A distributed control method and system for urban street lamps

Abstract

The application discloses a kind of urban street lamp distribution street lamp control method and system, it is related to the field of Internet of Things.The method is applied to central server, the method includes: when the load value of first proxy node reaches threshold value, obtain the assistance request sent by first proxy node;Based on assistance request, the amount of demand of first proxy node is evaluated power resource;Query the load information of multiple idle proxy nodes;According to the load information of multiple idle proxy nodes and the amount of demand of first proxy node, the service information of first proxy node is sent to multiple idle proxy nodes processing, obtain multiple processing results;Multiple processing results are returned to first proxy node, so that first proxy node controls the operating state of multiple urban street lamps according to multiple processing results.The technical scheme provided in the application solves the overload problem caused by excessive data of urban street lamp proxy node.

Description

Technical Field

[0001] This application relates to the technical field of the Internet of Things (IoT), specifically to a distributed control method and system for urban streetlights. Background Technology

[0002] With urbanization, the prevalence of streetlights in cities has been increasing year by year; this has brought about the problem of managing a large number of streetlights. The traditional management method is to pre-set the on or off times of streetlights, but this method lacks flexibility and cannot meet the needs of complex and ever-changing lighting environments.

[0003] Currently, a smart street light has been developed to address this problem. This smart street light is equipped with various types of sensors to monitor the current lighting environment in real time. In addition, it adopts a distributed management mode to achieve flexible management of a large number of street lights. Specifically, the distributed management mode divides the area where urban street lights are installed into multiple management areas, and sets up an agent node in each management area. The agent node dynamically adjusts the brightness and on / off time of the street lights based on the monitoring data uploaded by the street lights, thereby meeting the current lighting needs.

[0004] However, with the increase in business scenarios, a large amount of business data has overloaded the proxy nodes, resulting in the streetlights not responding in a timely manner. Summary of the Invention

[0005] To address the problem of untimely street light control caused by data overload at proxy nodes, this application provides a distributed control method and system for urban street lights.

[0006] In a first aspect, this application provides a distributed control method for urban streetlights, applied to a central server, the method comprising:

[0007] When the load value of the first proxy node reaches the threshold, the assistance request sent by the first proxy node is obtained, and the assistance request includes business information and business scenario.

[0008] Based on the assistance request, the computing power resource requirements of the first agent node are assessed, and the computing power resource requirements include the computing power resource requirements corresponding to multiple business types.

[0009] Query the load information of multiple idle proxy nodes, whereby the load information includes the load service type and the load amount corresponding to the load service type;

[0010] Based on the load information of the multiple idle proxy nodes and the computing power resource requirements of the first proxy node, the business information of the first proxy node is sent to the multiple idle proxy nodes for processing to obtain multiple processing results.

[0011] The processing results are returned to the first agent node so that the first agent node can control the operating status of multiple city streetlights based on the processing results.

[0012] Optionally, based on the business scenario of the first agent node, the initial data collection frequency of the first agent node is matched from the preset data collection frequency library. The business scenario can be any one of daily business scenario, special weather scenario, large-scale event scenario, and emergency fault scenario.

[0013] Obtain the performance counter data of the first agent node, the performance counter data including the number of CPU execution cycles, the number of memory read and write operations, and the number of disk I / O operations;

[0014] Based on the performance counter data, the initial data acquisition frequency is adjusted to obtain the target data acquisition frequency;

[0015] The business information of the first agent node is collected at the target data collection frequency.

[0016] Optionally, the overall deviation of the performance counter data can be calculated based on the performance threshold of the first agent node;

[0017] Based on a preset deviation threshold, the data acquisition frequency adjustment factor corresponding to the overall deviation degree is determined;

[0018] The initial data acquisition frequency is adjusted according to the data acquisition frequency adjustment factor to obtain the target data acquisition frequency.

[0019] Optionally, the service information of the first agent node is parsed to obtain multiple service types of the first agent node, including brightness adjustment service, fault detection service, environmental information analysis service and energy consumption statistics service.

[0020] Analyze the data volume of multiple business types to obtain the initial computing power resource requirements of multiple business types;

[0021] Based on the business scenario of the first agent node, determine the weight values ​​corresponding to multiple business types;

[0022] Based on the weight values ​​corresponding to the various business types and the initial computing resource requirements, the computing resource requirements for the various business types are calculated.

[0023] Optionally, based on the weight values ​​corresponding to the multiple business types, the caching ratio of the multiple processing results is obtained;

[0024] Based on the stated caching ratio, a cache sequence is generated from multiple processing results;

[0025] The cached sequence is sent to the first proxy node.

