Intelligent operation and maintenance method of photo-thermal energy storage key equipment and related device

Abstract

The invention discloses an intelligent operation and maintenance method of photo-thermal energy storage key equipment and a related device, and relates to the field of photo-thermal system risk management and control, and the method comprises the steps: obtaining a failure mode, a failure position, a failure reason and a failure result of groove type photo-thermal system key equipment; performing criticality analysis on the failure mode of each device to obtain risk factors of the failure mode of each device; determining the expert evaluation aggregation fuzzy number of each risk factor and the weight of each risk factor by adopting a Z-number improved AHP method; weighting the expert evaluation aggregation fuzzy number of each risk factor and the weight of each risk factor to obtain a risk priority number of the key equipment of the slot type photo-thermal system; sorting the risk priority numbers of the key equipment of the slot type photo-thermal system to obtain a failure mode risk sequence; and determining corresponding management and optimization measures based on the failure mode risk ranking. According to the invention, the safety risk management and control level of the photo-thermal system can be improved, so that the risk is stably controlled.

Description

Technical Field

[0001] The present application relates to the technical field of risk management of photothermal systems, and in particular to an intelligent operation and maintenance method and related devices for key photothermal energy storage equipment. Background Art

[0002] With the overconsumption of fossil energy and the worsening global environmental problems, the development and utilization of renewable and clean energy has been greatly promoted in recent years. With its unique technological advantages, solar thermal power generation and heat utilization have gained increasing attention and application, showing strong vitality and competitiveness.

[0003] Although trough solar thermal power generation technology has a long history of development and has made progress in technology and economy, its large-scale commercialization still faces challenges, especially in terms of long-term reliability. The stability of core equipment such as solar collectors, heat transfer medium pipelines and heat exchangers is crucial to the power generation efficiency and life of the system. However, due to the long-term exposure of the system to complex environments such as high temperature, corrosion, and high radiation, equipment fatigue, aging and failure problems gradually emerge. The reliability of the equipment directly affects the operational safety, maintenance costs and economic benefits of the system. Through systematic reliability research, the potential failure modes of the equipment can be identified and predicted, and optimization measures can be formulated to improve system availability, extend equipment life, and reduce downtime and maintenance costs.

[0004] Failure Mode and Effects Analysis (FMEA) prioritizes risks using the risk priority number (RPN) of the failure mode. The RPN is the product of three risk factors: occurrence (O), severity (S), and detection difficulty (D). Due to its ease of implementation, FMEA has been widely used in various fields, including construction, shipping, and the nuclear industry. However, the traditional FMEA method also has certain limitations. First, it uses fixed numbers to assess risks, making it difficult to effectively address the uncertainties in the system. Second, FMEA ignores the consensus of experts on failure mode risks, which may lead to low-quality intelligent operation and maintenance results. In addition, the aggregation process of risk assessment data does not fully consider the weights of experts, which affects the reliability of the analysis results. Summary of the Invention

[0005] The purpose of this application is to provide an intelligent operation and maintenance method and related devices for key solar thermal energy storage equipment, which can improve the safety risk management level of the solar thermal system to achieve the goal of stable risk control.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides an intelligent operation and maintenance method for key solar thermal energy storage equipment, the intelligent operation and maintenance method for key solar thermal energy storage equipment comprising:

[0008] Obtain the failure modes, failure locations, failure causes, and failure consequences of key equipment in the trough-type solar thermal system; the key equipment in the trough-type solar thermal system includes: vacuum collector tubes, thermal oil pipes, molten salt pipes, molten salt tanks, oil-salt heat exchangers, and steam generation system heat exchangers.

[0009] Perform a criticality analysis on the failure modes of each device to obtain risk factors for each device failure mode; the risk factors include severity, occurrence rate, and detectability.

[0010] The AHP method improved by Z number is used to determine the expert evaluation aggregate fuzzy number of each risk factor and the weight of each risk factor.

[0011] The expert evaluation aggregated fuzzy number of each risk factor and the weight of each risk factor are weighted to obtain the risk priority number of the key equipment of the trough solar thermal system.

[0012] The risk priority numbers of the key equipment of the trough solar thermal system are ranked to obtain a failure mode risk ranking.

[0013] Based on the failure mode risk ranking, determine the corresponding management and optimization measures.

[0014] In a second aspect, the present application provides an intelligent operation and maintenance device for key solar thermal energy storage equipment, which is used to implement the intelligent operation and maintenance method for key solar thermal energy storage equipment described above. The intelligent operation and maintenance device for key solar thermal energy storage equipment includes:

[0015] The data acquisition module is used to obtain the failure mode, failure location, failure cause and failure consequence of the key equipment of the trough-type solar thermal system; the key equipment of the trough-type solar thermal system includes: vacuum collector tubes, thermal oil pipes, molten salt pipelines, molten salt tanks, oil-salt heat exchangers and steam generation system heat exchangers.

