CN112308300A - Large-scale electronic product state prediction management system - Google Patents
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Abstract
本发明公开了一种大型电子产品状态预测管理系统，属于雷达设计技术领域，包括雷达系统监测组件、处理中心与显示平台。本发明通过联合分布式信息系统，对机内各模块自检、测试和工作数据的信息采集，完成雷达故障诊断、状态评估、状态预测等分析处理，从而对雷达的健康状态进行感知和评价，对雷达的操作、使用与维修保障提出预先对策及措施；并通过人机交互等功能，能够及时准确掌握雷达的技术状态，优化操作使用策略，支撑精准维修保障，提高系统运行可靠度，为雷达基于状态的维修提供技术支持，最大限度发挥雷达装备的作战性能，值得被推广使用。
The invention discloses a large-scale electronic product state prediction management system, belonging to the technical field of radar design, comprising a radar system monitoring component, a processing center and a display platform. By combining with the distributed information system, the invention collects the information of self-inspection, test and working data of each module in the machine, and completes the analysis and processing of radar fault diagnosis, state evaluation, state prediction, etc., so as to perceive and evaluate the health state of the radar. Put forward countermeasures and measures for the operation, use and maintenance support of radar; and through functions such as human-computer interaction, it can timely and accurately grasp the technical status of radar, optimize operation and use strategies, support precise maintenance support, improve system operation reliability, and improve the reliability of radar operation. Condition-based maintenance provides technical support and maximizes the combat performance of radar equipment, which is worthy of promotion.
Description
Technical Field
The invention relates to the technical field of radar design, in particular to a large-scale electronic product state prediction management system.
Background
In order to meet different application occasions, corresponding working reliability requirements such as mean fault time between serious faults, mean fault time between mean faults and the like are provided when large electronic products such as radars and the like are designed.
At present, large-scale electronic products such as radars are generally designed with fault location, so that the products can be quickly located and maintained when in fault. However, the design method for fault location does not consider the intermediate state between the fault and the normal state, so that the whole health condition of the operation of the large-scale electronic product is not fully mastered, the performance degradation condition of the device before the fault can not be found, the trend of the performance change of the device can not be mastered by a user, and the state of the device in a period of time in the future can not be predicted.
Because the performance and state changes of equipment in a future period cannot be predicted by fault location, the current maintenance mode mainly adopts post-fault maintenance and regular preventive maintenance. The post-repair mode is that equipment is repaired after a large electronic product fails, and the failure repair of the electronic products such as radars and the like during the on-duty process inevitably causes equipment shutdown, influences the normal on-duty of the equipment and generates immeasurable influence; a preventive maintenance mode, namely, large electronic products are overhauled at fixed time intervals, products are maintained, equipment which is likely to break down is replaced according to experience, and the replaced equipment has good performance and state, so that maintenance resources are wasted; therefore, the problems of 'under maintenance' and 'over maintenance' are bound to be brought about by the after maintenance and the preventive maintenance adopted by the current electronic products. Therefore, a status prediction management system for large electronic products is proposed.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: how to well meet the high-reliability work requirements of large electronic products such as radars and the like, solve the problems of 'under maintenance' and 'over maintenance' in the actual use process, and provide a large electronic product state prediction management system.
The invention solves the technical problems through the following technical scheme, and the radar system monitoring device comprises a radar system monitoring component, a processing center and a display platform;
the radar system monitoring component comprises system monitoring equipment, BITE of each single machine and a state information collector of each single machine, and is used as a basic data source of the whole state prediction management system;
the display platform is used for providing an interface between a user and the state prediction management system, coordinating and controlling to complete various functions, and providing a data visualization mode to display the processing results of each module in the processing center;
the processing center comprises a data acquisition module, a data processing module, a diagnosis and prediction module, a state evaluation module and a maintenance decision module which are connected in sequence; the data acquisition module is used for acquiring state parameter information of all monitored objects from the radar system; the data processing module is used for carrying out fusion, feature extraction and data conversion processing on the state parameter information data acquired by the data acquisition module; the diagnosis prediction module is used for finishing the judgment, detection and isolation of radar faults by establishing a radar diagnosis knowledge base; the state evaluation module is used for carrying out state evaluation on the radar as a whole and evaluating each technical parameter of the radar; the maintenance decision module is used for outputting radar fault information according to the diagnosis and prediction module, providing a comprehensive diagnosis suggestion of the radar fault information, comprehensively analyzing the data of the state evaluation module, and establishing a prediction model reflecting the actual operation trend of radar equipment to obtain a final diagnosis and prediction result and an optimal maintenance guarantee scheme;
the system monitoring equipment, the BITE of each single machine and the state information collector of each single machine are respectively in communication connection with the data collection module, and the data processing module, the diagnosis and prediction module, the state evaluation module and the maintenance decision module are respectively in communication connection with the display platform.
