Equipment intelligent guarantee method, system, storage medium and electronic device

Abstract

The present invention relates to the field of reliability engineering technology, and in particular to an intelligent equipment support method, system, storage medium and electronic device. In the method, first, the design data, historical usage data and real-time usage data of each preset equipment in the whole life cycle are obtained and filled into a comprehensive support database. Secondly, all sub-components to be replaced are determined by judging the probability of failure in a preset time period in the future; then, the number of spare parts for each sub-component to be replaced is determined based on all the sub-components to be replaced. On the one hand, the accumulation of rich support data and the improvement of the effectiveness of the support data can make the number of spare parts for each sub-component to be replaced more accurate. On the other hand, it can enable maintenance personnel to select spare parts according to the number of spare parts for each sub-component to be replaced, which is convenient for replacement and maintenance when a failure occurs, avoiding problems caused by the lack of spare parts or the remote location of spare parts, and improving the availability of preset equipment.

Description

Background Art

[0002] Equipment comprehensive support refers to ensuring the availability of equipment during its use, preventing equipment failures, and reducing the time required for repair. At present, the comprehensive support capability of equipment mainly comes from the operator's experience and the designer's initial design data. Equipment failures often occur during use, and the repair of failures may also be hindered by the lack of spare parts or the remote location of spare parts. When such a situation occurs, the availability of the equipment will be greatly reduced. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide an equipment intelligent protection method, system, storage medium and electronic device in view of the deficiencies in the prior art.

[0004] The technical solution of an equipment intelligent protection method of the present invention is as follows:

[0005] Obtain the design data, historical usage data and real-time usage data of each preset equipment throughout its life cycle and fill them into the comprehensive support database, where each preset equipment is the same;

[0006] Based on the comprehensive support database, determining the probability of each subcomponent of each preset equipment failing within a preset time period in the future;

[0007] Determine whether the probability of any subcomponent of any preset equipment failing within a preset time period in the future exceeds a preset probability threshold corresponding to the subcomponent; if so, determine the subcomponent of the preset equipment as a subcomponent to be replaced, until all subcomponents to be replaced are determined;

[0008] Based on all the sub-components to be replaced, determine the number of spare parts for each sub-component to be replaced.

[0009] The beneficial effects of an equipment intelligent protection method of the present invention are as follows:

[0010] By collecting the design data, historical usage data and real-time usage data of each preset equipment throughout its life cycle, we can accumulate rich support data and improve the effectiveness of support data. This will enable us to more accurately determine the number of spare parts for each sub-component to be replaced, and enable maintenance personnel to select spare parts based on the number of spare parts for each sub-component to be replaced, facilitating replacement and repair when a fault occurs, avoiding problems caused by the lack of spare parts or the remote location of spare parts, and improving the availability of preset equipment.

[0011] On the basis of the above solution, the equipment intelligent support method of the present invention can also be improved as follows.

[0012] Furthermore, it also includes:

[0013] Based on the comprehensive support database, the maintenance plan cycle of each preset equipment is obtained.

[0014] The beneficial effect of adopting the above further solution is that it is convenient for maintenance personnel to maintain each preset equipment according to the maintenance plan cycle of each preset equipment.

[0015] Furthermore, it also includes:

[0016] Based on the comprehensive support database, the mean time between failures, availability and mission success rate of any preset equipment in any preset stage are calculated.

[0017] The beneficial effect of adopting the above further solution is that it is convenient for maintenance personnel to check the performance status of any preset equipment at any preset stage.

[0018] Furthermore, it also includes:

[0019] When any preset equipment fails, the fault can be located through voice call or video call.

[0020] The beneficial effect of adopting the above further solution is: it is easier to locate the fault and saves time.

