Distributed control method and system for urban street lamps

Abstract

The invention discloses a distributed street lamp control method and system for urban street lamps, and relates to the field of Internet of Things. The method is applied to a central server, and comprises the following steps: when a load value of a first agent node reaches a threshold value, obtaining an assistance request sent by the first agent node; based on the assistance request, evaluating the computing power resource demand quantity of the first agent node; querying load information of a plurality of idle agent nodes; according to the load information of the plurality of idle agent nodes and the computing power resource demand quantity of the first agent node, service information of the first agent node is sent to the plurality of idle agent nodes for processing, and a plurality of processing results are obtained; and returning the plurality of processing results to the first agent node, so that the first agent node controls the operation states of the plurality of city street lamps according to the plurality of processing results. By implementing the technical scheme provided by the invention, the overload problem caused by excessive data volume of the urban street lamp agent node is solved.

Description

Technical Field

[0001] The present application relates to the technical field of the Internet of Things, and in particular to a distributed control method and system for urban street lamps. Background Art

[0002] With the construction of urbanization, the popularity of urban street lights has also increased year by year; along with it comes the problem of managing a large number of street lights. The traditional management method is to pre-set the on or off time of street lights, but this method lacks flexibility and cannot meet the complex and changing lighting environment.

[0003] At present, in response to this problem, an intelligent street lamp has been developed. This intelligent street lamp is equipped with various types of sensors for real-time monitoring of the current lighting environment. In addition, a distributed management mode is adopted to realize flexible management of a large number of street lamps. The distributed management mode is specifically to divide the area where urban street lamps are installed into multiple management areas, and set an agent node in each management area. The agent node dynamically adjusts the brightness and switching time of the street lamp according to the monitoring data uploaded by the street lamp, so as to meet 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 problem of untimely response of street lamps. Summary of the invention

[0005] In order to solve the problem that the proxy node cannot control the street lamps in a timely manner due to data overload, the present application provides a method and system for distributed control of urban street lamps.

[0006] In a first aspect, the present application provides a method for distributed control of urban street lamps, which is applied to a central server, and the method comprises: When the load value of the first proxy node reaches a threshold, obtaining an assistance request sent by the first proxy node, where the assistance request includes service information and a service scenario; Based on the assistance request, evaluating the computing resource demand of the first proxy node, the computing resource demand including computing resource demands corresponding to multiple business types; Querying load information of multiple idle proxy nodes, the load information including a load service type and a load amount corresponding to the load service type; According to the load information of the plurality of idle proxy nodes and the computing resource demand of the first proxy node, the business information of the first proxy node is sent to the plurality of idle proxy nodes for processing, to obtain a plurality of processing results; The plurality of processing results are returned to the first proxy node, so that the first proxy node controls the operating status of the plurality of city street lamps according to the plurality of processing results.

[0007] Optionally, based on a business scenario of the first proxy node, an initial data collection frequency of the first proxy node is matched from a preset data collection frequency library, where the business scenario is any one of a daily business scenario, a special weather scenario, a large-scale event scenario, and an emergency failure scenario; Obtaining performance counter data of the first proxy node, the performance counter data including the number of CPU execution cycles, the number of memory reads and writes, and the number of disk I / O operations; According to the performance counter data, the initial data collection frequency is adjusted to obtain a target data collection frequency; The business information of the first proxy node is collected at the target data collection frequency.

[0008] Optionally, calculating the comprehensive deviation degree of the performance counter data according to the performance threshold of the first proxy node; Based on a preset deviation threshold, determining a data collection frequency adjustment multiple corresponding to the comprehensive deviation degree; The initial data collection frequency is adjusted according to the data collection frequency adjustment multiple to obtain the target data collection frequency.

[0009] Optionally, parsing the service information of the first proxy node to obtain multiple service types of the first proxy node, the multiple service types including a brightness adjustment service, a fault detection service, an environmental information analysis service, and an energy consumption statistics service; Analyze the data volume of multiple business types to obtain initial computing resource requirements of multiple business types; Determining weight values ​​corresponding to the plurality of business types according to the business scenario of the first proxy node; Based on the weight values ​​corresponding to the multiple business types and the initial computing power resource requirements, the computing power resource requirements of the multiple business types are calculated.

[0010] Optionally, based on the weight values ​​corresponding to the multiple business types, a cache ratio of the multiple processing results is obtained; According to the cache ratio, generating a cache sequence from the plurality of processing results; The cache sequence is sent to the first proxy node.