Furthermore, the state parameter information acquired by the data acquisition module covers the whole radar system and is used for completely reflecting the working technical state of the radar equipment.
Furthermore, the working process of the data acquisition module is as follows:
s11: continuously or periodically acquiring state characteristic parameters of the radar equipment system by utilizing various sensors and detection equipment;
s12: receiving sensor monitoring information of each corresponding single machine and each corresponding component through radar main control software, peripheral management software and display control software;
s13: filtering, format conversion and fusion processing are carried out on the received monitoring information;
s14: and sending the extracted radar state effective data to the data processing module through a network.
Furthermore, the data collection symptoms are divided into an electrical effect, a temperature effect and a dynamic effect, wherein the electrical effect monitors the change of parameters of resistance, voltage and current in the radar equipment system, the temperature effect monitors the change of the radar equipment system and the ambient temperature, and the dynamic effect monitors vibration waves and pulse waves.
Furthermore, the data processing module integrates various effective data of the radar state from the data acquisition module through the Ethernet, performs preprocessing, feature extraction and data conversion by taking the external field replaceable unit as a unit, establishes a database table, binds a unique identifier of the external field replaceable unit for each piece of stored information, and stores the unique identifier into the database.
Further, the fault diagnosis operation process of the diagnosis and prediction module is as follows:
s21: receiving radar state effective data output by the data processing module;
s22: determining whether a relevant diagnosis object has a fault according to a radar fault judgment criterion, if the relevant diagnosis object has the fault, detecting and identifying the fault according to a fault event report and by combining a fault mode which is already recorded in a radar diagnosis knowledge base, analyzing the fault reason, and isolating a fault part;
s23: and evaluating the fault hazard, and determining the nature, reason and type of the fault.
Further, the failure trend prediction operation of the diagnosis prediction module is as follows:
s31: analyzing radar historical state data in a database by inquiring effective data in the database;
s32: and displaying the evolution trend of each module parameter of the radar in a certain time range in a visual mode, and performing state prediction on the components or the performance parameters with the gradual change rule.
Further, the state evaluation module includes a general performance evaluation unit for performing state evaluation on the radar as a whole and a dedicated performance evaluation unit; the special performance evaluation unit is used for evaluating each technical parameter of the radar.
Compared with the prior art, the invention has the following advantages: the large-scale electronic product state prediction management system completes analysis processing such as radar fault diagnosis, state evaluation, state prediction and the like on the self-checking, testing and working data information acquisition of each module in the large-scale electronic product through a combined distributed information system, so that the health state of the radar is sensed and evaluated, and advance countermeasures and measures are provided for operation, use and maintenance guarantee of the radar; through the functions of human-computer interaction and the like, the technical state of the radar can be timely and accurately mastered, the operation and use strategy is optimized, the accurate maintenance guarantee is supported, the system operation reliability is improved, the technical support is provided for the state-based maintenance of the radar, the combat performance of radar equipment is furthest exerted, and the radar device is worthy of being popularized and used.
Drawings
FIG. 1 is a block diagram of a status prediction management system of a medium-sized or large-sized electronic product according to an embodiment of the present invention;
fig. 2 is a diagram of a medium-and large-sized electronic product status prediction management software architecture according to an embodiment of the present invention.
Detailed Description
The following examples are given for the detailed implementation and specific operation of the present invention, but the scope of the present invention is not limited to the following examples.