[0026] Optionally, based on the load information of the multiple idle proxy nodes, the high-efficiency computing power resources of the multiple idle proxy nodes are determined;

[0027] Based on multiple high-efficiency computing resources, a business type processing chain is generated, which is composed of multiple idle agent nodes;

[0028] Based on the business type processing chain and the computing power resource requirements of the first agent node, the multiple business types of the first agent node are segmented to obtain multiple business sub-information corresponding to each of the multiple business types.

[0029] Based on the information type of multiple business sub-information of the business type to be processed, the multiple business sub-information of the business type to be processed are sent to multiple idle proxy nodes in the business information processing chain for processing to obtain the processing result of the business type to be processed. The business type to be processed is any one of the multiple business types of the first proxy node.

[0030] Optionally, the step of generating a business information processing chain based on multiple high-efficiency computing resources further includes:

[0031] Based on the high-efficiency computing resources of the second proxy node, the business information processing model of the second proxy node is compressed to generate a business information compression processing model, so as to process business information that can be processed by the high-efficiency computing resources. The second proxy node is any node in the business information processing chain.

[0032] Secondly, this application provides a distributed control system for urban streetlights, wherein the system is a central server, and the central server includes an acquisition module, a processing module, and a transmission module, wherein:

[0033] The acquisition module is used to acquire the assistance request sent by the first agent node when the load value of the first agent node reaches the threshold. The assistance request includes business information and business scenario.

[0034] The processing module is used to assess the computing power resource requirements of the first agent node based on the assistance request. The computing power resource requirements include the computing power resource requirements corresponding to multiple business types.

[0035] Query the load information of multiple idle proxy nodes, whereby the load information includes the load service type and the load amount corresponding to the load service type;

[0036] Based on the load information of the multiple idle proxy nodes and the computing power resource requirements of the first proxy node, the business information of the first proxy node is sent to the multiple idle proxy nodes for processing to obtain multiple processing results.

[0037] The sending module is used to return multiple processing results to the first agent node, so that the first agent node can control the operating status of multiple city streetlights according to the multiple processing results.

[0038] Thirdly, this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of the first aspects.

[0039] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in any one of the first aspects.

[0040] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0041] 1. To address the overload problem of proxy nodes caused by excessive data volume, this application analyzes multiple idle proxy nodes under the jurisdiction of the central server, reads the available computing resources from these idle proxy nodes, and then allocates the overloaded data volume to multiple idle proxy nodes based on the resource demand of the currently overloaded proxy nodes. During the allocation process, a processing chain for different business information is generated based on the efficient hardware resources of each idle proxy node. The business information is then divided into multiple business sub-information, and finally, the business sub-information is sent to multiple idle proxy nodes in the processing chain. This not only solves the overload problem of proxy nodes but also improves the resource utilization rate of multiple proxy nodes under the central server.

[0042] 2. Each idle agent node typically processes business sub-information using a data model. This data model is built for situations with large and complete data sets, but business sub-information is incomplete. This results in idle agent nodes spending more time parsing and processing the data. Therefore, this application performs low-rank decomposition compression on the data model of the idle agent nodes based on the business sub-information allocated to them. This removes model parameters that have little impact on the results, thereby reducing the amount of memory access and computational operations required, and ultimately reducing the data processing time of the idle agent nodes. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating a distributed control method for urban streetlights provided in an embodiment of this application.

[0044] Figure 2 This is a schematic diagram of the structure of a distributed control system for urban streetlights provided in an embodiment of this application.

[0045] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0046] Explanation of reference numerals in the attached drawings: 1. Acquisition module; 2. Processing module; 3. Transmission module; 300. Electronic device; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 305. Memory. Detailed Implementation

[0047] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0048] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0049] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0050] Driven by advanced network communication and artificial intelligence technologies, urban streetlights, as the core of urban infrastructure, are exhibiting a trend towards digitalization, networking, and intelligence. In particular, intelligent streetlights, as network edge devices, possess data computing and processing capabilities that are crucial for urban security and intelligent streetlight management, and have significant application value for urban smartification.

[0051] Current smart streetlights are equipped with various types of sensors to detect environmental information around the streetlights in real time and monitor their own status. They are also equipped with a control system to send the detected information to agent nodes and receive control commands from the agent nodes. Agent nodes can be understood as edge computing nodes that manage all streetlights in a city area. By processing the monitoring information uploaded by the streetlights in real time, they can intelligently control the brightness and on / off status of the streetlights, thereby meeting the real-time lighting needs of the area around the streetlights.

[0052] However, with the increase in business scenarios, the large amount of business data overloads the proxy nodes, resulting in untimely response of streetlights. For example, business scenarios may include lighting needs during large-scale events and lighting needs during severe weather. These business scenarios are characterized by large amounts of data and complex and variable characteristics, making it difficult for the proxy nodes to handle the large amount of business data, thus leading to overload.