[0021] A technical solution of an equipment intelligent security system of the present invention is as follows:

[0022] It includes an acquisition filling module and a determination module;

[0023] The acquisition and filling module is used to: acquire the design data, historical use data and real-time use data of each preset equipment in the whole life cycle, and fill them into the comprehensive support database, wherein each preset equipment is the same;

[0024] The determination module is used for:

[0025] Based on the comprehensive support database, determining the probability of each subcomponent of each preset equipment failing within a preset time period in the future;

[0026] Determine whether the probability of any subcomponent of any preset equipment failing within a preset time period in the future exceeds a preset probability threshold corresponding to the subcomponent; if so, determine the subcomponent of the preset equipment as a subcomponent to be replaced, until all subcomponents to be replaced are determined;

[0027] Based on all the sub-components to be replaced, determine the number of spare parts for each sub-component to be replaced.

[0028] The beneficial effects of an equipment intelligent security system of the present invention are as follows:

[0029] By collecting the design data, historical usage data and real-time usage data of each preset equipment throughout its life cycle, we can accumulate rich support data and improve the effectiveness of support data. This will enable us to more accurately determine the number of spare parts for each sub-component to be replaced, and enable maintenance personnel to select spare parts based on the number of spare parts for each sub-component to be replaced, facilitating replacement and repair when a fault occurs, avoiding problems caused by the lack of spare parts or the remote location of spare parts, and improving the availability of preset equipment.

[0030] Based on the above solution, the equipment intelligent support system of the present invention can also be improved as follows.

[0031] Furthermore, the determining module is also used for:

[0032] Based on the comprehensive support database, the maintenance plan cycle of each preset equipment is obtained.

[0033] The beneficial effect of adopting the above further solution is that it is convenient for maintenance personnel to maintain each preset equipment according to the maintenance plan cycle of each preset equipment.

[0034] Furthermore, a calculation module is included, and the calculation module is used to:

[0035] Calculate the mean time between failures, availability and mission success rate of any preset equipment at any preset stage.

[0036] The beneficial effect of adopting the above further solution is that it is convenient for maintenance personnel to check the performance status of any preset equipment at any preset stage.

[0037] Furthermore, a fault location module is included, and the fault location module is used to:

[0038] When any preset equipment fails, the fault can be located through voice call or video call.

[0039] The beneficial effect of adopting the above further solution is: it is easier to locate the fault and saves time.

[0040] A storage medium of the present invention stores instructions, and when a computer reads the instructions, the computer executes any one of the above-mentioned equipment intelligent support methods.

[0041] An electronic device of the present invention comprises a processor and the above-mentioned storage medium, wherein the processor executes instructions in the storage medium. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic diagram of a process flow of an equipment intelligent protection method according to an embodiment of the present invention;

[0043] Figure 2 A schematic diagram of the structure of an equipment intelligent protection method according to an embodiment of the present invention;

[0044] Figure 3 This is a schematic diagram of the home page of the handheld data collection terminal;

[0045] Figure 4 It is a functional schematic diagram of the visualization module;

[0046] Figure 5 One of the functional schematic diagrams of the evaluation module;

[0047] Figure 6 This is the second functional diagram of the evaluation module;

[0048] Figure 7 It is a functional diagram of the resource optimization module;

[0049] Figure 8 This is a schematic diagram of the remote support module;

[0050] Fig. 9 One of the functional diagrams of the security strategy generation module;

[0051] Fig.10 This is the second functional diagram of the security strategy generation module. DETAILED DESCRIPTION

[0052] like Figure 1 As shown, an equipment intelligent protection method according to an embodiment of the present invention includes the following steps:

[0053] S1. Obtain the design data, historical usage data and real-time usage data of each preset equipment during its entire life cycle, and fill them into the comprehensive support database, where each preset equipment is the same;

[0054] Among them, the preset equipment refers to equipment such as radars, vehicles or ships. The design data includes the size data and performance parameters designed for the preset equipment. The historical usage data includes: historical maintenance data and historical operation data of the preset equipment. The real-time usage data refers to: normally generated data, such as data in operation and data under maintenance.

