Street lamp intelligent adjusting method and system

Abstract

The invention provides an intelligent street lamp adjusting method and system, and relates to the technical field of intelligent street lamp control, and the method comprises the steps: carrying out the brightness tendency analysis of Q park image sets, and obtaining Q brightness tendency analysis results; carrying out regional grid fusion analysis to obtain a park brightness tropism distribution result; sending a plurality of brightness fusion concentrated values of the plurality of grid fusion areas in the distribution result to a street lamp intelligent adjustment platform to obtain a plurality of first template adjustment scheme sets; and performing fine tuning optimization on the plurality of first template adjustment scheme sets to obtain a first fine tuning optimization scheme set for brightness adjustment. According to the invention, the technical problems of unnecessary illumination resource waste and high energy consumption of the street lamps in the park due to the fact that the street lamp brightness cannot be adaptively regulated and controlled in time according to the actual illumination demand in the park can be solved, the technical target of intelligently regulating the street lamp brightness in the park can be realized, and the user experience is improved. The effect of effectively reducing energy consumption of street lamps in the park is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent control of street lamps, and in particular to an intelligent adjustment method and system for street lamps. Background Art

[0002] With the large-scale construction of industrial parks and commercial parks, the number of street lamps has increased dramatically. Although the energy consumption of a single street lamp is not high, the energy consumption of multiple street lamps in the park will result in a large amount of energy consumption.

[0003] At present, most industrial parks are still using traditional lighting methods, which is to control the industrial park street lights according to fixed time periods. This method cannot adaptively adjust the lighting status of the industrial park street lights according to the actual situation in the industrial park, resulting in a low match between the lighting status of the industrial park street lights and the actual lighting needs, resulting in frequent waste of lighting resources.

[0004] In summary, the existing park street lamp adjustment method cannot adaptively adjust the brightness of the park street lamps in time according to the actual lighting needs in the park, resulting in unnecessary waste of lighting resources and causing technical problems such as high energy consumption of park street lamps. Summary of the invention

[0005] The purpose of this application is to provide an intelligent street lamp adjustment method and system to solve the technical problem that the existing park street lamp adjustment method is unable to adaptively adjust the brightness of the park street lamps in time according to the actual lighting needs in the park, resulting in unnecessary waste of lighting resources and high energy consumption of the park street lamps.

[0006] In view of the above problems, the present application provides a method and system for intelligently adjusting street lamps.

[0007] In a first aspect, the present application provides a method for intelligently adjusting street lamps, which is implemented by a street lamp intelligent adjustment system, wherein the method is applied to a street lamp intelligent adjustment platform, and the street lamp intelligent adjustment platform is communicatively connected with a street lamp control unit and a camera component array, and the method comprises: using the camera component array to collect images of a target park within a preset monitoring window to obtain Q park image sets, wherein the camera component array has Q camera components; performing brightness trend analysis on the Q park image sets respectively to obtain Q brightness trend analysis results, wherein each brightness trend analysis result includes multiple grid areas, each grid area has a brightness concentration value and a trend mark, and the trend mark includes a brightness positive mark, a brightness steady-state mark, and a brightness negative mark; The trend analysis result is subjected to regional raster fusion analysis to obtain the target park brightness trend distribution result, wherein the target park brightness trend distribution result has multiple raster fusion areas, and the multiple raster fusion areas have multiple brightness fusion concentration values ​​and multiple trend identifications; the multiple brightness fusion concentration values ​​of the multiple raster fusion areas in the target park brightness trend distribution result are sent to the street light intelligent adjustment platform, and the adjustment scheme templates are respectively called for the multiple raster fusion areas to obtain multiple first template adjustment scheme sets; the multiple first template adjustment scheme sets are respectively fine-tuned and optimized according to the multiple brightness fusion concentration values ​​and the multiple trend identifications to obtain a first fine-tuning optimization scheme set; the street light control unit adjusts the brightness of the street lights in the target park according to the first fine-tuning optimization scheme set.

[0008] In a second aspect, the present application further provides a street light intelligent adjustment system for executing a street light intelligent adjustment method as described in the first aspect, wherein the system is communicatively connected to a street light intelligent adjustment platform, and the street light intelligent adjustment platform is communicatively connected to a street light control unit and a camera component layout array, and the system includes: a park image acquisition module, the park image acquisition module is used to use the camera component layout array to perform image acquisition on the target park within a preset monitoring window to obtain Q park image sets, wherein the camera component layout array has Q camera components; a brightness trend analysis module, the brightness trend analysis module is used to perform brightness trend analysis on the Q park image sets respectively to obtain Q brightness trend analysis results, wherein each brightness trend analysis result includes multiple grid areas, each grid area has a brightness concentration value and a trend mark, and the trend mark includes a brightness positive mark, a brightness steady-state mark and a brightness negative mark; a regional raster fusion analysis module, the regional raster fusion analysis module is used to perform brightness trend analysis on the Q park image sets respectively to obtain Q brightness trend analysis results. A brightness trend analysis result is used to perform regional grid fusion analysis to obtain a target park brightness trend distribution result, wherein the target park brightness trend distribution result has multiple grid fusion areas, and the multiple grid fusion areas have multiple brightness fusion concentration values ​​and multiple trend identifiers; an adjustment scheme template calling module, the adjustment scheme template calling module is used to send multiple brightness fusion concentration values ​​of multiple grid fusion areas in the target park brightness trend distribution result to the street light intelligent adjustment platform, and respectively call the adjustment scheme template for the multiple grid fusion areas to obtain multiple first template adjustment scheme sets; a template adjustment scheme optimization module, the template adjustment scheme optimization module is used to fine-tune and optimize the multiple first template adjustment scheme sets according to the multiple brightness fusion concentration values ​​and the multiple trend identifiers, and obtain a first fine-tuning optimization scheme set; a street light brightness adjustment module, the street light brightness adjustment module is used for the street light control unit to adjust the brightness of the street lights in the target park according to the first fine-tuning optimization scheme set.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages: 1. By using a camera assembly array to collect images of a target park within a preset monitoring window, Q park image sets are obtained, wherein the camera assembly array has Q camera assemblies; brightness trend analysis is performed on the Q park image sets respectively to obtain Q brightness trend analysis results, wherein each brightness trend analysis result includes multiple grid areas, each grid area has a brightness concentration value and a trend mark, and the trend mark includes a brightness positive mark, a brightness steady-state mark, and a brightness negative mark; regional grid fusion analysis is performed on the Q brightness trend analysis results to obtain a target park brightness trend distribution result, wherein the target park brightness trend The distribution result has multiple grid fusion areas, and the multiple grid fusion areas have multiple brightness fusion concentration values ​​and multiple trend identifications; the multiple brightness fusion concentration values ​​of the multiple grid fusion areas in the brightness trend distribution result of the target park are sent to the street light intelligent adjustment platform, and the adjustment scheme templates of the multiple grid fusion areas are called respectively to obtain multiple first template adjustment scheme sets; the multiple first template adjustment scheme sets are fine-tuned and optimized according to the multiple brightness fusion concentration values ​​and the multiple trend identifications to obtain a first fine-tuning optimization scheme set; the brightness of the street lights in the target park is adjusted according to the first fine-tuning optimization scheme set by the street light control unit. That is to say, by performing real-time brightness analysis and brightness trend analysis based on the park image set, multiple brightness trend analysis results are determined, and further integrated to obtain the overall park brightness trend distribution result; based on the brightness trend distribution result, the adjustment plan template is called, and the adjustment plan is fine-tuned and optimized according to the brightness fusion concentration value and the brightness trend mark, and the brightness of the park street lights is adjusted based on the optimization plan. On the premise of meeting the lighting needs, it can reduce or avoid additional lighting resource waste, improve the matching degree between the lighting needs and the brightness of the park street lights, achieve the technical goal of intelligently adjusting the brightness of the park street lights, and achieve the technical effect of effectively reducing the energy consumption of the park street lights.