[0011] Optionally, according to the load information of the plurality of idle proxy nodes, determining the high-efficiency computing power resources of the plurality of idle proxy nodes; Based on the plurality of high-efficiency computing resources, a service type processing chain is generated, wherein the service type processing chain is composed of a plurality of idle proxy nodes; According to the service type processing chain and the computing resource demand of the first proxy node, multiple service types of the first proxy node are segmented and processed to obtain multiple service sub-information corresponding to each of the multiple service types; 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 is sent to multiple idle proxy nodes in the business information processing chain for processing to obtain a processing result of the business type to be processed, and the business type to be processed is any one of the multiple business types of the first proxy node.

[0012] Optionally, generating a business information processing chain based on the plurality of high-efficiency computing resources specifically further includes: Based on the high-efficiency computing power 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 the business information that can be processed by the high-efficiency computing power resources. The second proxy node is any node in the business information processing chain.

[0013] In a second aspect, the present application provides a distributed control system for urban street lamps, wherein the system is a central server, and the central server includes an acquisition module, a processing module, and a sending module, wherein: The acquisition module is configured to acquire, when the load value of the first proxy node reaches a threshold, an assistance request sent by the first proxy node, wherein the assistance request includes service information and a service scenario; The processing module is used to evaluate the computing resource demand of the first proxy node based on the assistance request, where the computing resource demand includes computing resource demands corresponding to multiple business types; Querying load information of multiple idle proxy nodes, the load information including a load service type and a load amount corresponding to the load service type; According to the load information of the plurality of idle proxy nodes and the computing resource demand of the first proxy node, the business information of the first proxy node is sent to the plurality of idle proxy nodes for processing, to obtain a plurality of processing results; The sending module is used to return the multiple processing results to the first agent node, so that the first agent node controls the operating status of multiple city street lamps according to the multiple processing results.

[0014] In a third aspect, the present application provides an electronic device comprising a processor, a memory, a user interface and a network interface, wherein 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 so that the electronic device executes a method as described in any one of the first aspects.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, the method as described in any one of the first aspects is executed.

[0016] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. In response to 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 available computing resources from multiple idle proxy nodes, and then distributes the overloaded data volume to multiple idle proxy nodes based on the resource demand of the current overloaded proxy nodes. During the distribution process, a processing chain of different business information is generated based on the efficient hardware resources of each idle proxy node, and then the business information is cut into multiple business sub-information. Finally, the business sub-information is sent to multiple idle proxy nodes in the processing chain, which not only solves the overload problem of proxy nodes, but also improves the resource utilization of multiple proxy nodes under the central server.

[0017] 2. Each idle proxy node generally uses a data model to process business sub-information, and the data model it uses is established when the data volume is large and complete, but the business sub-information is incomplete data, which causes the idle proxy node to spend more time parsing and processing data when processing business sub-information. Based on this, the present application performs low-rank decomposition compression processing on the data model of the idle proxy node according to the business sub-information assigned to the idle proxy node, removes model parameters that have little impact on the results, thereby reducing the amount of memory access and the number of computing operations required for calculation, thereby reducing the data processing time of the idle proxy node. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of a distributed control method for urban street lamps provided in an embodiment of the present application.

[0019] Figure 2 It is a structural schematic diagram of a distributed control system for urban street lamps provided in an embodiment of the present application.

[0020] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0021] Explanation of the reference numerals: 1. Acquisition module; 2. Processing module; 3. Sending module; 300. Electronic device; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 305. Memory. DETAILED DESCRIPTION

[0022] In order 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 in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0023] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0024] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0025] Driven by the current advanced network communication and artificial intelligence technologies, urban street lights, as the core of urban infrastructure, are showing a trend of digitalization, networking and intelligence. In particular, as network edge devices, smart street lights have their own data computing and processing capabilities, which are the key guarantee for urban security and smart street light management and control, and have important application value for urban intelligence.

[0026] At present, smart street lights are equipped with various types of sensors for real-time detection of environmental information around the street lights and monitoring of their own status information. In addition, a control system is installed to send the detected information to the proxy node and receive control instructions sent by the proxy node. The proxy node can be understood as an edge computing node that manages all street lights in a city area. It intelligently controls the brightness and switch status of the street lights by real-time processing of the monitoring information uploaded by the street lights, thereby meeting the real-time lighting needs near the street lights.

