring system by the balanced mapping group, and dynamic adaptive allocation of computing power resources can be performed on each emergency task.

Description

Technical Field

[0001] This application relates to the field of smart city technology, and more specifically, to a city monitoring system and control method based on edge computing. Background Technology

[0002] A smart city is an urban model that uses information and internet technologies to improve urban governance, resource utilization, and service levels. By integrating various advanced technologies and innovative methods, it aims to optimize sustainable urban development, improve residents' quality of life, and increase the efficiency and transparency of urban operations. Among these, urban monitoring systems rely on a large number of sensors, cameras, and other IoT devices for data collection, communication, and computation. Edge computing is a core technology in urban monitoring systems, involving pushing computing tasks from the centralized cloud to edge computing devices closer to the data source to reduce latency, alleviate network burden, and improve the response speed of the urban monitoring system.

[0003] In urban surveillance systems, edge computing can effectively improve system response speed and efficiency, reduce the burden on central units, and process large amounts of real-time data at high speed and efficiency. However, currently, urban surveillance systems only have a limited number of edge computing devices for computation. When a large number of urgent tasks with high real-time requirements exist simultaneously, there may be competition for computing resources of edge computing devices, leading to performance degradation and extended task execution time. Therefore, under the condition of limited edge computing devices, how to dynamically allocate the computing resources of edge computing devices to ensure the response speed of urgent tasks when urban surveillance systems are handling a large number of urgent tasks is an urgent problem to be solved in the industry. Summary of the Invention

[0004] This application provides an urban monitoring system and its control method based on edge computing, which can dynamically and adaptively allocate the computing resources of edge computing devices for each emergency task when the urban monitoring system urgently needs to handle a large number of emergency tasks.

[0005] Firstly, this application provides a control method for an urban monitoring system based on edge computing, including: Monitor edge computing devices in urban surveillance systems to obtain emergency task flows and the maximum computing power of edge computing devices in urban surveillance systems; The maximum computing power group maps the emergency task flow into a mutual mapping data domain between the edge computing device and the emergency task flow; Determine the extreme latency cross-entropy corresponding to the emergency task flow, and determine the latency index of the emergency task by the cross-entropy data field and the extreme latency cross-entropy. Based on all edge emergency cross-referencing data in the cross-referencing data domain, determine the equalization response component corresponding to the cross-referencing data domain, and obtain the equalization mapping group of the cross-referencing data domain through the latency index of the emergency task and the equalization response component. The balanced mapping group allocates edge computing devices to the emergency task flow in the urban monitoring system.

[0006] In some embodiments, mapping the emergency task flow to a mutual mapping data domain between the edge computing device and the emergency task flow by the maximum computing power group specifically includes: Determine the computing power critical coefficient based on the maximum computing power group; For each emergency task in the emergency task flow, obtain the task urgency and task complexity corresponding to that emergency task; The urgency variation coefficient of the urgent task is determined based on the urgency and complexity of the task. Based on the emergency variation coefficient and the computing power critical coefficient, the limit emergency distance of the emergency task is obtained; By using the extreme emergency distance and the task complexity, an edge mapping table corresponding to each emergency task is obtained, and then the edge mapping table corresponding to all emergency tasks is determined. Based on the maximum computing power group and all edge mapping tables, the mutual mapping data domain is obtained.

[0007] In some embodiments, determining the extreme latency cross-entropy corresponding to the emergency task flow specifically includes: For each urgent task in the urgent task flow, obtain the task urgency, task complexity, and maximum urgency distance of that urgent task; By using the task urgency and the task complexity, the time delay cross-entropy corresponding to the urgent task is obtained, and then the time delay cross-entropy corresponding to each urgent task is determined. Based on the emergency task flow and all extreme emergency distances, multiple emergency task blocks are determined, and then the extreme delay cross-entropy corresponding to each emergency task block is obtained through the delay cross-entropy corresponding to all emergency tasks in each emergency task block.

[0008] In some embodiments, determining the latency metrics of an emergency task using the cross-referenced data domain and the extreme latency cross-referenced entropy specifically includes: Obtain the maximum latency cross-entropy corresponding to each emergency task block in the emergency task flow; Obtain the emergency limit distance for each emergency task in the cross-data domain; Determine the latency tolerance coefficient for the mirrored data domains; Determine the latency constraints for each emergency task block in the emergency task flow; The latency index of an emergency task is determined based on the extreme latency cross-entropy of each emergency task block in the emergency task flow, the emergency limit distance of each emergency task in the cross-entropy data domain, the latency tolerance coefficient of the cross-entropy data domain, and the latency constraint degree of each emergency task block in the emergency task flow.

[0009] In some embodiments, determining the equalization response component corresponding to the mutual mapping data domain based on all edge emergency mutual mapping data in the mutual mapping data domain specifically includes: For each edge emergency cross-mapped data in the cross-mapped data domain, obtain the computing power loss and transmission length corresponding to that edge emergency cross-mapped data; Based on the computing power loss and the transmission length, the equalization response value corresponding to the edge emergency mutual mapping data is determined, and then the equalization response value corresponding to each edge emergency mutual mapping data is obtained. The equalization response component corresponding to the mutual mapping data domain is determined by the equalization response value corresponding to each edge emergency mutual mapping data.

