An urban lighting energy efficiency management system based on Internet of Things technology

Abstract

The present invention discloses an urban lighting energy efficiency management system based on Internet of Things technology, specifically related to the technical field of urban lighting energy efficiency management. By means of a misdetection failure module, the misdetection failure coefficient is calculated to effectively evaluate the potential impact of sensor misdetection or failure on the operation of the system, avoiding the problem of frequent switching caused by misdetection. Through a network fault module, the data transmission interruption abnormal coefficient is calculated to accurately reflect the interference of network faults on system control and data transmission. Through a response delay module, the controller response delay coefficient is calculated to reduce the system instability caused by response delay. A hidden danger assessment model for frequent switching of lighting devices is constructed to generate a hidden danger assessment index for frequent switching of lighting devices, identifying potential hidden dangers of frequent switching faults of lighting devices. When there are hidden danger faults of frequent switching in lighting devices, the output sequence of the hidden danger assessment model for frequent switching of lighting devices is obtained, and early warning is carried out on the hidden danger faults of frequent switching existing in lighting devices according to the output sequence.

Description

Technical Field

[0001] The present invention relates to the technical field of urban lighting energy efficiency management. More specifically, the present invention relates to an urban lighting energy efficiency management system based on Internet of Things technology. Background Art

[0002] With the continuous advancement of urbanization, the urban lighting system, as an important part of the infrastructure, has gradually transformed from traditional lighting methods to intelligent and energy-saving ones. The traditional urban lighting system usually relies on fixed schedules for control, lacking real-time response to environmental changes, resulting in a large amount of unnecessary energy waste. With the progress of technology, especially the rapid development of Internet of Things (IoT) technology, more and more cities have started to adopt intelligent lighting systems to optimize energy use, reduce carbon emissions and improve management efficiency; IoT technology provides a new solution for urban lighting. Through the cooperation of sensors, intelligent controllers and communication networks, the intelligent lighting system can real-time monitor environmental light, traffic flow, climate change and other factors, and automatically adjust the on-off state, brightness and working hours of lamps according to this data. This flexible dynamic adjustment greatly improves the energy efficiency of the lighting system, enabling it to make intelligent adjustments according to actual needs, thus reducing energy waste.

[0003] However, although IoT technology brings significant energy-saving potential to the urban lighting system, there are still some challenges in the implementation process. The stability and reliability of the control system are a key issue. For example, sensors may have false detections or failures, network failures may cause data transmission interruptions, controller response delays or incorrect settings, etc., which will cause lighting equipment to turn on and off frequently, thus leading to energy waste. Therefore, when designing an efficient, stable and scalable urban lighting energy efficiency management system, while ensuring technological innovation, the reliability and fault tolerance of the system must be fully considered. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an urban lighting energy efficiency management system based on Internet of Things technology to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An urban lighting energy efficiency management system based on Internet of Things technology, including a false detection and failure module, a network failure module, a response delay module, a model construction module, and an early warning module;

[0007] The false detection and failure module is used to obtain false detection and failure information of sensors, calculate a false detection and failure coefficient according to the false detection and failure information, and evaluate the potential impact of false detection and failure of sensors on the frequent switching of lighting equipment;

[0008] A network failure module, which is used to obtain the data transmission interruption information of network data, calculate the data transmission interruption abnormal coefficient according to the data transmission interruption information, and evaluate the potential impact of the data transmission interruption of network data on the frequent switching of lighting devices;

[0009] A response delay module, which is used to obtain the response delay information of the controller, calculate the controller response delay coefficient according to the response delay information, and evaluate the potential impact of the response delay of the controller on the frequent switching of lighting devices;

[0010] A model construction module, which is used to construct a hidden danger evaluation model for the frequent switching of lighting devices according to the misdetection failure coefficient, the data transmission interruption abnormal coefficient, and the controller response delay coefficient, generate a hidden danger evaluation index for the frequent switching of lighting devices, and judge the hidden danger faults existing in the lighting devices due to frequent switching;

[0011] An early warning module, when there are hidden danger faults in the lighting devices due to frequent switching, obtains the output sequence of the hidden danger evaluation model for the frequent switching of lighting devices, and classifies the early warning of the hidden danger faults existing in the lighting devices due to frequent switching according to the output sequence.

