Coding-based equipment full life cycle management method and system

Abstract

The invention is suitable for the technical field of equipment management, and provides a coding-based equipment full life cycle management method and system, and the method comprises the steps: obtaining equipment information, and obtaining an equipment identification technology; acquiring scene information according to the equipment information, selecting an equipment identifier according to the scene information, uploading data to the equipment identifier through middleware, and realizing sharing among the equipment information through the middleware; obtaining operation data, constructing a prediction model according to the operation data, obtaining a prediction result, judging a subsequent working condition through the prediction result, comparing the prediction result with a threshold value, and judging whether the operation is abnormal or not according to a comparison result; according to the method, the operation data is subjected to visualization processing, the use condition, the failure rate and the maintenance plan of the equipment are displayed through visualization processing, and the visualization result is sent to the terminal, so that the problems that the existing equipment full-life-cycle management cannot be operated by multiple terminals at the same time, the operation accuracy cannot be ensured, and the operation efficiency is high are solved. And visual processing cannot be carried out on the whole life cycle of the equipment.

Description

Technical Field

[0001] The present invention belongs to the technical field of equipment management, and in particular, relates to a coding-based equipment full life cycle management method and system. Background Art

[0002] The foundation, direction, focus and measures of the current digital transformation of enterprises have opened a new chapter in the digital transformation of enterprises. As the most basic and core link in the production process, the digital and intelligent management of the entire life cycle of production equipment should be the focus of the digital transformation of enterprises.

[0003] Most companies use Excel spreadsheets to manage production equipment, or use a single automated management method for equipment, using a QR code to make the equipment information into a QR code and paste it on the equipment body, and scan it with a mobile phone to obtain information such as equipment model, manufacturer, and parameters. This solution can only manage a single device on site, and the management information only has the inherent information of the device.

[0004] There are problems in the full life cycle management of equipment, such as the inability to operate multiple terminals simultaneously, the inability to guarantee the accuracy of operations, and the inability to visualize the entire life cycle of the equipment. Summary of the invention

[0005] The purpose of the embodiment of the present invention is to provide a coding-based device full life cycle management method, aiming to solve the problem raised in the third part of the background technology.

[0006] The embodiment of the present invention is implemented as follows: a method and system for managing the entire life cycle of equipment based on coding, the method comprising:

[0007] Obtain device information, including device type and management program, and obtain device identification technology, including barcode, QR code, RFID and NFC;

[0008] Acquire scene information according to device information, the scene information includes device transportation and working scenes, select device identification according to the scene information, upload data to the device identification through middleware, the middleware includes an API interface, and realize sharing of device information through the middleware;

[0009] Acquire operation data, the operation data including operation records, fault records and maintenance records, build a prediction model based on the operation data, the prediction model adopts the ARIMA model, obtain prediction results, judge subsequent work conditions based on the prediction results, compare the prediction results with the threshold, and judge whether there is an abnormality based on the comparison results;

[0010] The operation data is visualized, including charts and dashboards, which display the equipment usage, failure rate and maintenance plan through visualization, and send the visualization results to the terminal.

[0011] Preferably, the scenario information includes equipment transportation and working scenarios, a device identifier is selected according to the scenario information, data is uploaded to the device identifier through a middleware, the middleware includes an API interface, and the steps of sharing device information through the middleware specifically include:

[0012] Acquire scene information according to device information, and select a device identifier according to the scene information, wherein the device identifier may be one or more types;

[0013] Upload data to the device ID through the middleware, share device information through the middleware, and upload information to the blockchain;

[0014] Obtain blockchain permissions, send permissions to user terminals, and obtain user terminal editing data on device information.

[0015] Preferably, the step of obtaining operation data, the operation data including operation records, fault records and maintenance records, building a prediction model based on the operation data, the prediction model using an ARIMA model, obtaining prediction results, judging subsequent work conditions based on the prediction results, comparing the prediction results with a threshold, and judging whether there is an abnormality based on the comparison results specifically includes:

[0016] Obtain operation data, obtain the operation records, fault records and maintenance records of the equipment through the operation data, and build a prediction model based on the operation data;

[0017] Obtain the prediction results of the prediction model, judge the subsequent work status according to the prediction results, and compare the prediction results with the threshold value, where the threshold value is the safe working range;

[0018] Obtain the comparison result and determine whether it is abnormal based on the comparison result. If the predicted result is greater than the threshold, it is determined to be abnormal.

[0019] Preferably, the step of performing visualization processing on the operation data, wherein the visualization processing includes charts and dashboards, and displaying the usage, failure rate and maintenance plan of the equipment through visualization processing, and sending the visualization results to the terminal specifically includes:

[0020] Visualize the operation data, prediction results and maintenance plan, make them into icons, and display the real-time status and trend of the equipment through the dashboard;

[0021] Display equipment usage, failure rate, and maintenance plan through visualization, and send visualization results to the terminal;

[0022] The terminal upload information is obtained, wherein the uploaded information includes improvement suggestions and processing methods, and the visual processing information is modified according to the uploaded information.