[0010] 2. Based on the intelligent street light adjustment platform, the adjustment plan template is called according to the brightness fusion concentration value, which can improve the efficiency and accuracy of the adjustment plan. At the same time, the adjustment plan is fine-tuned and optimized according to the brightness fusion concentration value and the brightness trend mark, which can further improve the adaptability of the adjustment plan to the actual lighting needs of the park and improve the accuracy of the adjustment plan setting, thereby improving the accuracy, efficiency and intelligence of the brightness adjustment of the park street lights.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0013] Figure 1 A flowchart of a method for intelligently adjusting street lamps is provided for this application; Figure 2 This is a schematic diagram of a process for obtaining Q brightness trend analysis results in a street lamp intelligent adjustment method of this application; Figure 3 The present invention is a schematic structural diagram of a street lamp intelligent adjustment system.

[0014] Description of reference numerals: Park image acquisition module 11, brightness trend analysis module 12, regional raster fusion analysis module 13, adjustment scheme template calling module 14, template adjustment scheme optimization module 15, street lamp brightness adjustment module 16. DETAILED DESCRIPTION

[0015] This application provides a method and system for intelligently adjusting street lamps, which solves the technical problem that the existing methods for adjusting street lamps in a park cannot timely and adaptively adjust the brightness of the park street lamps according to the actual lighting needs in the park, resulting in unnecessary waste of lighting resources and high energy consumption of the park street lamps. On the premise of meeting the lighting needs, it can reduce or avoid the waste of additional lighting resources, improve the matching degree between the lighting needs and the brightness of the park street lamps, achieve the technical goal of intelligently adjusting the brightness of the park street lamps, and achieve the technical effect of effectively reducing the energy consumption of the park street lamps.

[0016] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0017] Embodiment 1 Please see attached Figure 1 The present application provides a method for intelligently adjusting street lamps, wherein the method is applied to a street lamp intelligent adjustment system, the system is connected in communication with a street lamp intelligent adjustment platform, the street lamp intelligent adjustment platform is connected in communication with a street lamp control unit and a camera assembly arrangement array, and the method specifically comprises the following steps: Step 1: using the camera assembly array to collect images of the target park within a preset monitoring window to obtain Q park image sets, wherein the camera assembly array has Q camera assemblies; Specifically, the method provided in the present application is used to perform intelligent adjustment on street lights in a park. The method is specifically implemented in a street light intelligent adjustment system. The system and the street light intelligent adjustment platform can realize data transmission and information interaction. The street light intelligent adjustment platform inputs the adjustment plan into the street light control unit through signal transmission, thereby realizing intelligent adjustment of the street lights in the park; the camera component layout array includes multiple camera components, which are used to capture images of different areas of the park, and input the image capture results into the street light intelligent adjustment platform through signal transmission.

[0018] The camera component array is used to collect images of the target park within a preset monitoring window to obtain Q park image sets, wherein the preset monitoring window is a time period, and the preset monitoring window includes multiple monitoring time nodes, and the monitoring time nodes can be set by a person skilled in the art based on the street light adjustment accuracy requirement, wherein the higher the adjustment accuracy requirement, the smaller the interval of the monitoring time nodes, for example: the preset monitoring window can be set to 1 hour, and the monitoring time node can be set to 3 minutes, that is, within the preset monitoring window, an image of the target park is collected every 3 minutes; wherein the camera component array includes Q camera components, Q is an integer greater than 1, and the specific value of Q can be calculated based on the size of the target park and the coverage area of ​​each camera component.

[0019] By obtaining Q park image sets, data support is provided for the next step of park brightness identification and brightness change trend analysis.

[0020] Step 2: Performing brightness trend analysis on the Q park image sets respectively to obtain Q brightness trend analysis results, wherein each brightness trend analysis result includes multiple grid areas, each grid area has a brightness concentration value and a trend mark, and the trend mark includes a brightness positive mark, a brightness steady-state mark, and a brightness negative mark; Specifically, brightness trend analysis is performed on the Q park image sets respectively. First, the park image set is divided into grids, wherein each park image includes multiple grid areas. Then, brightness trend analysis is performed on the grid areas of each image set respectively. The brightness trend analysis includes brightness recognition and brightness change trend analysis, and Q brightness trend analysis results are obtained. Each brightness trend analysis result includes multiple grid areas, and each grid area has a brightness concentration value and a trend mark, and the brightness concentration value represents the brightness mean state of the area. The trend mark includes a brightness positive mark, a brightness steady-state mark and a brightness negative mark, wherein the brightness positive mark represents that the brightness in the area changes with time as an increasing trend, the brightness steady-state mark represents that the brightness in the area is almost unaffected by time, and the brightness negative mark represents that the brightness in the area changes with time as a decreasing trend.

[0021] By conducting brightness trend analysis, Q brightness trend analysis results are obtained, which can intuitively display the brightness status and brightness change trend of each area in the park, and at the same time provide data support for the overall brightness analysis and brightness change trend analysis of the park.