[0010] In some embodiments, obtaining the balanced mapping group of the mutual-mapped data domain through the latency index of the emergency task and the balanced response component specifically includes: For each edge computing device in the mutual mapping data domain, obtain all edge emergency mutual mapping data of that edge computing device to obtain an emergency mutual mapping table; By using the latency index of the emergency task and the balanced response component, the balanced correlation value of the emergency cross-mapping table is obtained, and then the balanced mapping group of the cross-mapping data domain is determined.

[0011] In some embodiments, the allocation of edge computing devices for emergency task flows in the urban monitoring system by the balanced mapping group specifically includes: The emergency edge mapping table is obtained by coordinating and adapting the mutually mapped data domains through the balanced mapping group. The emergency edge mapping table is used to map and allocate emergency task flows and all edge computing devices in the urban monitoring system.

[0012] Secondly, this application provides an urban monitoring system based on edge computing, including a control unit, the control unit comprising: The acquisition module is used to monitor edge computing devices in the urban monitoring system and acquire the emergency task flow and the maximum computing power of the edge computing devices in the urban monitoring system. The mapping module is used to map the emergency task flow to a mutual mapping data domain between the edge computing device and the emergency task flow by the maximum computing power group; The processing module is used to determine the extreme latency cross-entropy corresponding to the emergency task flow, and to determine the latency index of the emergency task by the cross-entropy data field and the extreme latency cross-entropy. The processing module is further configured to determine the balanced response component corresponding to the mutual mapping data domain based on all edge emergency mutual mapping data in the mutual mapping data domain, and obtain the balanced mapping group of the mutual mapping data domain through the latency index of the emergency task and the balanced response component. The allocation module is used to allocate edge computing devices to the emergency task flow in the urban monitoring system by the balanced mapping group.

[0013] Thirdly, this application provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the control method of the urban monitoring system based on edge computing described above.

[0014] Fourthly, this application provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the aforementioned control method for an edge computing-based urban monitoring system.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: In this application, firstly, by using the maximum computing power group of each edge computing device's computing power information, the emergency task flow is mapped to a mutual mapping data domain between the edge computing device and the emergency task flow by the maximum computing power group. This determines the extreme latency mutual mapping entropy corresponding to the emergency task flow. The latency index of the emergency task is determined by the mutual mapping data domain and the extreme latency mutual mapping entropy, thus determining the tolerance level of the emergency task for the dynamic latency of the edge computing device's processing. This allows for maximizing computing power utilization while meeting the dynamic latency requirements of the emergency task, achieving an adaptive allocation between latency and computing power consumption for the emergency task. Secondly, the mutual mapping is determined based on all edge emergency mutual mapping data in the mutual mapping data domain. The balanced response component corresponding to the data domain is used to determine the dynamic and real-time computing power consumption of the edge computing device. By using the balanced response component to evaluate the computing power loss of the edge computing device, the processing efficiency of emergency tasks on different edge computing devices can be dynamically and reasonably determined. Finally, the balanced mapping group of the mutual mapping data domain is obtained through the latency index of the emergency task and the balanced response component. The balanced mapping group is used to allocate emergency task flows in the urban monitoring system to edge computing devices. While ensuring the response speed of emergency tasks, it can more intelligently allocate emergency tasks to appropriate edge computing devices for processing, that is, realize the dynamic adaptive allocation of computing resources for each emergency task. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is an exemplary flowchart of a control method for an edge computing-based urban monitoring system according to some embodiments of this application; Figure 2 This is a flowchart illustrating the determination of the ultimate time delay cross-entropy according to some embodiments of this application; Figure 3 These are schematic diagrams of exemplary hardware and / or software of a control unit according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of a computer device for a control method of an edge computing-based urban monitoring system according to some embodiments of this application; Figure 5 This is an edge computing framework diagram shown according to some embodiments of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] This application provides an edge computing-based urban monitoring system and its control method. The system monitors edge computing devices within the urban monitoring system, obtains the emergency task flow and the maximum computing power group of the edge computing devices in the urban monitoring system. The maximum computing power group maps the emergency task flow into a mutual mapping data domain between the edge computing devices and the emergency task flow. The system determines the extreme latency mutual mapping entropy corresponding to the emergency task flow, and uses the mutual mapping data domain and extreme latency mutual mapping entropy to determine the latency index of the emergency task. Based on all edge emergency mutual mapping data in the mutual mapping data domain, the system determines the balanced response component corresponding to the mutual mapping data domain. Through the latency index of the emergency task and the balanced response component, a balanced mapping group of the mutual mapping data domain is obtained. The balanced mapping group is used to allocate edge computing devices to the emergency task flow in the urban monitoring system, enabling dynamic adaptive allocation of computing resources for each emergency task.