[0053] To address the aforementioned problems, this application provides a distributed control method for urban streetlights, which is applied to a central server, such as... Figure 1 As shown, the method includes steps S101 to S105, as detailed below:

[0054] S101. When the load value of the first agent node reaches the threshold, obtain the assistance request sent by the first agent node. The assistance request includes business information and business scenario.

[0055] In the above steps, the central server can be understood as the central control node for all agent nodes within the city. It is used daily to monitor the status information of the agent nodes and record their work logs. It also coordinates the resources of multiple agent nodes to ensure the normal operation of the streetlight system throughout the city. When a sudden increase in business data occurs in the area managed by a particular agent node, the load on that agent node will also increase sharply while processing this data. When the load reaches a threshold, it indicates that the agent node is about to enter an overload state. This threshold refers to the load limit of the agent node under normal operation. If an agent node is in an overload state for an extended period, it can easily lead to a more serious paralysis. Therefore, after reaching the threshold, the agent node will send an assistance request to the central server. This request includes the urgently needed business information and the business scenario of the streetlights causing the agent node's overload. The business information includes environmental information analysis, streetlight brightness adjustment, streetlight fault detection, and streetlight energy consumption statistics. The business scenario includes daily business operations. The scenarios include routine business scenarios such as daytime and nighttime street lighting needs, such as maintaining low brightness during the day for road marking and higher brightness at night for safety. Situations involving extreme weather conditions such as heavy rain, blizzards, and fog require streetlights to adjust brightness to enhance road visibility and may also necessitate the activation of special warning light modes. Situations involving large events such as concerts or sporting events require streetlights to adjust brightness and illumination range based on pedestrian and vehicular traffic. Emergency failure scenarios include situations such as streetlight malfunctions and power outages, requiring rapid fault location and appropriate measures, such as switching to backup power or isolating the faulty streetlight.

[0056] Under normal circumstances, the central server acquires data from proxy nodes at a fixed frequency. However, as business scenarios change, the amount of business information data fluctuates. Maintaining a fixed data acquisition frequency during periods of low workload can lead to unnecessary data collection and wasted system resources, while during periods of high workload, it may fail to capture critical data in a timely manner. Therefore, this application dynamically adjusts the central server's data acquisition frequency based on the business scenario and the current load of the proxy nodes. The acquisition frequency is reduced during periods of low workload to minimize system resource consumption, while the acquisition frequency is automatically increased during periods of increased workload. This ensures that sufficiently detailed data is acquired at critical moments while maintaining normal system operation, providing a guarantee for accurately assessing computing resource requirements.

[0057] Specifically, based on the business scenario of the proxy node, the initial data collection frequency of the proxy node is matched from a preset data collection frequency library. This library stores data collection frequencies for different business scenarios. It should be noted that the data in the preset data collection frequency library is derived from historical experience and extensive practical analysis. The data collection frequency settings are applicable to specific business scenarios. However, in real life, business scenarios can overlap. For example, during a large event, the weather might be foggy, and the initial data collection frequency matched from the preset library might not be suitable for the current business scenario. To address this, this application first obtains the amount of characteristic data generated by various business scenarios per unit time. This characteristic data amount can be understood as the basis for adjusting streetlights in specific business scenarios. For example, for large-scale event scenarios, the brightness of streetlights is mainly adjusted based on traffic and pedestrian flow; while for special weather scenarios, the brightness of streetlights is mainly adjusted based on visibility and light intensity. Then, the data volume ratio between various business scenarios is calculated to determine the weight ratio between various business scenarios. Finally, the data collection frequency of various business scenarios is matched from the preset data collection frequency library, and the weighted sum is calculated according to the weight ratio between various business scenarios to generate the initial data collection frequency of the central server. It should be noted that the sum of the weights of various business scenarios is equal to 1.