[0022] Step 3: Perform regional grid fusion analysis on the Q brightness trend analysis results to obtain the brightness trend distribution result of the target park, wherein the brightness trend distribution result of the target park has multiple grid fusion regions, and the multiple grid fusion regions have multiple brightness fusion concentration values ​​and multiple trend identifiers; Specifically, the Q brightness trend analysis results are subjected to regional grid fusion analysis, that is, the park image set is spliced ​​and fused according to the position coordinates of each camera component in the park, and then the splicing and fusion of the Q brightness trend analysis results are realized to obtain the target park brightness trend distribution result of the entire park, wherein the target park brightness trend distribution result includes multiple grid fusion areas, and each grid fusion area has a brightness fusion concentration value and a trend mark. By obtaining the target park brightness trend distribution result, a basis is provided for the next step of calling the street light adjustment plan template and intelligently generating the street light adjustment plan.

[0023] Step 4: sending multiple brightness fusion concentration values ​​of multiple grid fusion areas in the brightness trend distribution result of the target park to the street light intelligent adjustment platform, respectively calling the adjustment scheme templates for the multiple grid fusion areas, and obtaining multiple first template adjustment scheme sets; Specifically, a plurality of brightness fusion concentration values ​​of a plurality of grid fusion areas in the brightness trend distribution result of the target park are sent to the street light intelligent adjustment platform, wherein the street light intelligent adjustment platform is embedded with an adjustment scheme matching model, and the adjustment scheme matching model is constructed based on a BP neural network, which is a neural network model that can be iteratively optimized in machine learning, and is obtained through supervised training with sample data; further, the adjustment scheme matching model is used to perform adjustment scheme template matching on the plurality of brightness fusion concentration values ​​of the plurality of grid fusion areas, and since the brightness distribution results in each grid fusion area are not completely consistent, in order to subsequently select a better adjustment scheme, several scheme matchings may be performed to obtain a plurality of first template adjustment scheme sets, wherein each first template adjustment scheme set corresponds to a grid fusion area.

[0024] The adjustment scheme matching model includes an input layer, multiple hidden layers and an output layer, wherein the input data of the input layer is a brightness fusion concentration value, and the output data of the output layer is a template adjustment scheme. Sample data retrieval is performed based on big data technology. Big data technology refers to a series of technologies and methods that enable the value contained in massive data to be mined and displayed, including data planning, data collection, analysis and mining and other technical means. The sample data includes multiple sample brightness fusion concentration values ​​and multiple sample template adjustment schemes to obtain a sample data set; then the sample data set is used to supervise the training of the adjustment scheme matching model to obtain an adjustment scheme matching model that tends to converge.

[0025] By constructing an adjustment scheme matching model based on a BP neural network and performing intelligent matching of multiple brightness fusion concentration values ​​through the adjustment scheme matching model, the intelligence of template adjustment scheme generation can be improved, and the accuracy and efficiency of template adjustment schemes can be improved.

[0026] Step 5: fine-tuning and optimizing the plurality of first template adjustment solution sets respectively according to the plurality of brightness fusion concentration values ​​and the plurality of trend identifiers to obtain a first fine-tuning optimization solution set; Specifically, multiple fine-tuning directions and multiple fine-tuning steps are determined based on the multiple brightness fusion concentration values ​​and the multiple trend identifiers, and the multiple first template adjustment scheme sets are fine-tuned and optimized based on the multiple fine-tuning directions and the multiple fine-tuning steps to obtain a first fine-tuning optimization scheme set. The fine-tuning direction is the direction of brightness adjustment, and the fine-tuning step is the brightness value adjusted during a single brightness adjustment.

[0027] Since the template adjustment scheme obtained through intelligent matching only considers the brightness distribution of the park, but does not consider the impact of the brightness change trend on the adjustment scheme, the template adjustment scheme is fine-tuned and optimized by combining the brightness fusion concentration value and the brightness trend mark. The accuracy and rationality of the fine-tuning optimization scheme setting can be further improved, thereby improving the accuracy of the brightness adjustment of the park street lights.

[0028] Step six: the street light control unit adjusts the brightness of the street lights in the target park according to the first fine-tuning optimization solution set.

[0029] Specifically, the first fine-tuning optimization solution set is transmitted to the street light control unit, and the street light control unit adjusts the brightness of the target park street lights according to the first fine-tuning optimization solution set. By adjusting the brightness of the park street lights according to the fine-tuning optimization solution, the automation and intelligence of the brightness adjustment of the park street lights can be improved.

[0030] The described intelligent street light adjustment method is applied to an intelligent street light adjustment system. The system is communicatively connected to a street light intelligent adjustment platform. The street light intelligent adjustment platform is communicatively connected to a street light control unit and a camera component array. This can solve the technical problem that the existing park street light adjustment method cannot adaptively adjust the brightness of the park street lights in time according to the actual lighting needs in the park, resulting in unnecessary waste of lighting resources and high energy consumption of the park street lights.

[0031] First, the camera assembly array is used to collect images of the target park within a preset monitoring window to obtain Q park image sets, wherein the camera assembly array has Q camera assemblies; then, brightness trend analysis is performed on the Q park image sets respectively to obtain Q brightness trend analysis results, wherein each brightness trend analysis result includes multiple grid areas, each grid area has a brightness concentration value and a trend mark, and the trend mark includes a brightness positive mark, a brightness steady-state mark, and a brightness negative mark; then, regional grid fusion analysis is performed on the Q brightness trend analysis results to obtain a target park brightness trend distribution result, wherein the target park brightness trend analysis result includes a plurality of grid areas, each grid area has a brightness concentration value and a trend mark, and the trend mark includes a brightness positive mark, a brightness steady-state mark, and a brightness negative mark. The distribution result has multiple grid fusion areas, and the multiple grid fusion areas have multiple brightness fusion concentration values ​​and multiple trend identifications; next, the multiple brightness fusion concentration values ​​of the multiple grid fusion areas in the brightness trend distribution result of the target park are sent to the street light intelligent adjustment platform, and the adjustment scheme templates of the multiple grid fusion areas are called respectively to obtain multiple first template adjustment scheme sets; in addition, the multiple first template adjustment scheme sets are fine-tuned and optimized according to the multiple brightness fusion concentration values ​​and the multiple trend identifications to obtain a first fine-tuning optimization scheme set; finally, the street light control unit adjusts the brightness of the street lights in the target park according to the first fine-tuning optimization scheme set. By performing real-time brightness analysis and brightness trend analysis based on the park image set, multiple brightness trend analysis results are determined, and further integrated to obtain the overall park brightness trend distribution result; based on the brightness trend distribution result, the adjustment plan template is called, and the adjustment plan is fine-tuned and optimized according to the brightness fusion concentration value and the brightness trend mark. The brightness of the park street lights is adjusted based on the optimization plan. On the premise of meeting the lighting needs, it can reduce or avoid additional lighting resource waste, improve the matching degree between the lighting needs and the brightness of the park street lights, achieve the technical goal of intelligently adjusting the brightness of the park street lights, and achieve the technical effect of effectively reducing the energy consumption of the park street lights.