[0020] To better understand the above technical solutions, a detailed description of the solutions will be provided below in conjunction with the accompanying drawings and specific implementation methods. (Reference) Figure 1 The figure is an exemplary flowchart of a control method for an edge computing-based urban monitoring system according to some embodiments of this application. The control method for the edge computing-based urban monitoring system mainly includes the following steps: In step 101, the edge computing devices in the urban monitoring system are monitored to obtain the emergency task flow in the urban monitoring system and the maximum computing power of the edge computing devices.

[0021] In some embodiments, monitoring edge computing devices in an urban surveillance system to obtain the emergency task flow and maximum computing power of the edge computing devices in the urban surveillance system can be achieved through the following steps: The computing power of edge computing devices in the urban surveillance system is monitored to obtain a scatter plot of edge computing power. Obtain the emergency task flow from the city's surveillance system; The maximum computing power group of the edge computing device is obtained by using the edge computing power scatter plot.

[0022] It should be noted that edge computing devices are those used for edge computing in urban monitoring systems. They are distinguished by device identification numbers, with each edge computing device corresponding to a unique number. Edge computing pushes data processing and computation to the network edge, that is, a location closer to where the data is generated, thereby reducing the burden on the central cloud computing system and improving the real-time performance of data processing. The maximum computing power group consists of multiple limit computing power thresholds, with one limit computing power threshold corresponding to one edge computing device. The limit computing power threshold is an array of limit computing power information for edge computing devices, which includes the maximum load computing power, used computing power, and idle computing power of the edge computing device.

[0023] In practice, the location information of edge computing devices in the urban monitoring system can be obtained, the computing power information of edge computing devices in the urban monitoring system can be monitored in real time, and an edge computing power scatter plot can be generated based on the location information and computing power information of all edge computing devices using existing tools; for each edge computing device in the edge computing power scatter plot, the limit computing power information of the edge computing device can be extracted using existing tools.

[0024] It should be noted that in this application, by monitoring edge computing devices to obtain the maximum computing power group, it is possible to effectively understand the limit computing power of edge computing devices, which helps in the subsequent task allocation and scheduling of emergency tasks, and ensures that emergency tasks can be processed quickly on edge computing devices with sufficient computing power.

[0025] In some embodiments, obtaining the emergency task flow in an urban monitoring system involves arranging emergency tasks in descending order of urgency. Specifically, all tasks in the urban monitoring system can be used as a common task pool. Tasks in this pool with an urgency level exceeding a preset urgency threshold are considered emergency tasks, and their information is used as emergency task data. The task information of all emergency tasks in the common task pool is then obtained. It should be noted that the attributes of an emergency task include task urgency and task complexity. Existing tools can be used to process emergency tasks to obtain task urgency and task complexity. Task urgency describes the total latency requirement of the emergency task, while task complexity describes the computing power requirement of the emergency task on edge computing devices. The location of the emergency task can be used as the task source point.

[0026] In step 102, the maximum computing power group maps the emergency task flow to a mutual mapping data domain between the edge computing device and the emergency task flow.

[0027] In some embodiments, mapping the emergency task flow to a mutual mapping data domain between the edge computing device and the emergency task flow by the maximum computing power group can be achieved by the following steps: Determine the critical computing power coefficient based on the maximum computing power group; For each emergency task in the emergency task flow, obtain the task urgency and task complexity corresponding to that emergency task; The urgency variation coefficient of the urgent task is determined based on the urgency and complexity of the task. Based on the emergency variation coefficient and the computing power critical coefficient, the limit emergency distance of the emergency task is obtained; By using the extreme emergency distance and the task complexity, an edge mapping table corresponding to each emergency task is obtained, and then the edge mapping table corresponding to all emergency tasks is determined. Based on the maximum computing power group and all edge mapping tables, the mutual mapping data domain is obtained.

[0028] It should be noted that, in this application, the mutual mapping data field represents the mutual mapping relationship (i.e., the mutual mapping relationship) between all emergency tasks and all edge computing devices in the emergency task flow. This mutual mapping relationship is a many-to-many mapping relationship; the computing power threshold coefficient represents the idle computing power level of the edge computing devices, that is, the size of all idle computing power; the emergency variation coefficient is the priority weighting coefficient of the urgency and complexity of the emergency task on latency. The higher the emergency variation coefficient, the higher the priority on latency; the extreme emergency distance represents the farthest distance between the task source point of the emergency task and the edge computing device; the edge mapping table represents the one-to-many mapping relationship between the emergency task and the multiple edge computing devices corresponding to the emergency task.

[0029] In practical implementation, the product of the standard deviation and mean of the idle computing power of all edge computing devices in the maximum computing power group can be used as the computing power critical coefficient. For each emergency task in the emergency task flow, the urgency and complexity of the emergency task can be determined based on the existing equipment in the urban monitoring system, and the product of the urgency and complexity of the emergency task can be used as the emergency variation coefficient. All edge computing devices whose distance from the task source point to the edge computing device is less than the critical emergency distance and whose idle computing power is lower than the task complexity are selected. The set of all edge computing devices is used as the edge mapping table corresponding to the emergency task, and the edge mapping table corresponding to all emergency tasks in the emergency task flow is obtained. The edge computing devices with the same device number in all edge mapping tables can be merged through the cross-connection method in the existing technology to obtain the edge emergency mutual mapping table. Through the mapping relationship between the edge computing devices and emergency tasks in the edge emergency mutual mapping table, each edge computing device in the maximum computing power group and each emergency task can be connected to obtain the mutual mapping data domain.