[0023] Preferably, the management procedure includes a purchasing stage, a registration stage, a maintenance stage, an updating stage and a processing stage.

[0024] Another object of an embodiment of the present invention is to provide a coding-based equipment life cycle management system, the system comprising:

[0025] The device information module obtains device information, including device type and management program, and obtains device identification technology, including barcode, QR code, RFID and NFC;

[0026] An information uploading module obtains scene information according to device information, wherein the scene information includes device transportation and working scenes, selects a device identifier according to the scene information, and uploads data to the device identifier through a middleware, wherein the middleware includes an API interface, and sharing of device information is realized through the middleware;

[0027] The prediction module obtains operation data, including operation records, fault records and maintenance records, builds a prediction model based on the operation data, and adopts the ARIMA model to obtain prediction results, judge the subsequent work status based on the prediction results, compare the prediction results with the threshold, and judge whether it is abnormal based on the comparison results;

[0028] The visualization module performs visualization processing on the operation data. The visualization processing includes charts and dashboards. The usage, failure rate and maintenance plan of the equipment are displayed through visualization processing, and the visualization results are sent to the terminal.

[0029] Preferably, the information uploading module includes:

[0030] A device representation unit, which acquires scene information according to the device information and selects a device identifier according to the scene information, wherein the device identifier may be one or more types;

[0031] The blockchain unit uploads data to the device identifier through the middleware, realizes the sharing of device information through the middleware, and uploads the information to the blockchain;

[0032] The permission unit obtains blockchain permissions, sends permissions to user terminals, and obtains the editing data of device information by user terminals.

[0033] Preferably, the prediction module comprises:

[0034] A model building unit acquires operation data, obtains operation records, fault records and maintenance records of the equipment through the operation data, and builds a prediction model based on the operation data;

[0035] A prediction result unit obtains the prediction result of the prediction model, determines the subsequent work status according to the prediction result, and compares the prediction result with a threshold value, wherein the threshold value is a safe working range;

[0036] The judgment unit obtains the comparison result and judges whether it is abnormal according to the comparison result. If the prediction result is greater than a threshold, it is judged to be abnormal.

[0037] Preferably, the visualization module includes:

[0038] Visualize the operation data, prediction results and maintenance plan, make them into icons, and display the real-time status and trend of the equipment through the dashboard;

[0039] Display equipment usage, failure rate, and maintenance plan through visualization, and send visualization results to the terminal;

[0040] The terminal upload information is obtained, wherein the uploaded information includes improvement suggestions and processing methods, and the visual processing information is modified according to the uploaded information.

[0041] Preferably, the management procedure includes a purchasing stage, a registration stage, a maintenance stage, an updating stage and a processing stage.

[0042] The embodiment of the present invention provides a method for managing the entire life cycle of equipment based on coding, which obtains equipment information, knows the type of equipment and management procedures, obtains equipment identification technology, and the identification technology includes barcode, QR code, RFID and NFC, obtains scene information according to the equipment information, selects the equipment identification according to the scene information, and can use one or more equipment identifications, uploads data to the equipment identification through the middleware, realizes the sharing of equipment information through the middleware, uploads information to the blockchain, obtains blockchain permissions, sends permissions to the user terminal, obtains the editing data of the user terminal on the equipment information, obtains the operation data, obtains the operation record, fault record and maintenance record of the equipment through the operation data, builds a prediction model according to the operation data, obtains the prediction result of the prediction model, and judges the subsequent work according to the prediction result. The prediction result is compared with the threshold value, where the threshold value is the safe working range. The comparison result is obtained, and whether it is abnormal is determined based on the comparison result. If the prediction result is greater than the threshold value, it is determined to be abnormal. Visual processing is performed based on the operating data, prediction results and maintenance plan. The operating data, prediction results and maintenance plan are made into icons. The real-time status and trend of the equipment are displayed on the dashboard. The usage, failure rate and maintenance plan of the equipment are displayed through visualization processing. The visualization result is sent to the terminal, and the terminal upload information is obtained. The uploaded information includes improvement suggestions and processing methods. The visualization processing information is modified according to the uploaded information, which solves the problems of the existing equipment life cycle management that multiple terminals cannot be operated at the same time, the accuracy of the operation cannot be guaranteed, and the entire life cycle of the equipment cannot be visualized. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A flowchart of a method for managing the entire life cycle of equipment based on coding provided by an embodiment of the present invention;

[0044] Figure 2 A flowchart of the steps of acquiring scene information according to device information, selecting a device identifier according to the scene information, and sharing device information through middleware provided in an embodiment of the present invention;

[0045] Figure 3 A flowchart of the steps of obtaining operation data, building a prediction model based on the operation data, obtaining prediction results, and comparing the prediction results with a threshold value provided in an embodiment of the present invention;