[0032] Further, the Q park image sets are respectively subjected to brightness trend analysis to obtain Q brightness trend analysis results, as shown in the attached figure. Figure 2 As shown, step 2 of this application includes: Extracting a first park image set from the Q park image sets, each park image in the first park image set having M pixels; Taking the M pixels as indexes, extracting brightness values ​​of the first park image set in time sequence to obtain M brightness value sequences; Calculating the mean of the M brightness value sequences respectively to determine M brightness mean values; Taking the first park image set as the simulated terrain area and the M brightness mean values ​​as the terrain surface height of the simulated terrain area, the first park image set is divided into grid areas to obtain a first grid area set; Specifically, first, a first park image set is randomly extracted from the Q park image sets, wherein the first park image set is any one of the Q park image sets, and each park image in the first park image set has M pixels, wherein the value of M can be set based on the specifications and acquisition accuracy of the park image. Further, the brightness values ​​of the first park image set are extracted according to the order of image acquisition time nodes using the M pixels as indexes to obtain M brightness value sequences. Then, the mean values ​​of multiple brightness values ​​in the M brightness value sequences are respectively calculated to obtain M brightness mean values.

[0033] The park area corresponding to the first park image set is used as the simulated terrain area, the M brightness means are used as the terrain surface height of the simulated terrain area, the first park image set is divided into grid areas, and the first grid area set is obtained according to the division result.

[0034] Further, taking the first park image set as the simulated terrain area, the M brightness mean values ​​as the terrain surface height of the simulated terrain area, dividing the first park image set into grid areas to obtain a first grid area set, the present application further includes the following steps: Taking the minimum value of the M brightness averages as the starting point for flooding the simulated terrain area; The first average brightness value after the water level rises in the simulated terrain area after the water is filled is used as the average brightness value of the first stage; Calculate the brightness difference between the average brightness at the starting point and the average brightness in the first stage, and determine whether the calculation result meets the preset brightness difference. If so, continue to irrigate the simulated terrain area; If not, a first dividing ridge line is generated at the brightness mean value of the first stage, and water is continuously poured into the simulated terrain area, wherein the first dividing ridge line is used to separate the brightness mean value of the first stage from the starting point; When the water surface exceeds the maximum value among the M brightness averages, watering is stopped to obtain multiple segmentation ridge lines; The first park image set is divided into a plurality of first grid areas using the plurality of segmentation ridge lines, thereby obtaining the first grid area set.

[0035] Specifically, first, the minimum value of the M brightness averages is used as the starting point for flooding the simulated terrain area, and the simulated terrain area is flooded, and the first brightness average that the water surface rises after flooding the simulated terrain area is used as the first stage brightness average, wherein the first brightness average has the smallest deviation from the minimum brightness average. The brightness difference between the brightness average at the starting point and the brightness average of the first stage is further calculated to obtain a first brightness deviation, and then the first brightness deviation is judged according to a preset brightness difference value, which is the maximum brightness difference value that two pixels can be divided into the same area, and can be set by technicians in this field according to actual conditions.

[0036] If the first brightness difference is less than the preset brightness difference, water continues to be poured into the simulated terrain area; if the first brightness difference is greater than or equal to the preset brightness difference, a first segmentation ridge line is generated at the first stage brightness mean, and then water continues to be poured into the simulated terrain area, wherein the first segmentation ridge line is used to separate the first stage brightness mean and the starting point. Thus, the goal of using the preset brightness difference to divide pixels with similar brightness into the same area and pixels with large brightness differences into different areas, and performing grid area division on the park area corresponding to the first park image set is achieved. Then, the iterative simulation of watering is continuously performed until the water surface submerges the maximum value of the M brightness mean values, and then the watering is stopped to obtain multiple segmentation ridge lines. Then, the first park image set is divided into multiple first grid areas using the multiple segmentation ridge lines to obtain the first grid area set.

[0037] By using a simulated watering algorithm to perform grid area division on the first park image set, the accuracy and efficiency of grid area division can be improved, thereby achieving the technical goal of intelligent, efficient and accurate area division.

[0038] Performing brightness concentration value calculation and trend identification on the first grid area set according to the M brightness value sequences, and obtaining a first brightness trend analysis result in combination with the first grid area set; Specifically, according to the M brightness value sequences, the brightness concentration value calculation and the trend identification are performed on the first grid area set, wherein the trend identification includes a brightness positive identification, a brightness steady-state identification, and a brightness negative identification, to obtain a brightness concentration value calculation result and a trend identification result. Based on the brightness concentration value calculation result, the trend identification result, and the first grid area set, a first brightness trend analysis result is obtained, and the first brightness trend analysis result includes multiple first grid areas, and each grid area has a brightness concentration value and a trend identification.

[0039] Further, according to the M brightness value sequences, brightness concentration value calculation and trend identification are performed on the first grid area set, and a first brightness trend analysis result is obtained in combination with the first grid area set. The present application also includes the following steps: Matching the multiple first grid regions in the first grid region set with the M brightness mean values ​​respectively to obtain multiple brightness mean value sets, wherein the multiple brightness mean value sets correspond to the multiple first grid regions one by one; respectively taking the modes of the plurality of brightness mean value sets as a plurality of brightness concentration values ​​of a plurality of first grid regions; Using a linear regression method to fit multiple brightness value sequence sets in the multiple first grid areas respectively, determine the brightness development trend, and obtain multiple pixel trend identification sets; The pixel tendency identifiers with a larger proportion in the plurality of pixel tendency identifier sets are used as the tendency identifiers.

[0040] Specifically, a plurality of first grid regions in the first grid region set are matched with the M brightness mean values ​​to obtain a plurality of brightness mean value sets, wherein each brightness mean value set corresponds to a first grid region. A brightness mean mode in the brightness mean value set is further extracted as a brightness concentration value corresponding to the first grid region to obtain a plurality of brightness concentration values ​​corresponding to the plurality of first grid regions.