[0055] Among them, a hierarchical database, a network database or a relational database can be selected as the comprehensive support database. The specific implementation process of filling the design data, historical usage data and real-time usage data into the comprehensive support database is as follows:

[0056] 1) Design data and historical usage data can be imported into the database in the form of Excel tables;

[0057] 2) Real-time usage data can be collected through handheld devices. An Android version of data collection software is developed and installed on handheld devices such as mobile phones and tablets. Figure 3 As shown, operators can directly fill in the real-time usage data of preset equipment in the handheld device and upload the real-time usage data to the comprehensive support database through the network;

[0058] Through the above-mentioned electronic means, information such as the use, maintenance, and technical status of the equipment throughout its life cycle is collected, standardized management is carried out, rich support data is accumulated, and the effectiveness of the data is improved.

[0059] S2. Based on the comprehensive support database, determine the probability of each subcomponent of each preset equipment failing within a preset time period in the future;

[0060] For example, when the preset equipment is a radar, the radar includes multiple subcomponents such as an antenna, a transmitter, and a receiver, and the future preset time period may be the next month, the next two months, and so on.

[0061] Taking the radar transmitter as an example, the specific implementation process of S2 is explained, which includes the following forms:

[0062] 1) Obtain historical fault data and historical working conditions of the transmitter from the comprehensive support database, specifically from the historical usage data, and train a neural network model based on the historical fault data and historical working conditions of the transmitter obtained from the historical usage data. Determine the probability of the transmitter failing within a preset time period in the future through the neural network model, and so on, determine the probability of each subcomponent of each preset equipment failing within a preset time period in the future.

[0063] 2) From the comprehensive support database, specifically from the historical usage data, obtain the historical fault data and historical working conditions of the transmitter, determine the failure rate of the transmitter as λ, the number of installed units as N, and now consider the probability of the number of failures of the transmitter in half a year. Obviously, the calendar time of half a year is t = 24 × 365 / 2 = 4380h, assuming

[0064]

[0065] The probability of the transmitter having 0 failures is

[0066]

[0067] The probability of a maximum of one failure is

[0068]

[0069] By analogy, we can find P 2 , P 3, ..., and so on, determine the probability of failure of each subcomponent of each preset equipment within the future preset time period,

[0070] S3. Determine whether the probability of any subcomponent of any preset equipment failing within a preset time period in the future exceeds a preset probability threshold corresponding to the subcomponent. If so, determine the subcomponent of the preset equipment as a subcomponent to be replaced until all subcomponents to be replaced are determined.

[0071] S4. Determine the number of spare parts for each sub-component to be replaced based on all sub-components to be replaced, specifically:

[0072] For example, there are 10 radars in total. It is determined that the probability of the antenna of the first radar failing within a preset time period in the future is 65%, and the preset probability threshold corresponding to the antenna is 50%. Since 65%>50%, the antenna of the first radar is a sub-component to be replaced, until it is determined whether the antennas of all radars are sub-components to be replaced;

[0073] If the antennas of 6 radars among 10 radars are sub-components to be replaced, it means that the number of spare parts for this type of sub-component to be replaced, i.e., the antenna, is at least 6. Similarly, determining the number of spare parts for each sub-component to be replaced can enable maintenance personnel to select spare parts based on the number of spare parts for each sub-component to be replaced, which is convenient for replacement and repair when a fault occurs, avoiding problems caused by missing spare parts or remote locations of spare parts, and improving the availability of preset equipment.

[0074] An intelligent equipment maintenance method of the present application collects design data, historical usage data and real-time usage data of each preset equipment throughout its life cycle, accumulates rich maintenance data and improves the effectiveness of the maintenance data, and can determine the number of spare parts for each sub-component to be replaced more accurately, and enables maintenance personnel to purchase spare parts based on the number of spare parts for each sub-component to be replaced, thereby facilitating replacement and repair when a fault occurs, avoiding problems caused by the lack of spare parts or the remote location of spare parts, and improving the availability of the preset equipment.