[0046] Figure 4 A flowchart of the steps of visualizing the operation data and sending the visualization results to the terminal provided in an embodiment of the present invention;

[0047] Figure 5 An architecture diagram of a coding-based equipment life cycle management system provided by an embodiment of the present invention;

[0048] Figure 6An architectural diagram of an information upload module provided in an embodiment of the present invention;

[0049] Figure 7 An architectural diagram of a prediction module provided by an embodiment of the present invention;

[0050] Figure 8 An architectural diagram of a visualization module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0052] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0053] like Figure 1 As shown, a method for managing the entire life cycle of equipment based on coding is provided in an embodiment of the present invention, and the method includes:

[0054] S100, acquiring device information, the device information including device type and management program, acquiring device identification technology, the identification technology including barcode, QR code, RFID and NFC.

[0055] In this step, the device information is obtained. Obtaining device information is the first step in device management, which includes understanding the basic properties of the device, usage scenarios, management requirements and other information. This information provides a basis for the subsequent selection of appropriate device identification technology. Device identification technology is a means of uniquely identifying and managing devices. It is used to track the status and management process of the device. The types of equipment include the type, model and specifications of the equipment. For example, the equipment in a factory may include CNC machine tools, conveyor belts, wind turbines, etc. The usage scenarios and maintenance requirements of each device may be different;

[0056] Management procedures refer to the processes and rules for managing the equipment, including how to record equipment information, equipment maintenance plans, equipment access rights management, etc. Different types of equipment usually have different management procedures. For example, the management of heavy equipment requires special attention to safety inspection records, while the management of high-precision instruments may focus more on calibration and regular testing;

[0057] Device identification technology is used to assign unique identifiers to devices so that they can be recorded, queried, and tracked in management systems. Different device identification technologies have different characteristics and applicable scenarios. Barcodes are suitable for fixed devices or device management scenarios that do not require the storage of large amounts of information. QR codes are suitable for device management scenarios that require more data or provide quick information access. RFID is suitable for device management scenarios that require frequent movement, are difficult to scan manually, or are in harsh environments. NFC is suitable for device management scenarios that require close-range interaction and temporary data writing.

[0058] S200, acquiring scene information according to device information, wherein the scene information includes device transportation and working scenes, selecting a device identifier according to the scene information, and uploading data to the device identifier through middleware, wherein the middleware includes an API interface, and sharing of device information is achieved through the middleware.

[0059] In this step, scene information is obtained based on the device information. The scene information mainly includes the transportation scene and working scene of the device. This information will directly affect the selection of the device identification. The transportation scene mainly considers the possible requirements of the device during movement, storage, and loading and unloading. The working scene includes the daily working environment of the device. Environmental factors may affect the reading effect of the identification. The device identification is selected according to the scene information;

[0060] Upload data to the device identifier through the middleware, and upload the device data to the central database or cloud platform through the API interface. The middleware can connect multiple systems to complete real-time sharing and information updating of device data. Sharing of device information is achieved through the middleware, ensuring real-time synchronization and transparency of data throughout the life cycle of the device.

[0061] S300, obtaining operation data, wherein the operation data includes operation records, fault records and maintenance records, building a prediction model based on the operation data, wherein the prediction model adopts an ARIMA model, obtaining prediction results, judging subsequent work conditions based on the prediction results, comparing the prediction results with a threshold, and judging whether there is an abnormality based on the comparison results.

[0062] In this step, operation data is obtained. The operation data includes operation records, fault records and maintenance records. The operation records are the data of the equipment in daily operation, the fault records are the fault information of the equipment in operation, and the maintenance records are the detailed information of each maintenance or servicing.

[0063] After obtaining enough equipment operation data, the ARIMA model can be used to model and predict the data. The ARIMA model is a model commonly used in time series analysis, which can predict future equipment status by analyzing past data trends and fluctuations;

[0064] Get the prediction results, use the ARIMA model to get the prediction results of the future equipment status, judge the subsequent work status based on the prediction results, and compare the prediction results with the thresholds. If the prediction results show that certain indicators will exceed the normal thresholds, it may indicate that the equipment is about to become abnormal.

[0065] S400, performing visualization processing on the operation data, wherein the visualization processing includes charts and dashboards, and displaying the usage, failure rate and maintenance plan of the equipment through the visualization processing, and sending the visualization results to the terminal.

[0066] In this step, the operation data is visualized. By visualizing the operation data of the equipment, managers can more intuitively monitor the equipment usage, failure rate and maintenance plan. This visualization can not only generate charts, but also create dashboards to make complex data easier to understand, thereby helping managers make quick decisions;

[0067] Through visualization, the equipment usage, failure rate and maintenance plan can be displayed, the daily operation status and performance indicators of the equipment can be displayed, the equipment failure record and failure rate change trend can be displayed, and the historical and future maintenance arrangements of the equipment can be displayed. Different chart forms and dashboard layouts can be used according to different data types to make the information presentation clearer and more intuitive. Visualization methods include bar charts, line charts, pie charts, heat maps, dashboards, etc. Choosing the right visualization method can effectively convey data trends and status;

[0068] In order to facilitate managers to check the equipment status at any time, the visualization results are sent to the user terminal. Through the mobile terminal, managers can remotely access the equipment data outside the factory to ensure that the equipment status can be checked in real time.

