A smart lighting control method based on road detection

Abstract

The application discloses a kind of intelligent lighting control methods based on road detection, belong to road lighting control technical field, comprising: obtaining current road type, obtaining the road traffic of current road, the actual illuminance of current road environment and the road humidity of current road;According to current road type and lighting time period, query the expert knowledge base that has been created, reasoning obtains the illuminance expert recommended value of lighting, and the illuminance expert recommended value is used as illuminance setting value;Combining illuminance setting value, road traffic, actual illuminance of environment and road humidity, the opening percentage of single lamp controller is obtained, to realize the control of road lighting.The application comprehensively considers the traffic flow, illumination, road humidity and other parameters that influence road lighting, combines relevant standards and requirements, and obtains more intelligent and more reasonable intelligent lighting method.

Description

Technical Field

[0001] This invention belongs to the field of road lighting control technology, and more specifically, relates to a smart lighting control method based on road detection. Background Technology

[0002] Smart roads are an important component of smart cities. With societal development, the amount of road lighting in people's living environments is constantly increasing, presenting new challenges for lighting systems. On the one hand, the increasing number of road lighting facilities necessitates that lighting systems consider energy conservation; on the other hand, road lighting is related to safety, as poor lighting conditions in rainy or overcast weather greatly increase the risk of traffic accidents. Therefore, more intelligent and rational control of road lighting is required.

[0003] Current road lighting control mainly relies on timers or illuminance detectors. The on / off times are generally determined based on the experience of maintenance personnel or illuminance requirements. The control methods are relatively simple and consider few factors, resulting in insufficient intelligence in lighting control. Summary of the Invention

[0004] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention proposes a smart lighting control method based on road detection. Targeting the characteristics of road lighting systems, this method employs reliable and practical approaches, comprehensively considering parameters affecting road lighting such as traffic flow, illuminance, and road surface humidity, and combining relevant standards and requirements to arrive at a smarter and more reasonable smart lighting method.

[0005] To achieve the above objectives, the present invention provides a smart lighting control method based on road detection, comprising:

[0006] Get the current road type, the current road traffic volume, the current road ambient illuminance, and the current road humidity;

[0007] Based on the current road type and lighting time period, the existing expert knowledge base is queried to deduce the recommended illuminance value for lighting, and the recommended illuminance value is used as the illuminance setting value.

[0008] By combining the illuminance setpoint, road traffic flow, actual ambient illuminance, and road humidity, the opening percentage of the single lamp controller is obtained, thereby realizing the control of road lighting.

[0009] In some alternative implementations, by Obtain the opening percentage of the single-lamp controller, where k represents the current road type number; s(i) represents the measured road humidity near the i-th street lamp in the current road; M(k) represents the expert recommended illuminance value for the current road; n represents the number of road hygrometers installed on the road; p represents the traffic flow adjustment correction coefficient; t represents the actual ambient illuminance; and m represents the road traffic flow. m = HIGH, m = MID, m = LOW, where HIGH, MID, and LOW represent traffic flow, and the traffic flow decreases sequentially from HIGH to MID to LOW; s represents the decision to switch the lights on or off. T(k,s,n,p) represents the output of the single lamp controller.

[0010] In some alternative implementations, the expert knowledge base is determined as follows:

[0011] Collect expert knowledge on road lighting illuminance values ​​from existing lighting characteristic data;

[0012] The rule-based approach is used to express expert knowledge about road lighting illuminance values, generating two parts: a fact base and a rule base. The fact base is created by dividing and creating facts according to different road types and different lighting time periods. The rule base is a set of rules that combine the facts in the fact base and match the premise facts in the rule base with the facts of different road types and different lighting time periods.

[0013] In some alternative implementations, the reasoning to obtain expert-recommended values ​​for illuminance includes:

[0014] Based on the current road type and lighting time period, the system processes facts and rules from the expert knowledge base to deduce the expert-recommended illuminance value for the lighting.

[0015] In some optional implementations, the step of processing facts and rules in the expert knowledge base based on the current road type and lighting time period to infer and obtain the expert-recommended illuminance value for lighting includes:

[0016] Match the current road type and lighting time period with facts in the fact base one by one and generate facts;

[0017] Match the rule premises in the rule base with the generated road type and lighting time period facts;

[0018] Extract the premises of each rule and verify whether they are in the expert knowledge base. If they are in the expert knowledge base, the match is successful; otherwise, move on to the next rule for matching.