[0030] In the above embodiments, the critical emergency distance of the emergency task is obtained based on the emergency variation coefficient and the computing power criticality coefficient, wherein the critical emergency distance can be determined according to the following formula: in, Indicates the emergency limit distance. Indicates the emergency variation coefficient. This represents the computational energy critical coefficient. Represents the channel gain coefficient. Indicates the channel noise value. The specified distance refers to the channel gain coefficient, which describes the gain and attenuation of the signal during transmission in the urban monitoring system. The channel gain coefficient can be obtained through channel measurement using existing technologies. The channel noise value represents the magnitude of random noise in the channel and can be obtained through existing technologies. The specified distance represents the distance the signal is transmitted within a preset time, in meters, and can be obtained through extensive experimental measurement.

[0031] It should be noted that in this application, the emergency task flow is mapped to the edge computing device to form a mutual mapping data domain, so as to more effectively allocate emergency tasks to the appropriate edge computing device. Through mapping, the emergency task flow is associated with the edge computing device with sufficient computing power and a short enough transmission distance, thereby reducing the time for data transmission and processing.

[0032] In step 103, the extreme latency cross-entropy corresponding to the emergency task flow is determined, and the latency index of the emergency task is determined by the cross-entropy data field and the extreme latency cross-entropy.

[0033] In some embodiments, such as Figure 2 The diagram is a flowchart illustrating the process of determining the extreme latency cross-entropy corresponding to an emergency task flow in some embodiments of this application. In this embodiment, determining the extreme latency cross-entropy corresponding to an emergency task flow can be achieved using the following steps: In step 1031, for each emergency task in the emergency task flow, the task urgency, task complexity, and maximum emergency distance of the emergency task are obtained. In step 1032, the time delay cross-entropy corresponding to the urgent task is obtained through the task urgency and the task complexity, and then the time delay cross-entropy corresponding to each urgent task is determined. In step 1033, multiple emergency task blocks are determined based on the emergency task flow and all extreme emergency distances, and then the extreme time delay cross-entropy corresponding to each emergency task block is obtained through the time delay cross-entropy corresponding to all emergency tasks in each emergency task block.

[0034] It should be noted that the maximum latency cross-entropy represents the maximum latency limit of nearby emergency tasks. It is composed of multiple latency cross-entropy values. Each emergency task block corresponds to one latency cross-entropy value. The latency cross-entropy represents the total latency limit of each emergency task. An emergency task block is composed of multiple emergency tasks that are nearby from the task source point.

[0035] In practice, the set of other emergency tasks within the extreme emergency distance of each emergency task and the emergency task itself can be used as an emergency task block. During the merging process, if other emergency tasks within the extreme emergency distance have already been merged into other blocks, the distance between the two merges is compared, and the emergency task is assigned to the emergency task block with the smaller distance. The maximum value of the time delay cross-entropy corresponding to all emergency tasks in the emergency task block can be used as the extreme time delay cross-entropy corresponding to the emergency task block.

[0036] In the above embodiments, the time-delay cross-entropy corresponding to the urgent task is obtained through the task urgency and the task complexity. The time-delay cross-entropy corresponding to the urgent task can be determined using the following formula: in, Indicates the first in the emergency task flow The time delay cross-entropy corresponding to each urgent task This indicates the number of edge computing devices corresponding to emergency tasks. Indicates the first The idle computing power of an edge computing device Indicates the first in the emergency task flow The first urgent task to the The transmission distance of an edge computing device Indicates the first in the emergency task flow The urgency level of each urgent task. Indicates the first in the emergency task flow The task complexity of an emergency task can be determined by considering all edge computing devices within the extreme emergency distance as the corresponding edge computing devices for that emergency task.

[0037] In some embodiments, determining the latency index of an emergency task from the mutual mapping data domain and the extreme latency mutual mapping entropy can be achieved by the following steps: Obtain the maximum latency cross-entropy corresponding to each emergency task block in the emergency task flow; Obtain the emergency limit distance for each emergency task in the cross-data domain; Determine the latency tolerance coefficient for the mirrored data domains; Determine the latency constraints for each emergency task block in the emergency task flow; Based on the extreme latency cross-entropy of each emergency task block in the emergency task flow, the emergency limit distance of each emergency task in the cross-referenced data domain, the latency tolerance coefficient of the cross-referenced data domain, and the latency constraint degree of each emergency task block in the emergency task flow, the latency index of the emergency task is determined. The latency index can be determined according to the following formula: in, Indicates latency metrics, This indicates the number of emergency task blocks in the emergency task flow. This indicates the number of urgent tasks in the cross-referenced data domain. Indicates the verified distance. Indicates the first in the emergency task flow The extreme latency cross-entropy corresponding to each urgent task block Indicates the first in the mutually mapped data domain The emergency limit distance for an urgent mission. Indicates the first in the mutually mapped data domain The urgency level of each urgent task. Indicates the first in the emergency task flow The latency constraint of each urgent task block. This indicates the superiority of the latency constraint for the emergency task flow. This represents the latency tolerance coefficient of the mirrored data domain.