[0075] Optionally, in the above technical solution, it also includes:

[0076] S5. Based on the comprehensive support database, a maintenance plan cycle of each preset equipment is obtained. Taking radar as an example, the specific implementation forms are as follows:

[0077] 1) From the comprehensive support database, specifically from the historical usage data, obtain the historical maintenance plan record data of the radar, and determine the S-curve maintenance cycle of the radar through the algorithm model based on the historical maintenance plan record data. The relevant combinations of S-curve inspection items include: frequency synthesis combination, high-frequency receiving combination, intermediate frequency digital combination, signal processing combination, and wave control machine combination. According to the failure rate λ of all LRUs (minimum replaceable units) under these five combinations, calculate the respective failure rate P of the next month, assuming that the equipment power-on time per day is T.

[0078] For example, for a certain LRU under the frequency combination, calculate 1-e -λt1 =0.7, we know That is, after t1 hours, the frequency combination has a 0.7 probability of being broken.

[0079] Then converted to days, that is For comparison, if t1'>30, do not change the original maintenance cycle; if t1'<=30, change 0.7 to 0.9 and calculate again. If t2'>30, change to half-monthly maintenance; if t2'<=30, change to weekly maintenance. Perform the above calculation for each LRU in the five combinations and take the minimum maintenance cycle. Then:

[0080] If the shortest cycle among the five combinations is half-month maintenance: the monthly failure probability of a combination is greater than 70%, and half-month maintenance is recommended;

[0081] If the shortest cycle among the five combinations is weekly maintenance: The monthly failure probability of a combination is greater than 90%, weekly maintenance is recommended;

[0082] 2) Obtain the historical maintenance plan record data of the radar from the comprehensive support database, specifically from the historical usage data. The historical maintenance plan record data includes multiple historical maintenance plan cycles of the radar. The average value of the multiple historical maintenance plan cycles is used as the maintenance plan cycle of the radar, or any historical maintenance plan cycle of the multiple historical maintenance plan cycles is used as the maintenance plan cycle of the radar, and so on. The maintenance plan cycle of each preset equipment is obtained and a reminder is issued.

[0083] It is convenient for maintenance personnel to maintain each preset equipment according to the maintenance plan cycle of each preset equipment.

[0084] Optionally, in the above technical solution, it also includes:

[0085] S6. Based on the comprehensive support database, calculate the mean time between failures, availability and mission success rate of any preset equipment in any preset stage.

[0086] The preset stage can be understood as:

[0087] 1) For example, the period from the beginning of using the preset equipment to the first year of using the preset equipment is the first preset stage, and the second year of using the preset equipment is the second preset stage, etc.;

[0088] 2) The maintenance time given by the manufacturer of the preset equipment may also be divided into multiple preset stages, such as the time before the first maintenance time is the first preset stage, and the time between the first maintenance time and the second maintenance time is the second preset stage.

[0089] The process of obtaining the mean time between failures, availability and task success rate is as follows:

[0090] Assume that the historical cumulative power-on time of the equipment is T, the number of historical failures that have occurred is r, the equipment failure rate is W, the task execution time is t, and the mean failure recovery time MTTR = 0.5h, which may actually be longer.

[0091] 1) Calculate the mean time between failures (MTBF) using the following formula:

[0092]

[0093] 2) Calculate the availability α using the following formula:

[0094]

[0095] 3) The task success rate β is calculated by the following formula:

[0096] β=e -Wt ,in,

[0097] Optionally, in the above technical solution, it also includes:

[0098] S7. When any preset equipment fails, the fault location can be carried out through voice call or video call, which is more convenient for fault location and saves time.

