CN112085076A - Decision-making method and device based on smart street lamp big data and terminal - Google Patents
Decision-making method and device based on smart street lamp big data and terminal Download PDFInfo
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Abstract
The application relates to intelligent illumination and provides a decision-making method, a decision-making device and a terminal based on intelligent street lamp big data. The method comprises the following steps: acquiring initial intelligent street lamp big data, wherein the big data comprises information data of a plurality of intelligent street lamps in a designated geographical area, which are related to the lighting control strategy of the intelligent street lamps, and the information data of each intelligent street lamp comprises identification information, position information, environment information, user access data in a wireless signal coverage area and video monitoring data in a video monitoring area; the method comprises the steps that initial intelligent street lamp big data are subjected to cluster analysis, so that a plurality of intelligent street lamps in a designated geographic area are divided into a preset number of categories; and obtaining the illumination control strategy corresponding to the intelligent street lamp of each category by carrying out decision-making on the intelligent street lamps of each category. According to the scheme, the flexibility and the energy conservation of street lamp control are considered on the basis of reducing the load of a control system and reducing the system overhead.
Description
Technical Field
The application belongs to the technical field of intelligent illumination, and particularly relates to a decision-making method, a decision-making device, a terminal and a computer readable storage medium based on intelligent street lamp big data.
Background
The urban area is provided with a large number of street lamps, which is the most dense urban infrastructure and is convenient for information acquisition and release. The urban smart street lamp is an important information acquisition source of the Internet of things, is an important component and an important entrance of a smart city, can promote the intelligent municipal administration and the smart city to fall to the ground in the aspect of urban lighting service, and realizes the improvement of city and municipal service capacity.
At present, a smart street lamp can be equipped with various sensors or functional devices to realize information acquisition, such as acquiring environmental information through a temperature and humidity sensor, acquiring traffic flow information and pedestrian information through an image sensor or a radar device, and the like; or, the 5G base station may be configured to implement coverage of the 5G signal, and the wireless device may be configured to implement coverage of the wireless local area network.
However, how to effectively utilize massive big data collected by numerous intelligent street lamps, extract effective information from the big data and realize specific decisions is an urgent problem to be solved.
In the prior art, an intelligent street lamp generally has two lighting control strategies, one is a centralized control strategy, that is, most street lamps are turned on or off at the same time, and the other is an independent control strategy, that is, each street lamp can be independently controlled to be turned on or off; in practical application, the applicant of the present application finds that the centralized control strategy is not flexible enough in control mode and is not beneficial to energy saving; although the independent control strategy is flexible and can adapt to various scene requirements, when the number of the urban street lamps is large, the information processing is increased, and the control processing system of the intelligent street lamp is overloaded, so that the normal operation of the intelligent street lamp is influenced.
Content of application
In view of the above, the present application provides a decision method, a decision device, a terminal and a computer readable storage medium based on smart street lamp big data, so as to provide a new lighting control strategy based on smart street lamp big data.
The first aspect of the embodiment of the application provides a decision method based on intelligent street lamp big data, and the method comprises the following steps:
acquiring initial intelligent street lamp big data, wherein the initial intelligent street lamp big data comprises information data of a plurality of intelligent street lamps in a specified geographic area, the information data is related to an illumination control strategy of the intelligent street lamps, and the information data of each intelligent street lamp comprises identification information of the intelligent street lamp, position information of the intelligent street lamp, environment information corresponding to the position information of the intelligent street lamp, user access data in a wireless signal coverage area of the intelligent street lamp and video monitoring data in a video monitoring area of the intelligent street lamp;
performing cluster analysis on the initial intelligent street lamp big data to divide a plurality of intelligent street lamps in the designated geographic area into a preset number of categories;
and carrying out decision-making on the intelligent street lamps of each category to obtain the illumination control strategy corresponding to the intelligent street lamp of each category.
Based on the first aspect, in a first implementation manner, after the obtaining the lighting control strategy corresponding to each category of smart street lamps, the method further includes:
acquiring new intelligent street lamp big data;
performing cluster analysis on the new intelligent street lamp big data and the initial intelligent street lamp big data to obtain the category of the intelligent street lamp corresponding to the new intelligent street lamp big data;
and based on the category of the intelligent street lamp corresponding to the new intelligent street lamp big data, taking the illumination control strategy corresponding to the intelligent street lamp of the category as the illumination control strategy of the intelligent street lamp corresponding to the new intelligent street lamp big data.