[0038] It should be noted that the latency index represents the tolerance of emergency tasks for the processing latency of edge computing devices. The higher the latency index, the lower the latency requirement of emergency tasks for edge computing devices and the higher the tolerance. The latency constraint degree represents the degree of constraint on emergency tasks in emergency task blocks, and the latency constraint excellence degree represents the maximum degree of constraint on emergency tasks by all emergency task blocks in the emergency task flow. The latency tolerance coefficient represents the latency tolerance of the city monitoring system and can be preset according to different needs of the city monitoring system.

[0039] In practical implementation, the maximum value of the delay constraint degree of all emergency task blocks in the emergency task flow can be used as the delay constraint excellence. Here, the [missing value] in the emergency task flow... The latency constraint of each urgent task block can be determined according to the following formula: in, Indicates the first in the emergency task flow The latency constraint of each urgent task block. Indicates the first in the emergency task flow The number of urgent tasks in each urgent task block. Indicates the first in the emergency task flow The first in the emergency task block The transmission distance for an urgent mission. Indicates the first in the emergency task flow The first in the emergency task block The transmission distance for an urgent mission. Indicates the first in the emergency task flow Minimum transmission distance for each emergency task block.

[0040] In practice, the average distance between an emergency task and all edge computing devices within the scope of the emergency task block corresponding to that emergency task can be used as the transmission distance of that emergency task, and the minimum transmission distance of all emergency tasks in the emergency task block can be used as the minimum transmission distance.

[0041] It should be noted that in this application, the latency index is obtained by using the extreme latency cross-entropy of the emergency task flow, which better clarifies the time requirements of emergency tasks in the urban monitoring system. This helps to determine the latency constraints when allocating subsequent emergency tasks, thereby reducing transmission latency and facilitating the allocation of computing resources for each emergency task, thus improving transmission speed.

[0042] In step 104, the equalization response component corresponding to the mutual mapping data domain is determined based on all edge emergency mutual mapping data in the mutual mapping data domain. The equalization mapping group of the mutual mapping data domain is obtained through the latency index of the emergency task and the equalization response component.

[0043] In some embodiments, determining the equalization response component corresponding to the mutual mapping data domain based on all edge emergency mutual mapping data in the mutual mapping data domain can be achieved by the following steps: For each edge emergency cross-mapped data in the cross-mapped data domain, obtain the computing power loss and transmission length corresponding to that edge emergency cross-mapped data; Based on the computing power loss and transmission length, the equalization response value corresponding to the edge emergency mutual mapping data is determined, and then the equalization response value corresponding to each edge emergency mutual mapping data is obtained. The equalization response component corresponding to the mutual mapping data domain is determined by the equalization response value corresponding to each edge emergency mutual mapping data.

[0044] It should be noted that the balanced response component consists of multiple balanced response values. The balanced response value represents the degree of computing power consumption of edge computing devices in the edge emergency mapping data, that is, the priority of emergency task response efficiency. The larger the balanced response value, the more computing power the edge computing device consumes, the more computing power is allocated to the emergency task, and the higher the emergency task response efficiency.

[0045] In practice, the computing power requirement of the emergency task data in the edge emergency mutual mapping data can be used as the computing power loss, and the distance between the task source point of the emergency task in the edge emergency mutual mapping data and the edge computing device can be used as the transmission length.

[0046] In the above embodiments, the equalization response value corresponding to the edge emergency mutual mapping data can be determined by the following steps based on the computing power loss and transmission length: Obtain the computing power loss corresponding to the emergency edge mapping data; Obtain the transmission length corresponding to the edge emergency mapping data; Determine the coefficient of variation of loss for edge emergency cross-mapping data; Based on the computing power loss corresponding to the edge emergency cross-mapping data, the transmission length corresponding to the edge emergency cross-mapping data, and the loss variation coefficient of the edge emergency cross-mapping data, the equalization response value corresponding to the edge emergency cross-mapping data is determined, wherein the equalization response value corresponding to the edge emergency cross-mapping data can be determined according to the following formula: in, Indicates the first mutual mapping data field The balanced response value of emergency cross-referencing data at the edge. Indicates the first mutual mapping data field The computing power consumption corresponding to each edge emergency cross-mapping data. Indicates the first mutual mapping data field The transmission length corresponding to each edge emergency cross-mapping data. Indicates the first mutual mapping data field The coefficient of variation of loss for emergency tasks in the edge emergency mapping data should be noted. It is important to note that the coefficient of variation of loss represents the proportion of computing power loss in the computing power allocation process. It is used to adjust the priority of processing efficiency for emergency tasks. It can be preset according to the different urgency of emergency tasks in the edge emergency mapping data. The larger the coefficient of variation of loss, the higher the efficiency priority, the greater the computing power loss, the higher the processing efficiency of emergency tasks, and the more computing power is wasted.