[0069] like Figure 2 As shown, as a preferred embodiment of the present invention, the scene information is obtained according to the device information, the scene information includes the device transportation and working scenes, the device identification is selected according to the scene information, and the data is uploaded to the device identification through the middleware, the middleware includes an API interface, and the steps of sharing the device information through the middleware specifically include:

[0070] S201, acquiring scene information according to device information, and selecting a device identifier according to the scene information, wherein the device identifier may be one or more types.

[0071] In this step, scene information is obtained based on device information. Scene information is mainly obtained based on device information to analyze the environment, usage frequency, and movement requirements of the device during actual use and management. Scene information mainly includes two aspects: transportation scene and working scene of the device.

[0072] Transportation scenario: The state of the equipment during transportation and its requirements for identification technology. This includes whether it needs to be moved frequently, whether it is outdoors, whether it is subject to vibration or shock, whether the temperature and humidity change greatly, etc. Different transportation requirements will directly affect the choice of identification. For example, equipment that is frequently transported requires durable, non-contact identification technology.

[0073] Working scenario: The environmental conditions of the equipment in normal working state, including whether it is in a harsh environment with a lot of dust, oil, humidity, etc., whether it will be interfered by metal, whether it needs to be waterproof or high temperature resistant, etc. The characteristics of these working scenarios determine the durability and reading effect of the label.

[0074] After determining the scene information, you can select appropriate device identification for different environmental requirements. Common identification methods include barcodes, QR codes, RFID and NFC. You can even combine multiple identification methods to meet the needs of complex scenarios. Based on the scene information of the device, you can select one identification method or combine multiple identification methods to meet different needs.

[0075] Single identification method: For simple scenarios, select an appropriate identification method, such as using a barcode to identify devices in a static, indoor environment.

[0076] Combination of multiple identification methods: For complex or changing scenarios, a combination of multiple identification methods can be used. For example, for equipment that requires long-distance reading and close-range maintenance, RFID and NFC tags can be used in combination.

[0077] S202, upload data to the device identifier through the middleware, share device information through the middleware, and upload information to the blockchain.

[0078] In this step, data is uploaded to the device identifier through the middleware. The middleware is a data processing and forwarding module in the device management system, which can exchange information and share data between different hardware and software. Through the middleware, the real-time status and management information of the device can be uploaded to the central database or cloud, and the data can be distributed to other systems (such as ERP, MES, WMS, etc.) to form a unified device management view. The middleware also has functions such as data interface conversion, data cleaning, formatting, and identity authentication;

[0079] Through the middleware, information can be uploaded to the device identification (such as NFC, RFID tags). These tags have certain data storage and read-write functions, which makes it easy to check the device information at any time. For mobile devices or equipment that requires frequent maintenance, by updating the information on the tag, maintenance personnel and management systems can obtain the latest status of the device at any time;

[0080] Through the middleware, the key information of the equipment is encrypted and uploaded to the blockchain, such as maintenance records, service life, and fault records, to achieve traceability and secure storage of equipment data. The use of blockchain ensures the transparency and credibility of equipment information and is suitable for industries that require supervision and compliance.

[0081] S203, obtaining blockchain authority, sending the authority to the user terminal, and obtaining the editing data of the device information by the user terminal.

[0082] In this step, blockchain permissions are obtained. In the blockchain system, permission management is a key step to ensure data security and compliance. Permissions are usually obtained based on user identity authentication and role assignment to ensure that only qualified users can access or edit device information. The blockchain system will first verify the user's identity and assign specific permissions to the user based on the user's identity and responsibilities. The permissions of the blockchain system can be divided into different levels such as view, edit, review, and manage to control the scope of user operations on data. For example, ordinary users can only view the device status, while administrators can edit device information.

[0083] When the user's permissions are verified, the system will send the corresponding permissions to the user's terminal device, and the user terminal can view or edit the device data within the scope of permissions. When the user edits the device information on the terminal, such as updating maintenance records or uploading new status information, the terminal will send these edited data to the blockchain system. Through smart contracts, the blockchain can automatically verify and store these edited data to ensure the authenticity and immutability of the data.

[0084] like Figure 3 As shown, as a preferred embodiment of the present invention, the operation data is obtained, the operation data includes operation records, fault records and maintenance records, a prediction model is constructed according to the operation data, the prediction model adopts an ARIMA model, a prediction result is obtained, the subsequent work situation is judged by the prediction result, the prediction result is compared with a threshold, and whether it is abnormal according to the comparison result is judged, specifically including:

[0085] S301, acquiring operation data, obtaining operation records, fault records and maintenance records of the equipment through the operation data, and building a prediction model based on the operation data.

