Smart city data processing method and device, node and storage medium

Abstract

The invention provides a smart city data processing method and device, a node and a storage medium, relates to the technical field of computers, and is used for effectively meeting data processing requirements in a smart city scene. The method comprises the following steps: receiving point location data from a sensor; the point location data is data acquired by the sensor at a deployment position in a city; obtaining a task type and a task priority of a calculation task corresponding to the point location data, and a calculation capability of each distributed calculation node in the plurality of distributed calculation nodes; calling an intelligent contract to process the task type, the task priority and the computing power of each distributed computing node, and determining the task allocation weight of each distributed computing node; based on the task allocation weight of each distributed computing node, allocating the computing task to a target computing node; the probability that the distributed computing node is determined as the target computing node is positively correlated with the task allocation weight of the distributed computing node.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a smart city data processing method, device, node, and storage medium. Background Art

[0002] As IoT technology has been widely used in various fields such as intelligent transportation, environmental monitoring, urban security, and intelligent energy management, current smart city systems mainly rely on centralized cloud computing architecture for data storage and computing.

[0003] However, due to limitations in network bandwidth, data privacy, and real-time requirements, centralized cloud computing architectures have problems such as single points of failure, bandwidth bottlenecks, high latency, and data security risks, and cannot fully meet the data processing needs in smart city scenarios. Summary of the Invention

[0004] The present application provides a smart city data processing method, device, node and storage medium for effectively meeting the data processing needs in smart city scenarios.

[0005] To achieve the above objectives, this application adopts the following technical solutions:

[0006] In a first aspect, a smart city data processing method is provided, comprising: receiving point data from sensors. The point data is data collected by sensors at locations deployed in the city. The task type and task priority of the computing task corresponding to the point data, as well as the computing power of each distributed computing node among multiple distributed computing nodes, are obtained. A smart contract is called to process the task type, task priority, and computing power of each distributed computing node to determine the task allocation weight of each distributed computing node. Based on the task allocation weight of each distributed computing node, the computing task is allocated to a target computing node. The probability of a distributed computing node being determined as a target computing node is positively correlated with the task allocation weight of the distributed computing node.

[0007] Optionally, allocating a computing task to a target computing node based on the task allocation weights of each distributed computing node includes: determining computing resource consumption of the computing task. The computing resource consumption represents an estimated consumption of computing resources by the computing task. If the computing resource consumption exceeds a preset consumption threshold, splitting the computing task into multiple subtasks. Determining multiple target computing nodes based on the task allocation weights of each distributed computing node. Allocating the multiple subtasks to different target computing nodes among the multiple target computing nodes.

[0008] Optionally, determining the computing resource consumption of the computing task includes determining computing resource requirements corresponding to the computing task. The computing resource requirements include floating-point operation requirements, storage resource requirements, and bandwidth resource requirements. The computing resource consumption of the computing task is determined based on the computing resource requirements and multiple preset resource utilizations. The multiple preset resource utilizations include processor resource utilization, storage resource utilization, and bandwidth resource utilization.

[0009] Optionally, the method further includes: invoking a smart contract to store task assignment information corresponding to the computing task on the blockchain. The task assignment information includes the target computing node, computing task, task start time, task deadline, task execution duration, and task execution result.

[0010] Optionally, the method further includes: in response to a user device's request for access to the task assignment information, invoking a smart contract to perform a permission check on the user information in the access request. The invoking smart contract sends authorization information to the user device after the permission check passes based on the user information.

[0011] Optionally, the method further includes: invoking a smart contract to obtain a resource load of a first computing node. The first computing node is any one of the plurality of distributed computing nodes. Invoking the smart contract to migrate a computing task carried by the first computing node to a second computing node based on the resource load of the first computing node being greater than a first load threshold. The second computing node is a distributed computing node among the plurality of distributed computing nodes whose resource load is less than a second load threshold. The first load threshold is greater than the second load threshold.

[0012] Optionally, the method further includes: invoking a smart contract to store task migration information of the first computing node in a blockchain. The task migration information includes the resource load of the first computing node, the migrated computing task, and the computing task migration time.

[0013] In a second aspect, a smart city data processing device is provided, comprising: an acquisition unit, a calling unit and an allocation unit.

[0014] The acquisition unit is used to receive point data from sensors. Point data is data collected by sensors at locations deployed in the city.

[0015] The acquisition unit is further used to acquire the task type and task priority of the computing task corresponding to the point data, as well as the computing capacity of each distributed computing node in the multiple distributed computing nodes.

[0016] The calling unit is used to call the smart contract to process the task type, task priority and computing power of each distributed computing node, and determine the task allocation weight of each distributed computing node.

[0017] The allocating unit is configured to allocate the computing task to the target computing node based on the task allocation weights of the distributed computing nodes. The probability of the distributed computing node being determined as the target computing node is positively correlated with the task allocation weights of the distributed computing nodes.

