A method, device and medium for predicting the remaining life of an LED lamp

Abstract

The present invention discloses a method, device and medium for predicting the remaining life of an LED lamp. The method comprises: obtaining a working parameter sequence of an LED lamp; obtaining a fuzzy sequence of working parameters of the LED lamp according to the working parameter sequence of the LED lamp; obtaining a parameter solution of the exponential fuzzy prediction model according to the fuzzy sequence of working parameters of the LED lamp and a pre-constructed exponential fuzzy prediction model of the remaining life of the LED lamp; obtaining the remaining life of the LED lamp according to the working parameter sequence of the LED lamp and the parameter solution of the exponential fuzzy prediction model; the method proposed by the present invention can improve the robustness of the prediction method of the remaining life of the LED lamp and achieve a better prediction effect.

Description

Technical Field

[0001] The present invention relates to the field of smart street lamp systems, and in particular to a method, device and medium for predicting the remaining life of an LED lamp. Background Art

[0002] Compared with traditional lamps, the new generation of LED lamps has significant advantages in energy saving, environmental protection, life span and color rendering, so its application scope is expanding; currently, LED lamps are widely used in smart city street lighting systems as the main light-emitting devices. In smart city street lighting systems, the existing prediction methods for the remaining life of LED lamps have the disadvantage of poor robust performance. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a method for predicting the remaining life of an LED lamp, which can improve the robustness of the method for predicting the remaining life of an LED lamp and achieve a better prediction effect.

[0004] The present invention also provides an electronic device and a medium having the above-mentioned method for predicting the remaining life of an LED lamp.

[0005] A method for predicting the remaining life of an LED lamp according to a first aspect of an embodiment of the present invention includes:

[0006] Step S100, obtaining a sequence of working parameters of the LED lamp;

[0007] Step S200, obtaining a fuzzy sequence of working parameters of the LED lamp according to the working parameter sequence of the LED lamp;

[0008] Step S300, obtaining a parameter solution of the exponential fuzzy prediction model according to the fuzzy sequence of the working parameters of the LED lamp and a pre-constructed exponential fuzzy prediction model of the remaining life of the LED lamp;

[0009] Step S400: Obtain the remaining life of the LED lamp according to the operating parameter sequence of the LED lamp and the parameter solution of the exponential fuzzy prediction model.

[0010] A method for predicting the remaining life of an LED lamp according to an embodiment of the present invention has at least the following beneficial effects: the acquired working parameter sequence of the LED lamp is fuzzified to obtain a fuzzy sequence of the working parameters of the LED lamp; the working parameter sequence of the LED lamp is then input into a pre-constructed exponential fuzzy prediction model of the remaining life of the LED lamp, and the output result is the required remaining life of the LED lamp; the fuzzification process is combined with the exponential model to improve the robustness.

[0011] According to some embodiments of the present invention, in step S100, the sequence of obtaining the working parameters of the LED lamp includes:

[0012] The luminous flux data stream sequence of the LED lamp is sampled at a sampling time interval, and the obtained luminous flux data stream sequence is used as the working parameter sequence of the LED lamp.

[0013] According to some embodiments of the present invention, in step S200, obtaining a fuzzy sequence of operating parameters of the LED lamp according to the operating parameter sequence of the LED lamp includes:

[0014] Step S210, obtaining a minimum value sequence and a maximum value sequence respectively according to the working parameter sequence of the LED lamp;

[0015] Step S220, obtaining an upper deviation sequence and a lower deviation sequence according to the minimum value sequence and the maximum value sequence respectively;

[0016] Step S230: Obtain a fuzzy sequence of the working parameters of the LED lamp according to the working parameter sequence of the LED lamp, the upper deviation sequence, the lower deviation sequence, the minimum value sequence and the maximum value sequence.