[0058] While there are suitable initial data collection frequencies for specific business scenarios, these scenarios often contain multiple sub-scenarios with significant uncertainties, making it difficult to determine a suitable frequency through experimentation or historical experience. Furthermore, different business sub-scenarios have vastly different resource requirements. For example, in foggy weather, the focus is on adjusting the light's penetration, requiring sophisticated algorithms for brightness and color adjustment, thus necessitating substantial CPU memory and computing power. Conversely, in heavy rain, the focus shifts to the streetlights' waterproofing and electrical safety, necessitating frequent disk I / O operations to record and retrieve relevant data. Therefore, this application further adjusts the initial data collection frequency based on performance counter data. By appropriately adjusting the initial data collection frequency of the central server for different sub-scenarios within the same business scenario, the operational status of streetlights can be monitored more accurately under varying conditions. Specifically, the process involves: First, acquiring performance counter data from the proxy node. Performance counters record the operating status of each information processing device within the proxy node, including CPU execution cycles, memory read / write operations, and disk I / O operations. The operating status of these devices accurately reflects the specific circumstances of the current business scenario. Then, based on the proxy node's performance threshold, calculating the overall deviation of the performance counter data. This overall deviation can be understood as the impact of the business sub-environment on the overall operating status of the proxy node. The performance threshold includes relative lower and upper bound deviations for various operating states. For calculating the overall deviation, this application provides an example: for the CPU execution cycle count C, its relative lower bound deviation is C1, and its relative lower bound deviation is C2. If C is within the deviation range (C1, C2), then the deviation is 0. If C is greater than C2, it indicates that the CPU is in an overloaded or busy state. In this case, the deviation is calculated by subtracting the relative upper limit deviation from the current value and then dividing by the current value, i.e., deviation α = |(C-C2) / C|, thus knowing the ratio of the current overload to the current actual number of execution cycles. If C is less than C1, it indicates that the CPU is in a relatively idle state. In this state, the deviation is calculated by subtracting the current value from the relative lower limit deviation and then dividing by the relative lower limit deviation, i.e., deviation α = |(C1-C) / C1|, thus obtaining the deviation of the CPU's current idle state from the normal idle lower limit. Then, according to the above comprehensive deviation calculation method, the deviation of various operating states is calculated, and feature vectors representing the deviation of various operating states are generated. Finally, the Euclidean distance calculation formula is used to calculate the distance between the feature vector and the origin of the coordinate system, thus obtaining the comprehensive deviation.At this point, based on the preset deviation threshold, the data collection frequency adjustment factor corresponding to the overall degree is determined. It should be noted that the performance counter data of the proxy node under normal operating conditions often exhibits an approximately normal distribution. Therefore, based on the standard deviation interval around the mean of the normal distribution, three preset deviation thresholds are set, corresponding to intervals of 1 standard deviation, 2 standard deviations, and 3 standard deviations around the mean, respectively. The closer the overall deviation is to the mean, the more stable the operation of the proxy node; the farther it is from the mean, the closer the operation of the proxy node is to an overloaded state. The data collection frequency adjustment factor is used to adjust the initial data collection frequency of the central server for the proxy node under different operating conditions, so that the data collection frequency of the central server is closer to the current business sub-scenario, thereby responding promptly to the subsequent resource allocation between proxy nodes. The data collection frequency adjustment factor is determined based on the distance between the overall deviation and the mean. The specific setting rules are affected by the performance attributes of the proxy node and the central server, and can be set according to the actual situation. Finally, the initial data collection frequency is multiplied by the data collection frequency adjustment factor to obtain the target data collection frequency that fits the current business scenario. At this time, the central server collects the business information of the agent node at the target data collection frequency.

[0059] S102. Based on the assistance request, assess the computing resource requirements of the first agent node. The computing resource requirements include the computing resource requirements corresponding to multiple business types.

[0060] In the above steps, the business information in the assistance request is first parsed to obtain multiple business types of the proxy node. These business types contain business data that needs to be processed, including brightness adjustment, fault detection, environmental information analysis, and energy consumption statistics. It's understandable that regardless of the streetlight's business scenario, the data from these various business types must be processed to ensure the streetlight operates normally. Furthermore, the hardware resources consumed by each business type are consistent, including but not limited to CPU execution cycles, disk I / O operations, and memory read / write operations. However, the business scenario can affect the hardware resources consumed by various business types. For example, if the business scenario is a large-scale event, frequent adjustments to streetlight brightness are required, leading to a significant increase in CPU execution cycles. Finally, the data volume of multiple business types is analyzed to obtain the initial computing power resource requirements for each business type. The initial computing power resource requirements are a quantified resource requirement, including the CPU execution cycles, disk I / O operations, and memory read / write operations required for each business type.

[0061] In one possible implementation, when a proxy node is overloaded, an increase in the data volume of one business type will lead to an increase in the data volume of other business types. For example, when the business scenario is a large-scale event, the data volume of the brightness adjustment business will increase, resulting in frequent changes in the energy consumption of streetlights, which in turn causes a simultaneous increase in the data volume of the energy consumption statistics business. However, in large-scale event scenarios, the energy consumption statistics business is not essential, but it still consumes a significant amount of computing resources, thus increasing the burden on the proxy node. Based on this, this application determines the weight values ​​corresponding to multiple business types according to the business scenarios of the proxy node, thereby reducing the computing resources for non-essential businesses and increasing the computing resources for essential businesses. The importance of multiple business types can be determined using the analytic hierarchy process or historical experience, and weight values ​​for multiple business types can be generated based on their importance. Finally, the initial computing resource requirements of multiple business types are weighted and calculated based on the weight values ​​corresponding to the multiple business types to obtain the computing resource requirements for multiple business types.

[0062] S103. Query the load information of multiple idle proxy nodes. The load information includes the load service type and the load amount corresponding to the load service type.