[0012] In a preferred embodiment, by obtaining the misdetection failure information of the sensor, analyzing the misdetection failure situation in the actual operation of the sensor, and obtaining the misdetection failure coefficient, the misdetection failure degree in the actual operation of the sensor is measured;

[0013] The acquisition logic of the misdetection failure coefficient is as follows:

[0014] Obtain the number of error signals detected by the sensor within a preset time period T and the total number of detected signals , calculate the misdetection rate of the sensor, and the expression is as follows , where represents the misdetection rate of the sensor; obtain the number of failures of the sensor within the operation cycle , calculate the failure rate of the sensor, and the expression is as follows , where represents the failure rate of the sensor; obtain the intensity of the external environment interference type under which the sensor operates, calculate the external environment interference factor, and the expression is as follows , where represents the external environment interference factor, represents the weight coefficient of the intensity value of the i-th external environment interference type, represents the intensity value of the i-th external environment interference type, , is a positive integer;

[0015] Calculate the false detection failure coefficient based on the false detection rate of the sensor, the failure rate of the sensor, and the external environment interference factor , the expression is as follows , where represents the false detection rate of the j-th sensor, represents the failure rate of the j-th sensor, the external environment interference factor of the j-th sensor, , is a positive integer.

[0016] In a preferred embodiment, by obtaining the data transmission interruption information of the network data, analyzing the data transmission interruption situation of the network data, and obtaining the data transmission interruption anomaly coefficient, to measure the data transmission interruption degree of the network data;

[0017] The acquisition logic of the data transmission interruption anomaly coefficient is as follows:

[0018] Obtain the number of data transmission interruptions within the preset time period , calculate the interruption frequency factor , the expression is as follows , obtain the interruption duration of each interruption , calculate the interruption duration factor , the expression is as follows , where represents the interruption duration of the n-th interruption, n = {1, 2,..., N}, N is a positive integer; obtain the delay time from each interruption to recovery , calculate the recovery delay factor , the expression is as follows , where represents the delay time from the m-th interruption to recovery; calculate the data transmission interruption anomaly coefficient , the expression is as follows , where represents the number of data packets lost during the interruption.

[0019] In a preferred embodiment, by obtaining the response slowness information of the controller, analyzing the response delay situation of the controller when executing instructions, and obtaining the controller response slowness coefficient, to measure the response delay degree of the controller when executing instructions;

[0020] The acquisition logic of the controller response slowness coefficient is as follows:

[0021] Obtain the response time from each time the controller receives an instruction to the execution feedback within the preset time period T , calculate the average response delay time , the expression is as follows , where represents the response time of the b-th controller from receiving an instruction to executing feedback, represents the expected response time, b = {1, 2,..., B}, and B is a positive integer; calculate the controller response delay coefficient , and the expression is as follows .

[0022] In a preferred embodiment, an evaluation model for the hidden danger of frequent switching of lighting devices is constructed based on the misdetection failure coefficient, the data transmission interruption anomaly coefficient, and the controller response delay coefficient, and an evaluation index for the hidden danger of frequent switching of lighting devices is generated , and the formula on which the model is based is as follows , where respectively represent the preset proportionality coefficients of the misdetection failure coefficient, the data transmission interruption anomaly coefficient, and the controller response delay coefficient, and are all greater than 0.

[0023] In a preferred embodiment, the evaluation index for the hidden danger of frequent switching of lighting devices is compared with a preset threshold for the evaluation index of the hidden danger of frequent switching of lighting devices to determine whether there is a hidden danger fault of frequent switching of the lighting device, specifically as follows:

[0024] If the evaluation index for the hidden danger of frequent switching of lighting devices is greater than the threshold for the evaluation index of the hidden danger of frequent switching of lighting devices, a signal for the hidden danger of frequent lighting switching is generated;

[0025] If the evaluation index for the hidden danger of frequent switching of lighting devices is less than or equal to the threshold for the evaluation index of the hidden danger of frequent switching of lighting devices, there is no need to generate a signal for the hidden danger of frequent lighting switching.