[0019] Output the conclusions of the successfully matched rules to obtain the expert recommended values ​​for the illumination.

[0020] In some alternative implementations, the current road traffic flow is obtained through traffic flow detectors installed at the beginning and end points of the road.

[0021] In some alternative implementations, road humidity is obtained via a road hygrometer near a streetlight in the current road.

[0022] In some alternative implementations, the actual ambient illuminance is obtained via an illuminance meter at the control center.

[0023] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0024] This invention relates to a road lighting control method based on an expert system. Each streetlight is equipped with a single-lamp controller, traffic flow detectors are installed at the start and end points of the road, several road moisture meters are placed locally, and an illuminance meter is installed at the control center. Illuminance control is achieved by controlling the output percentage of each single-lamp controller. By comprehensively considering parameters affecting road lighting such as traffic flow, illuminance, and road surface moisture, and in conjunction with relevant standards and requirements, a smarter and more reasonable intelligent lighting method is derived. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of a road lighting control method provided in an embodiment of the present invention;

[0026] Figure 2 This is a schematic diagram of a road lighting illuminance value expert system provided in an embodiment of the present invention;

[0027] Figure 3 This is a flowchart of a road lighting control program provided in an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0029] This invention provides a smart lighting control method based on road detection, the principle of which is as follows: Figure 1As shown, the current road type number k and its illuminance setpoint are manually input. The traffic flow detector collects the road traffic flow, the illuminance meter ZT measures the actual environmental illuminance t, and the road humidity meter SE measures the road humidity s(i). Based on the current road type k and its different lighting time periods, the expert knowledge base that has been created is consulted to deduce the illuminance expert recommended value M(k) for lighting, which is used as the illuminance setpoint. Combining the illuminance setpoint, road traffic flow m, actual road environmental illuminance t, and road humidity s(i), the opening percentage T(k,s,n,p) of the single lamp controller is calculated according to the following formula (1), thereby realizing the control of road lighting.

[0030] exist Figure 1 In this diagram, ZT represents an illuminance meter; ME represents a traffic flow detector; SE represents a road humidity meter; k represents the current road type number; s(i) represents the measured road humidity near the i-th streetlight, in %; m represents the measured road traffic flow; M(k) represents the expert recommended illuminance value for the road, in %; and T(k,s,n,p) represents the single-lamp controller output percentage, in %.

[0031]

[0032]

[0033]

[0034] Where, k represents the current road type number; s(i) represents the measured road humidity near the i-th street light, in % (humidity influence is considered at a 20% rate; the higher the road humidity, the higher the lighting demand); M(k) represents the expert recommended illuminance value for the road, in %; n represents the number of road hygrometers installed on the road; p represents the traffic flow adjustment correction coefficient; t represents the actual environmental illuminance measurement value, in lx; m represents the measured road traffic flow value, where m = HIGH for high flow (adjustable if traffic flow ≥ 60 vehicles / minute), MID for moderate flow (adjustable if 10 < traffic flow < 60 vehicles / minute), and LOW for low flow (adjustable if traffic flow ≤ 10 vehicles / minute); s represents the light switching decision; and T(k,s,n,p) represents the output of the single-lamp controller, in %.

[0035] In this embodiment of the invention, the principle of the expert system for reasonable values ​​of road lighting illuminance is as follows: Figure 2As shown in the figure, X represents the input of the expert system's human-machine interface, including road type number, lighting time period, and management input of the knowledge base; Y represents the expert system output, which here refers to the expert recommended value M(k) for road lighting illuminance; the lighting system's human-machine interface refers to the lighting control system computer; knowledge management is the maintenance of knowledge in the knowledge base, such as adding, deleting, and modifying knowledge; the knowledge base is a collection of decision-making knowledge and experience knowledge of road lighting illuminance decision-makers; the inference engine is a set of programs that processes the knowledge base for road type and lighting time period and feeds back the inference results to the lighting system's human-machine interface.