[0063] S104. Based on the load information of multiple idle agent nodes and the computing power resource requirements of the first agent node, the business information of the first agent node is sent to multiple idle agent nodes for processing to obtain multiple processing results.

[0064] In the above steps, after the central server analyzes the required computing resources of the first agent node, it queries the load information of the idle agent nodes based on the status information and work logs uploaded periodically by the idle agent nodes. The load information includes the various business types currently being processed by the idle agent nodes and the computing resources occupied by each business type. It can be understood that the load information of the idle agent nodes also reflects their available resources. Since the business scenarios within the management areas of multiple idle agent nodes are different, the types and quantities of available resources of multiple idle agent nodes differ. Therefore, if an idle agent node has a large amount of available resources of a certain type, it can be defined as a high-efficiency computing resource. Finally, based on the high-efficiency computing resources corresponding to each of the multiple idle agent nodes, the business information of the first agent node's various business types is allocated to multiple idle agent nodes. For example, if an idle agent node has high-efficiency computing resources for the brightness adjustment business, then the business information of the first agent node's brightness adjustment business is allocated to that idle agent node for processing, thereby improving the processing efficiency of the brightness adjustment business.

[0065] In one possible implementation, the processing logic for any type of business information exists during processing. For example, for brightness adjustment business, it is necessary to first preprocess the collected environmental information, then formulate a brightness adjustment strategy based on the environmental information, and finally generate a brightness adjustment command. While multiple idle agent nodes with the same high-efficiency computing resources can all handle the same type of business, their processing efficiency is limited by hardware resources, resulting in differences in processing efficiency. For instance, if the first idle agent node has high-efficiency CPU resources, it will be more efficient in formulating a brightness adjustment strategy based on environmental information; if the second idle agent node has large-capacity and fast read / write memory resources, it will be more efficient in preprocessing the collected environmental information. In both scenarios, although both the first and second idle agent nodes are capable of handling brightness adjustment business overall, when there are too many brightness adjustment business requests, it will lead to an unbalanced load between the two idle agent nodes. That is, some hardware resources will be overloaded, while others will be idle or under low load, resulting in a decrease in the overall performance of the idle agent nodes.

[0066] Based on this, this application proposes a hardware resource analysis of the high-efficiency computing resources of multiple idle proxy nodes to identify their efficient hardware resources. Specifically, this can be achieved by analyzing the performance counter data of multiple idle proxy nodes to understand the processing efficiency of their hardware resources when handling the business types corresponding to the high-efficiency computing resources. Then, a processing chain for each business type is generated based on the efficient hardware resources of the multiple idle proxy nodes. Each node in the processing chain represents an efficient hardware resource of an idle proxy node. It should be noted that a logical processing step for a business type includes at least one efficient hardware resource of an idle proxy node. Finally, based on the processing chain or the processing logic of the business type, the business information of the business type is segmented into multiple business sub-information pieces. Each business sub-information piece corresponds to a processing logic piece for the business type, and these sub-information pieces are distributed to multiple nodes in the processing chain for processing. This improves the load balancing of the idle proxy nodes, thereby enhancing the efficiency and quality of business processing.

[0067] In one possible implementation, when generating a processing chain for a business type, the geographical distance between each idle proxy node and the central server varies, resulting in different data transmission times for multiple idle proxy nodes. This, in turn, increases the waiting time for the central server to receive data, reducing overall business processing efficiency. Therefore, based on the current computing resource requirements, a data transmission distance threshold is defined for multiple idle proxy nodes. Then, multiple usable idle proxy nodes are queried based on the data transmission distance threshold. Finally, these usable idle proxy nodes are used as nodes in the business type processing chain, thereby reducing the data waiting time for the central server.

[0068] In one possible implementation, when generating a processing chain for a business type, multiple idle proxy nodes may all possess efficient hardware resources for processing the same business sub-information. To further improve the load balancing of these multiple idle proxy nodes, this application obtains performance counter data from multiple idle proxy nodes of the same type, then reads the amount of idle efficient hardware resources of these nodes from the performance counters, calculates the processing ratio among the idle efficient hardware resources of these nodes, and finally, based on the processing ratio among these nodes, further divides the same business sub-information into multiple parts, and sends the divided business sub-information to their respective corresponding idle proxy nodes of the same type, thereby improving the load balancing of the multiple idle proxy nodes.