Based on the first aspect or the first possible implementation manner of the first aspect, in a second possible implementation manner, the performing cluster analysis on the smart street lamp big data to divide the plurality of smart street lamps in the designated geographic area into a preset number of categories includes:
evaluating the illumination requirements of various information data of each intelligent street lamp;
and dividing the plurality of intelligent street lamps in the designated geographical area into a preset number of categories by using the illumination demand evaluation result as a sample and using a preset clustering algorithm.
Based on the second possible implementation manner of the first aspect, in a third possible implementation manner, the lighting control strategy includes controlling a lighting time period of the corresponding smart street lamp;
correspondingly, the decision-making for the intelligent street lamps of each category to obtain the lighting control strategy corresponding to the intelligent street lamp of each category comprises:
and carrying out discrimination decision on the intelligent street lamps of each category by utilizing a preset classification and regression tree model, and determining the illumination time period of the intelligent street lamps of the category.
Based on the third possible implementation manner of the first aspect, in a fourth possible implementation manner, the lighting control strategy includes controlling the brightness of the corresponding smart street lamp in a lighting time period;
correspondingly, the decision-making for the intelligent street lamps of each category to obtain the lighting control strategy corresponding to the intelligent street lamp of each category comprises:
evaluating the brightness requirement of each item of information data of the intelligent street lamps of each category;
and determining the brightness type of the intelligent street lamp in the type in the starting time period based on the brightness requirement evaluation result.
Based on the fourth possible implementation manner of the first aspect, in a fifth possible implementation manner, the evaluating the brightness requirement of each item of information data of the intelligent street lamps of each category includes:
extracting environment brightness information from the environment information corresponding to the position information of the intelligent street lamp;
extracting the number of access users in the starting time period from the user access data;
extracting the pedestrian number and the traffic flow in the starting time period from the video monitoring data;
and based on the environment brightness information, the number of the access users, the number of pedestrians and the traffic flow, evaluating the brightness requirement of the intelligent street lamp of each category.
In a sixth possible implementation manner based on the fifth possible implementation manner of the first aspect, the evaluating the brightness requirement of the intelligent street lamps of each category based on the environment brightness information, the number of the access users, the number of pedestrians, and the traffic flow includes:
and inputting the environment brightness information, the number of the access users, the number of the pedestrians and the traffic flow into a preset binary tree classification model to obtain the brightness requirements of the intelligent street lamps of the corresponding categories.
A second aspect of the embodiments of the present application provides a decision device based on smart street lamp big data, the decision device includes:
the intelligent street lamp management system comprises a data acquisition unit, a video monitoring unit and a control unit, wherein the data acquisition unit is used for acquiring initial intelligent street lamp big data, the initial intelligent street lamp big data comprise information data of a plurality of intelligent street lamps in a specified geographic area, the information data are relevant to an illumination control strategy of the intelligent street lamps, and the information data of each intelligent street lamp comprise identification information of the intelligent street lamp, position information of the intelligent street lamp, environment information corresponding to the position information of the intelligent street lamp, user access data in a wireless signal coverage area of the intelligent street lamp and video monitoring data in a video monitoring area of the intelligent street lamp;
the cluster analysis unit is used for carrying out cluster analysis on the initial intelligent street lamp big data so as to divide a plurality of intelligent street lamps in the designated geographic area into a preset number of categories;
and the decision-making unit is used for making decision-making for the intelligent street lamps of each category to obtain the illumination control strategy corresponding to the intelligent street lamp of each category.
A third aspect of the embodiments of the present application provides a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor, when executing the computer program, implements the steps of the intelligent street lamp big data-based decision method as described in any one of the implementations of the first aspect.
A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the intelligent street lamp big data-based decision method are implemented as described in any one of the implementations of the first aspect.