[0018] Optionally, the allocation unit is specifically configured to: determine the computing resource consumption of the computing task. The computing resource consumption represents an estimated amount of computing resources consumed by the computing task. If the computing resource consumption exceeds a preset consumption threshold, split the computing task into multiple subtasks. Determine multiple target computing nodes based on task allocation weights of each distributed computing node. Allocate the multiple subtasks to different target computing nodes among the multiple target computing nodes.

[0019] Optionally, the allocation unit is specifically configured to determine computing resource requirements corresponding to the computing task. The computing resource requirements include floating-point operation requirements, storage resource requirements, and bandwidth resource requirements. Based on the computing resource requirements and a plurality of preset resource utilizations, the computing resource consumption of the computing task is determined. The plurality of preset resource utilizations include processor resource utilization, storage resource utilization, and bandwidth resource utilization.

[0020] Optionally, the calling unit is further configured to call the smart contract to store task assignment information corresponding to the computing task on the blockchain. The task assignment information includes the target computing node, computing task, task start time, task deadline, task execution duration, and task execution result.

[0021] Optionally, the calling unit is further configured to, in response to a user device's request for access to task assignment information, call the smart contract to perform permission verification on the user information in the access request. The calling unit is further configured to call the smart contract to send authorization information to the user device after the permission verification is completed based on the user information.

[0022] Optionally, the calling unit is further configured to call a smart contract to obtain the resource load of the first computing node. The first computing node is any one of the multiple distributed computing nodes. The calling unit is further configured to call the smart contract to migrate the computing task carried by the first computing node to a second computing node based on the resource load of the first computing node being greater than a first load threshold. The second computing node is a distributed computing node among the multiple distributed computing nodes whose resource load is less than a second load threshold. The first load threshold is greater than the second load threshold.

[0023] Optionally, the calling unit is further configured to call a smart contract to store task migration information of the first computing node in the blockchain. The task migration information includes the resource load of the first computing node, the migrated computing task, and the computing task migration time.

[0024] In a third aspect, an edge computing node is provided, comprising a memory and a processor; the memory is used to store computer execution instructions, and the processor and the memory are connected via a bus; when the edge computing node is running, the processor executes the computer execution instructions stored in the memory, so that the edge computing node executes any one of the smart city data processing methods in the first aspect.

[0025] The edge computing node can be a network device or a device within a network device, such as a chip system within the network device. The chip system is used to support the network device in implementing the functions involved in the first aspect and any possible implementation thereof, such as receiving, determining, and diverting the data and / or information involved in the above-mentioned smart city data processing method. The chip system includes a chip and may also include other discrete devices or circuit structures.

[0026] In a fourth aspect, a computer-readable storage medium is provided, the computer-readable storage medium including computer execution instructions. When the computer execution instructions are run on a computer, the computer executes any one of the smart city data processing methods in the first aspect.

[0027] It should be noted that the above-mentioned computer instructions may be stored in whole or in part on a computer-readable storage medium. The computer-readable storage medium may be packaged together with the processor of the smart city data processing device, or may be packaged independently of the processor of the smart city data processing device, and this application does not limit this.

[0028] In a fifth aspect, a computer program product is provided, which includes computer program instructions, and when the computer program instructions are executed by a computer, they implement any smart city data processing method as described in the first aspect.

[0029] In this application, the name of the smart city data processing device does not limit the device or functional module itself. In actual implementation, these devices or functional modules may appear with other names. As long as the functions of each device or functional module are similar to those of this application, they fall within the scope of the claims of this application and their equivalents.

[0030] These and other aspects of the present application will become more readily apparent from the following description.

[0031] The technical solution provided by this application brings at least the following beneficial effects:

[0032] Based on any of the above aspects, in this application, a distributed computing node can receive point data from a sensor and obtain the task type and task priority of the computing task corresponding to the point data, as well as the computing power of each distributed computing node among multiple distributed computing nodes, so as to further call a smart contract to process the task type, task priority and computing power of each distributed computing node, determine the task allocation weight of each distributed computing node, and then allocate the computing task to the target computing node based on the task allocation weight of each distributed computing node. Among them, the probability of a distributed computing node being determined as a target computing node is positively correlated with the task allocation weight of the distributed computing node.

[0033] Based on this, this application can reasonably distribute data computing tasks in smart cities among multiple distributed computing nodes, realize the intelligent, decentralized and efficient execution of urban management tasks such as intelligent traffic scheduling, environmental monitoring, security management, energy optimization, etc., and effectively meet the data processing needs in smart city scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A schematic diagram of the structure of a smart city data processing system provided in an embodiment of the present application;

[0035] Figure 2 A schematic diagram of the hardware structure of a computer device provided in an embodiment of the present application;

[0036] Figure 3 A flowchart of a smart city data processing method provided in an embodiment of the present application;

[0037] Figure 4 A schematic diagram of the structure of a smart city data processing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0039] It should be noted that in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being more preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0040] In order to facilitate a clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, words such as "first" and "second" are used to distinguish between identical or similar items with basically the same functions and effects. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order.

[0041] In addition, the terms "including" and "having" in the embodiments, claims, and drawings of this application are not exclusive. For example, a process, method, system, product, or device that includes a series of steps or modules is not limited to the listed steps or modules and may also include steps or modules that are not listed.