[0027] However, with the increase in business scenarios, a large amount of business data has caused the proxy nodes to be overloaded, resulting in untimely response of street lamps; for example, business scenarios may include lighting needs during large-scale events and lighting needs during bad weather, etc. These business scenarios have large amounts of data and are complex and changeable, which makes it difficult for proxy nodes to handle a large amount of business data, leading to overload.

[0028] In order to solve the above problems, the present application provides a distributed control method for urban street lamps, which is applied to a central server, such as Figure 1 As shown, the method includes steps S101 to S105, which are specifically as follows: S101. When a load value of a first proxy node reaches a threshold, an assistance request sent by the first proxy node is obtained, where the assistance request includes service information and a service scenario.

[0029] In the above steps, the central server can be understood as the central control node of all agent nodes in the city. It is used to monitor the status information of agent nodes and record the work logs of agent nodes on a daily basis. In addition, it can coordinate the resources of multiple agent nodes to ensure the normal operation of the street light system in the entire city. When there is a sharp increase in business data in the area managed by a certain agent node in the city, the load of the agent node will also increase sharply in the process of processing these business data, until the load value reaches the threshold, indicating that the agent node is about to enter an overloaded state. The threshold here refers to the load limit of the agent node when it is working normally. When the agent node is in an overloaded state for a long time, it is easy to cause a more serious paralysis state. Therefore, after the agent node reaches the threshold, it will send an assistance request to the central server. The assistance request contains the business information that needs to be processed urgently and the business scenario of the street light that causes the agent node to be overloaded. Among them, the business information includes environmental information analysis business, street light brightness adjustment business, street light fault detection business and street light energy consumption statistics business; in addition, the business scenario includes daily business Scenarios, special weather scenarios, large-scale event scenarios, and emergency fault business scenarios. Daily business scenarios include normal street lighting needs at different time periods during the day and night. For example, during the day, it may be necessary to maintain a lower brightness for road marking, while at night, a higher brightness is required to ensure safe lighting; special weather scenarios cover weather conditions such as heavy rain, heavy snow, and heavy fog. In these scenarios, street lights may need to adjust their brightness to enhance road visibility, and special warning light modes may need to be turned on; large-scale event scenarios refer to large-scale events such as concerts and sports events in a specific area, and the surrounding street lights need to adjust their brightness and lighting range according to changes in pedestrian and vehicle flow; emergency fault scenarios include situations such as street light damage and power failure, which require rapid fault location and corresponding measures, such as switching to backup power supplies or isolating faulty street lights.

[0030] Under normal circumstances, the frequency at which the central server obtains data from the proxy node is fixed, but as the business scenario changes, the amount of business information data will change. If a fixed information collection frequency is still used at this time, it may cause unnecessary data collection and waste of system resources when the business load is light, and fail to capture key data in time when the business is busy. Based on this, this application dynamically adjusts the information collection frequency of the central server according to the business scenario and the current load of the proxy node, reduces the collection frequency when the business load is light, and reduces the occupation of system resources; when the business load increases, the collection frequency is automatically increased. In this way, sufficiently detailed data can be obtained at critical moments while ensuring the normal operation of the system, providing a guarantee for accurately assessing computing power resource requirements.

[0031] Specifically: according to the business scenario of the proxy node, the initial data collection frequency of the proxy node is matched from the preset data collection frequency library. The preset data collection frequency library stores the data collection frequencies under different business scenarios. It should be noted that the data in the preset data collection frequency library is obtained from historical experience and a large amount of practical analysis, and the setting of the data collection frequency is applicable to specific business scenarios. However, in real life, business scenarios will appear in a superimposed state. For example, when holding large-scale events, the weather conditions may be foggy. At this time, the initial data collection frequency matched from the preset data collection frequency library is not applicable to the current business scenario. In response to this situation, the present application first obtains the amount of characteristic data generated by multiple business scenarios in unit time. The amount of characteristic data can be understood as the basis for adjusting street lights when adjusting them in specific business scenarios. For example, for large-scale event scenes, the brightness of street lights is mainly adjusted based on vehicle and pedestrian flow; for special weather scenes, the brightness of street lights 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 the various business scenarios, and finally the data collection frequencies of various business scenarios are matched from the preset data collection frequency library, and the weighted sum is calculated according to the weight ratio between the 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.