Compared with the prior art, the application has the beneficial effects that:
the method and the system can divide the plurality of intelligent street lamps in the designated urban area into the categories of the preset number by acquiring information data related to the lighting control strategy in the big data of the plurality of intelligent street lamps in the designated urban area and carrying out cluster analysis on the information data; and obtaining the illumination control strategy corresponding to the intelligent street lamp of each category by carrying out judgment and decision on the intelligent street lamps of each category. Therefore, the method and the device have the advantages that the clustering analysis of big data is utilized to classify a plurality of intelligent street lamps, and the corresponding lighting control strategy is executed on the intelligent street lamps of each category, so that on one hand, compared with an independent control strategy, the information processing amount is reduced, and the control system overhead is reduced; on the other hand, compared with a centralized control strategy, the method has the effects of flexible control and energy conservation.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a flowchart illustrating an implementation of a decision method based on smart street lamp big data according to an embodiment of the present application;
fig. 2 is a flowchart illustrating an implementation of a decision method based on smart street lamp big data according to another embodiment of the present application;
fig. 3 is a schematic structural diagram of a decision device based on smart street lamp big data according to an embodiment of the present application;
fig. 4 is a schematic diagram of a terminal provided in an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
To make the objects, technical solutions and advantages of the present application more clear, the following description is made by way of specific embodiments with reference to the accompanying drawings.
Referring to fig. 1, it shows a flowchart of an implementation of the intelligent street lamp big data-based decision method provided in the embodiment of the present application, which is detailed as follows:
in step 101, acquiring initial intelligent street lamp big data, wherein the initial intelligent street lamp big data comprises information data of a plurality of intelligent street lamps in a designated geographic area, and the information data are related to lighting control strategies of the intelligent street lamps;
in this embodiment, the information data of each intelligent street lamp may include identification information of the intelligent street lamp, location information of the intelligent street lamp, environment information corresponding to the location information of the intelligent street lamp, user access data in a wireless signal coverage area of the intelligent street lamp, and video monitoring data in a video monitoring area of the intelligent street lamp.
In the embodiment of the application, the identification information of the intelligent street lamp can be the number of the intelligent street lamp, and the number can be the only identification of the intelligent street lamp; the position information of the intelligent street lamp is the installation position of the intelligent street lamp, and can be longitude and latitude, for example; the environmental information indicates ambient environmental characteristics of the location of the smart street light, which in one embodiment may include commercial, residential, industrial, etc., indicating regional attributes of the area of the street light; in still other embodiments, the environmental information may also include ambient brightness, weather information, and the like.
In the embodiment of the application, the intelligent street lamp is provided with the 5G base station or the wireless local area network equipment, so that user access data in a coverage area of the 5G base station can be acquired, or user access data in a signal coverage area of the wireless local area network equipment can be acquired; in addition, video monitoring equipment can be configured on the intelligent street lamp, and monitoring video data in a video monitoring area can be acquired.
In step 102, cluster analysis is performed on the initial smart street lamp big data to divide the smart street lamps in the designated geographic area into a preset number of categories.
In the embodiment of the present application, normalization processing may be performed on the acquired information data, for example, location information is normalized to longitude and latitude, environment information is normalized to environment characteristics (for example, a number may be used to represent 1 for a business district, 2 for a residential district, 3 for an industrial district, and the like), user access data may be normalized to the number of access users, and monitoring video data may be normalized to the number of pedestrians and/or traffic flow.
After normalization processing is carried out on the acquired information data, a plurality of data sets are obtained, each data set corresponds to one intelligent street lamp, and one data element in each data set represents one information data of the intelligent street lamp. The normalized data can be subjected to cluster analysis by means of data processing, and the intelligent street lamps in the designated geographic area can be divided into a preset number of categories through clustering.
In an alternative embodiment, the step 102 may include:
evaluating the illumination requirements of various information data of each intelligent street lamp;
and dividing the plurality of intelligent street lamps in the designated geographical area into a preset number of categories by using the illumination demand evaluation result as a sample and using a preset clustering algorithm.