[0016] The criticality analysis module is used to perform criticality analysis on the failure modes of each device to obtain risk factors of each device failure mode; the risk factors include severity, occurrence rate and detectability.

[0017] The data determination module is used to determine the expert evaluation aggregate fuzzy number of each risk factor and the weight of each risk factor by using the AHP method improved by the Z number.

[0018] The weighted processing module is used to perform weighted processing on the expert evaluation aggregated fuzzy number of each risk factor and the weight of each risk factor to obtain the risk priority number of the key equipment of the trough solar thermal system.

[0019] The sorting module is used to sort the risk priority numbers of the key equipment of the trough type solar thermal system to obtain the failure mode risk ranking.

[0020] The management and measures determination module is used to determine corresponding management and optimization measures based on the failure mode risk ranking.

[0021] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the intelligent operation and maintenance method for the key solar thermal energy storage equipment described above.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the intelligent operation and maintenance method of the above-mentioned key solar thermal energy storage equipment.

[0023] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the intelligent operation and maintenance method of the above-mentioned key solar thermal energy storage equipment.

[0024] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0025] The present application provides an intelligent operation and maintenance method and related devices for key equipment of solar thermal energy storage. By obtaining the failure mode, failure location, failure cause and failure consequence of key equipment of the trough solar thermal system (the key equipment of the trough solar thermal system includes: vacuum heat collecting tubes, heat transfer oil pipes, molten salt pipes, molten salt tanks, oil-salt heat exchangers and steam generation system heat exchangers), it is possible to comprehensively identify various risk points that may exist in the trough solar thermal system, avoid missing important hidden dangers, and lay the foundation for subsequent risk assessment and management. By performing a hazard analysis on the failure mode of each device and obtaining the risk factors (severity, incidence and detectability) of the failure mode of each device, it is possible to achieve a quantitative assessment of the risk, making the size of the risk more intuitive and facilitating subsequent decision-making and priority sorting. By adopting the AHP method improved by the Z number, the expert evaluation aggregate fuzzy number of each risk factor and the weight of each risk factor are determined, which not only gives full play to the experience advantage of experts in complex system risk assessment, but also avoids the deviation that may be caused by relying solely on subjective judgment, and improves the scientificity and rationality of the determination of risk factor weights; at the same time, the introduction of fuzzy numbers can effectively deal with the uncertainty in risk factor assessment, better reflect the complexity and ambiguity of the actual situation, make the determination of risk factor weights closer to reality, and provide a more accurate basis for the subsequent calculation of risk priority numbers. By weighting the aggregated fuzzy number of expert evaluations of each risk factor and the weight of each risk factor, the risk priority number of the key equipment of the trough type solar thermal system is obtained; the risk priority numbers of the key equipment of the trough type solar thermal system are sorted to obtain the failure mode risk ranking; based on the failure mode risk ranking, the corresponding management and optimization measures are determined, which can comprehensively and objectively reflect the comprehensive risk level of the failure mode of each key equipment, provide a reliable basis for subsequent risk ranking and management, avoid the one-sidedness of risk assessment dominated by a single factor, and through risk ranking, timely discover the changing trend of risks, dynamically adjust the risk prevention and control strategy, ensure that risks are always under control, and effectively reduce the possibility and consequences of failure of key equipment, thereby improving the reliability and safety of the entire trough type solar thermal system. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 This is a diagram of the application environment of an intelligent operation and maintenance method for key solar thermal energy storage equipment in one embodiment of the present application.

[0028] Figure 2 A flowchart of an intelligent operation and maintenance method for key solar thermal energy storage equipment provided in one embodiment of the present application.

[0029] Figure 3 A schematic diagram of the membership of the TrFN in part A provided in one embodiment of the present application.

[0030] Figure 4 This is a schematic diagram of the Z-AHP risk factor weight assessment model provided in one embodiment of the present application.

[0031] Figure 5 A schematic diagram of the functional modules of an intelligent operation and maintenance device for key solar thermal energy storage equipment provided in one embodiment of the present application.

[0032] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0034] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0035] This application uses a failure mode, effects, and hazard analysis (FMEA) approach to identify the failure modes, causes, and consequences of critical equipment, including vacuum collector tubes, thermal oil pipes, molten salt pipelines, molten salt tanks, oil-salt heat exchangers, and steam generation system heat exchangers. By introducing fuzzy set theory based on the Z number and the Z-AHP method, the expert opinion aggregation process is improved, and the calculation of risk factor weights is optimized. Ultimately, a risk ranking of equipment failure modes is determined, and corresponding management and optimization measures are proposed to enhance system reliability and safety.