[0026] In a preferred embodiment, when a signal for the hidden danger of frequent switching of lighting devices is generated, the output sequence of the subsequent multiple time periods T of the evaluation model for the hidden danger of frequent switching of lighting devices is obtained, and the output sequence is marked as , where represents the evaluation index for the hidden danger of frequent switching of lighting devices output by the evaluation model for the hidden danger of frequent switching of lighting devices in the subsequent g-th time period T, g = {1, 2,..., G}, and G is a positive integer;

[0027] Calculate the average value of the evaluation index for the hidden danger of frequent switching of lighting devices , and the expression is as follows ; calculate the standard deviation of the evaluation index for the hidden danger of frequent switching of lighting devices , and the expression is as follows ;

[0028] Compare the standard deviation of the hidden danger assessment index of frequent switching of the lighting device with a preset standard deviation threshold, and conduct early warning classification on the hidden danger faults of frequent switching existing in the lighting device;

[0029] If the standard deviation of the hidden danger assessment index of frequent switching of the lighting device is greater than the standard deviation threshold, generate an early warning prompt signal;

[0030] If the standard deviation of the hidden danger assessment index of frequent switching of the lighting device is less than or equal to the standard deviation threshold, there is no need to generate an early warning prompt signal.

[0031] The technical effects and advantages of the present invention:

[0032] The present invention calculates the false detection failure coefficient through the false detection failure module, effectively evaluates the potential impact of sensor false detection or failure on the system operation, helps to quickly locate and repair faulty sensors, and avoids frequent switching problems caused by false detection. The data transmission interruption abnormal coefficient is calculated through the network failure module, which accurately reflects the interference of network failures on system control and data transmission, provides a basis for optimizing network configuration and improving data transmission reliability. The controller response delay coefficient is calculated through the response delay module to predict and solve the problem of controller performance degradation in advance, thereby reducing system instability caused by response delays. According to the false detection failure coefficient, the data transmission interruption abnormal coefficient, and the controller response delay coefficient, a hidden danger assessment model for frequent switching of lighting devices is constructed to generate a hidden danger assessment index for frequent switching of lighting devices, identify potential hidden danger faults of frequent switching of lighting devices. When there are hidden danger faults of frequent switching in the lighting device, obtain the output sequence of the hidden danger assessment model for frequent switching of the lighting device, and conduct early warning in advance on the hidden danger faults of frequent switching existing in the lighting device according to the output sequence. Description of the Drawings

[0033] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the accompanying drawings;

[0034] Figure 1 It is the flowchart of the system of the embodiment of the present invention. Specific Embodiments

[0035] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0036] Embodiment: The present invention provides as Figure 1A city lighting energy efficiency management system based on Internet of Things technology, including a misdetection failure module, a network failure module, a response delay module, a model construction module, and a warning module;

[0037] The misdetection failure module is used to obtain the misdetection failure information of the sensor, calculate the misdetection failure coefficient according to the misdetection failure information, and evaluate the potential impact of the misdetection failure of the sensor on the frequent switching of lighting equipment;

[0038] The network failure module is used to obtain the data transmission interruption information of network data, calculate the data transmission interruption abnormal coefficient according to the data transmission interruption information, and evaluate the potential impact of the data transmission interruption of network data on the frequent switching of lighting equipment;

[0039] The response delay module is used to obtain the response delay information of the controller, calculate the controller response delay coefficient according to the response delay information, and evaluate the potential impact of the response delay of the controller on the frequent switching of lighting equipment;

[0040] The model construction module is used to construct a hidden danger assessment model for the frequent switching of lighting equipment according to the misdetection failure coefficient, the data transmission interruption abnormal coefficient, and the controller response delay coefficient, generate a hidden danger assessment index for the frequent switching of lighting equipment, and judge the hidden danger faults of the lighting equipment with frequent switching;