[0041] Linear regression is a commonly used predictive modeling technique in statistics. It studies the linear relationship between dependent variables and independent variables, that is, the dependent variable is a linear combination of independent variables. The goal of linear regression is to find the best fitting straight line so that this straight line can reduce the square sum of the vertical distances between the data points and the straight line as much as possible. Then, the linear regression method is used to fit the multiple brightness value sequence sets in the multiple first grid areas respectively, and the brightness development trend is determined according to the fitting results. The brightness development trend includes an increasing trend, a stable trend, and a decreasing trend. The pixel trend mark is determined based on the brightness development trend, wherein the pixel trend mark corresponding to the increasing trend is a positive mark, the pixel trend mark corresponding to the stable trend is a steady-state mark, and the pixel trend mark corresponding to the decreasing trend is a negative mark, wherein each first grid area includes multiple pixels, and each pixel corresponds to a brightness value sequence, and multiple pixel trend mark sets are obtained. Finally, the pixel trend mark that accounts for a large proportion of the multiple pixel trend mark sets is used as the trend mark to obtain the trend mark result.

[0042] The Q brightness trend analysis results are obtained based on the Q park image sets.

[0043] Specifically, the Q park image sets are analyzed in sequence using the method for obtaining the first brightness trend analysis result to obtain Q brightness trend analysis results, wherein each park image set corresponds to one brightness trend analysis result.

[0044] Further, the Q brightness trend analysis results are subjected to regional grid fusion analysis to obtain the brightness trend distribution result of the target park. Step three of this application includes: According to the distribution positions of the Q camera components, the Q brightness trend analysis results are regionally grid-joined to obtain an initial park brightness trend distribution result, wherein the initial park brightness trend distribution result has a plurality of grid fusion regions, and the plurality of grid fusion regions include a plurality of overlapping regions and a plurality of non-overlapping regions; Performing mean processing on the multiple brightness concentration values ​​of the multiple overlapping areas to obtain multiple overlapping brightness fusion concentration values ​​of the overlapping areas; Using the brightness concentration values ​​of the multiple non-overlapping areas as multiple non-overlapping brightness fusion concentration values; A plurality of brightness fusion concentration values ​​of a plurality of grid fusion areas are obtained according to the plurality of overlapping brightness fusion concentration values ​​and the plurality of non-overlapping brightness fusion concentration values.

[0045] Specifically, according to the distribution positions of the Q camera components in the park, the Q brightness trend analysis results corresponding to the Q park image sets are regionally grid-stitched to obtain an initial park brightness trend distribution result, wherein the initial park brightness trend distribution result includes multiple grid fusion areas, and each grid fusion area includes an overlapping area and a non-overlapping area, wherein the overlapping area is an area where images overlap when two adjacent camera components perform image acquisition.

[0046] The multiple brightness concentration values ​​of the multiple overlapping areas are averaged, and the average calculation result is used as the overlapping brightness fusion concentration value of the corresponding overlapping area to obtain multiple overlapping brightness fusion concentration values ​​corresponding to the multiple overlapping areas; the brightness concentration value of the non-overlapping area is used as the non-overlapping brightness fusion concentration value to obtain multiple non-overlapping brightness fusion concentration values ​​corresponding to the multiple non-overlapping areas. Finally, based on the multiple overlapping brightness fusion concentration values ​​and the multiple non-overlapping brightness fusion concentration values, multiple brightness fusion concentration values ​​of the multiple grid fusion areas are obtained, wherein the brightness fusion concentration value is the sum of the product of the overlapping brightness fusion concentration value and the overlapping area proportion, and the product of the non-overlapping brightness fusion concentration value and the non-overlapping area proportion in the grid fusion area.

[0047] By dividing the grid fusion area into overlapping areas and non-overlapping areas, and calculating the brightness fusion concentrated value of the raster fusion area based on the overlapping brightness fusion concentrated value of the overlapping area and the non-overlapping brightness fusion concentrated value of the non-overlapping area, the accuracy of setting the brightness fusion concentrated value can be improved, thereby improving the precision and accuracy of subsequent street light adjustment scheme matching and optimization.

[0048] Further, the plurality of first template adjustment scheme sets are fine-tuned and optimized respectively according to the plurality of brightness fusion concentration values ​​and the plurality of trend identifiers. Step five of the present application includes: Determine a plurality of fine-tuning directions based on the plurality of trend identifiers, and determine a plurality of fine-tuning step sizes according to the plurality of brightness fusion concentration values; According to the multiple fine-tuning step sizes, the multiple first template adjustment solution sets are fine-tuned respectively according to the multiple fine-tuning directions to obtain multiple first template fine-tuning solution sets; Performing fitness analysis on the plurality of first template fine-tuning solution sets by using an expert survey method to obtain a plurality of first fitness sets; Respectively selecting a plurality of first template fine-tuning schemes corresponding to the maximum fitness values ​​in the plurality of first fitness sets as update fine-tuning directions, and performing fine-tuning according to the plurality of fine-tuning step sizes to obtain a plurality of second template fine-tuning scheme sets; Fitness analysis is performed again on the multiple second template fine-tuning solution sets to obtain multiple second fitness sets.

[0049] Specifically, first, multiple fine-tuning directions are determined based on the multiple trend indicators, wherein the fine-tuning direction of the positive brightness indicator is to reduce the brightness, the fine-tuning direction of the steady-state brightness indicator is to reduce or increase the brightness, and the fine-tuning direction of the negative brightness indicator is to increase the brightness. Multiple fine-tuning step sizes are determined based on the multiple brightness fusion concentration values, wherein the brightness deviation of the brightness fusion concentration value and the standard brightness is proportional to the fine-tuning step size, that is, the larger the brightness deviation, the larger the fine-tuning step size, and the fine-tuning step size can be matched by constructing a concentration value-step size mapping channel to obtain multiple fine-tuning step sizes.

[0050] The method for constructing a concentrated value-step mapping channel is as follows: first, multiple sample brightness fusion concentrated values ​​are set, and the fine-tuning step lengths corresponding to the multiple sample brightness fusion concentrated values ​​are analyzed by an expert system to determine multiple qualified fine-tuning step lengths, wherein the sample brightness fusion concentrated value and the qualified fine-tuning step length have a one-to-one mapping relationship, and then based on the decision tree principle, the sample brightness fusion concentrated value is used as a child node, and the qualified fine-tuning step length corresponding to the sample brightness fusion concentrated value is used as a leaf node of the child node to generate a concentrated value-step mapping channel.