In this embodiment, first, the lighting requirement evaluation may be performed on each item of information data, that is, each item of information data (except for the identification information and the location information, which are used for identifying a specific intelligent street lamp) in the big data corresponding to each intelligent street lamp is normalized to a number between 0 and 1, for example, 1, which indicates that the lighting requirement must be present, 0 indicates that the lighting requirement is not present, and 0.5 indicates that half of the lighting requirements may be present. In practical application, a preset lighting demand evaluation correspondence table can be used for lighting demand evaluation (namely, data normalization processing), and lighting demand scores corresponding to different information data can be recorded in the lighting demand evaluation correspondence table.
In the embodiment of the present application, a day may be divided into a plurality of time periods, for example, one time period per hour, and a total of 24 time periods per day, and the lighting requirement evaluation is performed for each time period, so as to obtain a lighting requirement evaluation result for each time period.
In practical application, a fuzzy C-means clustering (FCM) algorithm can be adopted to perform cluster analysis on data sets obtained after normalization, specifically, one data set corresponds to one vector, n vectors are obtained on the assumption that big data of n intelligent street lamps in an area are obtained, and the n vectors x can be usedi(1,2 …, n) are divided into c fuzzy groups (indicating that the intelligent street lamp is divided into c categories in advance), and the clustering center of each group is solved, so that the value function of the non-similarity index is minimized. FCM employs fuzzy partitioning such that each given data point determines its degree of belonging to each group by a degree of membership between 0 and 1. In response to the introduction of fuzzy partitions, the membership matrix U allows elements with values between 0 and 1. Plus the normalization provision, however, the sum of the membership of one dataset is always equal to 1, i.e.,
in the embodiment of the present application, the cost function of the FCM may be:
wherein u isijBetween 0 and 1; c. CiCluster center, d, representing fuzzy group iij＝||ci-xjI is the Euclidean distance between the ith clustering center and the jth data pointDistance, m ∈ [1, ∞) ] is a weighted index.
Furthermore, a new objective function can be constructed, a clustering center and a membership matrix are determined through iterative derivation, and finally the intelligent street lamps are divided into c categories.
In another embodiment, the clustering of the intelligent street lamps can be realized by a k-means clustering algorithm (k-means clustering algorithm).
In step 103, a decision is made for each category of intelligent street lamp to obtain a lighting control strategy corresponding to each category of intelligent street lamp.
In the embodiment of the application, after clustering, a decision can be determined for each category, so that the lighting control strategy corresponding to the intelligent street lamp of each category is determined, and the lighting condition of the intelligent street lamp of each category is controlled by using the lighting control strategy corresponding to the intelligent street lamp of each category.
In an optional embodiment, the lighting control strategy may include controlling a lighting time period of the corresponding smart street lamp; accordingly, the step 103 may include: and carrying out discrimination decision on the intelligent street lamps of each category by utilizing a preset classification and regression tree model, and determining the illumination time period of the intelligent street lamps of the category.
In this embodiment, for each category of smart street lamp, a decision may be determined according to a data set of the smart street lamp corresponding to the clustering center of the category to obtain an illumination control policy of the smart street lamp, and the policy is used as an illumination control policy of all the smart street lamps of the category. In addition, by dividing a day into multiple time periods, e.g., one time period per hour, for a total of 24 time periods per day, the classification and regression tree model may output the lighting control strategy results for each time period, i.e., whether lighting is turned on or not at each time period.
In an optional embodiment, the lighting control strategy includes controlling the brightness of the corresponding intelligent street lamp in a lighting time period; accordingly, the step 103 may include:
evaluating the brightness requirement of each item of information data of the intelligent street lamps of each category;
and determining the brightness type of the intelligent street lamp in the type in the starting time period based on the brightness requirement evaluation result.
In this embodiment, the brightness requirement evaluation may also be performed on the information data corresponding to the intelligent street lamps in the clustering center, and the brightness category of the intelligent street lamp of the category in the turn-on time period is obtained based on the evaluation result.
Optionally, the evaluating the brightness requirement of each item of information data of the intelligent street lamps of each category may include:
extracting environment brightness information from the environment information corresponding to the position information of the intelligent street lamp;
extracting the number of access users in the starting time period from the user access data;
extracting the pedestrian number and the traffic flow in the starting time period from the video monitoring data;
and based on the environment brightness information, the number of the access users, the number of pedestrians and the traffic flow, evaluating the brightness requirement of the intelligent street lamp of each category.