[0032] Although there is a more suitable initial data collection frequency in a specific business scenario, there will be multiple sub-scenarios in a specific business scenario, and the uncertainty of these sub-scenarios is too great, and it is difficult to find a suitable data collection frequency through experiments or historical experience. In addition, different business sub-scenarios have large differences in resource requirements. Taking special weather scenarios as an example, in foggy weather, more emphasis is placed on the adjustment of the penetration of lights, and higher requirements are placed on the brightness and color adjustment algorithms, which requires the CPU to have more memory and computing power; in rainy weather, more emphasis is placed on the waterproofing and circuit safety of street lights, and frequent disk I / O operations are required to record and read relevant data. Based on this, the present application further adjusts the initial data collection frequency according to the performance counter data, and reasonably adjusts the initial data collection frequency of the central server according to the actual conditions of different sub-scenarios in the same business scenario, so as to more accurately monitor the operating status of street lights in different sub-scenarios. Specifically: first, obtain the performance counter data of the proxy node. The performance counter records the operating status of each information processing device in the proxy node, including the number of CPU execution cycles, the number of memory reads and writes, and the number of disk I / O operations. The operating status of the information processing device can accurately reflect the specific situation of the current business scenario. Then, according to the performance threshold of the proxy node, calculate the comprehensive deviation degree of the performance counter data. The comprehensive deviation degree can be understood as the impact of the business sub-environment on the overall operating status of the proxy node. Among them, the performance threshold includes the relative lower limit deviation value and the relative upper limit deviation value of multiple operating states. For the calculation of the comprehensive deviation degree, this application gives an example. For the number of CPU execution cycles C, its relative lower limit deviation value is C1, and the relative lower limit deviation value is C2. If C is within the deviation value range, that is, C is in [C1, C2], then the deviation degree is 0. ; If C is greater than C2, it indicates that the CPU is in an overloaded or busy state. At this time, the deviation degree is calculated by subtracting the relative upper limit deviation value from the current value and then dividing it by the current value, that is, the deviation degree α=|(C-C2) / C|, so as to know the ratio of the current overload degree 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 relative lower limit deviation value is subtracted from the current value and then divided by the relative lower limit deviation value, that is, the deviation degree α=|(C1-C) / C1|, so as to obtain the deviation degree of the current idle degree of the CPU relative to the normal idle lower limit, and then according to the above-mentioned comprehensive deviation degree calculation method, the deviation degree of various operating states is calculated, and a characteristic vector representing the deviation degree of various operating states is generated. Finally, the Euclidean distance calculation formula is used to calculate the distance between the characteristic vector and the coordinate origin to obtain the comprehensive deviation degree.At this time, the data collection frequency adjustment multiple corresponding to the comprehensive degree is determined according to the preset deviation threshold. It should be noted that the performance counter data of the proxy node under normal operating conditions often presents the characteristics of an approximate normal distribution. Therefore, based on the standard deviation interval around the mean of the normal distribution, three preset deviation thresholds are set, corresponding to 1 standard deviation interval around the mean, 2 standard deviation intervals around the mean, and 3 standard intervals around the mean. The closer the comprehensive deviation degree is to the mean, the more stable the operation state of the proxy node is. The farther it is from the mean, the closer the operation state of the proxy node is to the overload state. The data collection frequency adjustment multiple is used to adjust the initial data collection frequency of the central server of 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, so as to respond to the resource allocation between subsequent proxy nodes in a timely manner. The data collection frequency adjustment multiple is determined according to the distance between the comprehensive deviation degree and the mean. The specific setting rules are affected by the performance attributes of the proxy node and the performance attributes of the central server, and can be set according to actual conditions. Finally, the initial data collection frequency is multiplied by the data collection frequency adjustment multiple to obtain the target data collection frequency that fits the current business scenario. At this time, the central server collects business information of the agent node at the target data collection frequency.

[0033] S102. Based on the assistance request, evaluate the computing resource demand of the first proxy node, where the computing resource demand includes computing resource demands corresponding to multiple business types.