[0051] Further based on the multiple fine-tuning step sizes, the multiple first template adjustment scheme sets are fine-tuned according to the multiple fine-tuning directions to obtain multiple first template fine-tuning scheme sets. Then, the fitness analysis of the multiple first template fine-tuning scheme sets is performed using the expert survey method to obtain multiple first fitness sets, wherein the expert survey method is also known as the Delphi method or the Delphi method, which is a survey method for soliciting opinions and views of relevant experts or authoritative persons around a certain topic or issue. The survey object of this method is limited to the expert level, and is usually conducted through multiple rounds of surveys to obtain more accurate results after multiple rounds of surveys and analyses. Then, multiple first template fine-tuning schemes corresponding to the maximum fitness values ​​in the multiple first fitness sets are respectively selected as update fine-tuning directions, and fine-tuned according to the multiple fine-tuning step sizes to obtain multiple second template fine-tuning scheme sets. Then, the fitness analysis of the multiple second template fine-tuning scheme sets is performed again using the expert survey method to obtain multiple second fitness sets.

[0052] Furthermore, after obtaining multiple second fitness sets, the present application further includes the following steps: Taking multiple second template fine-tuning schemes corresponding to the maximum fitness values ​​in the multiple second fitness sets as update fine-tuning directions, and performing fine-tuning according to the multiple fine-tuning step sizes to obtain multiple third template fine-tuning scheme sets; After a preset number of updates and fine-tuning, a plurality of target fine-tuning optimization schemes are obtained, wherein the plurality of target fine-tuning optimization schemes are template fine-tuning schemes corresponding to the maximum fitness values ​​in the plurality of fine-tuning processes; The first set of fine-tuning optimization solutions is generated according to the multiple target fine-tuning optimization solutions.

[0053] Specifically, after obtaining multiple second fitness sets, multiple second template fine-tuning schemes corresponding to the maximum fitness values ​​in the multiple second fitness sets are used as update fine-tuning directions, and fine-tuning is performed according to the multiple fine-tuning steps to obtain multiple third template fine-tuning scheme sets; then iterative fine-tuning is continuously performed until a preset number of iterative fine-tuning times is reached, and the preset number of iterative fine-tuning times can be set based on the optimization accuracy requirement, wherein the higher the required accuracy, the larger the preset number of iterative fine-tuning times; then the template fine-tuning scheme corresponding to the maximum fitness value in the multiple fine-tuning processes is set as the target fine-tuning optimization scheme, and multiple target fine-tuning optimization schemes are obtained; finally, the first fine-tuning optimization scheme set is generated according to the multiple target fine-tuning optimization schemes.

[0054] In summary, the intelligent street lamp adjustment method provided by this application has the following technical effects: 1. Through real-time brightness analysis and brightness trend analysis based on the park image set, multiple brightness trend analysis results are determined, and further integrated to obtain the overall park brightness trend distribution results; based on the brightness trend distribution results, the adjustment plan template is called, and the adjustment plan is fine-tuned and optimized according to the brightness fusion concentration value and the brightness trend mark. The brightness of the park street lights is adjusted based on the optimization plan. On the premise of meeting the lighting needs, it can reduce or avoid additional lighting resource waste, improve the matching degree between the lighting needs and the brightness of the park street lights, achieve the technical goal of intelligently adjusting the brightness of the park street lights, and achieve the technical effect of effectively reducing the energy consumption of the park street lights.

[0055] 2. Based on the intelligent street light adjustment platform, the adjustment plan template is called according to the brightness fusion concentration value, which can improve the efficiency and accuracy of the adjustment plan. At the same time, the adjustment plan is fine-tuned and optimized according to the brightness fusion concentration value and the brightness trend mark, which can further improve the adaptability of the adjustment plan to the actual lighting needs of the park and improve the accuracy of the adjustment plan setting, thereby improving the accuracy, efficiency and intelligence of the brightness adjustment of the park street lights.

[0056] 3. By using the simulated watering algorithm to divide the first park image set into grid areas, the accuracy and efficiency of the grid area division can be improved, thereby achieving the technical goal of intelligent, efficient and accurate area division.

[0057] Embodiment 2 Based on the same inventive concept as the street lamp intelligent adjustment method in the aforementioned embodiment, the present application also provides a street lamp intelligent adjustment system, the system is connected to the street lamp intelligent adjustment platform, and the street lamp intelligent adjustment platform is connected to the street lamp control unit and the camera assembly array. Figure 3 , the system comprising: The park image acquisition module 11 is used to acquire images of the target park within a preset monitoring window using the camera component array to obtain Q park image sets, wherein the camera component array has Q camera components; A brightness trend analysis module 12, the brightness trend analysis module 12 is used to perform brightness trend analysis on the Q park image sets respectively, and obtain Q brightness trend analysis results, wherein each brightness trend analysis result includes a plurality of grid areas, each grid area has a brightness concentration value and a trend mark, and the trend mark includes a brightness positive mark, a brightness steady-state mark, and a brightness negative mark; A regional grid fusion analysis module 13, the regional grid fusion analysis module 13 is used to perform regional grid fusion analysis on the Q brightness trend analysis results to obtain a target park brightness trend distribution result, wherein the target park brightness trend distribution result has multiple grid fusion areas, and the multiple grid fusion areas have multiple brightness fusion concentration values ​​and multiple trend identifiers; An adjustment scheme template calling module 14, which is used to send multiple brightness fusion concentration values ​​of multiple grid fusion areas in the brightness trend distribution result of the target park to the street light intelligent adjustment platform, and respectively call the adjustment scheme template for the multiple grid fusion areas to obtain multiple first template adjustment scheme sets; A template adjustment scheme optimization module 15, wherein the template adjustment scheme optimization module 15 is used to fine-tune and optimize the plurality of first template adjustment scheme sets respectively according to the plurality of brightness fusion concentration values ​​and the plurality of trend identifiers to obtain a first fine-tune optimization scheme set; The street light brightness adjustment module 16 is used by the street light control unit to adjust the brightness of the street lights in the target park according to the first fine-tuning optimization solution set.

[0058] Furthermore, the brightness trend analysis module 12 in the system is also used for: Extracting a first park image set from the Q park image sets, each park image in the first park image set having M pixels; Taking the M pixels as indexes, extracting brightness values ​​of the first park image set in time sequence to obtain M brightness value sequences; Calculating the mean of the M brightness value sequences respectively to determine M brightness mean values; Taking the first park image set as the simulated terrain area and the M brightness mean values ​​as the terrain surface height of the simulated terrain area, the first park image set is divided into grid areas to obtain a first grid area set; Performing brightness concentration value calculation and trend identification on the first grid area set according to the M brightness value sequences, and obtaining a first brightness trend analysis result in combination with the first grid area set; The Q brightness trend analysis results are obtained based on the Q park image sets.