[0036] The intelligent operation and maintenance method of key solar thermal energy storage equipment provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the failure mode, failure location, failure cause and failure consequences of the key equipment of the trough type solar thermal system to the server 104. The key equipment of the trough type solar thermal system includes: vacuum heat collecting tubes, heat transfer oil pipes, molten salt pipes, molten salt tanks, oil-salt heat exchangers and steam generation system heat exchangers; after the server 104 receives the failure mode, failure location, failure cause and failure consequences of the key equipment of the trough type solar thermal system, the server 104 performs a hazard degree on the failure mode of each device for the failure mode, failure location, failure cause and failure consequences of the key equipment of the trough type solar thermal system. Analyze and obtain risk factors for each device failure mode; the risk factors include severity, incidence, and detectability; use the Z-number improved AHP method to determine the expert evaluation aggregate fuzzy number and weight of each risk factor; perform weighted processing on the expert evaluation aggregate fuzzy number and weight of each risk factor to obtain the risk priority number of key devices in the trough solar thermal system; rank the risk priority numbers of key devices in the trough solar thermal system to obtain a failure mode risk ranking; and determine corresponding management and optimization measures based on the failure mode risk ranking. Server 104 can provide feedback on the obtained management and optimization measures to terminal 102. In addition, in some embodiments, the intelligent operation and maintenance method of key solar thermal energy storage equipment can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly perform intelligent operation and maintenance on the failure mode, failure location, failure cause and failure consequences of the key equipment of the trough type solar thermal system, or the server 104 can obtain the failure mode, failure location, failure cause and failure consequences of the key equipment of the trough type solar thermal system from the data storage system, and perform intelligent operation and maintenance on the failure mode, failure location, failure cause and failure consequences of the key equipment of the trough type solar thermal system.

[0037] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, and tablet computers. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or a cloud server.

[0038] In an exemplary embodiment, Figure 2 As shown, a method for intelligent operation and maintenance of key equipment of solar thermal energy storage is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps S1 to S6.

[0039] S1: Obtain the failure modes, failure locations, failure causes and failure consequences of key equipment in the trough-type solar thermal system; the key equipment in the trough-type solar thermal system includes: vacuum collector tubes, thermal oil pipes, molten salt pipes, molten salt tanks, oil-salt heat exchangers and steam generation system heat exchangers.

[0040] S2: Perform a criticality analysis on the failure mode of each device to obtain risk factors for the failure mode of each device; the risk factors include severity, occurrence rate, and detectability.

[0041] S3: The AHP method improved by Z number is used to determine the expert evaluation aggregate fuzzy number of each risk factor and the weight of each risk factor.

[0042] S4: Perform weighted processing on the aggregated fuzzy number of expert evaluation of each risk factor and the weight of each risk factor to obtain the risk priority number of key equipment of the trough solar thermal system.

[0043] S5: Sort the risk priority numbers of the key equipment of the trough solar thermal system to obtain a failure mode risk ranking.

[0044] S6: Based on the failure mode risk ranking, determine corresponding management and optimization measures.

[0045] By implementing steps S1 to S6 above, focusing on key equipment in a trough solar thermal system, the FMECA method was used to identify equipment failure modes, causes, and consequences based on on-site research, literature review, expert interviews, and relevant national standards. Expert opinions on the risk factors for each failure mode were obtained by combining fuzzy set theory based on the Z number. Expert opinions were aggregated using an improved similarity aggregation method. Risk factor weights were determined using the Z-AHP method. Finally, failure mode risk scores for trough solar thermal system equipment were derived and ranked according to their impact. Based on this, targeted recommendations and management methods were proposed to improve the safety risk management and control level of solar thermal systems, ultimately achieving the goal of maintaining stable risk control.

[0046] The following is an introduction to the fuzzy set Z number and the principle of FMECA method:

[0047] (1) Z-number fuzzy method.

[0048] The use of fuzzy set theory can be interpreted as performing calculations using linguistic variables provided by experts. This approach can overcome the challenges of insufficient historical data or the inability to determine data experimentally. The Z-number incorporates not only fuzzy information but also probabilistic information. Its most important characteristic, distinguishing it from classical fuzzy numbers, is its underlying probability distribution, making it more capable of capturing uncertainty and expert opinion. Parabolic trough (CSP) systems currently lack operational and maintenance data, making traditional fuzzy methods incapable of accurately assessing equipment risk. Therefore, this application employs the Z-number approach to obtain expert risk indicators for key CSP equipment. Generally speaking, the Z-number consists of two parts: Part A and Part B, which can be represented using triangular or trapezoidal fuzzy numbers. The conversion of decision-maker (expert) language terms into the Z-number is achieved through a combination of conversion rules (Table 1) and reliability associated with the constraints (Table 2). For the constraints, this application uses seven levels of language terms and then creates fuzzy ratings (represented by trapezoidal fuzzy numbers, TrFNs) through translation of expert language assessments. Table 1 lists the conversion rules. The reliability of Part A can be evaluated based on 21 language terms. These terms are defined based on confidence percentages. The conversion rules are shown in Table 2.