[0069] In one possible implementation, the business information of the proxy nodes is complex and numerous. When formulating streetlight brightness control strategies, a comprehensive analysis of this business information is necessary. Therefore, proxy nodes often employ trained neural network models to process this business information, achieving better comprehensive analysis results and thus more precise streetlight brightness control. Based on this, when an idle proxy node processes the business information of the first proxy node, it also uses a business information processing model. However, the business information processed by the idle proxy node is not complete, and the business information processing model consumes certain computing resources during runtime. When the idle proxy node only processes a portion of the business information, most of the model's functionality is not fully utilized, resulting in wasted computing resources. Furthermore, to handle various complex business situations, the business information processing model often has a complex structure. When processing incomplete business information, the model may need to spend more time parsing and processing the data. For example, the business information processing model contains a large number of neural network layers and nodes. When processing only the portion of business information related to the brightness adjustment strategy, the business information processing model needs to filter out the parts relevant to the current task from numerous connections and parameters for calculation, which increases computational time complexity and slows down processing speed. Therefore, to solve the above problems, this application compresses the business information processing model of the idle agent node based on the partial business information allocated to the idle agent node, resulting in a compressed business information processing model. This involves retaining the model functions that process this part of the business information while compressing other model functions. This process utilizes low-rank decomposition to remove model parameters that have a smaller impact on the results, thereby reducing the amount of memory access and computational operations required. Furthermore, during the compression process, some neural network layers in the compressed business information processing model are merged or simplified, enabling the model to converge to the results faster and reducing the time spent consuming hardware resources during computation, thus lowering the consumption of computing power.

[0070] S105. Return multiple processing results to the first agent node so that the first agent node can control the operation status of multiple city streetlights based on the multiple processing results.

[0071] In the above steps, when the central server returns the processing results from each idle agent node, due to the large number of processing results and the limited disk space of the first agent node, it is necessary to prioritize meeting the urgent needs of the current business scenario. This application analyzes the weight values ​​corresponding to multiple business types to generate multiple caching ratios for processing results, thereby clarifying the processing priority of multiple business types in the current business scenario. Then, the multiple processing results corresponding to each of the multiple business types are generated into a cache sequence according to the caching ratio. The cache sequence contains multiple sequence groups, and each sequence group contains the processing results of multiple business types corresponding to the current business scenario. For example, according to the caching ratio of multiple processing results, one sequence group may contain four environmental information analysis business processing results, three brightness adjustment business processing results, two energy consumption statistics business processing results, and one fault detection business processing result. Finally, the cache sequence generated from multiple processing results is sent to the first agent node, so that the first agent node executes them sequentially according to the cache order in the cache sequence to meet the urgent needs of the current business scenario.

[0072] Reference Figure 2 This application also provides a distributed control system for urban streetlights. The system is a central server, which includes an acquisition module 1, a processing module 2, and a transmission module 3, wherein:

[0073] The acquisition module 1 is used to acquire the assistance request sent by the first agent node when the load value of the first agent node reaches the threshold. The assistance request includes business information and business scenario.

[0074] Processing module 2 is used to assess the computing resource requirements of the first agent node based on the assistance request. The computing resource requirements include the computing resource requirements corresponding to multiple business types.

[0075] Query the load information of multiple idle proxy nodes. The load information includes the load service type and the load amount corresponding to the load service type.

[0076] Based on the load information of multiple idle agent nodes and the computing power resource requirements of the first agent node, the business information of the first agent node is sent to multiple idle agent nodes for processing, resulting in multiple processing results.

[0077] The sending module 3 is used to return multiple processing results to the first agent node, so that the first agent node can control the operating status of multiple city streetlights based on the multiple processing results.

[0078] Optionally, based on the business scenario of the first agent node, the initial data collection frequency of the first agent node is matched from the preset data collection frequency library. The business scenario can be any one of daily business scenario, special weather scenario, large-scale event scenario, and emergency fault scenario.

[0079] Obtain the performance counter data of the first agent node. The performance counter data includes the number of CPU execution cycles, memory read and write operations, and disk I / O operations.

[0080] Based on the performance counter data, the initial data acquisition frequency is adjusted to obtain the target data acquisition frequency;

[0081] Collect business information from the first agent node at the target data collection frequency.

[0082] Optionally, the overall deviation of the performance counter data can be calculated based on the performance threshold of the first agent node;

[0083] Based on a preset deviation threshold, determine the data acquisition frequency adjustment factor corresponding to the overall deviation degree;

[0084] The initial data acquisition frequency is adjusted by adjusting the multiple based on the data acquisition frequency to obtain the target data acquisition frequency.

[0085] Optionally, the business information of the first agent node is parsed to obtain multiple business types of the first agent node, including brightness adjustment business, fault detection business, environmental information analysis business and energy consumption statistics business.

[0086] Analyze the data volume of multiple business types to obtain the initial computing power resource requirements for each business type;

[0087] Based on the business scenario of the first agent node, determine the weight values ​​corresponding to multiple business types;

[0088] Based on the weight values ​​corresponding to multiple business types and the initial computing power resource requirements, the computing power resource requirements for multiple business types are calculated.

[0089] Optionally, based on the weight values ​​corresponding to multiple business types, the caching ratio of multiple processing results can be obtained;

[0090] Based on the caching ratio, generate a cache sequence from multiple processing results;

[0091] Send the cached sequence to the first agent node.