[0042] In order to facilitate understanding of this application, the relevant elements involved in this application are now described.

[0043] As IoT technology has been widely used in various fields such as intelligent transportation, environmental monitoring, urban security, and intelligent energy management, current smart city systems mainly rely on centralized cloud computing architecture for data storage and computing.

[0044] Although cloud computing provides powerful computing and storage capabilities, due to limitations in network bandwidth, data privacy, and real-time requirements, centralized cloud computing architectures have problems such as single points of failure, bandwidth bottlenecks, high latency, and data security risks. This makes it difficult to fully meet the processing requirements of low latency, high reliability, and data credibility in smart city scenarios.

[0045] Current smart city management systems still face numerous challenges in terms of data security, computing power, and response time. For example, data credibility is a concern. Because urban management involves sharing data from multiple parties, such as transportation, the environment, and security, traditional centralized databases are subject to the risk of data tampering, which can easily affect the accuracy of decision-making. Another example is the scheduling of computing and storage resources. City-level intelligent management requires large-scale computing and storage resources. Traditional cloud computing approaches can lead to bandwidth bottlenecks, wasted computing resources, or overloads, which can easily affect service quality. Furthermore, architectures combining traditional cloud computing with IoT technologies struggle to meet millisecond-level decision-making requirements.

[0046] To solve the above problems, this application proposes a smart city data processing method, in which distributed computing nodes can receive point data from sensors and obtain the task type and task priority of the computing task corresponding to the point data, as well as the computing power of each distributed computing node in multiple distributed computing nodes, so as to further call smart contracts to process the task type, task priority and computing power of each distributed computing node, determine the task allocation weight of each distributed computing node, and then allocate computing tasks to target computing nodes based on the task allocation weight of each distributed computing node. Among them, the probability of a distributed computing node being determined as a target computing node is positively correlated with the task allocation weight of the distributed computing node.

[0047] Based on this, this application can reasonably distribute data computing tasks in smart cities among multiple distributed computing nodes, realize the intelligent, decentralized and efficient execution of urban management tasks such as intelligent traffic scheduling, environmental monitoring, security management, energy optimization, etc., and effectively meet the data processing needs in smart city scenarios.

[0048] In response to the above problems, an embodiment of the present application provides a smart city data processing method, which is applicable to a smart city data processing system. Figure 1 FIG. 1 shows a structure of the smart city data processing system 100. Figure 1 As shown, the smart city data processing system 100 may include: multiple sensors 101 and multiple distributed computing nodes 102.

[0049] In practical applications, a sensor 101 can be connected to one or more distributed computing nodes 102 at the same time. A distributed computing node 102 can also be connected to one or more sensors 101 at the same time. Figure 1 The connection relationship shown in the figure is only an example and is not limited to this embodiment of the present application.

[0050] Figure 1 The sensor 101 is located in the perception layer of the smart city data processing system 100, which can be a visual sensor (such as a camera), a temperature and humidity sensor, a wind speed sensor, etc., deployed at specific points in the city to collect various data in the city.

[0051] Figure 1 Distributed computing nodes 102 are located in the data processing layer of smart city data processing system 100 and can be edge computing nodes deployed near sensors 101. They are used to receive and process point data from sensors 101. For example, distributed computing nodes 102 can be deployed with smart contracts, which can be used to allocate and schedule computing tasks and store various data generated during the processing on the blockchain.

[0052] Furthermore, edge computing can move computing tasks to the edge of the network, reducing data transmission delays, improving data processing efficiency, and enhancing data security. Furthermore, edge computing can effectively reduce network latency, placing computing tasks as close to the data source as possible, improving real-time response capabilities, and reducing bandwidth usage. This reduces the network resources consumed by data transmission back to the cloud, improving communication efficiency, and enhancing data security. Some sensitive data can be processed at the edge layer, reducing security pressure on the central cloud.

[0053] In addition, blockchain technology has the characteristics of decentralization, immutability, and traceability, which can effectively support data sharing and data security.

[0054] Or further, Figure 1 After completing data processing, the distributed computing nodes 102 in the smart city data processing system 100 can send the processing results to the management devices at the data application layer in the smart city data processing system 100. This allows the management devices to implement intelligent, decentralized, and efficient urban management tasks such as intelligent traffic scheduling, environmental monitoring, security management, and energy optimization based on the processing results. For example, the management devices can be traffic management devices, medical resource management devices, industrial production management devices, etc. in a smart city.

[0055] It should be understood that Figure 1 What is shown in the figure are examples of device forms of the sensor 101 and the distributed computing node 102, and are not intended to limit the specific forms of the sensor 101 and the distributed computing node 102.

[0056] Optionally, Figure 1 The distributed computing node 102 can be a terminal, a server, or other electronic device with smart city data processing capabilities. Figure 1 What is shown in FIG. 1 is an example of a device form of the distributed computing node 102 , and is not intended to limit the specific form of the distributed computing node 102 .

[0057] Optionally, Figure 1 The distributed computing node 102 in the embodiment can be a terminal or a server.

