Urban lighting energy efficiency management system based on Internet of Things technology

Abstract

The invention discloses an urban lighting energy efficiency management system based on the Internet of Things technology, and particularly relates to the technical field of urban lighting energy efficiency management, a false detection failure coefficient is calculated through a false detection failure module, the potential influence of false detection or failure of a sensor on system operation is effectively evaluated, the problem of frequent switching caused by false detection is avoided, and the system reliability is improved. The network fault module calculates a data transmission interruption abnormal coefficient to accurately reflect interference of a network fault on system control and data transmission, the response delay module calculates a controller response delay coefficient to reduce system instability caused by response delay, a lighting equipment frequent switching hidden danger assessment model is constructed, and a lighting equipment frequent switching hidden danger assessment model is established. Generating a lighting equipment frequent on-off hidden danger evaluation index, identifying a potential frequent on-off fault hidden danger of the lighting equipment, and when the lighting equipment has a frequent on-off hidden danger fault, obtaining an output sequence of the lighting equipment frequent on-off hidden danger evaluation model; and according to the output sequence, carrying out advanced early warning on a frequent switching hidden trouble fault of the lighting equipment.

Description

Technical Field

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

[0002] With the continuous advancement of urbanization, urban lighting systems, as an important part of infrastructure, have gradually transformed from traditional lighting methods to intelligent and energy-saving ones. Traditional urban lighting systems usually rely on fixed schedules for control and lack real-time response to environmental changes, resulting in a lot of unnecessary energy waste. With the advancement of technology, especially the rapid development of Internet of Things (IoT) technology, more and more cities are beginning 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 collaboration of sensors, intelligent controllers and communication networks, intelligent lighting systems can monitor factors such as ambient light, traffic flow, climate change in real time, and automatically adjust the switch status, brightness and working hours of lamps based on these data. This flexible dynamic adjustment greatly improves the energy efficiency of the lighting system, enabling it to be intelligently adjusted according to actual needs, thereby reducing energy waste.

[0003] However, although IoT technology brings significant energy-saving potential to urban lighting systems, there are still some challenges in the implementation process. The stability and reliability of the control system is a key issue. For example, sensors may misdetect or fail, network failures may cause data transmission interruptions, and controllers may respond slowly or have incorrect settings, which may cause lighting equipment to turn on or off frequently, leading to energy waste. Therefore, in order to design an efficient, stable, and scalable urban lighting energy efficiency management system, it is necessary to fully consider the reliability and fault tolerance of the system while ensuring technological innovation. 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-mentioned background technology.

[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 failure module, a network failure module, a slow response module, a model building module, and an early warning module;

[0007] 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;

[0008] 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;

[0009] 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;

[0010] 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;

[0011] 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 failure, and classifies the hidden danger of frequent switching failure of the lighting equipment according to the output sequence.

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

[0013] The logic for obtaining the false detection failure coefficient is as follows:

[0014] 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;

[0015] 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.

[0016] In a preferred embodiment, by acquiring data transmission interruption information of network data, analyzing the data transmission interruption situation of network data, and acquiring a data transmission interruption abnormality coefficient, the degree of data transmission interruption of network data is measured;

[0017] The logic for obtaining the data transmission interruption abnormality coefficient is as follows:

[0018] 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.

[0019] In a preferred embodiment, by acquiring the response delay information of the controller, analyzing the response delay of the controller when executing the instruction, and acquiring the response delay coefficient of the controller, the degree of response delay of the controller when executing the instruction is measured;

[0020] The logic for obtaining the controller response delay coefficient is as follows:

[0021] 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 .

[0022] In a preferred embodiment, a lighting equipment frequent switching hazard assessment model is constructed based on the false detection failure coefficient, the data transmission interruption abnormality coefficient, and the controller response delay coefficient to generate a lighting equipment frequent switching hazard 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 Both are greater than 0.

[0023] In a preferred embodiment, the lighting equipment frequent switching hidden danger assessment index is compared with a preset lighting equipment frequent switching hidden danger assessment index threshold, and the lighting equipment is judged to have a hidden danger fault of frequent switching, as follows:

[0024] 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;

[0025] 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.

[0026] In a preferred embodiment, when a 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;

[0027] 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 ;

[0028] 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;

[0029] 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;

[0030] 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.

[0031] 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 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0034] Figure 1 Flowchart of a system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Embodiment: The present invention provides as Figure 1An urban lighting energy efficiency management system based on the Internet of Things technology is shown, including a false detection failure module, a network failure module, a slow response module, a model building module, and an early warning module;

[0037] 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;

[0038] 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;

[0039] 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;

[0040] 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;

[0041] 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;

[0042] 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;

[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 weighting 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 the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0079] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

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 failure, and classifies the hidden danger of frequent switching failure of the lighting equipment according to the output sequence.

2. According to claim 1, the urban lighting energy efficiency management system based on Internet of Things technology is characterized by: 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: 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; 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.

3. The urban lighting energy efficiency management system based on Internet of Things technology according to claim 1 is characterized by: 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: 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.

4. The urban lighting energy efficiency management system based on Internet of Things technology according to claim 1 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 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 .

5. 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 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 Both are greater than 0.

6. The urban lighting energy efficiency management system based on Internet of Things technology according to claim 5 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.

7. The urban lighting energy efficiency management system based on Internet of Things technology according to claim 6 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 ,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; 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 ; 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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