[0086] In this step, the operation data is obtained. The operation data of the equipment is divided into three categories:

[0087] Operation records: include daily operation parameters and status information of the equipment, such as temperature, pressure, current, voltage, vibration frequency, operating time, etc. Operation records are usually collected by sensors in real time and reflect the current status and workload of the equipment.

[0088] Fault records: include all fault information that occurs during equipment operation, including the time, type, location and severity of the fault. This information can help us identify equipment weaknesses and high-risk components.

[0089] Maintenance records: include all maintenance and care information of the equipment, such as replaced parts, maintenance time, maintenance content, and responsible maintenance personnel. Maintenance records reveal the health status of the equipment and its maintenance cost and cycle.

[0090] The operation data is used to obtain the operation records, fault records, and maintenance records of the equipment. After data preprocessing and feature extraction are completed, the historical data can be used to build a prediction model. For example, we have the daily power load data (unit: MW, megawatt) for the past 10 days as follows:

[0091]

[0092]

[0093] Use ARIMA model for modeling: ARIMA (p, d, q) model parameters: p = 1: use the data of the past 1 day (autoregressive term).

[0094] d=1: Differencing once makes the data stable.

[0095] q=1: Use 1 moving average term.

[0096] First, perform a difference on the data, that is, calculate the difference in daily power load changes:

[0097]

[0098]

[0099] According to the differenced data, the ARIMA model is established:

[0100] Autoregressive part (AR): Assume that the difference value of the current day is affected by the difference value of the previous day, for example:

[0101] Difference value Dt≈0.5×D t-1

[0102] If the difference value on the 10th day is 5, then the predicted difference value on the 11th day is 0.5×5=2.5

[0103] Moving Average (MA): Use past errors to correct current forecasts, for example:

[0104] Dt≈Dt-1+0.3×E t-1 Where E t-1 It is the error on the 9th day.

[0105] Assuming that the error on the 10th day is 0 (i.e. the actual value of the difference value is consistent with the predicted value), we predict the difference value on the 11th day to be: Difference value (11th day) = 0.5 × 5 + 0.3 × 0 = 2.5

[0106] Add the difference back to the actual load value on the 10th day: Power load on the 11th day = Power load on the 10th day + difference = 230MW + 2.5MW = 232.5MW

[0107] Therefore, the ARIMA model predicts that the power load on day 11 is 232.5MW.

[0108] S302, obtaining the prediction result of the prediction model, judging the subsequent work status according to the prediction result, and comparing the prediction result with a threshold value, wherein the threshold value is a safe working range.

[0109] In this step, the prediction results of the prediction model are obtained. The ARIMA prediction model predicts the operating status of the equipment in the future by modeling the historical data of the equipment. The prediction results can be operating parameters such as temperature, vibration, pressure, and current of the equipment. The prediction results provide a basis for judging the health status and working condition of the equipment.

[0110] The safe working threshold refers to the normal operating range of the equipment. This threshold is usually set according to the equipment specifications, operating environment and industry standards. For example, indicators such as temperature, vibration frequency, and current have their own safe working ranges. When the predicted result exceeds the threshold, the operation of the equipment may be affected, and corresponding preventive or maintenance measures need to be taken. The safety threshold is set according to the working specifications of the equipment, and the various parameters in the predicted results are compared with the safety threshold. When the predicted result exceeds the safe range, the system will automatically generate an early warning notification to remind relevant personnel to take measures.

[0111] S303, obtaining the comparison result, and judging whether it is abnormal according to the comparison result. If the prediction result is greater than a threshold, it is judged to be abnormal.

[0112] In this step, the comparison results are obtained. By comparing the prediction results with the safety threshold, the system can quickly determine whether the status of the device is abnormal. If the prediction result is greater than the safety threshold, the system determines that the device is in an abnormal state, thereby triggering an early warning notification or an automated maintenance process;

[0113] If the prediction result is within the threshold range, that is, less than or equal to the threshold, the system will determine it as a normal state and the device can continue to operate. If the prediction result exceeds the safety threshold, that is, greater than the threshold, the system will determine it as an abnormal state. At this time, the device may face risks such as overload, overheating, and excessive vibration.

[0114] When the system determines that the equipment is in an abnormal state, it will automatically trigger the early warning and maintenance process. Based on the comparison results, the system can take a variety of treatment measures, such as notifying maintenance personnel, scheduling equipment to reduce load or shut down, and even arranging spare parts replacement. Abnormal state processing can reduce downtime losses caused by failures and prevent major safety hazards.

[0115] like Figure 4 As shown, as a preferred embodiment of the present invention, the operation data is visualized, and the visualization includes charts and dashboards. The usage, failure rate and maintenance plan of the equipment are displayed through visualization, and the visualization results are sent to the terminal. Specifically, the steps include:

[0116] S401, perform visualization processing based on the operation data, prediction results and maintenance plan, make the operation data, prediction results and maintenance plan into icons, and display the real-time status and trend of the equipment through the dashboard.