The embodiment provides a technical scheme: a large-scale electronic product state prediction management system adopts an open distributed hierarchical reasoning structure, and is divided into three layers from the aspect of hardware distribution: the bottom layer is hardware detection equipment distributed in each subsystem of the radar or built-in detection equipment of each single machine; the middle layer is a state prediction management processing center; the top layer is a management layer and is a radar state prediction management system terminal display platform.
The radar system mainly comprises the following parts belonging to a state prediction management system: the system monitoring equipment, BITE (built-in self-inspection equipment) of each single machine, a state information collector of each single machine and the like comprise the collection of relevant important analog quantity information.
And the radar state prediction management system terminal display platform provides an interface between a user and the state prediction management system, coordinates and controls to complete various functions, and provides a data visualization mode to display the processing results of each module of the processing center of the state prediction management system.
The state prediction management system processing center is the core of the radar state prediction management system, and the main modules comprise: the device comprises a data acquisition module, a data processing module, a diagnosis and prediction module, a state evaluation module, a maintenance decision module and the like.
And the data acquisition module is responsible for acquiring the state parameter information of all the monitored objects from the radar. The data acquisition covers the whole system of the radar, and completely reflects the working technical state of the radar equipment, particularly reflects the main parameters of the health condition of the radar, thereby realizing the full coverage of key parts related to the performance and the safety of the radar equipment.
The concrete mode is as follows: the data acquisition module continuously or periodically acquires state characteristic parameters of the radar equipment system by using various sensors and detection equipment, receives sensor monitoring information of each single machine and each component through radar main control software, peripheral management software and display control software, performs filtering, format conversion and fusion processing on the received monitoring information, and finally sends the extracted effective data of the radar state to the data processing module of the state prediction management system through a network.
The data acquisition symptoms are divided into electrical effects, temperature effects, kinetic effects, etc., wherein: the method comprises the steps of monitoring the change of resistance, voltage and current parameters in the radar equipment system through an electrical effect, monitoring the change of the temperature of the radar equipment system and the change of the ambient temperature through a temperature effect, monitoring vibration waves and pulse waves through a dynamic effect and the like, and evaluating the health state of the radar equipment system through the data. The acquisition mode is mainly online, offline and manual tests are assisted, the data transmission has online and offline transmission capacity, and the transmission interface adopts Ethernet and USB interfaces.
The data processing module mainly completes data fusion, feature extraction and data conversion processing. The data processing module integrates various effective data of radar states from the data acquisition module through Ethernet, preprocesses, extracts features and converts data by taking a replaceable unit (the radar is taken as a large system and consists of a plurality of replaceable parts, assemblies and the like) as a unit, establishes a database table, binds unique identification of the replaceable unit of an external field for each piece of stored information, and stores the unique identification into the database. The data processing module provides a state data retrieval function on the main interface, can inquire state data of the internal and external field replaceable unit in a certain time period, describes the change trend of parameters in a form and curve window mode, draws a radar state curve graph, and displays each module of the radar and a radar state evolution curve graph.
The diagnosis and prediction module mainly completes fault diagnosis, fault trend prediction and the like. The diagnosis and prediction module establishes a radar diagnosis knowledge base, converts a fault model, a fault representation and expert knowledge into a software description language which can be identified by a state prediction management system through key technologies such as a state knowledge representation technology, a fault tree diagnosis rule technology, an expert system fault diagnosis technology and the like, and positions the fault through automatic and manual guidance one-step recursion to complete the judgment, detection and isolation of the radar fault. The radar diagnosis knowledge base covers the main failure mode of the equipment, and can be modified and expanded (the radar diagnosis knowledge base is a knowledge base which is established by a designer according to the failures and the failure reasons which are possibly generated by the replaceable unit and the influence on the upper level and the lower level, and is used for assisting in judging the real reasons of the radar failures).
The diagnosis prediction module is embedded in the main interface menu function item, and the specific working mode of the fault diagnosis is as follows: and receiving the effective radar state data output by the data processing module, determining whether a relevant diagnosis object has a fault according to a radar fault judgment criterion, detecting and identifying the fault according to a fault event report and in combination with a fault mode which is already input into a radar diagnosis knowledge base if the relevant diagnosis object has the fault, analyzing the fault reason, isolating a fault part, and finally evaluating the fault hazard to determine the nature, the reason and the type of the fault.