[0041] The warning module, when there are hidden danger faults of frequent switching of lighting equipment, obtains the output sequence of the hidden danger assessment model for the frequent switching of lighting equipment, and classifies the hidden danger faults of the frequent switching of lighting equipment according to the output sequence;

[0042] The misdetection failure module is used to obtain the misdetection failure information of the sensor, calculate the misdetection failure coefficient according to the misdetection failure information, and evaluate the potential impact of the misdetection failure of the sensor on the frequent switching of lighting equipment;

[0043] The false detection failure coefficient is used to measure the degree of influence of the performance deviation of the sensor caused by false detection or failure on the control logic of the lighting equipment in actual operation. Its core function is to evaluate the reliability of sensors in environmental monitoring and the potential impact of sensor failure on the frequent switching of lighting equipment. By calculating the false detection failure coefficient, it is possible to quantify the potential impact of false detection or failure of sensors on the frequent switching of equipment, providing a scientific basis for subsequent optimization. Through real-time monitoring of the false detection failure coefficient, the working parameters of the sensor can be adjusted or the technology can be upgraded to improve the accuracy of data collection. The false detection failure coefficient can identify the frequent switching of lamps caused by false detection or failure, thereby dynamically adjusting the control strategy, avoiding unnecessary energy consumption, reducing equipment wear, and extending service life, thereby reducing maintenance and replacement costs; a larger false detection failure coefficient indicates that Problems with the environmental adaptability or hardware performance of the sensor may result in poor accuracy of the collected data, which may cause the lighting equipment to switch on and off frequently, resulting in energy waste and equipment loss, and at the same time have a negative impact on the stability and reliability of the system, increase maintenance costs and reduce overall energy efficiency; on the contrary, a smaller false detection failure coefficient indicates that the probability of false detection or failure of the sensor in actual operation is low, which indicates that the sensor has high environmental adaptability and hardware performance, and the accuracy of the collected data is high, which can effectively avoid frequent switching of lighting equipment, reduce energy waste and equipment wear, and at the same time improve the stability and reliability of the system, reduce maintenance costs and significantly improve overall energy efficiency;

[0044] Therefore, by obtaining the false detection failure information of the sensor, analyzing the false detection failure situation in the actual operation of the sensor, and obtaining the false detection failure coefficient, the false detection failure degree in the actual operation of the sensor is measured;

[0045] The logic for obtaining the false detection failure coefficient is as follows:

[0046] Get the number of error signals detected by the sensor within the preset time period T and the total number of heartbeats , calculate the sensor false detection rate, the expression is as follows ,in Indicates the sensor false detection rate; obtains the sensor in the operation cycle Number of failures within , calculate the sensor failure rate, the expression is as follows ,in Indicates the sensor failure rate; obtains the external environment interference type intensity of the sensor operation, and calculates the external environment interference factor. The expression is as follows ,in represents the external environmental interference factor, The weight coefficient representing the intensity value of the i-th external environmental interference type, represents the intensity value of the i-th external environment interference type, , is a positive integer;

[0047] It should be noted that the types of external environmental interference include strong light, rain, snow, dust, etc. The weight coefficient of the intensity value of the external environmental interference type is set according to the actual situation. For example, the expert empowerment method is adopted, that is, experts in related fields are invited to determine the preset weight coefficients of various indicators through professional opinion surveys and comprehensive evaluations;

[0048] Calculate the false detection failure coefficient based on the sensor false detection rate, sensor failure rate, and external environmental interference factors , the expression is as follows ,in represents the sensor false detection rate of the jth sensor, represents the sensor failure rate of the jth sensor, The external environmental interference factor of the jth sensor, , is a positive integer;

[0049] It should be noted that before calculating the false detection failure coefficient, it is necessary to ensure that the sensor false detection rate, sensor failure rate, and external environmental interference factors are normalized. Common normalization methods include Min-Max normalization and Z-Score normalization.