In this embodiment, the smart street lamps may be configured with a brightness sensor, the big data may include environment brightness information of each smart street lamp, and the brightness requirement evaluation of each category of smart street lamps is finally determined according to the environment brightness information, the number of users accessing, the number of pedestrians, and the traffic flow.
Optionally, based on the environment brightness information, the number of the access users, the number of the pedestrians, and the traffic flow, the evaluation of the brightness requirement of the intelligent street lamp in each category includes:
and inputting the environment brightness information, the number of the access users, the number of the pedestrians and the traffic flow into a preset binary tree classification model to obtain the brightness requirements of the intelligent street lamps of the corresponding categories.
In this embodiment, a binary tree classification model may be established in advance through actual research, and the model represents the influence weights of the environment brightness information, the number of the access users, the number of the pedestrians, and the traffic flow on the illumination brightness of the smart street lamp respectively. And inputting the information into a preset binary tree classification model so as to obtain the brightness requirement of the intelligent street lamp of the corresponding category.
According to the method, the information data related to the lighting control strategy in the big data of the intelligent street lamps in the designated urban area are obtained, the information data are subjected to cluster analysis, and the intelligent street lamps in the designated urban area are divided into the categories with the preset number; and obtaining the illumination control strategy corresponding to the intelligent street lamp of each category by carrying out judgment and decision on the intelligent street lamps of each category. Therefore, the method and the device have the advantages that the clustering analysis of big data is utilized to classify a plurality of intelligent street lamps, and the corresponding lighting control strategy is executed on the intelligent street lamps of each category, so that on one hand, compared with an independent control strategy, the information processing amount is reduced, and the control system overhead is reduced; on the other hand, compared with a centralized control strategy, the method has the effects of flexible control and energy conservation.
Fig. 2 shows a flowchart of an implementation of the intelligent street lamp big data-based decision method according to the embodiment of the present application, which is detailed as follows:
in the embodiment of the present invention, steps 201 to 203 may specifically refer to steps 101 to 103 (including alternative embodiments thereof) in the embodiment shown in fig. 1, and are not described herein again.
In step 204, new intelligent street lamp big data is obtained;
in step 205, performing cluster analysis on the new smart street lamp big data and the initial smart street lamp big data to obtain a category of the smart street lamp corresponding to the new smart street lamp big data;
in step 206, based on the category of the intelligent street lamp corresponding to the new intelligent street lamp big data, the lighting control strategy corresponding to the intelligent street lamp of the category is used as the lighting control strategy of the intelligent street lamp corresponding to the new intelligent street lamp big data.
In the embodiment of the present application, after performing cluster analysis and decision-making on the original big data of the intelligent street lamp in the embodiment shown in fig. 1, the lighting control strategy and the corresponding intelligent street lamp of each category may be obtained.
When newly built intelligent street lamps exist or new intelligent street lamps to be subjected to decision making exist, new intelligent street lamp big data corresponding to the intelligent street lamps can be obtained and subjected to cluster analysis with the original intelligent street lamp big data (the big data of the intelligent street lamps subjected to decision making) so as to obtain the category of the intelligent street lamps corresponding to the new intelligent street lamp big data.
After the category of the intelligent street lamp corresponding to the new intelligent street lamp big data is obtained, the lighting control strategy corresponding to the intelligent street lamp of the category can be directly used as the lighting control strategy of the intelligent street lamp corresponding to the new intelligent street lamp big data without carrying out decision again, so that the system overhead is further reduced.
According to the method, the information data related to the lighting control strategy in the big data of the intelligent street lamps in the designated urban area are obtained, the information data are subjected to cluster analysis, and the intelligent street lamps in the designated urban area are divided into the categories with the preset number; and obtaining the illumination control strategy corresponding to the intelligent street lamp of each category by carrying out judgment and decision on the intelligent street lamps of each category. Therefore, the method and the device have the advantages that the clustering analysis of big data is utilized to classify a plurality of intelligent street lamps, and the corresponding lighting control strategy is executed on the intelligent street lamps of each category, so that on one hand, compared with an independent control strategy, the information processing amount is reduced, and the control system overhead is reduced; on the other hand, compared with a centralized control strategy, the method has the effects of flexible control and energy conservation.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
The following are apparatus embodiments of the present application, and for details not described in detail therein, reference may be made to the corresponding method embodiments described above.