[0049] Table 1 Restrictive language term conversion rules (Part A)

[0050] Language terminology Trapezoidal fuzzy number / membership function Very Low (VL) (0，0，0.1，0.2) Low (L) (0.1，0.2，0.2，0.3) Slightly lower (SL) (0.2，0.3，0.4，0.5) Medium (M) (0.4，0.5，0.5，0.6) Slightly higher (SH) (0.5，0.6，0.7，0.8) High (H) (0.7，0.8，0.8，0.9) Very High (VH) (0.8，0.9，1，1)

[0051] Table 2 Conversion rules related to reliability language terms (Part B)

[0052] serial number Language terminology Trapezoidal fuzzy number 1 0% confidence (0，0，0.025，0.05) 2 5% certainty (0.025，0.05，0.075，0.1) 3 10% certainty (0.075，0.1，0.125，0.15) 4 15% certainty (0.125，0.15，0.175，0.2) 5 20% sure (0.175，0.2，0.225，0.25) 6 25% certainty (0.225，0.25，0.275，0.3) 7 30% certainty (0.275，0.3，0.325，0.35) 8 35% certainty (0.325，0.35，0.375，0.4) 9 40% certainty (0.375，0.4，0.425，0.45) 10 45% certainty (0.425，0.45，0.475，0.5) 11 50% sure (0.475，0.5，0.525，0.55) 12 55% certainty (0.525，0.55，0.575，0.6) 13 60% certainty (0.575，0.6，0.625，0.65) 14 65% certainty (0.625，0.65，0.675，0.7) 15 70% certainty (0.675，0.7，0.725，0.75) 16 75% certainty (0.725，0.75，0.775，0.8) 17 80% certainty (0.775，0.8，0.825，0.85) 18 85% confidence (0.825，0.85，0.875，0.9) 19 90% certainty (0.875，0.9，0.925，0.95) 20 95% certainty (0.925，0.95，0.975，1) 21 100% sure (0.975，1，1，1)

[0053] In addition, the membership of TrFN used for the transformation rule is as follows Figure 3 In this embodiment, trapezoidal fuzzy numbers are used to represent Z = [A, B]. For {A = (x, μ A )|x∈X}μ A (x):X∈[0,1],μ A (x) is the first membership function of TrFN; x is a random variable, x∈X, X∈R, R is a real-valued domain; A=(a1,a2,a3,a4), a1 is the first lower limit; a4 is the second lower limit; a2 is the first upper limit; a3 is the second upper limit. μ A (x) can be calculated by the following formula:

[0054]

[0055] The second component of the Z-score represents reliability (B), which can be converted to a clarity value using the following formula:

[0056]

[0057] Among them, α is the clarity value; μB (x) is the second membership function of TrFN; B = (b1, b2, b3, b4), where b1 is the left endpoint of the trapezoidal fuzzy number, b2 is the position where the left membership of the trapezoidal fuzzy number is 1, b3 is the position where the right membership of the trapezoidal fuzzy number is 1, and b4 is the right endpoint of the trapezoidal fuzzy number. Next, the weight of the reliability part is added to the first part, so the weighted Z number is defined as:

[0058]

[0059] Among them, Z α is the weighted Z number; is the membership function of the weighted Z number. Then the irregular fuzzy number (weighted Z number) is converted into a regular fuzzy number, which can be expressed as:

[0060]

[0061] Where Z′ is a regular fuzzy number, Z′=(z1,z2,z3,z4), z1 is the left endpoint of the regular fuzzy number, z2 is the position where the left membership of the regular fuzzy number is 1, z3 is the position where the right membership of the regular fuzzy number is 1, and z4 is the right endpoint of the regular fuzzy number; μ Z′ (x) is the third membership function of the trapezoidal fuzzy number. By combining the above formula, Z = [A, B] can be transferred to the new TrFN to obtain the fuzzy number Z″, which is expressed as:

[0062]

[0063] Among them, Z″ is the Z-number fusion fuzzy number.

[0064] Defuzzification method: This embodiment uses the most widely used Center of Area (COA) method for defuzzification. The COA defuzzification of the trapezoidal fuzzy number can be calculated as follows:

[0065]

[0066] in, de f(A) is the COA defuzzification function of the trapezoidal fuzzy number; r1 is the left endpoint of the Z-number fusion fuzzy number, r2 is the position where the left membership of the Z-number fusion fuzzy number is 1; r3 is the position where the right membership of the Z-number fusion fuzzy number is 1; and r4 is the right endpoint of the Z-number fusion fuzzy number.

[0067] (2)FMECA theory.