[0017] According to some embodiments of the present invention, in step S300, obtaining a parameter solution of the exponential fuzzy prediction model according to the fuzzy sequence of the working parameters of the LED lamp and a pre-constructed exponential fuzzy prediction model of the remaining life of the LED lamp includes:

[0018] Step S310, constructing an exponential fuzzy prediction model for the remaining life of an LED lamp; wherein the exponential fuzzy prediction model for the remaining life of an LED lamp comprises: a fuzzy sequence of working parameters of the LED lamp, a fuzzy quantity of an initial value of the working parameters of the LED lamp, and a fuzzy quantity of an attenuation value; the fuzzy quantity of the initial value of the working parameters of the LED lamp comprises: an initial value of the working parameters of the LED lamp and a first fuzzy quantity; the fuzzy quantity of the attenuation value comprises: an attenuation value and a second fuzzy quantity;

[0019] Step S320: Obtain a parameter solution of the exponential fuzzy prediction model according to the exponential fuzzy prediction model of the remaining life of the LED lamp and the fuzzy sequence of the operating parameters of the LED lamp.

[0020] According to some embodiments of the present invention, in step S320, obtaining a parameter solution of the exponential fuzzy prediction model according to the exponential fuzzy prediction model of the remaining life of the LED lamp and the fuzzy sequence of the working parameters of the LED lamp includes:

[0021] Step S321, obtaining a fuzzy fitting sequence of working parameters of the LED lamp according to the first fuzzy quantity and the second fuzzy quantity;

[0022] Step S322, obtaining a closeness according to the upper deviation sequence, the lower deviation sequence, the fuzzy fitting sequence of the working parameters of the LED lamp, and the fuzzy sequence of the working parameters of the LED lamp;

[0023] Step S323: Obtain a parameter solution of the exponential fuzzy prediction model according to the closeness, the preset threshold and the overall fuzziness.

[0024] According to some embodiments of the present invention, the initial value of the working parameter of the LED lamp and the attenuation value are obtained in the following manner:

[0025] According to the working parameter sequence of the LED lamp and the exponential model, the initial value and attenuation value of the working parameter of the LED lamp are obtained.

[0026] According to some embodiments of the present invention, the overall ambiguity is obtained by:

[0027] According to the initial value of the working parameter of the LED lamp and the attenuation value, a first fuzzy weight and a second fuzzy weight are obtained respectively;

[0028] An overall blur degree is obtained according to the first blur amount, the second blur amount, the first blur weight, and the second blur weight.

[0029] According to some embodiments of the present invention, in step S400, obtaining the remaining life of the LED lamp according to the operating parameter sequence of the LED lamp, the parameter solution of the exponential fuzzy prediction model and the exponential fuzzy prediction model of the remaining life of the LED lamp includes:

[0030] Step S401, obtaining a fuzzy remaining life according to a parameter solution of the exponential fuzzy prediction model and the exponential fuzzy prediction model of the remaining life of the LED lamp;

[0031] Step S402: defuzzify the fuzzy remaining life to obtain the remaining life of the LED lamp.

[0032] An electronic device according to a second aspect of an embodiment of the present invention includes:

[0033] Memory, used to store programs;

[0034] A processor is used to execute the program stored in the memory. When the processor executes the program stored in the memory, the processor is used to execute the method as described in any one of the first aspects.

[0035] A storage medium according to an embodiment of a third aspect of the present invention stores computer executable instructions, wherein the computer executable instructions are used to execute the method as described in any one of the first aspects.

[0036] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings are used to provide a further understanding of the technical solution of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the technical solution of the present invention and do not constitute a limitation to the technical solution of the present invention.

[0038] Figure 1 This is a flow chart of a method for predicting the remaining life of an LED lamp provided by an embodiment of the present invention;

[0039] Figure 2 yes Figure 1 Specific flow chart of step S200 in FIG.

[0040] Figure 3 yes Figure 1 Specific flow chart of step S300 in FIG.

[0041] Figure 4 yes Figure 3 Specific flow chart of step S320 in FIG.

[0042] Figure 5 It is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0044] It should be understood that in the description of the embodiments of the present invention, the meaning of multiple (or multiple) is more than two, greater than, less than, and exceeding are understood to exclude the number itself, and above, below, and within are understood to include the number itself. If there is a description of "first", "second", etc., it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0045] like Figure 1 As shown, an embodiment of the present invention provides a method for predicting the remaining life of an LED lamp, comprising:

[0046] Step S100, obtaining a sequence of working parameters of the LED lamp;

[0047] Step S200, obtaining a fuzzy sequence of working parameters of the LED lamp according to the working parameter sequence of the LED lamp;