[0050] A network fault module is used to obtain the data transmission interruption information of the network data, and calculate the data transmission interruption abnormality coefficient according to the data transmission interruption information, so as to evaluate the potential impact of the data transmission interruption of the network data on the frequent switching of the lighting equipment;

[0051] The data transmission interruption anomaly coefficient is an indicator used to measure the impact of network data transmission interruption in the Internet of Things system on the operation of the lighting system. It mainly reflects the frequency and duration of network failures and the degree of impact on the accuracy of control instructions or monitoring data transmission. Through real-time monitoring and evaluation of data transmission interruption anomalies, potential hidden dangers can be discovered in time, control failures or abnormal switching of equipment caused by network problems can be prevented, and the continuous and stable operation of the system can be guaranteed. The data transmission interruption anomaly coefficient provides a key basis for the adaptive optimization of the intelligent lighting system, enabling the system to dynamically adjust strategies according to network conditions and enhance its intelligence and resilience. The larger the data transmission interruption anomaly coefficient, the more serious the network data transmission interruption problem, which may cause the system to be unable to obtain environmental data or execute control instructions in real time, thereby causing lighting equipment to frequently switch on and off, abnormal status or unable to respond on demand, resulting in energy waste and reduced operating efficiency; on the contrary, the smaller the data transmission interruption anomaly coefficient, the better the network transmission stability, and the smaller the impact of data transmission interruption on system operation, which helps to ensure the efficient and stable operation of the lighting system.

[0052] Therefore, by acquiring the data transmission interruption information of the network data, analyzing the data transmission interruption situation of the network data, and acquiring the data transmission interruption abnormality coefficient, the degree of data transmission interruption of the network data is measured;

[0053] The logic for obtaining the data transmission interruption abnormality coefficient is as follows:

[0054] At a preset time period Get the number of data transmission interruptions within , calculate the interrupt frequency factor , the expression is as follows , get the interruption duration of each interruption , calculate the interruption duration factor , the expression is as follows ,in Indicates the duration of the nth interruption, n={1,2,...,N}, N is a positive integer; obtains the delay time from each interruption to recovery , calculate the recovery delay factor , the expression is as follows ,in Indicates the delay time from the mth interruption to recovery; calculates the data transmission interruption abnormality coefficient , the expression is as follows ,in Indicates the number of packets lost during the outage;

[0055] It should be noted that before calculating the data transmission interruption anomaly coefficient, it is necessary to ensure that the interruption frequency factor, interruption duration factor, recovery delay factor, and number of data packets are all normalized;

[0056] A response delay module is used to obtain the response delay information of the controller, and calculate the response delay coefficient of the controller according to the response delay information, so as to evaluate the potential impact of the response delay of the controller on the frequent switching of the lighting equipment;

[0057] The controller response delay coefficient is an indicator used to measure the potential impact caused by the response delay when the controller executes instructions. It mainly reflects the response time deviation of the controller and the severity of the problems caused by it, such as reduced system operation efficiency, frequent switching of equipment, and increased energy consumption. A larger controller response delay coefficient indicates that the controller response delay is significant, which may cause frequent switching of lighting equipment, reduced energy efficiency and poor user experience. A smaller controller response delay coefficient indicates that the controller operation is relatively stable and the delay has less impact on the operation of the lighting system.

[0058] Therefore, by obtaining the response delay information of the controller, the response delay of the controller when executing instructions is analyzed, and the response delay coefficient of the controller is obtained to measure the degree of response delay of the controller when executing instructions;

[0059] The logic for obtaining the controller response delay coefficient is as follows:

[0060] Get the response time from each controller receiving a command to executing feedback within the preset time period T , calculate the average response delay time , the expression is as follows ,in It represents the response time from the controller receiving the instruction to executing the feedback for the bth time. Represents the expected response time, b={1,2,...,B}, B is a positive integer; calculate the controller response delay coefficient , the expression is as follows ;