[0047] In practice, the above formula can be used to determine the equalization response value corresponding to each edge emergency mutual mapping data in the mutual mapping data domain, and the set of all equalization response values ​​can be used as the equalization response component.

[0048] In some embodiments, obtaining the balanced mapping group of the mutual mapping data domain using the latency index of the emergency task and the balanced response component can be achieved through the following steps: For each edge computing device in the mutual mapping data domain, obtain all edge emergency mutual mapping data of that edge computing device to obtain an emergency mutual mapping table; By using the latency index of the emergency task and the balanced response component, the balanced correlation value of the emergency cross-mapping table is obtained, and then the balanced mapping group of the cross-mapping data domain is determined.

[0049] In practice, all edge emergency mutual mapping data containing edge computing devices are filtered in the mutual mapping data domain, and all edge emergency mutual mapping data are stored in a preset data table to obtain the emergency mutual mapping table; all balanced association values ​​are used as balanced mapping groups.

[0050] In the above embodiments, obtaining the balanced correlation value of the emergency cross-mapping table using the latency index of the emergency task and the balanced response component can be achieved through the following steps: Obtain the equalization response value corresponding to each edge emergency cross-mapping data in the equalization response component of the emergency cross-mapping table; Obtain the latency metrics for the emergency mapping table; Based on the equalization response value corresponding to each edge emergency mapping data in the equalization response component and the latency index of the emergency mapping table, the equalization correlation value of the emergency mapping table is determined. The equalization correlation value of the emergency mapping table can be determined using the following steps: in, This represents the balanced correlation value of the emergency cross-mapping table. This indicates the number of edge emergency cross-referenced data in the emergency cross-reference table. The table of emergency mappings indicates the first item. The balanced response value of emergency cross-referencing data at the edge. This represents the peak value of the balanced emergency response in the emergency cross-mapping table. The table of emergency mappings indicates the first item. The time scale for the mutual mapping of emergency edge mapping data. This indicates the latency of the emergency mapping table.

[0051] It should be noted that the mutual mapping timescale represents the length of time that edge computing devices in the edge emergency mutual mapping data are allocated to respond to emergency tasks. The larger the mutual mapping timescale, the longer the response time.

[0052] In specific implementation, the maximum value of the balanced response value of all edge emergency mutual mapping data in the emergency mutual mapping table can be used as the peak value of the balanced emergency response; the sum of the transmission time scale and the computation time scale in the edge emergency mutual mapping data can be used as the mutual mapping time scale; the ratio of the task complexity of the emergency task to the computing power can be used as the computation time scale; and the latency index determined in step 103 can be used as the latency index of the emergency mutual mapping table.

[0053] It should be noted that, in this application, the balanced response component is obtained based on the mutual mapping data domain, which can more reasonably evaluate the processing efficiency of the entropy of emergency tasks on different edge computing devices, provide a more reliable basis for the subsequent allocation of emergency tasks, thereby improving the processing speed of the allocated emergency computing devices for emergency tasks, and helping to realize the allocation of computing resources for each emergency task.

[0054] In step 105, the balanced mapping group allocates edge computing devices to the emergency task flow in the urban monitoring system.

[0055] In some embodiments, the allocation of edge computing devices for emergency task flows in the urban monitoring system by the balanced mapping group can be achieved through the following steps: The emergency edge mapping table is obtained by coordinating and adapting the mutually mapped data domains through the balanced mapping group. The emergency edge mapping table is used to map and allocate emergency task flows and all edge computing devices in the urban monitoring system.

[0056] In specific implementation, all emergency tasks in the mutual mapping data domain are sorted from highest to lowest according to their task urgency. The sorting result is used as an emergency task sequence. For each emergency task in the emergency task sequence, among the idle edge computing devices that can meet the task complexity of the emergency task, the edge computing device closest to the task source of the emergency task is selected. It is determined whether the edge computing device meets the task urgency requirement of the emergency task. If the edge computing device meets the task urgency requirement of the emergency task, then the edge computing device is used as the edge computing device for mapping the emergency task. The emergency tasks in the emergency task sequence are mapped in turn to obtain an emergency edge mapping table. For each emergency task in the emergency task flow of the urban monitoring system, the edge computing device corresponding to the emergency task in the emergency edge mapping table is used as the edge computing device assigned to the emergency task.

[0057] It should be noted that in this application, a balanced mapping group is obtained by using latency index and balanced response component, which is used for better allocation of edge computing devices. The urban monitoring system can more intelligently allocate emergency tasks to suitable edge computing devices, and can realize dynamic adaptive allocation of computing resources for each emergency task.