[0034] In the above steps, the business information in the assistance request is first parsed to obtain multiple business types of the proxy node. The multiple business types contain business data to be processed, including brightness adjustment business, fault detection business, environmental information analysis business, and energy consumption statistics business. It can be understood that no matter what business scenario the street lamp is in, the data of the above multiple business types must be processed to ensure that the street lamp is in normal operation. In addition, the hardware resources consumed by each business type are also consistent, and the consumed hardware resources include but are not limited to the number of CPU execution cycles, the number of disk I / O operations, and the number of memory reads and writes. The business scenario will affect the hardware resources consumed by multiple business types. For example, if the business scenario is a large-scale event scenario, the brightness of the street lamp needs to be adjusted frequently, which leads to a significant increase in the number of CPU execution cycles. Finally, the data volume of multiple business types is analyzed to obtain the initial computing power resource requirements of multiple business types. The initial computing power resource requirements are a quantified resource requirement, which includes the number of CPU execution cycles, the number of disk I / O operations, and the number of memory reads and writes required for each business type.

[0035] In one possible implementation, when the proxy node is in an overloaded state, when the data volume of one type of business increases, the data volume of other types of business will also increase. For example, when the business scenario is a large-scale event scenario, the data volume of the brightness adjustment business will increase, causing the energy consumption of the street lamp to be in a state of frequent changes, thereby causing the data volume of the energy consumption statistics business to increase at the same time; however, in large-scale event scenarios, the energy consumption statistics business is a non-essential business, but it still occupies a large amount of computing power resources, thereby increasing the burden on the proxy node. Based on this, the present application determines the weight values ​​corresponding to multiple business types according to the business scenario of the proxy node, thereby reducing the computing power resources of non-essential businesses and increasing the computing power resources of necessary businesses. Among them, the hierarchical analysis method or historical experience can be used to determine the importance of multiple business types, and the weight values ​​of multiple business types are generated according to the importance. Finally, according to the weight values ​​corresponding to the multiple business types, the initial computing power requirements of the multiple business types are weighted and calculated to obtain the computing power resource requirements of the multiple business types.

[0036] S103: Query load information of multiple idle proxy nodes, where the load information includes load service types and load amounts corresponding to the load service types.

[0037] S104. According to the load information of the plurality of 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 plurality of idle proxy nodes for processing to obtain a plurality of processing results.

[0038] In the above steps, after the central server analyzes the required computing power resources of the first proxy node, it queries the load information of the idle proxy node according to the status information and work logs uploaded by the idle proxy node at regular intervals, wherein the load information includes the multiple business types currently being processed by the idle proxy node and the computing power resources occupied by each of the multiple business types; it can be understood that the load information of the idle proxy node also reflects its available resources. Since the business scenarios in the management areas of the multiple idle proxy nodes are different, the available resources of the multiple idle proxy nodes are different in type and quantity. At this time, if the available resources of a certain type of the idle proxy node are more, it can be defined as a high-efficiency computing power resource. Finally, according to the high-efficiency computing power resources corresponding to each of the multiple idle proxy nodes, the business information of the multiple business types of the first proxy node is allocated to the multiple idle proxy nodes. For example, if the idle proxy node has high-efficiency computing power resources in the brightness adjustment business, the business information of the brightness adjustment business of the first proxy node is allocated to the idle proxy node for processing, thereby improving the processing efficiency of the brightness adjustment business.

[0039] In a possible implementation, there is a processing logic when processing the business information of any business type. For example, for the brightness adjustment business, it is necessary to pre-process the collected environmental information first, then formulate a brightness adjustment strategy based on the environmental information, and finally generate a brightness adjustment instruction. Although multiple idle proxy nodes with the same high-efficiency computing power resources can handle the same business type, the processing efficiency is limited by hardware resources, resulting in differences in processing efficiency. For example, if the first idle proxy node has efficient CPU resources, the first idle proxy node has higher efficiency when formulating a brightness adjustment strategy based on environmental information. If the second idle proxy node has a large capacity and fast read and write memory resource, the second idle proxy node has higher efficiency when pre-processing the collected environmental information. In the above two situations, although from the overall point of view, the first idle proxy node and the second idle proxy node both have the ability to process brightness adjustment business, when there are too many brightness adjustment businesses, it will cause the two idle proxy nodes to have a load imbalance problem, that is, some hardware resources are overloaded, while some hardware resources are in an idle or low-load state, resulting in the overall performance of the idle proxy node.