[0061] A model building module is used to build a lighting equipment frequent switching hidden danger assessment model based on the false detection failure coefficient, the data transmission interruption abnormality coefficient, and the controller response delay coefficient, generate a lighting equipment frequent switching hidden danger assessment index, and judge the lighting equipment frequent switching hidden danger fault;

[0062] According to the false detection failure coefficient, data transmission interruption abnormal coefficient, and controller response delay coefficient, a lighting equipment frequent switching hidden danger assessment model is constructed to generate a lighting equipment frequent switching hidden danger assessment index The model is based on the following formula , where They represent the preset proportional coefficients of the false detection failure coefficient, the data transmission interruption abnormality coefficient, and the controller response delay coefficient, respectively, and All are greater than 0;

[0063] It should be noted that before constructing the frequent switching hazard assessment model of lighting equipment, it is necessary to ensure that the false detection failure coefficient, data transmission interruption abnormality coefficient, and controller response delay coefficient are all normalized; Set it according to the actual situation. For example, adopt the expert empowerment method, that is, invite experts in relevant fields to determine the preset proportion coefficients of various indicators through professional opinion surveys and comprehensive evaluations;

[0064] It can be seen from the above calculation expression that the larger the false detection failure coefficient, the larger the data transmission interruption abnormal coefficient, and the larger the controller response delay coefficient, the larger the lighting equipment frequent switching hidden danger assessment index is, indicating that the false detection failure of the sensor, the data transmission interruption of the network data, and the slow response of the controller have a deeper potential impact on the frequent switching of the lighting equipment. On the contrary, the smaller the false detection failure coefficient, the smaller the data transmission interruption abnormal coefficient, and the smaller the controller response delay coefficient, the smaller the lighting equipment frequent switching hidden danger assessment index is, indicating that the false detection failure of the sensor, the data transmission interruption of the network data, and the slow response of the controller have a shallower potential impact on the frequent switching of the lighting equipment.

[0065] The lighting equipment frequent switching hidden danger assessment index is compared with the preset lighting equipment frequent switching hidden danger assessment index threshold, and the lighting equipment frequent switching hidden danger fault is judged, as follows:

[0066] If the lighting equipment frequent switching hidden danger assessment index is greater than the lighting equipment frequent switching hidden danger assessment index threshold, it indicates that the lighting equipment has a high probability of hidden danger failure due to frequent switching, and a lighting frequent switching hidden danger signal is generated;

[0067] If the lighting equipment frequent switching hazard assessment index is less than or equal to the lighting equipment frequent switching hazard assessment index threshold, it indicates that the lighting equipment switching state is relatively stable, and there is no need to generate a lighting frequent switching hazard signal;

[0068] The early warning module obtains the output sequence of the frequent switching hidden danger assessment model of the lighting equipment when the lighting equipment has a hidden danger of frequent switching on and off, and classifies the hidden danger of frequent switching on and off of the lighting equipment according to the output sequence;

[0069] When the lighting frequent switching hazard signal is generated, the output sequence of the lighting equipment frequent switching hazard assessment model for subsequent multiple time periods T is obtained and the output sequence is marked as ,in represents the lighting equipment frequent switching hazard assessment index output by the lighting equipment frequent switching hazard assessment model in the subsequent g-th time period T, g={1,2,...,G}, where G is a positive integer;

[0070] Calculate the average value of the frequent switching hazard assessment index of lighting equipment , the expression is as follows ; Calculate the standard deviation of the lighting equipment frequent switching hazard assessment index , the expression is as follows ;

[0071] Compare the standard deviation of the lighting equipment frequent switching hidden danger assessment index with the preset standard deviation threshold, and classify the frequent switching hidden danger faults of the lighting equipment for early warning;

[0072] If the standard deviation of the lighting equipment frequent switching hidden danger assessment index is greater than the standard deviation threshold, it indicates that the hidden danger failure of the lighting equipment frequent switching is continuously fluctuating, and an early warning signal is generated;

[0073] If the standard deviation of the lighting equipment frequent switching hidden danger assessment index is less than or equal to the standard deviation threshold, it indicates that the probability of the lighting equipment frequent switching hidden danger failure being an accidental event is relatively high, and there is no need to generate an early warning signal;