And the failure trend prediction utilizes the failure diagnosis result to describe the real-time state of each module of the radar in two modes of a graph and a table. Effective data in a database is inquired, radar historical state data in the database is analyzed, the evolution trend of parameters of each module of the radar in a certain time range is displayed in a table, curve window and other visual modes, and state prediction including parameter threshold, time life and time life is carried out on components or performance parameters with gradient rules.
The state evaluation module state evaluation includes a general performance evaluation and a specific performance evaluation. The general performance evaluation is used for carrying out state evaluation on the radar on the whole, such as equivalent service time, equivalent overhaul times, intangible loss and the like, and the health state of the whole radar is established. The special performance evaluation mainly evaluates various technical parameters of the radar, such as fault states, technical indexes, operational performance and the like.
The maintenance decision module provides radar fault information comprehensive diagnosis suggestions according to the radar fault information output by the diagnosis and prediction module, automatically generates a maintenance work order according to key information such as fault levels, component redundancy, maintenance resources and the like by inquiring a fault information table of a database, and manages and stores the whole life process of the maintenance work order. Through the data of the comprehensive analysis state evaluation module, a prediction model reflecting the actual operation trend of the equipment is established (for example, the change trend of the transmitter power can connect all the test values into a curve, and the change direction of the transmission power is predicted according to the rising or falling trend of the curve, and similarly, the change curve of the motor driving current is collected, whether the driving current is gradually increased or not can be judged, and the larger the resistance of the motor rotation is, the larger the required driving current is, and further the state of the motor can be predicted according to the driving current), and the relevant knowledge of the prediction knowledge base is read, so that the final diagnosis and prediction result and the optimal maintenance guarantee scheme are formed. The prediction knowledge base comprises knowledge of failure trend prediction, service life prediction and the like. And reasonable equipment operation use suggestions and information use suggestions are provided aiming at the conclusions of radar detection power preservation degree, equipment function loss and degradation, equipment residual combat capability and the like sensed by the state evaluation module.
As shown in fig. 1, the architecture diagram of the status prediction management system of the large-scale electronic product of this embodiment mainly includes three major parts, namely a radar system, a processing center, and a display platform. The radar system comprises system monitoring equipment, BITE of each single machine, a state information collector of each single machine and the like, and is a basic data source of the whole system. The processing center collects, arranges, fuses and converts basic data of the system through the data acquisition module and the data processing module, and transfers the processed data to the diagnosis and prediction module, the state evaluation module and the maintenance decision module to form a series of product state prediction and evaluation results, including diagnosis and prediction of faults and evaluation of radar general and special performances, and finally forms a final decision of guarantee through comprehensive analysis and combination of related database knowledge. The display platform visually displays the result through human-computer interaction including on-site and remote operation modes.
As shown in fig. 2, for the large-scale electronic product state prediction management software system architecture of this embodiment, the state prediction management system provides a friendly visual interface and a human-computer interaction function, and is mainly used for managing a radar state prediction management system basic database, collecting, storing, processing, displaying, transmitting and managing radar state data, determining/detecting and isolating radar faults, evaluating radar fighting performance and general quality performance, predicting trends and lives of equipment or components, and finally providing operation decision suggestions, maintenance suggestions, guarantee suggestions and the like for equipment health conditions.