[0058] When the distributed computing node 102 is a terminal, the terminal can be a device that provides voice and / or data connectivity to a user, a handheld device with wireless connection capabilities, or other processing devices connected to a wireless modem. The wireless terminal can communicate with one or more core networks via a radio access network (RAN). The wireless terminal can be a mobile terminal, such as a computer with a mobile terminal, or a portable, pocket-sized, handheld, or computer-built-in mobile device that exchanges language and / or data with a radio access network, such as a mobile phone, tablet computer, laptop computer, netbook, or personal digital assistant (PDA). The embodiments of the present application do not impose any restrictions on this.

[0059] When the distributed computing node 102 is a server, the server may be a single server, or a server cluster composed of multiple servers. In some implementations, the server cluster may also be a distributed cluster. This embodiment of the present application does not impose any restrictions on this.

[0060] like Figure 2 FIG. 1 is a schematic diagram of a hardware structure of a computer device provided in an embodiment of the present application. The computer device may be a distributed computing node 102. The computer device includes a processor 21, a memory 22, a communication interface 23, and a bus 24. The processor 21, the memory 22, and the communication interface 23 may be connected via a bus 24.

[0061] The processor 21 is the control center of the computer device and can be a single processor or a collective term for multiple processing elements. For example, the processor 21 can be a CPU or other general-purpose processor. The general-purpose processor can be a microprocessor or any conventional processor.

[0062] As an embodiment, the processor 21 may include one or more CPUs, such as Figure 2 CPU0 and CPU1 are shown in the figure.

[0063] The memory 22 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0064] In one possible implementation, the memory 22 can exist independently of the processor 21 and can be connected to the processor 21 via a bus 24 to store instructions or program codes. When the processor 21 calls and executes the instructions or program codes stored in the memory 22, the smart city data processing method provided in the following embodiments of the present application can be implemented.

[0065] In another possible implementation, the memory 22 may also be integrated with the processor 21 .

[0066] The communication interface 23 is used to connect the computer device to other devices via a communication network, which may be Ethernet, wireless access network, wireless local area network (WLAN), etc. The communication interface 23 may include a receiving unit for receiving data and a sending unit for sending data.

[0067] The bus 24 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of presentation, Figure 2 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0068] It should be pointed out that Figure 2 The structure shown in the figure does not constitute a limitation on the computer equipment, except Figure 2 In addition to the components shown, a computer device may include more or fewer components than shown, or combine certain components, or arrange components differently.

[0069] like Figure 3 The figure is a flow chart of a smart city data processing method provided by an embodiment of the present application. The smart city data processing method can be applied to Figure 1 The distributed computing node 102 in the smart city data processing system 100 is shown. The smart city data processing method includes: S301-S304.

[0070] S301. The distributed computing node receives point data from the sensor.

[0071] Point data refers to data collected by sensors deployed in the city. This sensor can be any one of the numerous sensors deployed in smart city scenarios. These sensors are used to achieve comprehensive data detection of the physical world.

[0072] Point data may include the timestamp when the sensor collects data, the geographic location information of the sensor deployment location, the sensor data collected by the sensor, and the data confidence of the sensor data.

[0073] In one possible approach, different sensors can collect data at different acquisition frequencies. The bandwidth required for a sensor to send point data to a distributed computing node can be calculated by multiplying the sensor's acquisition frequency by the size of a single data packet.

[0074] After collecting sensor data, sensors generate point data and send it to distributed computing nodes via wireless communication networks such as 5G (fifth-generation mobile communication technology), Wi-Fi 6, narrowband Internet of Things (NB-IoT), or long-range wide area networks (LoraWAN). The transmission latency of point data between sensors and distributed computing nodes can be calculated by dividing the amount of point data by the available network bandwidth.

[0075] Furthermore, considering that the network bandwidth in wireless communication networks may have problems such as network fluctuations, data congestion, and packet loss, sensors can transmit point data to distributed computing nodes via the optimal path. For example, the set of all available transmission paths between sensors and distributed computing nodes can be recorded as P = (P1, P2, ..., Pn). Among them, Pn is the nth available transmission path. The path transmission cost of the nth available transmission path is Cn = w1 × Tn + w2 × Ln + w3 × Bn. w1, w2, and w3 are weight coefficients. Tn is the transmission delay of the nth available transmission path. Ln is the packet loss rate of the nth available transmission path. Bn is the bandwidth required by the nth available transmission path. Based on this, the path transmission cost of each available transmission path can be determined, and the available transmission path with the smallest path transmission cost can be selected as the optimal path, so that the point data can be uplinked on the optimal path.

[0076] Furthermore, when network load is excessive, edge caching can be used to cache row point data, effectively improving the stability and reliability of data transmission and reducing computing task delays caused by insufficient bandwidth. In this case, cache capacity utilization can be calculated by dividing the amount of point data by the cache bandwidth, thereby optimizing storage.

[0077] S302: The distributed computing node obtains the task type and task priority of the computing task corresponding to the point data, as well as the computing capability of each distributed computing node among the multiple distributed computing nodes.