[0068] FMECA (Failure Mode, Effects and Criticality Analysis) is mainly divided into two steps. The first step is to complete the failure mode and effect analysis (qualitative analysis) of the research object, and then conduct the second step: CA analysis (criticality analysis). CA analysis uses the probability and severity of each failure mode to comprehensively calculate its criticality, and then sorts them according to the numerical value of the criticality to find the key failures and determine the weak links, so as to fully evaluate the impact of different failures on system products.

[0069] This embodiment calculates the criticality of the fault mode using the improved risk priority number method (RPN). The risk priority number method is a commonly used method for calculating the criticality of the fault. The core is to evaluate the severity (S), occurrence rate (O) and detectability (D) of the fault mode. During the evaluation process, in order to make the evaluation results more reasonable, the AHP method improved by the Z number is used to obtain the weight of each risk factor. The risk base scores of each failure mode obtained by the evaluation are combined with the risk factor weights. Finally, the improved risk priority number is obtained, and the failure mode risk size is sorted according to the numerical value of the RPN. The larger the improved RPN score, the higher the criticality of the fault mode. The risk base scores of each failure mode refer to regular fuzzy numbers, which are defined as follows:

[0070] 1. Its membership function is continuous.

[0071] 2. The membership degree is in the range of [0, 1].

[0072] 3. There is a unique position where the maximum membership value is 1 (i.e. the "kernel" of the fuzzy number).

[0073] 4. The membership function is left-right monotonic (non-decreasing on the left and non-increasing on the right).

[0074] 5. The domain is a finite interval.

[0075] The Z-AHP risk factor weight assessment model used in this example is as follows: Figure 4 shown.

[0076] As an optional implementation, in step S3, the AHP method improved by the Z number is used to determine the expert evaluation aggregate fuzzy number of each risk factor and the weight of each risk factor, specifically including:

[0077] S31: Convert the risk factors of each equipment failure mode into a Z-number evaluation in the form of a Z-number; the Z-number form is represented by a triangular fuzzy number or a trapezoidal fuzzy number.

[0078] S32: Convert the Z-number evaluation into a regular fuzzy number.

[0079] S33: Using the Z-number fuzzy method to fuse the regular fuzzy numbers to obtain a Z-number fused fuzzy number.

[0080] S34: A similarity aggregation method is used to aggregate the Z-number fusion fuzzy number to obtain an expert evaluation aggregated fuzzy number.

[0081] S35: Based on the expert evaluation aggregated fuzzy number, the weight of the risk factor of each equipment failure mode is calculated using the AHP method.

[0082] As an optional implementation, in step S33, the regular fuzzy numbers are fused using a Z-number fuzzy method to obtain a Z-number fused fuzzy number, which specifically includes:

[0083] S331: Determine the first membership function and the second membership function of the trapezoidal fuzzy number.

[0084] S332: Determine a Z number conversion rule based on the first membership function.

[0085] S333: Determine the restriction-related reliability of the Z number based on the second membership function.

[0086] S334: Convert the constraint-related reliability of the Z number into a clarity value.

[0087] S335: Add the clarity value to the conversion rule of the Z number to obtain a weighted Z number.

[0088] S336: Convert the weighted Z number into a regular fuzzy number.

[0089] S337: Add the regular fuzzy number to the conversion rule of the Z number to obtain the Z number fusion fuzzy number.

[0090] This step refers to formula (2) to formula (5).

[0091] The FMECA ranking of the parabolic trough system is as follows:

[0092] (1) Analysis of failure modes, causes and consequences of key trough-type solar thermal equipment.

[0093] Considering the failure modes, causes and consequences of six key equipment including the trough solar thermal system vacuum collector tubes, thermal oil pipes, molten salt pipelines, molten salt tanks, oil-salt heat exchangers and steam generation system heat exchangers, as well as the specific location of each failure mode in the equipment, based on on-site investigations, literature review and expert interviews, the FMEA of each equipment is finally obtained (see Table 3).

[0094] Table 3 FMEA of trough-type solar thermal system

[0095]

[0096]

[0097]

[0098] (2) CA analysis of key trough-type solar thermal equipment.

[0099] After the failure modes, failure locations, failure causes, and failure consequences of key equipment in the trough solar thermal system are explored and FMEA tables are produced, the steps for CA analysis of the failure modes are as follows: Taking the expert-language term evaluation as an example, the experts' Z-number evaluation language terms for the three risk factors of each failure mode are collected, as shown in Table 4.

[0100] Table 4 Expert-level risk factor Z-score evaluation language terminology

[0101]

[0102]

[0103] After converting the linguistic terms given by the experts in the form of Z numbers into regular fuzzy numbers, Table 5 is obtained by adding part B to part A using equations (1)-(5) to obtain the Z number fusion fuzzy numbers.

[0104] Table 5 Expert-Z number fusion fuzzy number

[0105]

[0106]

[0107] The aggregated fuzzy numbers of the three experts’ evaluations obtained by the similarity aggregation method are shown in Table 6.