[0036] In this embodiment of the invention, a knowledge base of road lighting illuminance values ​​(%) can be created in the following manner:

[0037] (1) Knowledge Collection

[0038] Expert knowledge on road lighting illuminance values ​​is collected from recognized lighting characteristic data. Table 1 lists common road illuminance values ​​based on actual road lighting conditions.

[0039] Table 1. Road Lighting Illuminance Table (%)

[0040]

[0041] (2) Knowledge Expression

[0042] The standard procedural framework for expressing expert knowledge of illuminance measurements using the rule-based method is "IF-THEN," which means evaluating a situation and taking action if the situation is true. Based on the expert knowledge in Table 1, a fact base and a rule base are generated after the rule-based knowledge expression.

[0043] 1) Generate a fact base

[0044] Facts were categorized and created based on different road types and lighting time periods. Facts were refined as control requirements increased and coarser as control requirements decreased. The facts were categorized according to the information in Table 1, and the resulting fact database is shown in Table 2, containing facts such as "Fact 1", ..., "Fact D", etc.

[0045] Table 2 Fact Base

[0046] Serial Number fact Serial Number fact Fact 1 Main road Fact A Lights on until 8 PM Fact 2 Secondary arterial road Fact B 8 PM to 11 PM Fact 3 branch road Fact C 11 PM to 1 AM Fact D Lights out at 1 AM

[0047] 2) Generate a rule base

[0048] The rule base is constructed by combining facts from the existing fact base, as shown in Table 3, which includes facts such as "Rule 1A", ..., "Rule 3D". Among them, rule "Rule 2A" expresses the expert knowledge that "if the road is a secondary arterial road AND the lights are on until 8:00 PM; then the road lighting illuminance value M(k) = 95%".

[0049] Table 3 Rule Base

[0050] Serial Number rule Rule 1A IF Fact 1 AND Fact A; THEN M(1) = 100% Rule 1B IF Fact 1 AND Fact B; THEN M(1) = 80% Rule 1C IF Fact 1 AND Fact C; THEN M(1) = 60% Rule 1D IF Fact 1 AND Fact D; THEN M(1) = 40% Rule 2A IF fact 2 AND fact A; THEN M(2) = 95% Rule 2B IF Fact 2 AND Fact B; THEN M(2) = 75% Rule 2C IF fact 2 AND fact C; THEN M(2) = 50% Rule 2D IF fact 2 AND fact D; THEN M(2) = 30% Rule 3A IF fact 3 AND fact A; THEN M(3) = 90% Rule 3B IF fact 3 AND fact B; THEN M(3) = 70% Rule 3C IF fact 3 AND fact C; THEN M(3) = 40% Rule 3D IF fact 3 AND fact D; THEN M(3) = 20%

[0051] (3) Reasoning about road lighting illuminance values

[0052] The road lighting illuminance value expert system uses an inference engine to perform knowledge reasoning to obtain the required illuminance values ​​for various roads at different stages.

[0053] (3.1) Reasoning Methods

[0054] The inference engine of the road lighting illuminance value expert system employs forward reasoning. It processes facts and rules from the system's knowledge base based on known conditions input by the user, such as road type and lighting time period. Its reasoning principle is as follows:

[0055] If fact M is true, and there exists a rule "TF M THEN N", then N is true.

[0056] Therefore, if the known conditions input by the user satisfy facts 1 and facts A in the fact base, and the rule "IF fact 1 AND fact A; THEN M(1) = 100%" exists in the rule base, then the road lighting illuminance value M(k) = 100% can be obtained.

[0057] The working process of an inference engine is as follows:

[0058] 1) Match the known conditions entered by the user with the facts in the fact base one by one and generate facts.

[0059] 2) Match the premises of the rules in the rule base with the generated road type and lighting time period facts; extract the premises of each rule and verify whether these premises are in the expert knowledge base. If they are all in the expert knowledge base, the match is successful; otherwise, take the next rule for matching.

[0060] 3) Output the <Conclusion> of the successfully matched rule to obtain the road lighting value M(k), and proceed to the next step of calculation.

[0061] 4) Following the steps above, deduce the parameters M(k) of all road lighting in all areas.

[0062] Combine the actual measured road humidity value s(i), the actual environmental illuminance value t, and the road traffic flow determination m, and substitute them into equations (1), (1), and (3) to obtain the output of the single lamp controller.