[0059] Furthermore, the brightness trend analysis module 12 in the system is also used for: Taking the minimum value of the M brightness averages as the starting point for flooding the simulated terrain area; The first average brightness value after the water level rises in the simulated terrain area after the water is filled is used as the average brightness value of the first stage; Calculate the brightness difference between the average brightness at the starting point and the average brightness in the first stage, and determine whether the calculation result meets the preset brightness difference. If so, continue to irrigate the simulated terrain area; If not, a first dividing ridge line is generated at the brightness mean value of the first stage, and water is continuously poured into the simulated terrain area, wherein the first dividing ridge line is used to separate the brightness mean value of the first stage from the starting point; When the water surface exceeds the maximum value among the M brightness averages, watering is stopped to obtain multiple segmentation ridge lines; The first park image set is divided into a plurality of first grid areas using the plurality of segmentation ridge lines, thereby obtaining the first grid area set.

[0060] Furthermore, the brightness trend analysis module 12 in the system is also used for: Matching the multiple first grid regions in the first grid region set with the M brightness mean values ​​respectively to obtain multiple brightness mean value sets, wherein the multiple brightness mean value sets correspond to the multiple first grid regions one by one; respectively taking the modes of the plurality of brightness mean value sets as a plurality of brightness concentration values ​​of a plurality of first grid regions; Using a linear regression method to fit multiple brightness value sequence sets in the multiple first grid areas respectively, determine the brightness development trend, and obtain multiple pixel trend identification sets; The pixel tendency identifiers with a larger proportion in the plurality of pixel tendency identifier sets are used as the tendency identifiers.

[0061] Furthermore, the regional grid fusion analysis module 13 in the system is also used for: According to the distribution positions of the Q camera components, the Q brightness trend analysis results are regionally grid-joined to obtain an initial park brightness trend distribution result, wherein the initial park brightness trend distribution result has a plurality of grid fusion regions, and the plurality of grid fusion regions include a plurality of overlapping regions and a plurality of non-overlapping regions; Performing mean processing on the multiple brightness concentration values ​​of the multiple overlapping areas to obtain multiple overlapping brightness fusion concentration values ​​of the overlapping areas; Using the brightness concentration values ​​of the multiple non-overlapping areas as multiple non-overlapping brightness fusion concentration values; A plurality of brightness fusion concentration values ​​of a plurality of grid fusion areas are obtained according to the plurality of overlapping brightness fusion concentration values ​​and the plurality of non-overlapping brightness fusion concentration values.

[0062] Furthermore, the template adjustment scheme optimization module 15 in the system is also used for: Determine a plurality of fine-tuning directions based on the plurality of trend identifiers, and determine a plurality of fine-tuning step sizes according to the plurality of brightness fusion concentration values; According to the multiple fine-tuning step sizes, the multiple first template adjustment solution sets are fine-tuned respectively according to the multiple fine-tuning directions to obtain multiple first template fine-tuning solution sets; Performing fitness analysis on the plurality of first template fine-tuning solution sets by using an expert survey method to obtain a plurality of first fitness sets; Respectively selecting a plurality of first template fine-tuning schemes corresponding to the maximum fitness values ​​in the plurality of first fitness sets as update fine-tuning directions, and performing fine-tuning according to the plurality of fine-tuning step sizes to obtain a plurality of second template fine-tuning scheme sets; Fitness analysis is performed again on the multiple second template fine-tuning solution sets to obtain multiple second fitness sets.

[0063] Furthermore, the template adjustment scheme optimization module 15 in the system is also used for: Taking multiple second template fine-tuning schemes corresponding to the maximum fitness values ​​in the multiple second fitness sets as update fine-tuning directions, and performing fine-tuning according to the multiple fine-tuning step sizes to obtain multiple third template fine-tuning scheme sets; After a preset number of updates and fine-tuning, a plurality of target fine-tuning optimization schemes are obtained, wherein the plurality of target fine-tuning optimization schemes are template fine-tuning schemes corresponding to the maximum fitness values ​​in the plurality of fine-tuning processes; The first set of fine-tuning optimization solutions is generated according to the multiple target fine-tuning optimization solutions.

[0064] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The intelligent street lamp adjustment method and specific examples in the aforementioned embodiment 1 are also applicable to an intelligent street lamp adjustment system in this embodiment. Through the aforementioned detailed description of an intelligent street lamp adjustment method, those skilled in the art can clearly understand an intelligent street lamp adjustment system in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0065] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0066] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A street lamp intelligent adjustment method, characterized in that: The method is applied to a street lamp intelligent adjustment platform, the street lamp intelligent adjustment platform is in communication connection with a street lamp control unit and a camera assembly arrangement array, and the method comprises: Using the camera assembly array to collect images of the target park within a preset monitoring window to obtain Q park image sets, wherein the camera assembly array has Q camera assemblies; Performing brightness trend analysis on the Q park image sets respectively to obtain Q brightness trend analysis results, wherein each brightness trend analysis result includes a plurality of grid areas, each grid area has a brightness concentration value and a trend mark, and the trend mark includes a brightness positive mark, a brightness steady-state mark, and a brightness negative mark; Performing regional grid fusion analysis on the Q brightness trend analysis results to obtain a target park brightness trend distribution result, wherein the target park brightness trend distribution result has multiple grid fusion regions, and the multiple grid fusion regions have multiple brightness fusion concentration values ​​and multiple trend identifiers; Sending multiple brightness fusion concentration values ​​of multiple grid fusion areas in the target park brightness trend distribution result to the street lamp intelligent adjustment platform, respectively calling the adjustment scheme templates for the multiple grid fusion areas, and obtaining multiple first template adjustment scheme sets; Fine-tune and optimize the plurality of first template adjustment solution sets respectively according to the plurality of brightness fusion concentration values ​​and the plurality of trend identifiers to obtain a first fine-tune optimization solution set; The street light control unit adjusts the brightness of the street lights in the target park according to the first fine-tuning optimization solution set.

2. The method according to claim 1, characterized in that Performing brightness trend analysis on the Q park image sets respectively to obtain Q brightness trend analysis results, the method comprising: Extracting a first park image set from the Q park image sets, each park image in the first park image set having M pixels; Taking the M pixels as indexes, extracting brightness values ​​of the first park image set in time sequence to obtain M brightness value sequences; Calculating the mean of the M brightness value sequences respectively to determine M brightness mean values; Taking the first park image set as the simulated terrain area and the M brightness mean values ​​as the terrain surface height of the simulated terrain area, the first park image set is divided into grid areas to obtain a first grid area set; Performing brightness concentration value calculation and trend identification on the first grid area set according to the M brightness value sequences, and obtaining a first brightness trend analysis result in combination with the first grid area set; The Q brightness trend analysis results are obtained based on the Q park image sets.