[0108] Table 6 Aggregate fuzzy number table of three experts' evaluation

[0109]

[0110] The aggregated fuzzy number of expert evaluation is clarified according to formula (6), and the obtained clarified score is weighted and processed with the risk factor weight value to obtain the risk priority number of the key equipment improvement of the trough solar thermal system, as shown in Table 7 below.

[0111] Table 7 Failure mode weighted risk priority number (RPN) results

[0112] serial number O S D RPN weighted sum FM1 0.4135 0.5807 0.5162 0.1239 FM2 0.28 0.5102 0.3248 0.0464 FM3 0.5275 0.6265 0.257 0.0849 FM4 0.547 0.705 0.522 0.301 FM5 0.3475 0.6357 0.4775 0.1055 FM6 0.6123 0.4298 0.486 0.1279 FM7 0.3568 0.6353 0.36 0.0816 FM8 0.5225 0.5315 0.5725 0.159 FM9 0.4723 0.4378 0.3335 0.069 FM10 0.4973 0.6602 0.4802 0.1577 FM11 0.7721 0.7397 0.527 0.2013 FM12 0.653 0.8239 0.522 0.2808 FM13 0.505 0.7343 0.3718 0.1379 FM14 0.6841 0.7643 0.3878 0.2028 FM15 0.505 0.6353 0.261 0.0837 FM16 0.7404 0.646 0.5205 0.249 FM17 0.559 0.6413 0.5543 0.1987 FM18 0.3762 0.6217 0.4752 0.1111 FM19 0.251 0.5485 0.4053 0.0558 FM20 0.5213 0.4305 0.3515 0.0789 FM21 0.5935 0.549 0.4815 0.1569 FM22 0.6387 0.594 0.4977 0.1888 FM23 0.551 0.4677 0.22 0.0567 FM24 0.467 0.5742 0.5082 0.1363

[0113] The risk factor weight values ​​are obtained as follows:

[0114] The three experts gave their respective values ​​for O, S, and D, and took the weighted average. The result is: ω O=0.3125,ω S =0.25,ω D =0.4375.

[0115] process:

[0116] Expert 1: ω 1O ,ω 1S ,ω 1D They represent expert 1’s judgment on the weights of O, S, and D, respectively.

[0117] Expert 2: ω 2O ,ω 2S ,ω 2D They represent expert 2’s judgment on the weights of O, S, and D, respectively.

[0118] Expert 3: ω 3O ,ω 3S ,ω 3D They represent expert 3’s judgment on the weights of O, S, and D, respectively.

[0119] Expert's own weight: ω1 = 0.3125, ω2 = 0.25, ω3 = 0.4375 Weighted sum algorithm formula:

[0120]

[0121] Among them, N represents O, S, and D.

[0122] Experts 1, 2, and 3 gave the following evaluations:

[0123] Expert 1's O weight is 0.5728, S weight is 0.2928, and D weight is 0.1344.

[0124] Expert 2's O weight is 0.5, S weight is 0.3, and D weight is 0.2.

[0125] Expert 3's O weight is 0.4, S weight is 0.2, and D weight is 0.4.

[0126] As an optional implementation, in step S4, the expert evaluation aggregated fuzzy number of each risk factor and the weight of each risk factor are weighted to obtain the risk priority number of the key equipment of the parabolic trough system, which specifically includes:

[0127] S41: Defuzzifying the aggregated fuzzy number of the expert evaluation to obtain a clarity score.

[0128] S42: Perform weighted processing on the clarification score and the weight of each risk factor to obtain the risk priority number of the key equipment of the trough solar thermal system.

[0129] Finally, they are ranked as shown in Table 8.

[0130] Table 8 Failure mode risk ranking

[0131]

[0132]

[0133] The improved RPN score calculation results of the failure modes of key equipment in the trough-type solar thermal system were ranked. The results showed that molten salt corrosion of vacuum collector tubes, molten salt pipelines, and molten salt tanks, as well as freezing of molten salt pipelines and heat exchanger shells were the five failure modes with the highest risks. It is recommended to strengthen the prevention of pipeline corrosion, strengthen the layout of equipment sensors to detect equipment abnormalities in the first time, and install heating devices to prevent freezing.

[0134] This application adopts a method combining Z number and trapezoidal fuzzy number to obtain the risk indicator factors of the failure modes of trough-type solar thermal system equipment by experts, taking into account the fuzziness of the rating while reducing the subjectivity of the experts, making the evaluation results more reasonable; using the AHP method based on Z number to obtain the risk indicator factor weights of the FMECA method, to avoid the evaluation results of the weight being too objective and subjective, which affects the evaluation results; completing the risk ranking of the failure modes through the weighted sum method, and analyzing the key equipment of the trough-type solar thermal system through the improved FMECA method, it was found that the five failure modes with the highest risk were molten salt corrosion of the vacuum collector tube, molten salt pipeline, and molten salt tank, and freezing of the molten salt pipeline and heat exchanger shell. Subsequently, prevention and treatment of key failure modes can be carried out to achieve the purpose of providing assistance for improving the risk management level of trough-type solar thermal power stations.