[0048] Step S300, obtaining a parameter solution of the exponential fuzzy prediction model according to the fuzzy sequence of the working parameters of the LED lamp and the pre-constructed exponential fuzzy prediction model of the remaining life of the LED lamp;

[0049] Step S400, obtaining the remaining life of the LED lamp according to the working parameter sequence of the LED lamp and the parameter solution of the exponential fuzzy prediction model;

[0050] It should be noted that the obtained working parameter sequence of the LED lamp is fuzzified to obtain a fuzzy sequence of the working parameters of the LED lamp; the working parameter sequence of the LED lamp is then input into the constructed exponential fuzzy prediction model of the remaining life of the LED lamp, and the output result is the required remaining life of the LED lamp; by combining the fuzzy processing with the exponential model, the robustness is improved and the prediction accuracy is improved.

[0051] In one embodiment, the above-mentioned method for predicting the remaining life of an LED lamp is executed on a lower computer;

[0052] It should be noted that in an LED lamp control system, executing the prediction method of the remaining life of the LED lamp on the lower computer greatly reduces the transmission pressure caused by simultaneously sending the LED lamp's own parameters to the upper computer, and reduces the impact of low robustness performance caused by noise interference in the transmission.

[0053] In one embodiment, in step S100, obtaining the operating parameter sequence of the LED lamp includes:

[0054] The luminous flux data stream sequence of the LED lamp is sampled at a sampling time interval, and the obtained luminous flux data stream sequence is used as the working parameter sequence of the LED lamp; in one embodiment, the sampling time interval is 1 hour. When the luminous flux of the LED lamp drops to 70% of the original factory value, the LED lamp is considered to have failed and needs to be replaced.

[0055] like Figure 2 As shown, in one embodiment, in step S200, obtaining the fuzzy sequence of the working parameters of the LED lamp according to the working parameter sequence of the LED lamp includes:

[0056] Step S210, obtaining a minimum value sequence and a maximum value sequence according to the working parameter sequence of the LED lamp;

[0057] Step S220, obtaining an upper deviation sequence and a lower deviation sequence according to the minimum value sequence and the maximum value sequence respectively;

[0058] Step S230: Obtain a fuzzy sequence of the working parameters of the LED lamp according to the working parameter sequence, the upper deviation sequence, the lower deviation sequence, the minimum value sequence and the maximum value sequence of the LED lamp.

[0059] In one embodiment, in step S210, according to the working parameter sequence of the LED lamp, obtaining the minimum value sequence and the maximum value sequence respectively includes:

[0060] The working parameter sequence of the LED lamp is recorded as φ n (n=1, 2, ..., m); n is the number of each working parameter in the working parameter sequence of the LED lamp;

[0061] According to the working parameter sequence of LED lamp φ n (n=1,2,…,m) respectively determine the maximum value sequence φ_P n and the minimum sequence φ_Q n ; Among them, the maximum value sequence φ_P n The calculation formula is:

[0062]

[0063] Minimum sequence φ_Q n The calculation formula is:

[0064]

[0065] In one embodiment, in step S220, obtaining an upper deviation sequence and a lower deviation sequence according to the minimum value sequence and the maximum value sequence respectively includes:

[0066] According to the maximum value sequence φ_P n and the minimum sequence φ_Q n , and get the upper deviation sequence φ_A n :

[0067]

[0068] And according to the maximum value sequence φ_P n and the minimum sequence φ_Q n , and get the lower deviation sequence φ_B n :

[0069]

[0070] In one embodiment, in step S230, according to the working parameter sequence, the upper deviation sequence, the lower deviation sequence, the minimum value sequence and the maximum value sequence of the LED lamp, the fuzzy sequence of the working parameters of the LED lamp is obtained, which includes:

[0071] According to the working parameter sequence, upper deviation sequence, lower deviation sequence, minimum value sequence and maximum value sequence of the LED lamp, the triangular fuzzy working data flow is obtained.