[0099] The present invention discloses an equipment intelligent support method, the implementation process of which is divided into two steps: data collection and data analysis. Specifically:

[0100] 1) Collect information on the use, maintenance, and technical status of equipment throughout its life cycle through electronic means, conduct standardized management, accumulate rich support data, and improve the effectiveness of the data;

[0101] The premise for the operation and function of the intelligent support system is data. First, according to the system function design, a comprehensive support database is established as the data basis for the system operation. The required data is divided into two parts, of which the historical usage data and design data can be imported into the database through Excel tables. Through the analysis of system functions and algorithm models, the most critical parameters for equipment evaluation are sorted out. The historical data and design data are filled into Excel tables and imported into the comprehensive support database. The real-time use data of the equipment is collected through the handheld terminal. An Android version of the data collection software is developed and installed in the handheld device. The operator can directly fill in the real-time use data of the equipment in the handheld device, and upload the data to the comprehensive support database through the network. The use, maintenance, technical status and other information of the equipment throughout its life cycle are collected by electronic means, and standardized management is carried out to accumulate rich support data and improve the effectiveness of the data.

[0102] Step 2: Use scientific algorithms to analyze equipment support data to achieve real-time evaluation and accurate prediction of equipment status, provide accurate support decisions for equipment preventive maintenance, etc., scientifically analyze and equip equipment support resources, pre-store equipment maintenance equipment, and use remote support and intelligent fault identification technologies to achieve intelligent positioning of complex equipment faults. This invention breaks away from the traditional experience-based equipment use support method, uses the data generated by the equipment, connects to the actual use requirements of the equipment, and achieves precise and customized support. This invention can mainly improve work efficiency, reduce labor costs, and contribute to the use and support of equipment.

[0103] The present invention provides an equipment intelligent support method, which collects equipment usage data in a standardized manner, uses relevant algorithm models to calculate and analyze a large amount of data, obtains the evaluation results of equipment support capabilities, intuitively sees the equipment support capabilities, predicts possible future equipment failures, adjusts equipment function inspection and maintenance plans, and uses data and algorithms as drivers to scientifically guide equipment support business and improve equipment support efficiency. This method belongs to the field of reliability engineering technology.

[0104] In the above embodiments, although the steps are numbered S1, S2, etc., these are only specific embodiments given in the present application. Those skilled in the art may adjust the execution order of S1, S2, etc. according to actual conditions, which is also within the scope of protection of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0105] like Figure 2 As shown, an equipment intelligent security system 200 according to an embodiment of the present invention includes an acquisition and filling module 210 and a determination module 220;

[0106] The acquisition and filling module 210 is used to: acquire the design data, historical use data and real-time use data of each preset equipment in the whole life cycle, and fill them into the comprehensive support database, wherein each preset equipment is the same;

[0107] The determination module 220 is used to:

[0108] Based on the comprehensive support database, determining the probability of each subcomponent of each preset equipment failing within a preset time period in the future;

[0109] Determine whether the probability of any subcomponent of any preset equipment failing within a preset time period in the future exceeds a preset probability threshold corresponding to the subcomponent; if so, determine the subcomponent of the preset equipment as a subcomponent to be replaced, until all subcomponents to be replaced are determined;

[0110] Based on all the sub-components to be replaced, determine the number of spare parts for each sub-component to be replaced.

[0111] By collecting the design data, historical usage data and real-time usage data of each preset equipment throughout its life cycle, we can accumulate rich support data and improve the effectiveness of support data. This will enable us to more accurately determine the number of spare parts for each sub-component to be replaced, and enable maintenance personnel to select spare parts based on the number of spare parts for each sub-component to be replaced, facilitating replacement and repair when a fault occurs, avoiding problems caused by the lack of spare parts or the remote location of spare parts, and improving the availability of preset equipment.

[0112] Optionally, in the above technical solution, the determining module 220 is further used to:

[0113] Based on the comprehensive support database, the maintenance plan cycle of each preset equipment is obtained.

[0114] It is convenient for maintenance personnel to maintain each preset equipment according to the maintenance plan cycle of each preset equipment.

[0115] Optionally, in the above technical solution, a calculation module is further included, and the calculation module is used to:

[0116] Calculate the mean time between failures, availability and mission success rate of any preset equipment at any preset stage, so that maintenance personnel can check the performance status of any preset equipment at any preset stage.