[0117] In this step, the operation data, prediction results and maintenance plan are visualized as charts to more clearly display the various indicators of the equipment, including temperature, pressure, vibration, current and operation time.

[0118] The dashboard integrates operating data, forecast results and maintenance plan charts into one interface, allowing managers to quickly view the comprehensive status of equipment. The focus of dashboard design is on reasonable information layout, real-time updates and highlighting key indicators.

[0119] S402, display the usage, failure rate and maintenance plan of the equipment through visualization processing, and send the visualization results to the terminal.

[0120] In this step, the equipment usage, failure rate and maintenance plan are displayed through visualization. The equipment usage displays the equipment's operating status, including real-time operating parameters (such as temperature, vibration, current, etc.) and historical usage data (such as operating hours, load conditions, etc.). The visualization of usage can help managers determine the current health status of the equipment and identify potential problems;

[0121] Failure rate shows the equipment's failure history and current failure trend, including the frequency, cause, and duration of failures. Visualization of failure rates helps managers identify high-failure components and possible risk points, providing support for maintenance decisions;

[0122] The maintenance plan displays the historical maintenance records and future maintenance arrangements of the equipment, helping managers understand the health management of the equipment. Through the visualization of the maintenance plan, managers can reasonably arrange the use and maintenance of the equipment to ensure the continued normal operation of the equipment.

[0123] S403, obtaining terminal uploaded information, wherein the uploaded information includes improvement suggestions and processing methods, and modifying the visual processing information according to the uploaded information.

[0124] In this step, the terminal upload information is obtained. In the device management system, users upload improvement suggestions and processing methods through terminal devices. These feedback contents usually include problems encountered by users in actual use and improvement suggestions. For example, some data display may not be intuitive enough, or some important information is not highlighted. Managers can put forward opinions and specific processing methods.

[0125] Based on the improvement suggestions and processing methods uploaded by users, the system will adjust the visualization processing. This may include changing the chart type, adjusting the refresh frequency, adding new charts, rearranging the layout, etc., to optimize the data display effect and make it more in line with user needs.

[0126] like Figure 5 As shown, a coding-based equipment life cycle management system provided by an embodiment of the present invention includes:

[0127] The device information module 100 is used to obtain device information, including device type and management program, and obtain device identification technology, including barcode, QR code, RFID and NFC.

[0128] In this system, the device information module 100 obtains device information. Obtaining device information is the first step in device management, which includes understanding the basic properties of the device, usage scenarios, management requirements and other information. This information provides a basis for the subsequent selection of appropriate device identification technology. Device identification technology is a means of uniquely identifying and managing equipment, and is used to track the status and management process of equipment. Equipment types include equipment type, model and specifications. For example, the equipment in a factory may include CNC machine tools, conveyor belts, wind turbines, etc. The usage scenarios and maintenance requirements of each device may be different;

[0129] Management procedures refer to the processes and rules for managing the equipment, including how to record equipment information, equipment maintenance plans, equipment access rights management, etc. Different types of equipment usually have different management procedures. For example, the management of heavy equipment requires special attention to safety inspection records, while the management of high-precision instruments may focus more on calibration and regular testing;

[0130] Device identification technology is used to assign unique identifiers to devices so that they can be recorded, queried, and tracked in management systems. Different device identification technologies have different characteristics and applicable scenarios. Barcodes are suitable for fixed devices or device management scenarios that do not require the storage of large amounts of information. QR codes are suitable for device management scenarios that require more data or provide quick information access. RFID is suitable for device management scenarios that require frequent movement, are difficult to scan manually, or are in harsh environments. NFC is suitable for device management scenarios that require close-range interaction and temporary data writing.

[0131] The information upload module 200 is used to obtain scene information based on device information, the scene information includes device transportation and working scenes, select a device identifier based on the scene information, and upload data to the device identifier through the middleware. The middleware includes an API interface, and the sharing of device information is achieved through the middleware.

[0132] In this system, the information uploading module 200 obtains scene information according to the device information. The scene information mainly includes the transportation scene and working scene of the device. This information will directly affect the selection of the device identification. The transportation scene mainly considers the possible requirements of the device during movement, storage and loading and unloading. The working scene includes the daily working environment of the device. Environmental factors may affect the reading effect of the identification. The device identification is selected according to the scene information;

[0133] Upload data to the device identifier through the middleware, and upload the device data to the central database or cloud platform through the API interface. The middleware can connect multiple systems to complete real-time sharing and information updating of device data. Sharing of device information is achieved through the middleware, ensuring real-time synchronization and transparency of data throughout the life cycle of the device.

[0134] The prediction module 300 is used to obtain operation data, which includes operation records, fault records and maintenance records, and to build a prediction model based on the operation data. The prediction model adopts the ARIMA model to obtain prediction results, judge the subsequent work status based on the prediction results, compare the prediction results with the threshold, and judge whether it is abnormal based on the comparison results.