[0072]

[0073] It is easy to understand that the data flow of triangle fuzzy work is the fuzzy sequence of the working parameters of the LED lamp. Figure 3 As shown, in one embodiment, in step S300, according to the fuzzy sequence of the working parameters of the LED lamp and the pre-constructed exponential fuzzy prediction model of the remaining life of the LED lamp, the parameter solution of the exponential fuzzy prediction model is obtained, which includes:

[0074] Step S310, constructing an exponential fuzzy prediction model for the remaining life of an LED lamp; wherein the exponential fuzzy prediction model for the remaining life of an LED lamp includes: a fuzzy sequence of working parameters of the LED lamp, a fuzzy quantity of initial values ​​of working parameters of the LED lamp, and a fuzzy quantity of attenuation value; the fuzzy quantity of initial values ​​of working parameters of the LED lamp includes: an initial value of working parameters of the LED lamp and a first fuzzy quantity; the fuzzy quantity of attenuation value includes: an attenuation value and a second fuzzy quantity;

[0075] Step S320: Obtain a parameter solution of the exponential fuzzy prediction model according to the exponential fuzzy prediction model of the remaining life of the LED lamp and the fuzzy sequence of the working parameters of the LED lamp.

[0076] In one embodiment, the exponential fuzzy prediction model of the remaining life of an LED lamp constructed according to the general formula of the exponential model is:

[0077]

[0078] Among them, T, and They are the remaining life of the LED lamp, the initial fuzzy value of the working parameters of the LED lamp, the fuzzy sequence of the working parameters of the LED lamp, and the fuzzy value of the attenuation value. Since they are all fuzzy variables, they can be represented by triangular fuzzy numbers, as follows:

[0079]

[0080] Among them, φ0 is the initial value of the working parameters of the LED lamp, is the first blur value, θ is the attenuation value, is the second fuzzy quantity; the initial value of the working parameters of the LED lamp, the first fuzzy quantity, the attenuation value and the second fuzzy quantity are all unknown parameters, that is, the parameter solution of the exponential fuzzy prediction model; the parameter solution of the exponential fuzzy prediction model must be obtained first, and the parameters of the exponential fuzzy prediction model of the remaining life of the LED lamp must be clear before further solving the remaining life of the LED lamp.

[0081] like Figure 4 As shown, in one embodiment, in step S320, according to the exponential fuzzy prediction model of the remaining life of the LED lamp and the fuzzy sequence of the working parameters of the LED lamp, the parameter solution of the exponential fuzzy prediction model is obtained, including:

[0082] Step S321, obtaining a fuzzy fitting sequence of working parameters of the LED lamp according to the first fuzzy quantity and the second fuzzy quantity;

[0083] Step S322, obtaining a closeness according to the upper deviation sequence, the lower deviation sequence, the fuzzy fitting sequence of the working parameters of the LED lamp, and the fuzzy sequence of the working parameters of the LED lamp;

[0084] Step S323: Obtain a parameter solution of the exponential fuzzy prediction model according to the closeness, the preset threshold and the overall fuzziness.

[0085] In one embodiment, in step S321, obtaining the fuzzy fitting sequence of the working parameters of the LED lamp according to the first fuzzy amount and the second fuzzy amount includes:

[0086] According to the first blur and the second blur amount Get the fuzzy fitting sequence of the working parameters of the LED lamp

[0087] In one embodiment, in step S322, according to the upper deviation sequence, the lower deviation sequence, the fuzzy fitting sequence of the working parameters of the LED lamp, and the fuzzy sequence of the working parameters of the LED lamp, the closeness is obtained, which includes:

[0088] According to the upper deviation sequence φ_A n , lower deviation sequence φ_B n , Fuzzy fitting sequence of working parameters of LED lamp and the fuzzy sequence of working parameters of LED lights Get close

[0089] Among them, closeness The calculation formula is:

[0090]

[0091] In one embodiment, in step S323, obtaining the parameter solution of the exponential fuzzy prediction model according to the closeness, the preset threshold and the overall fuzziness includes:

[0092] According to the closeness Preset threshold λ and overall fuzziness The first set of equations is obtained:

[0093] Wherein, the preset threshold λ is a preset fit threshold;

[0094] The closeness Substituting into the first system of equations, we get the second system of equations:

[0095]

[0096] Simplifying the second set of equations, we get the third set of equations:

[0097]

[0098] According to the third equation group, the standard linear programming equation group is obtained:

[0099]

[0100] The working parameter sequence of the LED lamp is solved according to the linear programming standard equation group to obtain the optimal solution vector, which is used as the parameter solution of the exponential fuzzy prediction model.