3. The method according to claim 2, characterized in that Taking the first park image set as a simulated terrain area, the M brightness mean values ​​as the terrain surface height of the simulated terrain area, dividing the first park image set into grid areas, and obtaining a first grid area set, the method includes: Taking the minimum value of the M brightness averages as the starting point for flooding the simulated terrain area; The first average brightness value after the water level rises in the simulated terrain area after the water is filled is used as the average brightness value of the first stage; Calculate the brightness difference between the average brightness at the starting point and the average brightness in the first stage, and determine whether the calculation result meets the preset brightness difference. If so, continue to irrigate the simulated terrain area; If not, a first dividing ridge line is generated at the brightness mean value of the first stage, and water is continuously poured into the simulated terrain area, wherein the first dividing ridge line is used to separate the brightness mean value of the first stage from the starting point; When the water surface exceeds the maximum value among the M brightness averages, watering is stopped to obtain multiple segmentation ridge lines; The first park image set is divided into a plurality of first grid areas using the plurality of segmentation ridge lines, thereby obtaining the first grid area set.

4. The method according to claim 2, characterized in that The method comprises: performing brightness concentration value calculation and trend identification on the first grid area set according to the M brightness value sequences, and obtaining a first brightness trend analysis result in combination with the first grid area set. Matching the multiple first grid regions in the first grid region set with the M brightness mean values ​​respectively to obtain multiple brightness mean value sets, wherein the multiple brightness mean value sets correspond to the multiple first grid regions one by one; respectively taking the modes of the plurality of brightness mean value sets as a plurality of brightness concentration values ​​of a plurality of first grid regions; Using a linear regression method to fit multiple brightness value sequence sets in the multiple first grid areas respectively, determine the brightness development trend, and obtain multiple pixel trend identification sets; The pixel tendency identifiers with a larger proportion in the plurality of pixel tendency identifier sets are used as the tendency identifiers.

5. The method according to claim 1, characterized in that Performing regional grid fusion analysis on the Q brightness trend analysis results to obtain the brightness trend distribution result of the target park, the method comprising: According to the distribution positions of the Q camera components, the Q brightness trend analysis results are regionally grid-joined to obtain an initial park brightness trend distribution result, wherein the initial park brightness trend distribution result has a plurality of grid fusion regions, and the plurality of grid fusion regions include a plurality of overlapping regions and a plurality of non-overlapping regions; Performing mean processing on the multiple brightness concentration values ​​of the multiple overlapping areas to obtain multiple overlapping brightness fusion concentration values ​​of the overlapping areas; Using the brightness concentration values ​​of the multiple non-overlapping areas as multiple non-overlapping brightness fusion concentration values; A plurality of brightness fusion concentration values ​​of a plurality of grid fusion areas are obtained according to the plurality of overlapping brightness fusion concentration values ​​and the plurality of non-overlapping brightness fusion concentration values.

6. The method according to claim 1, characterized in that The method comprises: fine-tuning and optimizing the plurality of first template adjustment scheme sets respectively according to the plurality of brightness fusion concentration values ​​and the plurality of trend identifiers; Determine a plurality of fine-tuning directions based on the plurality of trend identifiers, and determine a plurality of fine-tuning step sizes according to the plurality of brightness fusion concentration values; According to the multiple fine-tuning step sizes, the multiple first template adjustment solution sets are fine-tuned respectively according to the multiple fine-tuning directions to obtain multiple first template fine-tuning solution sets; Performing fitness analysis on the plurality of first template fine-tuning solution sets by using an expert survey method to obtain a plurality of first fitness sets; Respectively selecting a plurality of first template fine-tuning schemes corresponding to the maximum fitness values ​​in the plurality of first fitness sets as update fine-tuning directions, and performing fine-tuning according to the plurality of fine-tuning step sizes to obtain a plurality of second template fine-tuning scheme sets; Fitness analysis is performed again on the multiple second template fine-tuning solution sets to obtain multiple second fitness sets.

7. The method according to claim 6, characterized in that After obtaining a plurality of second fitness sets, the method comprises: Taking multiple second template fine-tuning schemes corresponding to the maximum fitness values ​​in the multiple second fitness sets as update fine-tuning directions, and performing fine-tuning according to the multiple fine-tuning step sizes to obtain multiple third template fine-tuning scheme sets; After a preset number of updates and fine-tuning, a plurality of target fine-tuning optimization schemes are obtained, wherein the plurality of target fine-tuning optimization schemes are template fine-tuning schemes corresponding to the maximum fitness values ​​in the plurality of fine-tuning processes; The first set of fine-tuning optimization solutions is generated according to the multiple target fine-tuning optimization solutions.

8. A street lamp intelligent adjustment system, characterized in that: The method of any one of claims 1 to 7 is implemented by the system being in communication with a street light intelligent adjustment platform, the street light intelligent adjustment platform being in communication with a street light control unit and a camera assembly arrangement array, the method comprising: A park image acquisition module, the park image acquisition module is used to use the camera assembly array to acquire images of the target park within a preset monitoring window to obtain Q park image sets, wherein the camera assembly array has Q camera assemblies; A brightness trend analysis module, the brightness trend analysis module is used to perform brightness trend analysis on the Q park image sets respectively to obtain Q brightness trend analysis results, wherein each brightness trend analysis result includes a plurality of grid areas, each grid area has a brightness concentration value and a trend mark, and the trend mark includes a brightness positive mark, a brightness steady-state mark, and a brightness negative mark; A regional raster fusion analysis module, wherein the regional raster fusion analysis module is used to perform regional raster fusion analysis on the Q brightness trend analysis results to obtain a target park brightness trend distribution result, wherein the target park brightness trend distribution result has multiple raster fusion regions, and the multiple raster fusion regions have multiple brightness fusion concentration values ​​and multiple trend identifiers; An adjustment scheme template calling module, wherein the adjustment scheme template calling module is used to send multiple brightness fusion concentration values ​​of multiple grid fusion areas in the brightness trend distribution result of the target park to the street light intelligent adjustment platform, and respectively call the adjustment scheme template for the multiple grid fusion areas to obtain multiple first template adjustment scheme sets; A template adjustment scheme optimization module, the template adjustment scheme optimization module is used to fine-tune and optimize the multiple first template adjustment scheme sets according to the multiple brightness fusion concentration values ​​and the multiple trend identifiers, to obtain a first fine-tuning optimization scheme set; A street light brightness adjustment module is used by the street light control unit to adjust the brightness of the street lights in the target park according to the first fine-tuning optimization solution set.

Images