[0135] In this system, the prediction module 300 obtains operation data, which includes operation records, fault records and maintenance records. The operation records are data of the equipment in daily operation, the fault records are fault information of the equipment in operation, and the maintenance records are detailed information of each maintenance or servicing.

[0136] After obtaining enough equipment operation data, the ARIMA model can be used to model and predict the data. The ARIMA model is a model commonly used in time series analysis, which can predict future equipment status by analyzing past data trends and fluctuations;

[0137] Get the prediction results, use the ARIMA model to get the prediction results of the future equipment status, judge the subsequent work status based on the prediction results, and compare the prediction results with the thresholds. If the prediction results show that certain indicators will exceed the normal thresholds, it may indicate that the equipment is about to become abnormal.

[0138] The visualization module 400 is used to perform visualization processing on the operation data. The visualization processing includes charts and dashboards. The usage, failure rate and maintenance plan of the equipment are displayed through visualization processing, and the visualization results are sent to the terminal.

[0139] In this system, the visualization module 400 visualizes the operation data. By visualizing the operation data of the equipment, managers can more intuitively monitor the use, failure rate and maintenance plan of the equipment. This visualization can not only generate charts, but also create dashboards to make complex data easier to understand, thereby helping managers to make quick decisions;

[0140] Through visualization, the equipment usage, failure rate and maintenance plan can be displayed, the daily operation status and performance indicators of the equipment can be displayed, the equipment failure record and failure rate change trend can be displayed, and the historical and future maintenance arrangements of the equipment can be displayed. Different chart forms and dashboard layouts can be used according to different data types to make the information presentation clearer and more intuitive. Visualization methods include bar charts, line charts, pie charts, heat maps, dashboards, etc. Choosing the right visualization method can effectively convey data trends and status;

[0141] In order to facilitate managers to check the equipment status at any time, the visualization results are sent to the user terminal. Through the mobile terminal, managers can remotely access the equipment data outside the factory to ensure that the equipment status can be checked in real time.

[0142] like Figure 6 As shown, as a preferred embodiment of the present invention, the information uploading module 200 includes:

[0143] The device representation unit 201 is used to obtain scene information according to the device information, and select a device identifier according to the scene information. The device identifier may be one or more.

[0144] In this module, the device representation unit 201 obtains scene information based on the device information. The scene information is mainly obtained by analyzing the environment, usage frequency, and movement requirements of the device during actual use and management based on the device information. The scene information mainly includes two aspects: the transportation scene and the working scene of the device.

[0145] Transportation scenario: The state of the equipment during transportation and its requirements for identification technology. This includes whether it needs to be moved frequently, whether it is outdoors, whether it is subject to vibration or shock, whether the temperature and humidity change greatly, etc. Different transportation requirements will directly affect the choice of identification. For example, equipment that is frequently transported requires durable, non-contact identification technology.

[0146] Working scenario: The environmental conditions of the equipment in normal working state, including whether it is in a harsh environment with a lot of dust, oil, humidity, etc., whether it will be interfered by metal, whether it needs to be waterproof or high temperature resistant, etc. The characteristics of these working scenarios determine the durability and reading effect of the label.

[0147] After determining the scene information, you can select appropriate device identification for different environmental requirements. Common identification methods include barcodes, QR codes, RFID and NFC. You can even combine multiple identification methods to meet the needs of complex scenarios. Based on the scene information of the device, you can select one identification method or combine multiple identification methods to meet different needs.

[0148] Single identification method: For simple scenarios, select an appropriate identification method, such as using a barcode to identify devices in a static, indoor environment.

[0149] Combination of multiple identification methods: For complex or changing scenarios, a combination of multiple identification methods can be used. For example, for equipment that requires long-distance reading and close-range maintenance, RFID and NFC tags can be used in combination.

[0150] The blockchain unit 202 is used to upload data to the device identification through the middleware, realize the sharing of device information through the middleware, and upload the information to the blockchain.

[0151] In this module, the blockchain unit 202 uploads data to the device identifier through the middleware. The middleware is a data processing and forwarding module in the device management system, which can exchange information and share data between different hardware and software. Through the middleware, the real-time status and management information of the device can be uploaded to the central database or cloud, and the data can be distributed to other systems (such as ERP, MES, WMS, etc.), thereby forming a unified device management view. The middleware also has functions such as data interface conversion, data cleaning, formatting, and identity authentication;

[0152] Through the middleware, information can be uploaded to the device identification (such as NFC, RFID tags). These tags have certain data storage and read-write functions, which makes it easy to check the device information at any time. For mobile devices or equipment that requires frequent maintenance, by updating the information on the tag, maintenance personnel and management systems can obtain the latest status of the device at any time;

[0153] Through the middleware, the key information of the equipment is encrypted and uploaded to the blockchain, such as maintenance records, service life, and fault records, to achieve traceability and secure storage of equipment data. The use of blockchain ensures the transparency and credibility of equipment information and is suitable for industries that require supervision and compliance.