[0101] In one embodiment, the working parameter sequence of the LED lamp is solved according to the linear programming standard equation group to obtain the optimal solution vector, including:

[0102] The working parameter sequence of LED lamp φ n Substitute (n=1,2,…,m) into the standard linear programming equation system and obtain the optimal solution vector based on the simplex method Among them, the optimal solution vector Including: LED lamp working parameter initial value φ0, the first fuzzy quantity The attenuation value θ and the second blur amount

[0103] In one embodiment, the initial value and attenuation value of the working parameter of the LED lamp are obtained by:

[0104] According to the working parameter sequence and exponential model of the LED lamp, the initial value and attenuation value of the working parameter of the LED lamp are obtained.

[0105] In one embodiment, according to the working parameter sequence and the exponential model of the LED lamp, obtaining the initial value and the attenuation value of the working parameter of the LED lamp includes:

[0106] The LED lamp working parameter sequence {(n=1,φ n＝1 );(n=2,φ n＝2 );……;(n=m,φ n＝m )} is substituted into the above exponential fuzzy prediction model to obtain the fourth set of equations:

[0107]

[0108] The fourth set of equations is solved based on the least squares method to obtain the initial value φ0 and the attenuation value θ of the working parameters of the LED lamp.

[0109] In one embodiment, the overall blur is obtained by:

[0110] According to the initial value and attenuation value of the working parameter of the LED lamp, a first fuzzy weight and a second fuzzy weight are obtained respectively;

[0111] An overall blur degree is obtained according to the first blur amount, the second blur amount, the first blur weight, and the second blur weight.

[0112] In one embodiment, the overall blur is obtained by:

[0113] According to the initial value φ0 and attenuation value θ of the working parameters of the LED lamp, the first fuzzy weight ω1 and the second fuzzy weight ω2 are obtained by the following formulas:

[0114]

[0115] According to the first blur Second blur amount The first fuzzy weight ω1 and the second fuzzy weight ω2 are used to obtain the overall fuzziness, where the overall fuzziness is:

[0116] In one embodiment, in step S400, according to the working parameter sequence of the LED lamp, the parameter solution of the exponential fuzzy prediction model and the exponential fuzzy prediction model of the remaining life of the LED lamp, obtaining the remaining life of the LED lamp includes:

[0117] Step S401, obtaining the fuzzy remaining life according to the parameter solution of the exponential fuzzy prediction model and the exponential fuzzy prediction model of the remaining life of the LED lamp;

[0118] Step S402: defuzzify the fuzzy remaining life to obtain the remaining life of the LED lamp.

[0119] In one embodiment, in step S401, according to the parameter solution of the exponential fuzzy prediction model and the exponential fuzzy prediction model of the remaining life of the LED lamp, the fuzzy remaining life is obtained, which includes:

[0120] Substituting the parameter solution of the exponential fuzzy prediction model into the exponential fuzzy prediction model of the remaining life of the LED lamp, the fifth set of equations is obtained:

[0121]

[0122] , due to the fuzzy sequence of LED lamp working parameters is a triangular fuzzy number. As the remaining life T changes, the fuzzy sequence of the working parameters of the LED lamp It is represented as a cluster of curves. It is easy to understand that the remaining life T is the remaining use time of the LED lamp.

[0123] When the working parameters of the LED lamp drop to 70% of the original factory value, it is considered a failure state, and the sixth set of equations is obtained:

[0124] The sixth set of equations is the fuzzy remaining life.

[0125] In one embodiment, in step S402, the fuzzy remaining life is defuzzified to obtain the remaining life of the LED lamp, including:

[0126] The above fuzzy remaining life is defuzzified by weighted average method to obtain the remaining life T of the LED lamp:

[0127]

[0128] In one embodiment, the method for predicting the remaining life of an LED lamp further includes:

[0129] The remaining life T of the LED lamp is sent to the host computer, so that the host computer receives and displays the prediction result of the remaining life of the LED lamp, that is, the remaining life T of the LED lamp.