[0078] Considering that different computing tasks have different real-time requirements, computational complexity, and data dependencies, computing tasks can be divided into three types: real-time, periodic, and batch. For example, real-time computing tasks can correspond to accelerometers used to monitor seismic activity or visual sensors used to collect road traffic data. Periodic computing tasks can correspond to temperature and humidity sensors. Batch computing tasks can correspond to visual sensors used for quality inspection on industrial production lines. In this way, distributed computing nodes can determine the task type of computing tasks based on the sensor type.

[0079] The task priority of a computing task can be determined based on the manually pre-set priority for the sensor. Alternatively, the task priority of a computing task can be determined based on the time period in which the timestamp of the sensor data was collected. For example, during the time periods corresponding to the morning and evening rush hours, the computing task processing road traffic data has the highest task priority. Another example is that during the time periods corresponding to rainy and snowy weather, the computing task processing meteorological data has the highest task priority.

[0080] The computing power of a distributed computing node refers to the currently available computing power of the distributed computing node. In a smart city data processing system, a distributed computing node can send its currently available computing power to other distributed computing nodes in real time or periodically. Each distributed computing node can store the computing power of each of its multiple distributed computing nodes. The currently available computing power of a distributed computing node is R = αC + βM + γB. C is the currently available processor computing power of the distributed computing node. M is the currently available memory resources of the distributed computing node. B is the currently available network bandwidth of the distributed computing node. α, β, and γ are weight coefficients.

[0081] S303: The distributed computing node calls the smart contract to process the task type, task priority, and computing power of each distributed computing node, and determines the task allocation weight of each distributed computing node.

[0082] To optimize computing resource utilization, distributed computing nodes can call smart contracts to schedule computing tasks. For example, distributed computing nodes call smart contracts to process task types, task priorities, and the computing power of each distributed computing node, and determine the task allocation weights for each distributed computing node.

[0083] Specifically, when the task type of a computing task is a real-time computing task and the task priority is high, the distributed computing node can determine the task allocation weight of each distributed computing node based on the computing power of each distributed computing node. In this case, the greater the computing power of the distributed computing node, the greater the task allocation weight of the distributed computing node.

[0084] Alternatively, when the task type is a periodic or batch computing task and the task priority is low, the distributed computing node may determine the task allocation weight for each distributed computing node based on the computing power of each distributed computing node. In this case, the closer the computing power of a distributed computing node is to the median computing power, the greater the task allocation weight of the distributed computing node. The median computing power is the median of the computing power of multiple distributed computing nodes.

[0085] S304: The distributed computing node allocates the computing task to the target computing node based on the task allocation weight of each distributed computing node.

[0086] Among them, the probability of a distributed computing node being determined as a target computing node is positively correlated with the task allocation weight of the distributed computing node.

[0087] For example, the distributed computing node may determine a distributed computing node with the largest task allocation weight among all distributed computing nodes as a target computing node, and allocate the computing task to the target computing node.

[0088] In one embodiment, in the above S304, that is, when the distributed computing node allocates computing tasks to the target computing node based on the task allocation weights of each distributed computing node, the embodiment of the present application provides an optional implementation method, including: S3041-S3044.

[0089] S3041. The distributed computing node determines the computing resource consumption of the computing task.

[0090] The computing resource consumption is used to indicate the estimated consumption of computing resources by a computing task.

[0091] For example, the distributed computing node may determine the computing resource consumption of the computing task based on the amount of point data that needs to be processed in the computing task.

[0092] S3042. When the computing resource consumption is greater than a preset consumption threshold, the distributed computing node splits the computing task into multiple subtasks.

[0093] Optionally, the preset consumption threshold can be flexibly set based on experience without restriction.

[0094] If computing resource consumption exceeds a preset threshold, it indicates that the task is causing excessive load. The distributed computing node can then split the computing task into multiple subtasks, allowing them to be distributed to different distributed computing nodes for parallel processing, reducing time overhead.

[0095] S3043. The distributed computing node determines multiple target computing nodes based on the task allocation weights of each distributed computing node.

[0096] For example, the distributed computing node may sort the distributed computing nodes in descending order according to the task allocation weights, and determine the distributed computing nodes that come first in the order as the target computing nodes.

[0097] S3044. The distributed computing node distributes the multiple subtasks to different target computing nodes among the multiple target computing nodes.

[0098] Based on this, multiple target computing nodes can process multiple subtasks in parallel to improve task processing efficiency.

[0099] In one embodiment, in the above S3041, that is, when the distributed computing node determines the computing resource consumption of the computing task, the embodiment of the present application provides an optional implementation method, including: S30411-S30412.

[0100] S30411. The distributed computing node determines the computing resource requirements corresponding to the computing task.

[0101] Among them, computing resource requirements include floating-point operation requirements, storage resource requirements and bandwidth resource requirements.

[0102] S30412. The distributed computing node determines the computing resource consumption of the computing task based on the computing resource demand and multiple preset resource utilization rates.

[0103] The plurality of preset resource utilizations include processor resource utilization, storage resource utilization, and bandwidth resource utilization, and the plurality of preset resource utilizations can be pre-set in the distributed computing node.