[0074] The present invention calculates the false detection failure coefficient through the false detection failure module, effectively evaluates the potential impact of sensor false detection or failure on system operation, helps to quickly locate and repair faulty sensors, and avoids frequent switching problems caused by false detection. The data transmission interruption abnormality coefficient is calculated through the network fault module to accurately reflect the interference of network failures on system control and data transmission, and provide a basis for optimizing network configuration and improving data transmission reliability. The controller response delay coefficient is calculated through the response delay module to predict and solve the problem of controller performance degradation in advance, thereby reducing system instability caused by response delays. A lighting equipment frequent switching hidden danger assessment model is constructed based on the false detection failure coefficient, the data transmission interruption abnormality coefficient, and the controller response delay coefficient, and a lighting equipment frequent switching hidden danger assessment index is generated to identify potential frequent switching fault hidden dangers of lighting equipment. When the lighting equipment has a hidden danger failure of frequent switching, the output sequence of the lighting equipment frequent switching hidden danger assessment model is obtained, and advance warning of the frequent switching hidden danger failure of the lighting equipment is given according to the output sequence.

[0075] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0076] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0077] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0078] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0079] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims described above.

Claims

1. An urban lighting energy efficiency management system based on Internet of Things technology, characterized by: It includes false detection failure module, network failure module, slow response module, model building module and early warning module; The false detection failure module is used to obtain the false detection failure information of the sensor, calculate the false detection failure coefficient based on the false detection failure information, and evaluate the potential impact of the false detection failure of the sensor on the frequent switching of the lighting equipment; A network fault module is used to obtain the data transmission interruption information of the network data, and calculate the data transmission interruption abnormality coefficient according to the data transmission interruption information, so as to evaluate the potential impact of the data transmission interruption of the network data on the frequent switching of the lighting equipment; A response delay module is used to obtain the response delay information of the controller, and calculate the response delay coefficient of the controller according to the response delay information, so as to evaluate the potential impact of the response delay of the controller on the frequent switching of the lighting equipment; A model building module is used to build a lighting equipment frequent switching hidden danger assessment model based on the false detection failure coefficient, the data transmission interruption abnormality coefficient, and the controller response delay coefficient, generate a lighting equipment frequent switching hidden danger assessment index, and judge the lighting equipment frequent switching hidden danger fault; The early warning module obtains the output sequence of the frequent switching hidden danger assessment model of the lighting equipment when the lighting equipment has a hidden danger of frequent switching on and off, and classifies the hidden danger of frequent switching on and off of the lighting equipment according to the output sequence; By obtaining the false detection failure information of the sensor, analyzing the false detection failure situation in the actual operation of the sensor, and obtaining the false detection failure coefficient, the false detection failure degree in the actual operation of the sensor is measured; The logic for obtaining the false detection failure coefficient is as follows: The number of error signals S1 and the total number of detection signals S2 detected by the sensor are obtained within the preset time period T, and the sensor false detection rate is calculated. The expression is as follows Where WJ represents the sensor false detection rate; obtain the number of failures XC of the sensor within the operating cycle ZQ, and calculate the sensor failure rate. The expression is as follows Where SX represents the sensor failure rate; obtain the external environment interference type intensity of the sensor operation and calculate the external environment interference factor. The expression is as follows Where GR represents the external environmental interference factor, qz i The weight coefficient representing the intensity value of the i-th external environment interference type, lx i represents the intensity value of the i-th external environment interference type, i={1,2,...,I}, I is a positive integer; The false detection failure coefficient Wxjx is calculated based on the sensor false detection rate, sensor failure rate, and external environmental interference factors. The expression is as follows Among them WJ j represents the sensor false detection rate of the jth sensor, SX j represents the sensor failure rate of the jth sensor, GR j The external environment interference factor of the jth sensor, j = {1, 2, ..., J}, J is a positive integer; By acquiring the data transmission interruption information of the network data, analyzing the data transmission interruption situation of the network data, and acquiring the data transmission interruption abnormality coefficient, the degree of data transmission interruption of the network data is measured; The logic for obtaining the data transmission interruption abnormality coefficient is as follows: Get the number of data transmission interruptions ZD within the preset time period T, and calculate the interruption frequency factor PL. The expression is as follows Get the interruption duration sj of each interruption and calculate the interruption duration factor ZC. The expression is as follows: Among them sj n Represents the interruption duration of the nth interruption, n={1,2,...,N}, N is a positive integer; obtain the delay time yc from each interruption to recovery, and calculate the recovery delay factor HF, the expression is as follows Among them, yc m Represents the delay time from the mth interruption to recovery; calculates the data transmission interruption abnormality coefficient Scsz, the expression is as follows: Scsz = sjb*(PL+ZC+HF), where sjb represents the number of data packets lost during the interruption.