[0130] In one embodiment, sending the remaining life T of the LED lamp to the host computer includes:

[0131] Encode in the order of the start code, the number of the LED street lamp, the binary code of the prediction result (i.e., the remaining life T of the LED lamp), the stop code, and the cyclic redundancy check code to obtain the data packet to be sent;

[0132] The data packet to be sent is sent to the host computer via a wireless communication network; in one embodiment, the data packet is uploaded to the host computer via GRPS and the Internet;

[0133] It should be noted that the sequential format of the start code, the number of the LED street lamp, the binary code of the prediction result (i.e., the remaining life T of the LED lamp), the stop code, and the cyclic redundancy check code can be obtained or directly acquired by those skilled in the art according to the prediction result;

[0134] In one embodiment, the upper computer performs redundancy check and data unpacking on the prediction results sent by each lower computer based on multi-threading technology, and displays and outputs each successfully recovered prediction result in the form of a GUI.

[0135] After comparative experiments, the prediction method for the remaining life of an LED lamp proposed in the embodiment of the present invention is compared with the existing prediction method for the remaining life of an LED lamp, and the following table is obtained:

[0136]

[0137]

[0138] Among them, the polynomial fitting prediction method, the AR autoregressive prediction method and the exponential prediction method are all existing prediction methods for the remaining life of LED lamps; MAPE is the mean absolute percentage error, which is usually used to measure the prediction accuracy of the prediction method. The prediction method with a MAPE within 10% is a high-precision prediction method, and the prediction method with a MAPE exceeding 10% and within 20% is a good prediction method; RMSE is the root mean square error, which reflects the stability of the prediction algorithm. The smaller the RMSE, the stronger the stability of the prediction algorithm.

[0139] It can be seen from the above table that the MAPE of the method proposed in the embodiment of the present invention is within 10%, which is a high-precision prediction method; and compared with other existing prediction methods for the remaining life of LED lamps, the RMSE is significantly reduced, the stability is greatly enhanced, and good robustness is shown.

[0140] An embodiment of the present invention provides an electronic device. The electronic device includes but is not limited to:

[0141] Memory, used to store programs;

[0142] The processor is used to execute the program stored in the memory. When the processor executes the program stored in the memory, the processor is used to execute the above-mentioned method for predicting the remaining life of an LED lamp.

[0143] The processor and the memory may be connected via a bus or other means.

[0144] In addition, refer to Figure 5 An embodiment of the present application further provides an electronic device, which includes: a memory 11, a processor 12, and a computer program stored in the memory 11 and executable on the processor 12.

[0145] The processor 12 and the memory 11 may be connected via a bus or other means.

[0146] The non-transient software program and instructions required to implement the method for predicting the remaining life of an LED lamp in the above embodiment are stored in the memory 11, and when executed by the processor 12, the method for predicting the remaining life of an LED lamp in the above embodiment is executed.

[0147] An embodiment of the present invention further provides a storage medium storing computer executable instructions, wherein the computer executable instructions are used to execute the above-mentioned method for predicting the remaining life of an LED lamp.

[0148] In one embodiment, the storage medium stores computer executable instructions that are executed by one or more control processors.

[0149] The above described embodiments are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0150] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically include computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0151] Embodiments of the present invention are described herein, including preferred embodiments known to the inventor for performing the present invention. After reading the above description, the variations of these described embodiments will become apparent to those skilled in the art. The inventor wishes that the technician adopt such variations as appropriate, and the inventor intends to practice the embodiments of the present invention in a manner different from that specifically described herein. Therefore, as permitted by applicable law, the scope of the present invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto. In addition, the scope of the present invention encompasses any combination of the above-mentioned elements in all possible variations thereof, unless otherwise indicated herein or otherwise clearly contradicted by context.