[0104] For example, a distributed computing node can determine the computing resource consumption of a computing task as Lt = Wt × fcpu + Mt × fmen + Bt × fnet. Lt represents the computing resource consumption of the computing task. Wt represents the floating-point operation requirement of the computing task. Mt represents the storage resource requirement of the computing task. Bt represents the bandwidth resource requirement of the computing task. fcpu represents the processor resource utilization. fmen represents the storage resource utilization. fnet represents the bandwidth resource utilization.

[0105] In one embodiment, the smart city data processing method provided in the embodiment of the present application further includes: S401.

[0106] S401. The distributed computing node calls the smart contract to store the task allocation information corresponding to the computing task in the blockchain.

[0107] The task allocation information includes the target computing node, computing task, task start time, task deadline, task execution duration, and task execution result.

[0108] The execution time of a computing task is Tt = T1 + T2 + T3. T1 is the data transmission time, which is equal to the data volume corresponding to the computing task divided by the network bandwidth. T2 is the execution time, which is equal to the floating-point operation requirement of the computing task divided by the processor power of the distributed computing node. T3 is the synchronization time after task splitting, which is determined by the synchronization time between each subtask.

[0109] To ensure the traceability of computing tasks, distributed computing nodes can call smart contracts to store the task allocation information corresponding to the computing tasks on the blockchain after the computing tasks are completed, and support traceability queries on the task execution process through the blockchain.

[0110] The task allocation information of the computing tasks stored in the block may include various parts such as storage data, hash verification and storage location, and each part may be hashed separately and stored in the blockchain block.

[0111] In one embodiment, the smart city data processing method provided in the embodiment of the present application further includes: S501-S502.

[0112] S501. In response to a user device's access request for task allocation information, a distributed computing node calls a smart contract to perform permission verification on the user information in the access request.

[0113] S502: The distributed computing node calls the smart contract to verify the permissions based on the user information and sends authorization information to the user device.

[0114] Based on this, distributed computing nodes can manage data access permissions through smart contracts. For example, a smart contract can grant user data access rights through permission values. Each user ID can be associated with a permission value. The permission value is used to verify the user's access rights. If the smart contract determines that the user has access rights based on the user ID, it can send an access authorization key to the user's device, preventing unauthorized users from accessing the data.

[0115] In one embodiment, the smart city data processing method provided in the embodiment of the present application further includes: S601-S602.

[0116] S601. The distributed computing node calls a smart contract to obtain the resource load of the first computing node.

[0117] The first computing node is any one of the multiple distributed computing nodes.

[0118] S602. The distributed computing node calls the smart contract to migrate the computing task carried by the first computing node to the second computing node based on the resource load of the first computing node being greater than the first load threshold.

[0119] The second computing node is a distributed computing node whose resource load is less than a second load threshold among the multiple distributed computing nodes.

[0120] The first load threshold is greater than the second load threshold. The first load threshold and the second load threshold can be set according to needs. For example, the first load threshold can be the maximum value of the resource loads of each distributed computing node.

[0121] Based on this, the distributed computing node can call the smart contract in real time or periodically to perform computing resource detection on the first computing node and obtain the resource load of the first computing node. For example, the resource load of the first computing node is RL, and RL = Cu / Ca+Mu / Ma+Bu / Ba. Where Cu is the processor resource usage. Ca is the total processor resource. Mu is the storage resource usage. Ma is the total storage resource. Bu is the bandwidth resource usage. Ba is the total bandwidth resource.

[0122] The smart contract can trigger task migration when the resource load of a distributed computing node exceeds a first load threshold. Based on the resource load of other distributed computing nodes, the smart contract selects a second computing node to migrate the task to, and then migrates the computing task carried by the first computing node to the second computing node. Furthermore, the smart contract can synchronize the state after the migration is complete to ensure that the task can continue to execute.

[0123] In one embodiment, the smart city data processing method provided in the embodiment of the present application further includes: S701.

[0124] S701. The distributed computing node calls the smart contract to store the task migration information of the first computing node in the blockchain.

[0125] The task migration information includes the resource load of the first computing node, the migrated computing task, and the computing task migration time.

[0126] Based on this, distributed computing nodes can call smart contracts to store records of operations such as task migration and resource scheduling on the blockchain to achieve operation proof.

[0127] The above-mentioned embodiments of this application achieve efficient allocation, dynamic optimization, and task migration of computing tasks in a decentralized computing architecture through an intelligent scheduling process for blockchain computing tasks and edge computing resources. Compared to the problems of low computing resource utilization, slow computing task response speed, and high cost of dynamic migration of computing tasks that exist in a centralized cloud computing architecture, the combination of distributed computing and blockchain trusted storage adopted in the embodiments of this application can ensure low-latency execution of computing tasks, high availability, and efficient utilization of computing resources through real-time monitoring of computing resources, dynamic optimization of task scheduling, and elastic scaling management of computing nodes.