2. According to claim 1, the urban lighting energy efficiency management system based on Internet of Things technology is characterized by: By acquiring the response delay information of the controller, analyzing the response delay of the controller when executing instructions, and acquiring the response delay coefficient of the controller, the degree of response delay of the controller when executing instructions is measured; The logic for obtaining the controller response delay coefficient is as follows: Get the response time xys from each controller receiving a command to executing feedback within the preset time period T, and calculate the average response delay time Dmean, which is expressed as follows: where xys b represents the response time from the bth controller receiving the instruction to executing the feedback, yqs represents the expected response time, b={1,2,...,B}, B is a positive integer; calculate the controller response delay coefficient Vxyz, the expression is as follows 3. The urban lighting energy efficiency management system based on Internet of Things technology according to claim 1 is characterized by: According to the false detection failure coefficient, data transmission interruption abnormal coefficient and controller response delay coefficient, a lighting equipment frequent switching hidden danger assessment model is constructed to generate the lighting equipment frequent switching hidden danger assessment index QNFA. The model is based on the following formula QNFA=ln(1+a1*Wxjx+a2*Scsz+a3*Vxyz), where a1, a2 and a3 represent the preset proportional coefficients of the false detection failure coefficient, data transmission interruption abnormal coefficient and controller response delay coefficient, respectively, and a1, a2 and a3 are all greater than 0.

4. The urban lighting energy efficiency management system based on Internet of Things technology according to claim 3 is characterized by: The lighting equipment frequent switching hidden danger assessment index is compared with the preset lighting equipment frequent switching hidden danger assessment index threshold, and the lighting equipment frequent switching hidden danger fault is judged, as follows: If the lighting equipment frequent switching hazard assessment index is greater than the lighting equipment frequent switching hazard assessment index threshold, a lighting frequent switching hazard signal is generated; If the lighting equipment frequent switching hazard assessment index is less than or equal to the lighting equipment frequent switching hazard assessment index threshold, there is no need to generate a lighting frequent switching hazard signal.

5. The urban lighting energy efficiency management system based on Internet of Things technology according to claim 4 is characterized by: When the lighting frequent switching hazard signal is generated, the output sequence of the lighting equipment frequent switching hazard assessment model for subsequent multiple time periods T is obtained, and the output sequence is marked as SC = {QNFA g }, where QNFA g represents the lighting equipment frequent switching hazard assessment index output by the lighting equipment frequent switching hazard assessment model in the subsequent g-th time period T, g={1,2,...,G}, G is a positive integer; Calculate the average value PJZ of the frequent switching hazard assessment index of lighting equipment, the expression is as follows Calculate the standard deviation BZC of the lighting equipment frequent switching hazard assessment index, the expression is as follows Compare the standard deviation of the lighting equipment frequent switching hidden danger assessment index with the preset standard deviation threshold, and classify the frequent switching hidden danger faults of the lighting equipment for early warning; If the standard deviation of the lighting equipment frequent switching hidden danger assessment index is greater than the standard deviation threshold, an early warning signal is generated; If the standard deviation of the lighting equipment frequent switching hazard assessment index is less than or equal to the standard deviation threshold, there is no need to generate an early warning signal.

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