Fig. 3 shows a schematic structural diagram of a decision device based on smart street lamp big data according to an embodiment of the present application, and for convenience of description, only the relevant portions of the embodiment of the present application are shown, which are detailed as follows:
as shown in fig. 3, the intelligent street lamp big data-based decision device 3 includes: a data acquisition unit 31, a cluster analysis unit 32 and a discrimination decision unit 33.
The data acquisition unit 31 is configured to acquire initial smart street lamp big data, where the initial smart street lamp big data includes information data of a plurality of smart street lamps in a specified geographic area, the information data being related to lighting control strategies of the smart street lamps, and the information data of each smart street lamp includes identification information of the smart street lamp, location information of the smart street lamp, environment information corresponding to the location information of the smart street lamp, user access data in a wireless signal coverage area of the smart street lamp, and video monitoring data in a video monitoring area of the smart street lamp;
the cluster analysis unit 32 is configured to perform cluster analysis on the initial smart street lamp big data to divide the plurality of smart street lamps in the designated geographic area into a preset number of categories;
and the decision-making unit 33 is configured to perform decision-making on the intelligent street lamps of each category to obtain an illumination control strategy corresponding to the intelligent street lamp of each category.
Optionally, the data obtaining unit 31 is further configured to obtain new big data of the intelligent street lamp after the decision unit 33 obtains the lighting control strategy corresponding to each category of the intelligent street lamp;
the cluster analysis unit 32 is further configured to perform cluster analysis on the new smart street lamp big data and the initial smart street lamp big data to obtain a category of the smart street lamp corresponding to the new smart street lamp big data;
the decision-making unit 33 is further configured to, based on the category of the intelligent street lamp corresponding to the new intelligent street lamp big data, use the lighting control strategy corresponding to the intelligent street lamp of the category as the lighting control strategy of the intelligent street lamp corresponding to the new intelligent street lamp big data.
Optionally, the decision device 3 based on the smart street lamp big data further includes:
the lighting demand evaluation unit is used for evaluating the lighting demand of each item of information data of each intelligent street lamp;
correspondingly, the cluster analysis unit 32 is specifically configured to divide the plurality of intelligent street lamps in the designated geographic area into a preset number of categories by using the illumination demand evaluation result as a sample and using a preset clustering algorithm.
Optionally, the lighting control strategy includes controlling a lighting time period of the corresponding intelligent street lamp;
correspondingly, the decision-making unit 33 is specifically configured to perform decision-making on the intelligent street lamps of each category by using a preset classification and regression tree model, and determine the lighting time period of the intelligent street lamps of the category.
Optionally, the lighting control strategy includes controlling the brightness of the corresponding intelligent street lamp in the lighting time period; decision device 3 based on wisdom street lamp big data still includes:
the brightness requirement evaluation unit is used for evaluating the brightness requirement of each item of information data of the intelligent street lamps of each category;
correspondingly, the decision-making unit 33 is further configured to determine the brightness category of the intelligent street lamp in the category in the turn-on time period based on the brightness requirement evaluation result.
Optionally, the decision device 3 based on the smart street lamp big data further includes:
the first information extraction unit is used for extracting environment brightness information from the environment information corresponding to the position information of the intelligent street lamp;
a second information extraction unit, configured to extract, from the user access data, the number of access users in the enabled time period;
the third information extraction unit is used for extracting the number of pedestrians and the traffic flow in the starting time period from the video monitoring data;
correspondingly, the brightness requirement evaluation unit is specifically configured to evaluate the brightness requirements of the intelligent street lamps of each category based on the environment brightness information, the number of the access users, the number of pedestrians, and the traffic flow.
Optionally, the brightness requirement evaluation unit is further configured to input the environment brightness information, the number of the access users, the number of the pedestrians, and the traffic flow into a preset binary tree classification model, so as to obtain the brightness requirements of the intelligent street lamps of the corresponding category.