[0117] Optionally, in the above technical solution, a fault location module is further included, and the fault location module is used to:

[0118] When any preset equipment fails, the fault can be located through voice call or video call.

[0119] In another embodiment, the equipment intelligent support system mainly includes a plurality of functional modules such as a comprehensive support database, a system management module, a visualization module, an evaluation module, a prediction module, a resource optimization module, a remote support module and a support strategy generation module. The following is a brief introduction to the functions of each module:

[0120] 1) Real-time usage data can be collected through handheld devices. An Android version of data collection software is developed and installed on handheld devices such as mobile phones and tablets. Figure 3 As shown, operators can directly fill in the real-time usage data of preset equipment in the handheld device and upload the real-time usage data to the comprehensive support database through the network; design data and historical usage data can be imported into the database in the form of Excel tables;

[0121] 2) The system management module includes user authority management, data import and export management, etc. The system uses username and password login, and the administrator configures users uniformly with clear role division, which is a security line of defense for the intelligent security support system.

[0122] 3) The visualization module displays the historical and real-time data of the equipment in the form of charts. The fault information visualization counts the fault conditions of each unit of equipment by year, and counts the equipment fault conditions by level according to the physical structure of the equipment, accurate to the smallest replaceable unit. The functions of the visualization module are as follows: Figure 4 As shown;

[0123] 4) The evaluation module counts the failure and working conditions of the equipment at each stage, and uses mathematical models to calculate the average failure-free time and availability of the equipment at each stage, such as Figure 5 and Figure 6 As shown;

[0124] 5) The prediction module counts the equipment's failure and working conditions. On the premise that the equipment's availability meets the requirements, it uses mathematical models to calculate and predict the combinations or equipment that may fail in the future. Scientific algorithms analyze equipment support data to achieve real-time evaluation and accurate prediction of equipment status, providing precise support decisions for equipment preventive maintenance, etc.

[0125] 6) The resource optimization module calculates the design failure rate and fault condition data of all replaceable units of the equipment, and appropriately raises the required standards on the premise that the equipment availability meets the requirements. It uses mathematical models to calculate and predict the combination or equipment that may fail in the future, and calculates the number of spare parts required. Users can edit the number of spare parts or set parameter standards by themselves to make all spare parts meet their own spare parts satisfaction requirements. Scientifically analyze and equip equipment support resources to pre-store equipment maintenance materials. Avoid the problem that the large variety of resources required for support increases the burden and difficulty of support, while the truly required and large-demand resources are not fully supported, such as Figure 7 As shown;

[0126] 7) Remote support module: The remote support module provides a quick troubleshooting solution. The handheld operator quickly establishes a communication link with the intelligent support system. The handheld operator sends the fault description or fault picture to the intelligent support system server. The system quickly resolves the fault after receiving the fault information. The software is equipped with an image matching algorithm, which can quickly match the cause of the fault and the repair method from the historical fault resource library, and assist in guiding on-site quick troubleshooting. Remote support supports multiple communication methods such as messaging, recording, voice calls, and video calls. Technologies such as remote support and intelligent fault identification can realize the intelligent positioning of complex equipment faults, such as Figure 8 As shown;

[0127] 8) The support strategy generation module mainly solves the problem of equipment maintenance according to the situation. At present, all equipment of the same type have the same maintenance plan cycle and project. However, in actual use, the failure and working conditions of each equipment are different. By analyzing the collected data of each equipment failure, working condition, key parameter indicators, etc., and using appropriate mathematical model algorithms, a dedicated maintenance plan cycle is generated for each equipment to improve the efficiency of equipment maintenance inspection, such as Fig. 9 and Fig.10 shown.

[0128] The system runs stably, has complete functions and produces accurate and reasonable results.

[0129] The above parameters and steps for each unit module to implement corresponding functions in the equipment intelligent support system 200 of the present invention can refer to the parameters and steps in the embodiment of the equipment intelligent support method above, and will not be repeated here.