[0040] Based on this, the present application proposes to perform hardware resource analysis on the high-efficiency computing power resources of multiple idle proxy nodes, and clarify the high-efficiency hardware resources of multiple idle proxy nodes. Specifically, the performance counter data of multiple idle proxy nodes can be analyzed to understand the processing efficiency of multiple hardware resources of idle proxy nodes when processing business types corresponding to high-efficiency computing power resources; then a processing chain of business types is generated for the high-efficiency hardware resources of multiple idle proxy nodes, and each node in the processing chain represents the high-efficiency hardware resources of an idle proxy node. It should be explained that a logical processing step of a business type includes at least one high-efficiency hardware resource of an idle proxy node. Finally, according to the processing chain of the business type or the processing logic of the business type, the business information of the business type is cut to obtain multiple business sub-information, wherein one business sub-information corresponds to a processing logic of the business type, and multiple business sub-information is distributed to multiple nodes in the processing chain for processing, so as to improve the load balancing of each idle proxy node, thereby improving the efficiency and quality of business processing.

[0041] In one possible implementation, when generating a processing chain for a business type, there is a difference in the geographical distance between each idle proxy node and the central server, resulting in different data transmission times for multiple idle proxy nodes, which in turn increases the time the central server spends waiting for data and reduces the overall business processing efficiency. Based on this, according to the current demand for computing resources, a data transmission distance threshold for multiple idle proxy nodes is defined, and then multiple available idle proxy nodes are queried based on the data transmission distance threshold, and finally multiple available idle proxy nodes are used as nodes in the business type processing chain, thereby reducing the data waiting time for the central server.

[0042] In a possible implementation, when generating a processing chain of a business type, multiple idle proxy nodes may all have efficient hardware resources for processing the same business sub-information. In order to further improve the load balancing of multiple idle proxy nodes, the present application obtains performance counter data of multiple idle proxy nodes of the same type, then reads the idle efficient hardware resource amounts of multiple idle proxy nodes of the same type from the performance counters, and calculates the processing ratio between the idle efficient hardware resource amounts of multiple idle proxy nodes of the same type. Finally, according to the processing ratio between multiple idle proxy nodes of the same type, the same business sub-information is further cut into multiple parts, and the cut multiple business sub-information is sent to the corresponding idle proxy nodes of the same type, so as to improve the load balancing of multiple idle proxy nodes.

[0043] In a possible implementation, the business information of the proxy node is complex and numerous, and when formulating the control strategy of the street lamp brightness, it is necessary to conduct a comprehensive analysis in combination with the business information. Therefore, the proxy node often uses a trained neural network model to process these business information in the processing process to achieve a better comprehensive analysis effect, so as to make the final street lamp brightness control more accurate. Based on this, when the idle proxy node processes the business information of the first proxy node, it will also use the business information processing model, but the business information processed by the idle proxy node is not complete business information, and the business information processing model will occupy certain computing resources when running. When the idle proxy node only processes part of the business information, most of the functions of the entire model are not fully utilized, resulting in a waste of computing resources; in addition, in order to be able to handle various complex business situations, the business information processing model is often more complex in structure. When processing incomplete business information, the model may need to spend more time to parse and process data. For example, the business information processing model contains a large number of neural network layers and network nodes. When processing only part of the business information related to the brightness adjustment strategy, the business information processing model needs to filter out the part related to the current task from many connections and parameters for calculation, which will increase the time complexity of the calculation, thereby slowing down the processing speed. Therefore, in order to solve the above problems, the present application compresses the business information processing model of the idle proxy node based on part of the business information allocated to the idle proxy node, and obtains a business information compression processing model, that is, the model function of processing this part of the business information in the business information processing model is retained, and other model functions are compressed; this process uses a low-rank decomposition method to remove model parameters that have little impact on the results, thereby reducing the amount of memory access and the number of computing operations required for calculation; in addition, during the compression process, some neural network layers in the business information compression processing model are merged or simplified, so that the model can converge to the result faster during calculation, reducing the time occupied by hardware resources during the calculation process, and thereby reducing the consumption of computing power resources.

[0044] S105: Return the multiple processing results to the first proxy node, so that the first proxy node controls the operating status of the multiple city street lamps according to the multiple processing results.