The software system adopts an open software system architecture, realizes centralized deployment or distributed deployment by a universal service processing software middleware, can conveniently expand and upgrade the radar health management function, and improves the maintainability of the system. The software system adopts an open type layered architecture and can be dynamically expanded. The functions of bottom layer driving, storage allocation, queue management, memory management, communication management, task scheduling, load balancing, database management and the like are packaged in a mode of basic software components, centralized and effective management of data and equipment is achieved, and each basic software component can be dynamically loaded. To increase the openness and versatility of software systems, network communications may be dynamically configured and managed. The system has good openness, configurability and expandability. The service processing core in the software system adopts a generalized software component, and the service processing functions can be combined and reconstructed. All the service processing software components adopt parameterized and generalized debugging interfaces, and software modules and public data can be dynamically loaded and flexibly configured and combined, so that the reconfigurability of a software system is fully embodied, the software universality and maintainability are improved, and the radar health management function expansion and upgrading are facilitated. The method adopts a model-parameter design mode, realizes the separation of data and processing on a software structure, designs different processing modes and display modes aiming at the data content of the same organization structure, can realize the diversity of the processing modes and the display modes, and ensures the uniformity of the data and the expandability of the software. The software system comprises a basic database, wherein the basic database comprises a radar equipment database, a radar state database, a fault information database, a spare part database, a related knowledge base and the like, and can be used for creating, managing, inquiring, displaying, structuring and content. The database is provided with an export interface, and table contents in an excel format, including fault information, spare part information and periodic maintenance information, can be directly imported into the corresponding database. And providing a basic data query page on the main interface for querying and displaying each basic database.
To sum up, the large-scale electronic product state prediction management system of the above embodiment completes analysis processing such as radar fault diagnosis, state evaluation, state prediction and the like by combining the distributed information system to perform self-checking, testing and information acquisition of working data of each module in the machine, thereby sensing and evaluating the health state of the radar and providing advance countermeasures and measures for operation, use and maintenance guarantee of the radar; through the functions of human-computer interaction and the like, the technical state of the radar can be timely and accurately mastered, the operation and use strategy is optimized, the accurate maintenance guarantee is supported, the system operation reliability is improved, the technical support is provided for the state-based maintenance of the radar, the combat performance of radar equipment is furthest exerted, and the radar device is worthy of being popularized and used.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
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|---|---|---|---|---|
| CN113281711A (en) * | 2021-05-19 | 2021-08-20 | 北京无线电测量研究所 | Method and system for detecting health state |
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Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105809255A (en) * | 2016-03-07 | 2016-07-27 | 大唐淮南洛河发电厂 | IoT-based heat-engine plantrotary machine health management method and system |
| CN106952028A (en) * | 2017-03-13 | 2017-07-14 | 杭州安脉盛智能技术有限公司 | Dynamoelectric equipment failure is examined and health control method and system in advance |
| CN107035602A (en) * | 2017-06-14 | 2017-08-11 | 衢州市江氏电子科技有限公司 | A kind of turbine state monitoring and fault diagnosis system |
| CN107327366A (en) * | 2017-06-28 | 2017-11-07 | 华中科技大学 | A kind of pump-storage generator prognostic and health management system and method |
| CN107832867A (en) * | 2017-10-10 | 2018-03-23 | 北京交通大学 | A kind of railway equipment based on failure predication technology is health management system arranged |