[0130] A storage medium according to an embodiment of the present invention stores instructions, and when a computer reads the instructions, the computer executes any one of the above-mentioned equipment intelligent support methods.

[0131] An electronic device according to an embodiment of the present invention comprises a processor and the above-mentioned storage medium, wherein the processor executes instructions in the storage medium. The electronic device may be a computer, a mobile phone, etc.

[0132] Those skilled in the art will appreciate that the present invention may be implemented as a system, method or computer program product.

[0133] Therefore, the present disclosure may be specifically implemented in the following forms, namely: it may be completely hardware, it may be completely software (including firmware, resident software, microcode, etc.), or it may be a combination of hardware and software, generally referred to herein as a "circuit", "module" or "system". In addition, in some embodiments, the present invention may also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0134] Any combination of one or more computer-readable media can be used. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, computer-readable storage media can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0135] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for intelligent equipment protection, It is characterized in that include: Obtain the design data, historical usage data and real-time usage data of each preset equipment throughout its life cycle and fill them into the comprehensive support database, where each preset equipment is the same; Based on the comprehensive support database, determining the probability of each subcomponent of each preset equipment failing within a preset time period in the future; From the comprehensive support database, specifically from the historical usage data, the historical failure data and historical working conditions of the transmitter are obtained, and the failure rate of the transmitter is determined to be λ. The number of installed units is N. Now consider the probability of the number of failures of the transmitter in half a year. The calendar time of half a year is t = 24 × 365 / 2 = 4380h. Assume: The probability of the transmitter having 0 failures is The probability of at most one failure is Until the probability of each subcomponent of each preset equipment failing within a preset time period in the future is determined; whether the probability of any subcomponent of any preset equipment failing within a preset time period in the future exceeds the preset probability threshold corresponding to the subcomponent is determined, and if so, the subcomponent of the preset equipment is determined as a subcomponent to be replaced, until all subcomponents to be replaced are determined; Determine the number of spare parts for each sub-component to be replaced based on all sub-components to be replaced; Also includes: Based on the comprehensive support database, a maintenance plan cycle for each preset equipment is obtained; From the comprehensive support database, specifically from the historical usage data, the historical maintenance plan record data of the radar is obtained, and the S-curve maintenance cycle of the radar is determined through the algorithm model based on the historical maintenance plan record data. The relevant combinations of S-curve inspection items include: frequency synthesis combination, high-frequency receiving combination, intermediate frequency digital combination, signal processing combination, and wave control machine combination. According to the failure rate λ of all the minimum replaceable units LRU under these five combinations, the respective failure rates P of the next month are calculated. Assuming that the historical cumulative power-on time of the equipment is T, for any LRU under the frequency synthesis combination, 1-e is calculated. -λt1 =0.7, we know After t1 hour, the frequency combination has a probability of 0.7 to be broken, and then converted to days, Compare, if t1>30, do not change the original maintenance cycle; if t1<=30, change 0.7 to 0.9 and calculate again. If t2>30, change to half-monthly maintenance; if t2<=30, change to weekly maintenance. Perform the above calculation for each LRU in the five combinations and take the minimum maintenance cycle. If the shortest cycle among the five combinations is half-monthly maintenance: the monthly failure probability of a combination is greater than 70%, and half-monthly maintenance is performed; if the shortest cycle among the five combinations is weekly maintenance: the monthly failure probability of the combination is greater than 90%, and weekly maintenance is performed; it also includes: Based on the comprehensive support database, the mean time between failures, availability and mission success rate of any preset equipment in any preset stage are calculated; The process of obtaining the mean time between failures, availability and task success rate is as follows: Assume that the historical cumulative power-on time of the equipment is T, the number of historical failures is r, the equipment failure rate is W, the task execution time is t, and the mean failure recovery time MTTR = 0.5h: 1) Calculate the mean time between failures (MTBF) using the following formula: 2) Calculate the availability α using the following formula: 3) The task success rate β is calculated by the following formula: β=e -Wt ,in, 2. According to the equipment intelligent support method of claim 1, It is characterized in that Also includes: When any preset equipment fails, the fault can be located through voice or video calls.