[0128] Furthermore, intelligent collaborative scheduling of computing tasks between blockchain computing nodes and edge computing networks ensures flexible allocation of computing tasks to optimal resources and dynamically adjusts resource utilization. Through blockchain smart contracts, resource status assessments are performed before task allocation, ensuring transparency and security in task scheduling. After task execution, the results are stored on the blockchain and distributed storage systems, ensuring data security, traceability, and immutability. This enables intelligent adaptation and efficient execution of computing tasks and edge computing resources, ensuring efficient operation and dynamic management of computing tasks in complex environments.

[0129] Furthermore, through real-time monitoring of computing tasks and smart contract scheduling, efficient task migration and automatic scaling of computing resources are achieved, ensuring stable task execution and efficient resource utilization. If a computing node experiences resource overload, uneven load balancing, or network fluctuations during task execution, the task will be migrated in real time to a less-loaded node, preventing failure due to resource bottlenecks. Computing resource matching analysis is used to select the optimal migration target from the candidate computing node pool, and blockchain-based evidence storage ensures traceability of the task's execution history.

[0130] In addition, the above-mentioned embodiments of the present application introduce a dynamic resource optimization mechanism during the execution of computing tasks, combined with automatic scheduling of smart contracts, computing resource load balancing, and adaptive computing resource expansion to ensure that computing tasks always maintain optimal resource utilization efficiency during execution. During the execution of computing tasks, the system will monitor the usage of computing resources in real time, and automatically trigger resource optimization strategies through smart contracts to adjust the allocation of computing resources. A computing task load balancing strategy is adopted to ensure that computing tasks are evenly distributed among multiple computing nodes to prevent overload of certain computing nodes. Therefore, the computing resource optimization mechanism in the embodiments of the present application can ensure that computing tasks are always in an efficient operating state during execution, reduce the execution delay of computing tasks, and improve the throughput of computing tasks.

[0131] After completing the prototype system's architectural design and modular decomposition, the solution in the aforementioned embodiments of this application has been demonstrated through functional verification and performance evaluation within a smart city integrated governance simulation platform, demonstrating its ability to improve task execution efficiency, resource utilization, system latency control, and security assurance capabilities. This is particularly evident in core scenarios such as concurrent multi-point task execution, high-frequency dynamic resource scheduling, and trusted recording and querying of task results.

[0132] Furthermore, the aforementioned embodiments of this application have good interface compatibility with current mainstream open-source edge computing platforms and blockchain platforms, enabling rapid integration into existing city-level information infrastructure. The relevant adapter interfaces facilitate technical packaging and cross-platform deployment, facilitating customized deployment and implementation for multiple smart city projects.

[0133] To sum up, the technical solutions provided in the above embodiments of this application have a clear engineering implementation path, specific product transformation objects and a basis for standardization work docking, providing a replicable and popularizable efficient solution for edge computing resource scheduling and collaborative scenarios in smart cities.

[0134] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of method. In order to realize the above functions, it includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily appreciate that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven hardware manner depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0135] The embodiment of the present application can divide the distributed computing nodes into functional modules according to the above method examples. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. Optionally, the division of modules in the embodiment of the present application is schematic and is only a logical functional division. In actual implementation, there may be other division methods.

[0136] like Figure 4 The figure is a schematic diagram of the structure of a smart city data processing device provided by an embodiment of the present application. The smart city data processing device can be used to perform the following Figure 3 The smart city data processing method shown in FIG. The smart city data processing device includes: an acquisition unit 801 , a calling unit 802 and an allocation unit 803 .

[0137] The acquisition unit 801 is configured to receive point data from sensors. The point data is data collected by sensors at locations deployed in the city.

[0138] The acquisition unit 801 is further configured to acquire the task type and task priority of the computing task corresponding to the point data, and the computing capability of each of the multiple distributed computing nodes.

[0139] The calling unit 802 is used to call the smart contract to process the task type, task priority and computing power of each distributed computing node, and determine the task allocation weight of each distributed computing node.

[0140] The allocating unit 803 is configured to allocate computing tasks to target computing nodes based on the task allocation weights of the distributed computing nodes. The probability of a distributed computing node being determined as a target computing node is positively correlated with the task allocation weights of the distributed computing nodes.

[0141] Optionally, allocation unit 803 is specifically configured to determine computing resource consumption of a computing task. The computing resource consumption represents an estimated consumption of computing resources by the computing task. If the computing resource consumption exceeds a preset consumption threshold, the computing task is split into multiple subtasks. Based on the task allocation weights of each distributed computing node, multiple target computing nodes are determined. The multiple subtasks are respectively allocated to different target computing nodes among the multiple target computing nodes.

[0142] Optionally, allocation unit 803 is specifically configured to determine computing resource requirements corresponding to the computing task. The computing resource requirements include floating-point operation requirements, storage resource requirements, and bandwidth resource requirements. Based on the computing resource requirements and multiple preset resource utilizations, the computing resource consumption of the computing task is determined. The multiple preset resource utilizations include processor resource utilization, storage resource utilization, and bandwidth resource utilization.

[0143] Optionally, the calling unit 802 is further configured to call the smart contract to store task assignment information corresponding to the computing task on the blockchain. The task assignment information includes the target computing node, computing task, task start time, task deadline, task execution duration, and task execution result.