[0058] Furthermore, in another aspect of this application, in some embodiments, this application provides an edge computing-based urban surveillance system, which includes a control unit, referenced... Figure 3 The figure is a schematic diagram of exemplary hardware and / or software of a control unit according to some embodiments of this application. The control unit includes: an acquisition module 201, a mapping module 202, a processing module 203, and an allocation module 204, which are described below: The acquisition module 201 in this application is mainly used to monitor the edge computing devices in the urban monitoring system and to acquire the emergency task flow and the maximum computing power of the edge computing devices in the urban monitoring system. Mapping module 202, in this application, is mainly used to map the emergency task flow to a mutual mapping data domain between the edge computing device and the emergency task flow by the maximum computing power group; Processing module 203, in this application, is used to determine the extreme latency cross-entropy corresponding to the emergency task flow, and to determine the latency index of the emergency task by the cross-entropy data field and the extreme latency cross-entropy. In addition, the processing module 203 in this application is also used to determine the equalization response component corresponding to the mutual mapping data domain based on all edge emergency mutual mapping data in the mutual mapping data domain, and obtain the equalization mapping group of the mutual mapping data domain through the latency index of the emergency task and the equalization response component. The allocation module 204 in this application is mainly used by the balanced mapping group to allocate edge computing devices to the emergency task flow in the urban monitoring system.

[0059] The foregoing has detailed examples of an edge computing-based urban monitoring system and its control method provided in the embodiments of this application. It is understood that the corresponding apparatus, in order to achieve the above functions, includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0060] In some embodiments, this application also provides a computer device, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for calling and running the computer program from the memory, so that the computer device executes the control method of the urban monitoring system based on edge computing described above.

[0061] In some embodiments, reference Figure 4 The dashed lines in the figure indicate that the unit or module is optional. This figure is a schematic diagram of the structure of a computer device implementing a control method for an edge-computing-based urban monitoring system according to an embodiment of this application. The control method for an edge-computing-based urban monitoring system described in the above embodiments can be... Figure 4 The computer device shown is used to implement this, and the computer device includes at least one processor 301, a memory 302 and at least one communication unit 305. The computer device may be a terminal device, a server or a chip.

[0062] Processor 301 can be a general-purpose processor or a special-purpose processor. For example, processor 301 can be a central processing unit (CPU), which can be used to control computer devices, execute software programs, and process data from software programs. The computer device may also include a communication unit 305 for inputting (receiving) and outputting (transmitting) signals.

[0063] For example, the computer device may be a chip, and the communication unit 305 may be the input and / or output circuit of the chip, or the communication unit 305 may be the communication interface of the chip, which may be a component of a terminal device, network device or other device.

[0064] For example, the computer device may be a terminal device or a server, and the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.

[0065] The computer device may include one or more memories 302 storing a program 304. The program 304 can be executed by a processor 301 to generate instructions 303, causing the processor 301 to execute the method described in the above method embodiments according to the instructions 303. Optionally, the memory 302 may also store data (such as a target audit model). Optionally, the processor 301 may also read data stored in the memory 302, which may be stored at the same storage address as the program 304, or it may be stored at a different storage address than the program 304.

[0066] The processor 301 and memory 302 can be configured separately or integrated together, for example, integrated on the system on chip (SOC) of the terminal device.

[0067] It should be understood that each step of the above method embodiment can be completed by hardware logic circuits or software instructions in the processor 301. The processor 301 can be a CPU, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, such as discrete gate, transistor logic devices, or discrete hardware components.

[0068] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0069] For example, in some embodiments, this application also provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the control method of the urban monitoring system based on edge computing described above.

[0070] In some embodiments, reference Figure 5 This diagram illustrates an edge computing framework provided according to an embodiment of this application. The edge computing architecture is mainly divided into three layers: edge controller, edge gateway, and edge cloud. The edge controller is primarily responsible for network control, development, and motion control. The edge gateway is primarily responsible for device management, storage, and intelligent computing. The edge cloud is primarily responsible for business application management, such as uplink and downlink transmission of business instructions, task editing and scheduling, application deployment, and lifecycle management; platform management, such as virtualization platform management and operation and maintenance management; and intelligent tasks such as edge-cloud collaboration and heterogeneous computing. Data processing using edge computing mainly involves the following three steps: First, massive amounts of data generated by terminal devices and service platform systems are collected and uploaded via the Internet. Second, multi-source heterogeneous data is converted and normalized according to protocols and then inherited at the edge. Finally, edge servers are used to aggregate and process the underlying data and integrate the edge layer data into the cloud center.

[0071] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0072] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A control method for an urban monitoring system based on edge computing, characterized in that, Includes the following steps: Monitor edge computing devices in urban surveillance systems to obtain emergency task flows and the maximum computing power of edge computing devices in urban surveillance systems; The maximum computing power group maps the emergency task flow into a mutual mapping data domain between the edge computing device and the emergency task flow; Determine the extreme latency cross-entropy corresponding to the emergency task flow, and determine the latency index of the emergency task by the cross-entropy data field and the extreme latency cross-entropy. Based on all edge emergency cross-referencing data in the cross-referencing data domain, determine the equalization response component corresponding to the cross-referencing data domain, and obtain the equalization mapping group of the cross-referencing data domain through the latency index of the emergency task and the equalization response component. The balanced mapping group allocates edge computing devices to the emergency task flow in the urban monitoring system.