[0135] This application also provides an application scenario that applies the above-mentioned intelligent operation and maintenance method for key solar thermal energy storage equipment. Specifically: The intelligent operation and maintenance method for key solar thermal energy storage equipment provided in this embodiment can be applied in the intelligent operation and maintenance scenario of key solar thermal energy storage equipment. The intelligent operation and maintenance scenario of key solar thermal energy storage equipment includes: data acquisition, hazard analysis, data determination, weighted processing, ranking, and management and measure determination. First, the failure mode, failure location, failure cause, and failure consequence of the key equipment of the trough solar thermal system are obtained. The key equipment of the trough solar thermal system includes: vacuum collector tubes, thermal oil pipes, molten salt pipes, molten salt tanks, oil-salt heat exchangers, and steam generation system heat exchangers. Second, the hazard level of the failure mode of each device is analyzed to obtain the risk factors of each device failure mode. The risk factors include severity, incidence, and detectability. Then, the Z-number improved AHP method is used to determine the expert evaluation aggregate fuzzy number of each risk factor and the weight of each risk factor. Finally, the expert evaluation aggregate fuzzy number of each risk factor and the weight of each risk factor are weighted to obtain the risk priority number of the key equipment of the trough solar thermal system. The risk priority numbers of the key equipment of the trough solar thermal system are ranked to obtain the failure mode risk ranking. Based on the failure mode risk ranking, corresponding management and optimization measures are determined.

[0136] Based on the same inventive concept, embodiments of the present application also provide an intelligent operation and maintenance device for a key solar thermal energy storage device, for implementing the intelligent operation and maintenance method for key solar thermal energy storage devices described above. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of the embodiments of the intelligent operation and maintenance device for one or more key solar thermal energy storage devices provided below can be found in the limitations of the intelligent operation and maintenance method for key solar thermal energy storage devices described above, and will not be further elaborated here.

[0137] In an exemplary embodiment, Figure 5 As shown, an intelligent operation and maintenance device for key solar thermal energy storage equipment is provided, and the intelligent operation and maintenance device for key solar thermal energy storage equipment includes:

[0138] The data acquisition module M1 is used to obtain the failure mode, failure location, failure cause and failure consequence of the key equipment of the trough type solar thermal system; the key equipment of the trough type solar thermal system includes: vacuum collector tubes, thermal oil pipes, molten salt pipes, molten salt tanks, oil-salt heat exchangers and steam generation system heat exchangers.

[0139] The criticality analysis module M2 is used to perform criticality analysis on the failure mode of each device to obtain the risk factors of the failure mode of each device; the risk factors include severity, occurrence rate and detectability.

[0140] The data determination module M3 is used to determine the expert evaluation aggregate fuzzy number of each risk factor and the weight of each risk factor by using the AHP method improved by the Z number.

[0141] The weighted processing module M4 is used to perform weighted processing on the expert evaluation aggregated fuzzy number of each risk factor and the weight of each risk factor to obtain the risk priority number of the key equipment of the trough solar thermal system.

[0142] The sorting module M5 is used to sort the risk priority numbers of the key equipment of the trough type solar thermal system to obtain a failure mode risk ranking.

[0143] The management and measures determination module M6 is used to determine corresponding management and optimization measures based on the failure mode risk ranking.

[0144] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store the failure mode, failure location, failure cause and failure consequence of the key equipment of the trough-type solar thermal system. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an intelligent operation and maintenance method for key solar thermal energy storage equipment is realized.

[0145] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0146] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the above method embodiments when executing the computer program.

[0147] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the above-mentioned method embodiments when executed by a processor.

[0148] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the above method embodiments are implemented.

[0149] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0150] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0151] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0152] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0153] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. An intelligent operation and maintenance method for key solar thermal energy storage equipment, characterized in that: The intelligent operation and maintenance method of the key solar thermal energy storage equipment includes: Obtain the failure modes, locations, causes, and consequences of key equipment in the trough solar thermal system, including vacuum collector tubes, thermal oil pipes, molten salt pipes, molten salt tanks, oil-salt heat exchangers, and steam generation system heat exchangers. Performing a criticality analysis on the failure modes of each device to obtain risk factors for each failure mode of the device; the risk factors include severity, occurrence rate, and detectability; The AHP method improved by Z number is used to determine the expert evaluation aggregate fuzzy number of each risk factor and the weight of each risk factor; The expert evaluation aggregated fuzzy number of each risk factor and the weight of each risk factor are weighted to obtain the risk priority number of key equipment in the parabolic trough system. Sorting the risk priority numbers of the key equipment of the trough solar thermal system to obtain a failure mode risk ranking; Based on the failure mode risk ranking, determine the corresponding management and optimization measures.