[0092] Optionally, based on the load information of multiple idle agent nodes, determine the high-efficiency computing resources of multiple idle agent nodes;

[0093] Based on multiple high-efficiency computing resources, a business type processing chain is generated, which consists of multiple idle agent nodes.

[0094] Based on the business type processing chain and the computing power resource requirements of the first agent node, the multiple business types of the first agent node are segmented to obtain multiple business sub-information corresponding to each business type.

[0095] Based on the information type of multiple business sub-information of the business type to be processed, the multiple business sub-information of the business type to be processed are sent to multiple idle agent nodes in the business information processing chain for processing, and the processing result of the business type to be processed is obtained. The business type to be processed is any one of the multiple business types of the first agent node.

[0096] Optionally, a business information processing chain can be generated based on multiple high-efficiency computing resources, specifically including:

[0097] Based on the high-efficiency computing resources of the second agent node, the business information processing model of the second agent node is compressed to generate a business information compression processing model, which can process business information that can be processed by the high-efficiency computing resources. The second agent node is any node in the business information processing chain.

[0098] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above 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. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0099] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0100] The communication bus 302 is used to enable communication between these components.

[0101] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0102] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0103] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0104] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a distributed control method of urban streetlights.

[0105] exist Figure 3In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and to acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for a distributed control method of urban streetlights. When executed by one or more processors 301, the electronic device 300 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0106] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0107] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0108] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0109] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0110] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0111] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0112] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A distributed control method for urban streetlights, characterized in that, Applied to a central server, the method includes: When the load value of the first proxy node reaches the threshold, the assistance request sent by the first proxy node is obtained, and the assistance request includes business information and business scenario. Based on the assistance request, the computing power resource requirements of the first agent node are assessed, and the computing power resource requirements include the computing power resource requirements corresponding to multiple business types. Query the load information of multiple idle proxy nodes, whereby the load information includes the load service type and the load amount corresponding to the load service type; Based on the load information of the multiple idle proxy nodes and the computing power resource requirements of the first proxy node, the business information of the first proxy node is sent to the multiple idle proxy nodes for processing to obtain multiple processing results. The processing results are returned to the first agent node, so that the first agent node controls the operating status of multiple city streetlights based on the processing results. The step involves sending the business information of the first proxy node to the multiple idle proxy nodes for processing based on the load information of the multiple idle proxy nodes and the computing resource requirements of the first proxy node, thereby obtaining multiple processing results, specifically including: Based on the load information of the multiple idle proxy nodes, determine the high-efficiency computing resources of the multiple idle proxy nodes; Based on multiple high-efficiency computing resources, a business type processing chain is generated. This business type processing chain consists of multiple idle proxy nodes, specifically including: Hardware resource analysis is performed on the high-efficiency computing resources of multiple idle agent nodes to identify the high-efficiency hardware resources of multiple idle agent nodes; The service type processing chain is generated for the efficient hardware resources of multiple idle proxy nodes. Each node in the service type processing chain represents the efficient hardware resources of an idle proxy node. A logical processing step of a service type includes the efficient hardware resources of at least one idle proxy node. Based on the business type processing chain and the computing power resource requirements of the first agent node, the multiple business types of the first agent node are segmented to obtain multiple business sub-information corresponding to each of the multiple business types, specifically including: Obtain performance counter data from multiple idle agent nodes of the same type; Read the amount of idle high-efficiency hardware resources of multiple idle agent nodes of the same type from the performance counter, and calculate the processing ratio among the amount of idle high-efficiency hardware resources of multiple idle agent nodes of the same type. Based on the processing ratio among multiple idle proxy nodes of the same type, the same business sub-information is further divided into multiple parts, and the multiple business sub-information after division is sent to their respective corresponding idle proxy nodes of the same type. Based on the information type of multiple business sub-information of the business type to be processed, the multiple business sub-information of the business type to be processed are sent to multiple idle proxy nodes in the business information processing chain for processing to obtain the processing result of the business type to be processed. The business type to be processed is any one of the multiple business types of the first proxy node. The generation of the business information processing chain based on multiple high-efficiency computing resources further includes: Based on the high-efficiency computing resources of the second proxy node, the business information processing model of the second proxy node is compressed to generate a business information compression processing model, so as to process business information that can be processed by the high-efficiency computing resources. The second proxy node is any node in the business information processing chain.

2. The method according to claim 1, characterized in that, The step of obtaining the assistance request sent by the first proxy node specifically includes: Based on the business scenario of the first agent node, the initial data collection frequency of the first agent node is matched from the preset data collection frequency library. The business scenario can be any one of daily business scenario, special weather scenario, large-scale event scenario, and emergency fault scenario. Obtain the performance counter data of the first agent node, the performance counter data including the number of CPU execution cycles, the number of memory read and write operations, and the number of disk I / O operations; Based on the performance counter data, the initial data acquisition frequency is adjusted to obtain the target data acquisition frequency; The business information of the first agent node is collected at the target data collection frequency.