[0063] (3.2) Implementation

[0064] A set of inference engine programs is compiled according to the above reasoning method. When controlling road lighting, the lighting control method of the present invention is implemented according to the following steps, such as the control program flow. Figure 3 As shown.

[0065] Step 1: The user enters the road type k and the illuminance setting values ​​for different time periods (i.e., the values ​​in Table 1, which are adjustable), and then creates a fact base and a rule base based on expert knowledge.

[0066] Step 2: Measure the ambient illuminance value t to determine whether to turn on the lights.

[0067] Step 3: If the conditions for turning on the lights are met, determine the current lighting time period. If not, proceed to step 10.

[0068] Step 4: Using the road type k and the illuminance setting values ​​for different time periods, the lighting time period is repeatedly matched with the rules in the knowledge base through forward reasoning using the inference engine.

[0069] Step 5: The inference engine outputs the expert recommended value M(k) for road lighting illuminance.

[0070] Step 6: Detect traffic flow and determine p.

[0071] Step 7: Detect road humidity and determine s(i).

[0072] Step 8: Combine the road humidity measurement value s(i), the actual ambient illuminance measurement value t, and the traffic flow determination m to obtain the output T(k,s,n,p) of each single lamp controller.

[0073] Step 9: Control the output of each single lamp controller by pressing T(k,s,n,p) to achieve lighting power control and realize smart lighting.

[0074] Step 10: End, return to step 2.

[0075] It should be noted that, depending on the implementation needs, the various steps / components described in this application can be broken down into more steps / components, or two or more steps / components or parts of the operation of steps / components can be combined into new steps / components to achieve the purpose of this invention.

[0076] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A smart lighting control method based on road detection, characterized in that, include: The system obtains the current road type, the current road traffic volume, the current road ambient light level, and the current road humidity. It also obtains the current road traffic volume through traffic flow detectors installed at the start and end points of the road. Based on the current road type and lighting time period, the system queries the existing expert knowledge base, infers the recommended illuminance value for lighting, and uses the recommended illuminance value as the illuminance setting value. By combining the illuminance setpoint, road traffic flow, actual ambient illuminance, and road humidity, the opening percentage of the single lamp controller is obtained, thereby realizing the control of road lighting; Depend on Obtain the opening percentage of the single-lamp controller, where k represents the current road type number; s(i) represents the measured road humidity near the i-th street lamp in the current road; M(k) represents the expert recommended illuminance value for the current road; n represents the number of road hygrometers installed on the road; p represents the traffic flow adjustment correction coefficient; t represents the actual ambient illuminance; and m represents the road traffic flow. m = HIGH, m = MID, m = LOW, where HIGH, MID, and LOW represent traffic flow, and the traffic flow decreases sequentially from HIGH to MID to LOW; s represents the decision to switch lights on or off. T(k,s,n,p) represents the output of the single-lamp controller; The expert knowledge base is determined as follows: Collect expert knowledge on road lighting illuminance values ​​from existing lighting characteristic data; The rule-based approach is used to express expert knowledge about road lighting illuminance values, generating two parts: a fact base and a rule base. The fact base is created by dividing and creating facts according to different road types and different lighting time periods. The rule base is a set of rules that combine the facts in the fact base and match the premise facts in the rule base with the facts of different road types and different lighting time periods. The reasoning process yields expert-recommended illuminance values ​​for the lighting, including: Based on the current road type and lighting time period, the system processes facts and rules from the expert knowledge base to deduce the expert-recommended illuminance value for the lighting.

2. The method according to claim 1, characterized in that, The process of processing facts and rules in the expert knowledge base based on the current road type and lighting time period to deduce the expert-recommended illuminance value includes: Match the current road type and lighting time period with facts in the fact base one by one and generate facts; Match the rule premises in the rule base with the generated road type and lighting time period facts; Extract the premises of each rule and verify whether they are in the expert knowledge base. If they are in the expert knowledge base, the match is successful; otherwise, move on to the next rule for matching. Output the conclusions of the successfully matched rules to obtain the expert recommended values ​​for the illumination.

3. The method according to claim 1, characterized in that, The road humidity is obtained by using a road hygrometer near the streetlights in the current road.

4. The method according to claim 1, characterized in that, The actual ambient illuminance is obtained through the illuminance meter in the control center.

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