According to the method, the information data related to the lighting control strategy in the big data of the intelligent street lamps in the designated urban area are obtained, the information data are subjected to cluster analysis, and the intelligent street lamps in the designated urban area are divided into the categories with the preset number; and obtaining the illumination control strategy corresponding to the intelligent street lamp of each category by carrying out judgment and decision on the intelligent street lamps of each category. Therefore, the method and the device have the advantages that the clustering analysis of big data is utilized to classify a plurality of intelligent street lamps, and the corresponding lighting control strategy is executed on the intelligent street lamps of each category, so that on one hand, compared with an independent control strategy, the information processing amount is reduced, and the control system overhead is reduced; on the other hand, compared with a centralized control strategy, the method has the effects of flexible control and energy conservation.
Fig. 4 is a schematic diagram of a terminal according to an embodiment of the present application. As shown in fig. 4, the terminal 4 of this embodiment includes: a processor 40, a memory 41 and a computer program 42 stored in said memory 41 and executable on said processor 40. The processor 40 executes the computer program 42 to implement the steps of the above-mentioned decision method embodiments based on smart street lamp big data, such as steps 101 to 103 shown in fig. 1. Alternatively, the processor 40, when executing the computer program 42, implements the functions of the modules/units in the above-mentioned device embodiments, such as the functions of the units 31 to 33 shown in fig. 3.
Illustratively, the computer program 42 may be partitioned into one or more modules/units that are stored in the memory 41 and executed by the processor 40 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 42 in the terminal 4. For example, the computer program 42 may be divided into a data acquisition unit, a cluster analysis unit and a decision unit, and each unit has the following specific functions:
the intelligent street lamp management system comprises a data acquisition unit, a video monitoring unit and a control unit, wherein the data acquisition unit is used for acquiring initial intelligent street lamp big data, the initial intelligent street lamp big data comprise information data of a plurality of intelligent street lamps in a specified geographic area, the information data are relevant to an illumination control strategy of the intelligent street lamps, and the information data of each intelligent street lamp comprise identification information of the intelligent street lamp, position information of the intelligent street lamp, environment information corresponding to the position information of the intelligent street lamp, user access data in a wireless signal coverage area of the intelligent street lamp and video monitoring data in a video monitoring area of the intelligent street lamp;
the cluster analysis unit is used for carrying out cluster analysis on the initial intelligent street lamp big data so as to divide a plurality of intelligent street lamps in the designated geographic area into a preset number of categories;
and the decision-making unit is used for making decision-making for the intelligent street lamps of each category to obtain the illumination control strategy corresponding to the intelligent street lamp of each category.
The terminal 4 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal may include, but is not limited to, a processor 40, a memory 41. Those skilled in the art will appreciate that fig. 4 is only an example of a terminal 4 and does not constitute a limitation of terminal 4 and may include more or less components than those shown, or some components in combination, or different components, for example, the terminal may also include input output devices, network access devices, buses, etc.
The Processor 40 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the terminal 4, such as a hard disk or a memory of the terminal 4. The memory 41 may also be an external storage device of the terminal 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) and the like provided on the terminal 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the terminal 4. The memory 41 is used for storing the computer program and other programs and data required by the terminal. The memory 41 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal and method may be implemented in other ways. For example, the above-described apparatus/terminal embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A decision-making method based on intelligent street lamp big data is characterized by comprising the following steps:
acquiring initial intelligent street lamp big data, wherein the initial intelligent street lamp big data comprises information data of a plurality of intelligent street lamps in a specified geographic area, the information data is related to an illumination control strategy of the intelligent street lamps, and the information data of each intelligent street lamp comprises identification information of the intelligent street lamp, position information of the intelligent street lamp, environment information corresponding to the position information of the intelligent street lamp, user access data in a wireless signal coverage area of the intelligent street lamp and video monitoring data in a video monitoring area of the intelligent street lamp;
performing cluster analysis on the initial intelligent street lamp big data to divide a plurality of intelligent street lamps in the designated geographic area into a preset number of categories;
and carrying out decision-making on the intelligent street lamps of each category to obtain the illumination control strategy corresponding to the intelligent street lamp of each category.