[0045] In the above steps, when the central server returns the processing results of each idle proxy node, due to the large number of processing results and the limited disk space of the first proxy node, it is necessary to give priority to meeting the urgent needs of the current business scenario. This application generates a cache ratio of multiple processing results by analyzing the weight values ​​corresponding to multiple business types to clarify the processing priority of multiple business types in the current business scenario, and then generates a cache sequence according to the cache ratio for the multiple processing results corresponding to each of the multiple business types. The cache sequence contains multiple sequence groups, and a sequence group contains the processing results of multiple business types corresponding to the current business scenario. For example, according to the cache ratio of multiple processing results, one sequence group may contain 4 environmental information analysis business processing results, 3 brightness adjustment business processing results, 2 energy consumption statistics business processing results, and 1 fault detection business processing result. Finally, the cache sequence generated by multiple processing results is sent to the first proxy node, so that the first proxy node executes them in sequence according to the cache order in the cache sequence to meet the urgent needs of the current business scenario.

[0046] Reference Figure 2 The present application also provides a distributed control system for urban street lamps, the system is a central server, the central server includes an acquisition module 1, a processing module 2 and a sending module 3, wherein: An acquisition module 1 is used to acquire an assistance request sent by the first proxy node when the load value of the first proxy node reaches a threshold, where the assistance request includes service information and a service scenario; Processing module 2, used to evaluate the computing resource demand of the first proxy node based on the assistance request, where the computing resource demand includes computing resource demand corresponding to each of multiple business types; Querying load information of multiple idle proxy nodes, the load information including load service type and load amount corresponding to the load service type; According to the load information of the plurality of idle proxy nodes and the computing resource demand of the first proxy node, the business information of the first proxy node is sent to the plurality of idle proxy nodes for processing, so as to obtain a plurality of processing results; The sending module 3 is used to return the multiple processing results to the first proxy node, so that the first proxy node controls the operating status of multiple city street lamps according to the multiple processing results.

[0047] Optionally, based on the business scenario of the first proxy node, the initial data collection frequency of the first proxy node is matched from a preset data collection frequency library, where the business scenario is any one of a daily business scenario, a special weather scenario, a large-scale event scenario, and an emergency failure scenario; Obtaining performance counter data of the first proxy node, the performance counter data including the number of CPU execution cycles, the number of memory reads and writes, and the number of disk I / O operations; According to the performance counter data, the initial data collection frequency is adjusted to obtain the target data collection frequency; The business information of the first agent node is collected at a target data collection frequency.

[0048] Optionally, calculating a comprehensive deviation degree of the performance counter data according to a performance threshold of the first proxy node; Based on the preset deviation threshold, determine the data collection frequency adjustment multiple corresponding to the comprehensive deviation degree; According to the data collection frequency adjustment multiple, the initial data collection frequency is adjusted to obtain the target data collection frequency.

[0049] Optionally, the service information of the first proxy node is parsed to obtain multiple service types of the first proxy node, where the multiple service types include a brightness adjustment service, a fault detection service, an environmental information analysis service, and an energy consumption statistics service; Analyze the data volume of multiple business types and obtain the initial computing resource requirements of multiple business types; Determine weight values ​​corresponding to multiple business types according to the business scenario of the first proxy node; Based on the weight values ​​corresponding to multiple business types and the initial computing power resource requirements, the computing power resource requirements of multiple business types are calculated.

[0050] Optionally, based on weight values ​​corresponding to multiple business types, cache ratios of multiple processing results are obtained; According to the cache ratio, multiple processing results are generated into a cache sequence; Send the cache sequence to the first proxy node.

[0051] Optionally, determining high-efficiency computing resources of the plurality of idle proxy nodes according to load information of the plurality of idle proxy nodes; Based on multiple high-efficiency computing resources, a business type processing chain is generated, which is composed of multiple idle proxy nodes; According to the business type processing chain and the computing resource demand of the first proxy node, multiple business types of the first proxy node are cut and processed to obtain multiple business sub-information corresponding to each of the multiple business types; 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 is sent to multiple idle proxy nodes in the business information processing chain for processing to obtain a processing result of the business type to be processed, and the business type to be processed is any one of the multiple business types of the first proxy node.

[0052] Optionally, a business information processing chain is generated based on multiple high-efficiency computing resources, including: Based on the high-efficiency computing power 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 the business information that can be processed by the high-efficiency computing power resources. The second proxy node is any node in the business information processing chain.

[0053] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0054] The present application also discloses an electronic device. Figure 3 , Figure 3 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 .

[0055] The communication bus 302 is used to realize the connection and communication between these components.

[0056] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0057] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0058] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0059] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, 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 a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a distributed control method for urban street lamps.