2. The intelligent operation and maintenance method for key solar thermal energy storage equipment according to claim 1 is characterized in that: The AHP method improved by Z number is used to determine the expert evaluation aggregate fuzzy number of each risk factor and the weight of each risk factor, including: Convert the risk factors of each equipment failure mode into a Z-number evaluation in the form of a Z-number; the Z-number form is represented by a triangular fuzzy number or a trapezoidal fuzzy number; Converting the Z-number evaluation into a regular fuzzy number; The regular fuzzy numbers are fused using the Z-number fuzzy method to obtain the Z-number fused fuzzy numbers; The Z-number fusion fuzzy number is aggregated using a similarity aggregation method to obtain an expert evaluation aggregated fuzzy number; Based on the expert evaluation aggregated fuzzy number, the weight of the risk factor of each equipment failure mode is calculated using the AHP method.

3. The intelligent operation and maintenance method for key solar thermal energy storage equipment according to claim 2 is characterized in that: The Z-number fuzzy method is used to fuse the regular fuzzy numbers to obtain the Z-number fused fuzzy numbers, which specifically includes: determining a first membership function and a second membership function of a trapezoidal fuzzy number; Determining a Z number conversion rule based on the first membership function; determining a restriction-related reliability of a Z number based on the second membership function; converting the constraint-related reliability of the Z number into a clarity value; Adding the clarity value to the conversion rule of the Z number to obtain a weighted Z number; Converting the weighted Z number into a regular fuzzy number; The regular fuzzy number is added to the conversion rule of the Z number to obtain the Z number fusion fuzzy number.

4. The intelligent operation and maintenance method for key solar thermal energy storage equipment according to claim 2 is characterized in that: The expression of the Z number fuzzy method is: Among them, μ A (x) is the first membership function of the trapezoidal fuzzy number; x is a random variable; a1 is the first lower limit; a4 is the second lower limit; a2 is the first upper limit; a3 is the second upper limit; μ B (x) is the second membership function of the trapezoidal fuzzy number; α is the clarity value; Z α is the weighted Z number; is the membership function of the weighted Z number; Z′ is a regular fuzzy number; μ Z′ (x) is the third membership function of the trapezoidal fuzzy number; Z″ is the Z-number fusion fuzzy number.

5. The intelligent operation and maintenance method for key solar thermal energy storage equipment according to claim 1 is characterized in that: The expert evaluation aggregated fuzzy number of each risk factor and the weight of each risk factor are weighted to obtain the risk priority number of key equipment in the parabolic trough system, which includes: Defuzzifying the aggregated fuzzy number of the expert evaluation to obtain a clarified score; The clarification score is weighted with the weight of each risk factor to obtain the risk priority number of the key equipment of the trough solar thermal system.

6. The intelligent operation and maintenance method for key solar thermal energy storage equipment according to claim 5 is characterized in that The expression of the defuzzification process is: Where def(A) is the COA defuzzification function of the trapezoidal fuzzy number; r1 is the left endpoint of the trapezoidal fuzzy number, r2 is the position where the left membership of the trapezoidal fuzzy number is 1; r3 is the position where the right membership of the trapezoidal fuzzy number is 1; and r4 is the right endpoint of the trapezoidal fuzzy number.

7. An intelligent operation and maintenance device for key solar thermal energy storage equipment, characterized in that: A method for implementing the intelligent operation and maintenance method of a key solar thermal energy storage device according to any one of claims 1 to 6, wherein the intelligent operation and maintenance device of the key solar thermal energy storage device comprises: A data acquisition module for acquiring failure modes, locations, causes, and consequences of key equipment in a trough-type solar thermal system, including vacuum collector tubes, thermal oil pipes, molten salt pipes, molten salt tanks, oil-salt heat exchangers, and steam generation system heat exchangers; A criticality analysis module is used to perform criticality analysis on the failure modes of each device to obtain risk factors for each device failure mode; the risk factors include severity, occurrence rate, and detectability; The data determination module is used to determine the expert evaluation aggregate fuzzy number of each risk factor and the weight of each risk factor using the AHP method improved by the Z number; The weighted processing module is used to perform weighted processing on the expert evaluation aggregated fuzzy number of each risk factor and the weight of each risk factor to obtain the risk priority number of the key equipment of the parabolic trough system; A ranking module is used to sort the risk priority numbers of key equipment of the trough solar thermal system to obtain a failure mode risk ranking; The management and measures determination module is used to determine corresponding management and optimization measures based on the failure mode risk ranking.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the intelligent operation and maintenance method for key solar thermal energy storage equipment according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the intelligent operation and maintenance method of the key solar thermal energy storage equipment according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the intelligent operation and maintenance method of the key solar thermal energy storage equipment according to any one of claims 1 to 6 is implemented.

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