[0144] Optionally, the calling unit 802 is further configured to, in response to a user device's request for access to task assignment information, call the smart contract to perform permission verification on the user information in the access request. The calling unit 802 is further configured to call the smart contract to pass the permission verification based on the user information and send authorization information to the user device.

[0145] Optionally, calling unit 802 is further configured to call a smart contract to obtain the resource load of a first computing node. The first computing node is any one of the multiple distributed computing nodes. Calling unit 802 is further configured to call a smart contract to migrate a computing task carried by the first computing node to a second computing node based on the resource load of the first computing node being greater than a first load threshold. The second computing node is a distributed computing node among the multiple distributed computing nodes whose resource load is less than a second load threshold. The first load threshold is greater than the second load threshold.

[0146] Optionally, the calling unit 802 is further configured to call a smart contract to store task migration information of the first computing node in the blockchain. The task migration information includes the resource load of the first computing node, the migrated computing task, and the computing task migration time.

[0147] Those skilled in the art will appreciate that, in one or more of the examples above, the functions described herein can be implemented using hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer-readable storage media and communication media, wherein communication media include any media that facilitates the transmission of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0148] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0149] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place, or they may be distributed in multiple different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0150] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A smart city data processing method, characterized in that: include: Receive point data from sensors; The point data is data collected by the sensor at the deployment location in the city; Obtaining a task type and a task priority of a computing task corresponding to the point data, and a computing capability of each of the plurality of distributed computing nodes; Invoke a smart contract to process the task type, the task priority, and the computing power of each distributed computing node, and determine the task allocation weight of each distributed computing node; Allocate the computing task to the target computing node based on the task allocation weight of each distributed computing node; The probability that the distributed computing node is determined as the target computing node is positively correlated with the task allocation weight of the distributed computing node.

2. The smart city data processing method according to claim 1, characterized in that: The allocating the computing task to the target computing node based on the task allocation weight of each distributed computing node includes: Determining the computing resource consumption of the computing task; the computing resource consumption is used to represent the estimated consumption of computing resources by the computing task; When the computing resource consumption exceeds a preset consumption threshold, splitting the computing task into multiple subtasks; Determine multiple target computing nodes based on the task allocation weights of each distributed computing node; The multiple subtasks are respectively allocated to different target computing nodes among the multiple target computing nodes.

3. The smart city data processing method according to claim 2, characterized in that: Determining the computing resource consumption of the computing task includes: Determining computing resource requirements corresponding to the computing task; the computing resource requirements include floating-point operation requirements, storage resource requirements, and bandwidth resource requirements; The computing resource consumption of the computing task is determined based on the computing resource demand and a plurality of preset resource utilizations; the plurality of preset resource utilizations include processor resource utilization, storage resource utilization, and bandwidth resource utilization.

4. The smart city data processing method according to claim 2, characterized in that: The method further comprises: The smart contract is called to store the task assignment information corresponding to the computing task in the blockchain; the task assignment information includes the target computing node, the computing task, the task start time, the task deadline, the task execution duration, and the task execution result.

5. The smart city data processing method according to claim 4, characterized in that: The method further comprises: In response to a request from a user device to access the task assignment information, calling a smart contract to perform an authority verification on the user information in the access request; The smart contract is called to pass the permission verification based on the user information and send authorization information to the user device.

6. The smart city data processing method according to claim 1, characterized in that: The method further comprises: Calling a smart contract to obtain a resource load of a first computing node; the first computing node is any one of the multiple distributed computing nodes; The smart contract is called to migrate the computing tasks carried by the first computing node to the second computing node based on the resource load of the first computing node being greater than a first load threshold; the second computing node is a distributed computing node among the multiple distributed computing nodes whose resource load is less than the second load threshold; the first load threshold is greater than the second load threshold.

7. The smart city data processing method according to claim 6, characterized in that: The method further comprises: Calling a smart contract to store task migration information of the first computing node in a blockchain; the task migration information includes the resource load of the first computing node, the migrated computing task, and the computing task migration time.

8. A smart city data processing device, characterized in that: include: Get unit, call unit and allocate unit; An acquisition unit, used for receiving point data from a sensor; The point data is data collected by the sensor at the deployment location in the city; The acquisition unit is further configured to acquire a task type and a task priority of a computing task corresponding to the point data, and a computing capability of each of the plurality of distributed computing nodes; A calling unit, configured to call a smart contract to process the task type, the task priority, and the computing power of each distributed computing node, and determine a task allocation weight for each distributed computing node; an allocating unit, configured to allocate the computing task to a target computing node based on the task allocation weights of the distributed computing nodes; The probability that the distributed computing node is determined as the target computing node is positively correlated with the task allocation weight of the distributed computing node.

9. An edge computing node, characterized in that: It includes a memory and a processor; the memory is used to store computer execution instructions, and the processor is connected to the memory through a bus; when the edge computing node is running, the processor executes the computer execution instructions stored in the memory, so that the edge computing node executes the smart city data processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes computer-executable instructions, and when the computer-executable instructions are executed on a computer, the computer executes the smart city data processing method according to any one of claims 1 to 7.

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