[0060] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program of a distributed control method of urban street lamps stored in the memory 305. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0061] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0062] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, 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 service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0063] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0064] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0065] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, 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, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0066] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0067] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A distributed control method for urban street lamps, characterized in that: Applied to a central server, the method comprises: When the load value of the first proxy node reaches a threshold, obtaining an assistance request sent by the first proxy node, where the assistance request includes service information and a service scenario; Based on the assistance request, evaluating the computing resource demand of the first proxy node, the computing resource demand including computing resource demands corresponding to multiple business types; Querying load information of multiple idle proxy nodes, the load information including a load service type and a load amount corresponding to the load service type; According to the load information of the plurality of idle proxy nodes and the computing resource demand of the first proxy node, the business information of the first proxy node is sent to the plurality of idle proxy nodes for processing, to obtain a plurality of processing results; The plurality of processing results are returned to the first proxy node, so that the first proxy node controls the operating status of the plurality of city street lamps according to the plurality of processing results.

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

3. The method according to claim 2, characterized in that The adjusting the initial data collection frequency according to the performance counter data to obtain a target data collection frequency specifically includes: Calculating the comprehensive deviation degree of the performance counter data according to the performance threshold of the first proxy node; Based on a preset deviation threshold, determining a data collection frequency adjustment multiple corresponding to the comprehensive deviation degree; The initial data collection frequency is adjusted according to the data collection frequency adjustment multiple to obtain the target data collection frequency.

4. The method according to claim 1, characterized in that The evaluating, based on the assistance request, the computing resource requirement of the first proxy node specifically includes: Parsing the service information of the first proxy node to obtain multiple service types of the first proxy node, wherein the multiple service types include a brightness adjustment service, a fault detection service, an environmental information analysis service, and an energy consumption statistics service; Analyze the data volume of multiple business types to obtain initial computing resource requirements of multiple business types; Determining weight values ​​corresponding to the plurality of business types according to the business scenario of the first proxy node; Based on the weight values ​​corresponding to the multiple business types and the initial computing power resource requirements, the computing power resource requirements of the multiple business types are calculated.

5. The method according to claim 4, characterized in that The returning the plurality of processing results to the first proxy node specifically includes: Based on the weight values ​​corresponding to the multiple business types, obtaining cache ratios of the multiple processing results; According to the cache ratio, generating a cache sequence from the plurality of processing results; The cache sequence is sent to the first proxy node.

6. The method according to claim 1, characterized in that The step of sending the service information of the first proxy node to the plurality of idle proxy nodes for processing according to the load information of the plurality of idle proxy nodes and the computing resource requirements of the first proxy node to obtain a plurality of processing results specifically includes: Determine the high-efficiency computing resources of the plurality of idle proxy nodes according to the load information of the plurality of idle proxy nodes; Based on the plurality of high-efficiency computing resources, a service type processing chain is generated, wherein the service type processing chain is composed of a plurality of idle proxy nodes; According to the service type processing chain and the computing resource demand of the first proxy node, multiple service types of the first proxy node are segmented and processed to obtain multiple service sub-information corresponding to each of the multiple service types; 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 is sent to multiple idle proxy nodes in the business information processing chain for processing to obtain a processing result of the business type to be processed, and the business type to be processed is any one of the multiple business types of the first proxy node.

7. The method according to claim 6, characterized in that The generating of a business information processing chain based on the plurality of high-efficiency computing resources specifically includes: Based on the high-efficiency computing power 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 the business information that can be processed by the high-efficiency computing power resources. The second proxy node is any node in the business information processing chain.

8. A distributed control system for urban street lamps, characterized in that: The system is a central server, and the central server comprises an acquisition module (1), a processing module (2) and a sending module (3), wherein: The acquisition module (1) is used to acquire an assistance request sent by the first proxy node when the load value of the first proxy node reaches a threshold, wherein the assistance request includes service information and a service scenario; The processing module (2) is used to evaluate the computing resource demand of the first proxy node based on the assistance request, wherein the computing resource demand includes computing resource demands corresponding to multiple business types; Querying load information of multiple idle proxy nodes, the load information including a load service type and a load amount corresponding to the load service type; According to the load information of the plurality of idle proxy nodes and the computing resource demand of the first proxy node, the business information of the first proxy node is sent to the plurality of idle proxy nodes for processing, to obtain a plurality of processing results; The sending module (3) is used to return the multiple processing results to the first proxy node, so that the first proxy node controls the operating status of multiple city street lamps according to the multiple processing results.

9. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (305), a user interface (303) and a network interface (304), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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