3. The method according to claim 2, characterized in that, The step of adjusting the initial data acquisition frequency based on the performance counter data to obtain the target data acquisition frequency specifically includes: Calculate the overall deviation of the performance counter data based on the performance threshold of the first agent node; Based on a preset deviation threshold, the data acquisition frequency adjustment factor corresponding to the overall deviation degree is determined; The initial data acquisition frequency is adjusted according to the data acquisition frequency adjustment factor to obtain the target data acquisition frequency.

4. The method according to claim 1, characterized in that, The step of assessing the computing resource requirements of the first proxy node based on the assistance request specifically includes: The service information of the first agent node is parsed to obtain multiple service types of the first agent node, including brightness adjustment service, fault detection service, environmental information analysis service and energy consumption statistics service. Analyze the data volume of multiple business types to obtain the initial computing power resource requirements of multiple business types; Based on the business scenario of the first agent node, determine the weight values ​​corresponding to multiple business types; Based on the weight values ​​corresponding to the various business types and the initial computing resource requirements, the computing resource requirements for the various business types are calculated.

5. The method according to claim 4, characterized in that, The step of returning multiple processing results to the first proxy node specifically includes: Based on the weight values ​​corresponding to the multiple business types, the caching ratio of the multiple processing results is obtained; Based on the stated caching ratio, a cache sequence is generated from multiple processing results; The cached sequence is sent to the first proxy node.

6. A distributed control system for urban streetlights, characterized in that, The system is a central server, which includes an acquisition module (1), a processing module (2), and a sending module (3), wherein: The acquisition module (1) is used to acquire the assistance request sent by the first agent node when the load value of the first agent node reaches the threshold. The assistance request includes business information and business scenario. The processing module (2) is used to evaluate the computing power resource requirements of the first agent node based on the assistance request. The computing power resource requirements include the computing power resource requirements corresponding to multiple business types. Query the load information of multiple idle proxy nodes, whereby the load information includes the load service type and the load amount corresponding to the load service type; Based on the load information of the multiple idle proxy nodes and the computing resource requirements of the first proxy node, the business information of the first proxy node is sent to the multiple idle proxy nodes for processing, resulting in multiple processing results, which specifically include: Based on the load information of the multiple idle proxy nodes, determine the high-efficiency computing resources of the multiple idle proxy nodes; Based on multiple high-efficiency computing resources, a business type processing chain is generated. This business type processing chain consists of multiple idle proxy nodes, specifically including: Hardware resource analysis is performed on the high-efficiency computing resources of multiple idle agent nodes to identify the high-efficiency hardware resources of multiple idle agent nodes; The service type processing chain is generated for the efficient hardware resources of multiple idle proxy nodes. Each node in the service type processing chain represents the efficient hardware resources of an idle proxy node. A logical processing step of a service type includes the efficient hardware resources of at least one idle proxy node. Based on the business type processing chain and the computing power resource requirements of the first agent node, the multiple business types of the first agent node are segmented to obtain multiple business sub-information corresponding to each of the multiple business types, specifically including: Obtain performance counter data from multiple idle agent nodes of the same type; Read the amount of idle high-efficiency hardware resources of multiple idle agent nodes of the same type from the performance counter, and calculate the processing ratio among the amount of idle high-efficiency hardware resources of multiple idle agent nodes of the same type. Based on the processing ratio among multiple idle proxy nodes of the same type, the same business sub-information is further divided into multiple parts, and the multiple business sub-information after division is sent to their respective corresponding idle proxy nodes of the same type. Based on the information type of multiple business sub-information of the business type to be processed, the multiple business sub-information of the business type to be processed are sent to multiple idle proxy nodes in the business information processing chain for processing to obtain the processing result of the business type to be processed. The business type to be processed is any one of the multiple business types of the first proxy node. The generation of the business information processing chain based on multiple high-efficiency computing resources further includes: Based on the high-efficiency computing resources of the second proxy node, the business information processing model of the second proxy node is compressed to generate a business information compression processing model to process business information that can be processed by the high-efficiency computing resources. The second proxy node is any node in the business information processing chain. The sending module (3) is used to return multiple processing results to the first agent node so that the first agent node can control the operating status of multiple city streetlights according to the multiple processing results.

7. An electronic device, characterized in that, The device includes a processor (301), a memory (305), a user interface (303), and a network interface (304). The memory (305) is used to store instructions. The user interface (303) and the network interface (304) are used to communicate with other devices. The processor (301) is used to execute the instructions stored in the memory (305) to cause the electronic device (300) to perform the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 5.

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