3. An equipment intelligent support system, It is characterized in that It includes an acquisition filling module and a determination module; The acquisition and filling module is used to: acquire the design data, historical use data and real-time use data of each preset equipment in the whole life cycle, and fill them into the comprehensive support database, wherein each preset equipment is the same; The determination module is used for: Based on the comprehensive support database, determining the probability of each subcomponent of each preset equipment failing within a preset time period in the future; From the comprehensive support database, specifically from the historical usage data, the historical failure data and historical working conditions of the transmitter are obtained, and the failure rate of the transmitter is determined to be λ. The number of installed units is N. Now consider the probability of the number of failures of the transmitter in half a year. The calendar time of half a year is t = 24 × 365 / 2 = 4380h. Assume: The probability of the transmitter having 0 failures is The probability of at most one failure is Until the probability of failure of each subcomponent of each preset equipment within a preset time period in the future is determined; Determine whether the probability of any subcomponent of any preset equipment failing within a preset time period in the future exceeds a preset probability threshold corresponding to the subcomponent; if so, determine the subcomponent of the preset equipment as a subcomponent to be replaced, until all subcomponents to be replaced are determined; Determine the number of spare parts for each sub-component to be replaced based on all sub-components to be replaced; The determining module is also used for: Based on the comprehensive support database, a maintenance plan cycle for each preset equipment is obtained; From the comprehensive support database, specifically from the historical usage data, the historical maintenance plan record data of the radar is obtained, and the S-curve maintenance cycle of the radar is determined through the algorithm model based on the historical maintenance plan record data. The relevant combinations of S-curve inspection items include: frequency synthesis combination, high-frequency receiving combination, intermediate frequency digital combination, signal processing combination, and wave control machine combination. According to the failure rate λ of all the minimum replaceable units LRU under these five combinations, the respective failure rates P of the next month are calculated. Assuming that the historical cumulative power-on time of the equipment is T, for any LRU under the frequency synthesis combination, 1-e is calculated. -λt1 =0.7, we know After t1 hour, the frequency combination has a probability of 0.7 to be broken, and then converted to days, Compare, if t1,>30, do not change the original maintenance cycle; if t1,<=30, change 0.7 to 0.9 and calculate again, If t2,>30, it is replaced with half-monthly maintenance; if t2,<=30, it is replaced with weekly maintenance. The above calculation is performed for each LRU in the five combinations, and the minimum maintenance cycle is taken. If the shortest cycle among the five combinations is half-monthly maintenance: the monthly failure probability of a combination is greater than 70%, and half-monthly maintenance is performed; if the shortest cycle among the five combinations is weekly maintenance: the monthly failure probability of the combination is greater than 90%, and weekly maintenance is performed; Also included is a calculation module, the calculation module is used to: Calculate the mean time between failures, availability and mission success rate of any preset equipment at any preset stage; The process of obtaining the mean time between failures, availability and task success rate is as follows: Assume that the historical cumulative power-on time of the equipment is T, the number of historical failures is r, the equipment failure rate is W, the task execution time is t, and the mean failure recovery time MTTR = 0.5h: 1) Calculate the mean time between failures (MTBF) using the following formula: 2) Calculate the availability α using the following formula: 3) The task success rate β is calculated by the following formula: β=e -Wt ,in, 4. An equipment intelligent support system according to claim 3, It is characterized in that It also includes a fault location module, which is used to: When any preset equipment fails, the fault can be located through voice or video calls.

5. A storage medium, It is characterized in that The storage medium stores instructions, and when a computer reads the instructions, the computer executes an equipment intelligent support method as claimed in claim 1 or 2.

6. An electronic device, It is characterized in that The invention comprises a processor and the storage medium as claimed in claim 5, wherein the processor executes instructions in the storage medium.

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