2. The intelligent street lamp big data-based decision method according to claim 1, further comprising, after obtaining the lighting control strategy corresponding to each category of intelligent street lamps:
acquiring new intelligent street lamp big data;
performing cluster analysis on the new intelligent street lamp big data and the initial intelligent street lamp big data to obtain the category of the intelligent street lamp corresponding to the new intelligent street lamp big data;
and based on the category of the intelligent street lamp corresponding to the new intelligent street lamp big data, taking the illumination control strategy corresponding to the intelligent street lamp of the category as the illumination control strategy of the intelligent street lamp corresponding to the new intelligent street lamp big data.
3. The intelligent street lamp big data-based decision method as claimed in claim 1 or 2, wherein the performing cluster analysis on the intelligent street lamp big data to classify the plurality of intelligent street lamps in the designated geographic area into a predetermined number of categories comprises:
evaluating the illumination requirements of various information data of each intelligent street lamp;
and dividing the plurality of intelligent street lamps in the designated geographical area into a preset number of categories by using the illumination demand evaluation result as a sample and using a preset clustering algorithm.
4. The intelligent street lamp big data-based decision method as claimed in claim 3, wherein the lighting control strategy comprises controlling a lighting time period of the corresponding intelligent street lamp;
correspondingly, the decision-making for the intelligent street lamps of each category to obtain the lighting control strategy corresponding to the intelligent street lamp of each category comprises:
and carrying out discrimination decision on the intelligent street lamps of each category by utilizing a preset classification and regression tree model, and determining the illumination time period of the intelligent street lamps of the category.
5. The intelligent street lamp big data-based decision method as claimed in claim 4, wherein the lighting control strategy comprises controlling the brightness of the corresponding intelligent street lamp in a lighting time period;
correspondingly, the decision-making for the intelligent street lamps of each category to obtain the lighting control strategy corresponding to the intelligent street lamp of each category comprises:
evaluating the brightness requirement of each item of information data of the intelligent street lamps of each category;
and determining the brightness type of the intelligent street lamp in the type in the starting time period based on the brightness requirement evaluation result.
6. The intelligent street lamp big data-based decision method as claimed in claim 5, wherein the evaluation of the brightness requirement of each item of information data of each category of intelligent street lamps comprises:
extracting environment brightness information from the environment information corresponding to the position information of the intelligent street lamp;
extracting the number of access users in the starting time period from the user access data;
extracting the pedestrian number and the traffic flow in the starting time period from the video monitoring data;
and based on the environment brightness information, the number of the access users, the number of pedestrians and the traffic flow, evaluating the brightness requirement of the intelligent street lamp of each category.
7. The intelligent street lamp big data-based decision method as claimed in claim 6, wherein the evaluation of the brightness requirement of the intelligent street lamps of each category based on the environment brightness information, the number of the access users, the number of pedestrians and the traffic flow comprises:
and inputting the environment brightness information, the number of the access users, the number of the pedestrians and the traffic flow into a preset binary tree classification model to obtain the brightness requirements of the intelligent street lamps of the corresponding categories.
8. A decision-making device based on wisdom street lamp big data, its characterized in that, the decision-making device includes:
the intelligent street lamp management system comprises a data acquisition unit, a video monitoring unit and a control unit, wherein the data acquisition unit is used for acquiring initial intelligent street lamp big data, the initial intelligent street lamp big data comprise information data of a plurality of intelligent street lamps in a specified geographic area, the information data are relevant to an illumination control strategy of the intelligent street lamps, and the information data of each intelligent street lamp comprise identification information of the intelligent street lamp, position information of the intelligent street lamp, environment information corresponding to the position information of the intelligent street lamp, user access data in a wireless signal coverage area of the intelligent street lamp and video monitoring data in a video monitoring area of the intelligent street lamp;
the cluster analysis unit is used for carrying out cluster analysis on the initial intelligent street lamp big data so as to divide a plurality of intelligent street lamps in the designated geographic area into a preset number of categories;
and the decision-making unit is used for making decision-making for the intelligent street lamps of each category to obtain the illumination control strategy corresponding to the intelligent street lamp of each category.
9. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the intelligent street lamp big data-based decision method according to any one of the above claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the steps of the intelligent street lamp big data-based decision method according to any one of the above claims 1 to 7.
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