2. The method as described in claim 1, characterized in that, The mapping of the emergency task flow to a mutual mapping data domain between the edge computing device and the emergency task flow by the maximum computing power group specifically includes: Determine the computing power critical coefficient based on the maximum computing power group; For each emergency task in the emergency task flow, obtain the task urgency and task complexity corresponding to that emergency task; The urgency variation coefficient of the urgent task is determined based on the urgency and complexity of the task. Based on the emergency variation coefficient and the computing power critical coefficient, the limit emergency distance of the emergency task is obtained; By using the extreme emergency distance and the task complexity, an edge mapping table corresponding to each emergency task is obtained, and then the edge mapping table corresponding to all emergency tasks is determined. Based on the maximum computing power group and all edge mapping tables, the mutual mapping data domain is obtained.

3. The method as described in claim 1, characterized in that, Determining the extreme latency cross-entropy corresponding to the emergency task flow specifically includes: For each urgent task in the urgent task flow, obtain the task urgency, task complexity, and maximum urgency distance of that urgent task; By using the task urgency and the task complexity, the time delay cross-entropy corresponding to the urgent task is obtained, and then the time delay cross-entropy corresponding to each urgent task is determined. Based on the emergency task flow and all extreme emergency distances, multiple emergency task blocks are determined, and then the extreme delay cross-entropy corresponding to each emergency task block is obtained through the delay cross-entropy corresponding to all emergency tasks in each emergency task block.

4. The method as described in claim 1, characterized in that, The time delay metrics for determining urgent tasks, determined by the mutual mapping data domain and the extreme time delay mutual mapping entropy, specifically include: Obtain the maximum latency cross-entropy corresponding to each emergency task block in the emergency task flow; Obtain the emergency limit distance for each emergency task in the cross-data domain; Determine the latency tolerance coefficient for the mirrored data domains; Determine the latency constraints for each emergency task block in the emergency task flow; The latency index of an emergency task is determined based on the extreme latency cross-entropy of each emergency task block in the emergency task flow, the emergency limit distance of each emergency task in the cross-entropy data domain, the latency tolerance coefficient of the cross-entropy data domain, and the latency constraint degree of each emergency task block in the emergency task flow.

5. The method as described in claim 1, characterized in that, Determining the equalization response component corresponding to the mutual mapping data domain based on all edge emergency mutual mapping data in the mutual mapping data domain specifically includes: For each edge emergency cross-mapped data in the cross-mapped data domain, obtain the computing power loss and transmission length corresponding to that edge emergency cross-mapped data; Based on the computing power loss and the transmission length, the equalization response value corresponding to the edge emergency mutual mapping data is determined, and then the equalization response value corresponding to each edge emergency mutual mapping data is obtained. The equalization response component corresponding to the mutual mapping data domain is determined by the equalization response value corresponding to each edge emergency mutual mapping data.

6. The method as described in claim 1, characterized in that, The balanced mapping group of the mutual-reflection data domain, obtained through the latency index of the emergency task and the balanced response component, specifically includes: For each edge computing device in the mutual mapping data domain, obtain all edge emergency mutual mapping data of that edge computing device to obtain an emergency mutual mapping table; By using the latency index of the emergency task and the balanced response component, the balanced correlation value of the emergency cross-mapping table is obtained, and then the balanced mapping group of the cross-mapping data domain is determined.

7. The method as described in claim 1, characterized in that, The allocation of edge computing devices for emergency task flows in the urban monitoring system by the balanced mapping group specifically includes: The emergency edge mapping table is obtained by coordinating and adapting the mutually mapped data domains through the balanced mapping group. The emergency edge mapping table is used to map and allocate emergency task flows and all edge computing devices in the urban monitoring system.

8. A city surveillance system based on edge computing, characterized in that, It includes a control unit, which includes: The acquisition module is used to monitor edge computing devices in the urban monitoring system and acquire the emergency task flow and the maximum computing power of the edge computing devices in the urban monitoring system. The mapping module is used to map the emergency task flow to a mutual mapping data domain between the edge computing device and the emergency task flow by the maximum computing power group; The processing module is used to determine the extreme latency cross-entropy corresponding to the emergency task flow, and to determine the latency index of the emergency task by the cross-entropy data field and the extreme latency cross-entropy. The processing module is further configured to determine the balanced response component corresponding to the mutual mapping data domain based on all edge emergency mutual mapping data in the mutual mapping data domain, and obtain the balanced mapping group of the mutual mapping data domain through the latency index of the emergency task and the balanced response component. The allocation module is used to allocate edge computing devices to the emergency task flow in the urban monitoring system by the balanced mapping group.

9. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to call and run the computer programs from the memory, so that the computer device performs the control method of the urban monitoring system based on edge computing as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the control method for an edge computing-based urban monitoring system as described in any one of claims 1 to 7.

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