| CN109447048A (en) * | 2018-12-25 | 2019-03-08 | 苏州闪驰数控系统集成有限公司 | A kind of artificial intelligence early warning system |
| CN109742852A (en) * | 2018-12-11 | 2019-05-10 | 诚启(武汉)智能机器有限公司 | A kind of controller switching equipment state-detection diagnostic system |
| CN110107441A (en) * | 2019-05-27 | 2019-08-09 | 浙江大学 | Hydraulic turbine inline diagnosis forecasting system |
| CN110206676A (en) * | 2019-05-27 | 2019-09-06 | 浙江大学 | The hydraulic turbine remotely monitors, diagnosis prediction and intelligent maintenance regulator control system |
| CN110766277A (en) * | 2018-10-24 | 2020-02-07 | 中国核电工程有限公司 | Health assessment and diagnostic systems and mobile terminals for nuclear industry sites |
| CN111365158A (en) * | 2020-03-02 | 2020-07-03 | 东方电气集团东方电机有限公司 | Real-time state evaluation and life cycle management prediction system for water turbine runner |
| CN111444055A (en) * | 2020-04-26 | 2020-07-24 | 昆山润石智能科技有限公司 | System for health assessment and maintenance of equipment |
| CN111553590A (en) * | 2020-04-27 | 2020-08-18 | 中国电子科技集团公司第十四研究所 | A radar embedded health management system |
| CN111784207A (en) * | 2020-07-30 | 2020-10-16 | 中国电子科技集团公司第十四研究所 | An open radar health management system |
-
2020
- 2020-10-19 CN CN202011120365.XA patent/CN112308300A/en active Pending
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105809255A (en) * | 2016-03-07 | 2016-07-27 | 大唐淮南洛河发电厂 | IoT-based heat-engine plantrotary machine health management method and system |
| CN106952028A (en) * | 2017-03-13 | 2017-07-14 | 杭州安脉盛智能技术有限公司 | Dynamoelectric equipment failure is examined and health control method and system in advance |
| CN107035602A (en) * | 2017-06-14 | 2017-08-11 | 衢州市江氏电子科技有限公司 | A kind of turbine state monitoring and fault diagnosis system |
| CN107327366A (en) * | 2017-06-28 | 2017-11-07 | 华中科技大学 | A kind of pump-storage generator prognostic and health management system and method |
| CN107832867A (en) * | 2017-10-10 | 2018-03-23 | 北京交通大学 | A kind of railway equipment based on failure predication technology is health management system arranged |
| CN110766277A (en) * | 2018-10-24 | 2020-02-07 | 中国核电工程有限公司 | Health assessment and diagnostic systems and mobile terminals for nuclear industry sites |
| CN109742852A (en) * | 2018-12-11 | 2019-05-10 | 诚启(武汉)智能机器有限公司 | A kind of controller switching equipment state-detection diagnostic system |
| CN109447048A (en) * | 2018-12-25 | 2019-03-08 | 苏州闪驰数控系统集成有限公司 | A kind of artificial intelligence early warning system |
| CN110107441A (en) * | 2019-05-27 | 2019-08-09 | 浙江大学 | Hydraulic turbine inline diagnosis forecasting system |
| CN110206676A (en) * | 2019-05-27 | 2019-09-06 | 浙江大学 | The hydraulic turbine remotely monitors, diagnosis prediction and intelligent maintenance regulator control system |
| CN111365158A (en) * | 2020-03-02 | 2020-07-03 | 东方电气集团东方电机有限公司 | Real-time state evaluation and life cycle management prediction system for water turbine runner |
| CN111444055A (en) * | 2020-04-26 | 2020-07-24 | 昆山润石智能科技有限公司 | System for health assessment and maintenance of equipment |
| CN111553590A (en) * | 2020-04-27 | 2020-08-18 | 中国电子科技集团公司第十四研究所 | A radar embedded health management system |
| CN111784207A (en) * | 2020-07-30 | 2020-10-16 | 中国电子科技集团公司第十四研究所 | An open radar health management system |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113281711A (en) * | 2021-05-19 | 2021-08-20 | 北京无线电测量研究所 | Method and system for detecting health state |
| CN113306510B (en) * | 2021-06-15 | 2023-10-20 | 北京小马易行科技有限公司 | Method and device for monitoring radar state, vehicle, storage medium and processor |
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| CN114236484A (en) * | 2021-12-17 | 2022-03-25 | 四川九洲空管科技有限责任公司 | Secondary radar and anti-collision system health management method, device, equipment and medium |
| CN114236484B (en) * | 2021-12-17 | 2024-12-06 | 四川九洲空管科技有限责任公司 | Secondary radar and collision avoidance system health management method, device, equipment and medium |
| CN114676899A (en) * | 2022-03-17 | 2022-06-28 | 安徽维德工业自动化有限公司 | A method for health status assessment and maintenance decision-making of automobile tire manufacturing equipment |
| CN115327496A (en) * | 2022-08-11 | 2022-11-11 | 中国人民解放军32181部队 | Radar equipment state real-time evaluation method and system |
| CN115857757A (en) * | 2022-12-26 | 2023-03-28 | 中国电子科技集团公司第十四研究所 | A human-computer interaction system for radar health management |
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| CN119199865A (en) * | 2024-11-27 | 2024-12-27 | 上海几何伙伴智能驾驶有限公司 | A millimeter-wave radar that meets functional safety requirements |
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