Claims

1. A method for predicting the remaining life of an LED lamp, characterized in that: include: Step S100, obtaining a sequence of working parameters of the LED lamp; Step S200, obtaining a fuzzy sequence of working parameters of the LED lamp according to the working parameter sequence of the LED lamp; Step S300, obtaining a parameter solution of the exponential fuzzy prediction model according to the fuzzy sequence of the working parameters of the LED lamp and a pre-constructed exponential fuzzy prediction model of the remaining life of the LED lamp; Step S400, obtaining the remaining life of the LED lamp according to the operating parameter sequence of the LED lamp and the parameter solution of the exponential fuzzy prediction model; In step S100, the sequence of obtaining the working parameters of the LED lamp includes: Sampling the luminous flux data stream sequence of the LED lamp at a sampling time interval, and using the obtained luminous flux data stream sequence as the working parameter sequence of the LED lamp; In step S200, obtaining a fuzzy sequence of working parameters of the LED lamp according to the working parameter sequence of the LED lamp includes: Step S210, obtaining a minimum value sequence and a maximum value sequence respectively according to the working parameter sequence of the LED lamp; Step S220, obtaining an upper deviation sequence and a lower deviation sequence according to the minimum value sequence and the maximum value sequence respectively; Step S230, obtaining a fuzzy sequence of the working parameters of the LED lamp according to the working parameter sequence of the LED lamp, the upper deviation sequence, the lower deviation sequence, the minimum value sequence and the maximum value sequence; In step S300, the parameter solution of the exponential fuzzy prediction model is obtained according to the fuzzy sequence of the working parameters of the LED lamp and the pre-constructed exponential fuzzy prediction model of the remaining life of the LED lamp, including: Step S310, constructing an exponential fuzzy prediction model for the remaining life of an LED lamp; wherein the exponential fuzzy prediction model for the remaining life of an LED lamp comprises: a fuzzy sequence of working parameters of the LED lamp, a fuzzy quantity of an initial value of the working parameters of the LED lamp, and a fuzzy quantity of an attenuation value; the fuzzy quantity of the initial value of the working parameters of the LED lamp comprises: an initial value of the working parameters of the LED lamp and a first fuzzy quantity; the fuzzy quantity of the attenuation value comprises: an attenuation value and a second fuzzy quantity; Step S320, obtaining a parameter solution of the exponential fuzzy prediction model according to the exponential fuzzy prediction model of the remaining life of the LED lamp and the fuzzy sequence of the working parameters of the LED lamp; In step S320, according to the exponential fuzzy prediction model of the remaining life of the LED lamp and the fuzzy sequence of the working parameters of the LED lamp, the parameter solution of the exponential fuzzy prediction model is obtained, including: Step S321, obtaining a fuzzy fitting sequence of working parameters of the LED lamp according to the first fuzzy quantity and the second fuzzy quantity; Step S322, obtaining a closeness according to the upper deviation sequence, the lower deviation sequence, the fuzzy fitting sequence of the working parameters of the LED lamp, and the fuzzy sequence of the working parameters of the LED lamp; Step S323: Obtain a parameter solution of the exponential fuzzy prediction model according to the closeness, the preset threshold and the overall fuzziness.

2. The method for predicting the remaining life of an LED lamp according to claim 1, characterized in that: The initial value of the working parameter of the LED lamp and the attenuation value are obtained by the following method: According to the working parameter sequence and the exponential model of the LED lamp, the initial value and attenuation value of the working parameter of the LED lamp are obtained.

3. The method for predicting the remaining life of an LED lamp according to claim 1, characterized in that: The overall ambiguity is obtained as follows: According to the initial value of the working parameter of the LED lamp and the attenuation value, a first fuzzy weight and a second fuzzy weight are obtained respectively; An overall blur degree is obtained according to the first blur amount, the second blur amount, the first blur weight, and the second blur weight.

4. The method for predicting the remaining life of an LED lamp according to claim 1, characterized in that: In step S400, according to the working parameter sequence of the LED lamp, the parameter solution of the exponential fuzzy prediction model and the exponential fuzzy prediction model of the remaining life of the LED lamp, the remaining life of the LED lamp is obtained, which includes: Step S401, obtaining a fuzzy remaining life according to a parameter solution of the exponential fuzzy prediction model and the exponential fuzzy prediction model of the remaining life of the LED lamp; Step S402: defuzzify the fuzzy remaining life to obtain the remaining life of the LED lamp.

5. An electronic device, characterized in that: include: Memory, used to store programs; A processor, configured to execute the program stored in the memory. When the processor executes the program stored in the memory, the processor is configured to execute the method as claimed in any one of claims 1 to 4.

6. A storage medium, characterized in that: Computer executable instructions are stored, and the computer executable instructions are used to execute the method according to any one of claims 1 to 4.

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