[0154] The permission unit 203 is used to obtain blockchain permissions, send permissions to the user terminal, and obtain the editing data of the user terminal on the device information.

[0155] In this module, the permission unit 203 obtains blockchain permissions. In the blockchain system, permission management is a key step to ensure data security and compliance. Permissions are usually obtained based on user identity authentication and role assignment to ensure that only qualified users can access or edit device information. The blockchain system will first verify the identity of the user and assign specific permissions to the user based on the user's identity and responsibilities. The permissions of the blockchain system can be divided into different levels such as viewing, editing, reviewing, and management to control the scope of user operations on data. For example, ordinary users can only view the device status, while administrators can edit device information.

[0156] When the user's permissions are verified, the system will send the corresponding permissions to the user's terminal device, and the user terminal can view or edit the device data within the scope of permissions. When the user edits the device information on the terminal, such as updating maintenance records or uploading new status information, the terminal will send these edited data to the blockchain system. Through smart contracts, the blockchain can automatically verify and store these edited data to ensure the authenticity and immutability of the data.

[0157] like Figure 7 As shown, as a preferred embodiment of the present invention, the prediction module 300 includes:

[0158] The model building unit 301 is used to obtain operation data, obtain operation records, fault records and maintenance records of the equipment through the operation data, and build a prediction model according to the operation data.

[0159] In this module, the model building unit 301 obtains operation data, and the operation data of the equipment is divided into three categories:

[0160] Operation records: include daily operation parameters and status information of the equipment, such as temperature, pressure, current, voltage, vibration frequency, operating time, etc. Operation records are usually collected by sensors in real time and reflect the current status and workload of the equipment.

[0161] Fault records: include all fault information that occurs during equipment operation, including the time, type, location and severity of the fault. This information can help us identify equipment weaknesses and high-risk components.

[0162] Maintenance records: include all maintenance and care information of the equipment, such as replaced parts, maintenance time, maintenance content, and responsible maintenance personnel. Maintenance records reveal the health status of the equipment and its maintenance cost and cycle.

[0163] The operation data is used to obtain the operation records, fault records, and maintenance records of the equipment. After data preprocessing and feature extraction are completed, the historical data can be used to build a prediction model. For example, we have the daily power load data (unit: MW, megawatt) for the past 10 days as follows:

[0164]

[0165]

[0166] Use ARIMA model for modeling: ARIMA (p, d, q) model parameters: p = 1: use the data of the past 1 day (autoregressive term).

[0167] d=1: Differencing once makes the data stable.

[0168] q=1: Use 1 moving average term.

[0169] First, perform a difference on the data, that is, calculate the difference in daily power load changes:

[0170] time Power load (MW) Difference value (MW) Day 1 200 - Day 2 205 5 Day 3 207 2 Day 4 210 3 Day 5 215 5 Day 6 213 -2 Day 7 218 5 Day 8 220 2 Day 9 225 5 Day 10 230 5

[0171] According to the differenced data, the ARIMA model is established:

[0172] Autoregressive part (AR): Assume that the difference value of the current day is affected by the difference value of the previous day, for example:

[0173] Difference value Dt≈0.5×D t-1

[0174] If the difference value on the 10th day is 5, then the predicted difference value on the 11th day is 0.5×5=2.5

[0175] Moving Average (MA): Use past errors to correct current forecasts, for example:

[0176] Dt≈Dt-1+0.3×E t-1 Where E t-1 It is the error on the 9th day.

[0177] Assuming that the error on the 10th day is 0 (i.e. the actual value of the difference value is consistent with the predicted value), we predict the difference value on the 11th day to be: Difference value (11th day) = 0.5 × 5 + 0.3 × 0 = 2.5

[0178] Add the difference back to the actual load value on the 10th day: Power load on the 11th day = Power load on the 10th day + difference = 230MW + 2.5MW = 232.5MW

[0179] Therefore, the ARIMA model predicts that the power load on day 11 is 232.5MW.

[0180] The prediction result unit 302 is used to obtain the prediction result of the prediction model, determine the subsequent work status according to the prediction result, and compare the prediction result with a threshold value, where the threshold value is a safe working range.

[0181] In this module, the prediction result unit 302 obtains the prediction result of the prediction model. The ARIMA prediction model predicts the operating status of the equipment in the future by modeling the historical data of the equipment. The prediction result can be the operating parameters of the equipment such as temperature, vibration, pressure and current. The prediction result provides a basis for judging the health status and working condition of the equipment.

[0182] The safe working threshold refers to the normal operating range of the equipment. This threshold is usually set according to the equipment specifications, operating environment and industry standards. For example, indicators such as temperature, vibration frequency, and current have their own safe working ranges. When the predicted result exceeds the threshold, the operation of the equipment may be affected, and corresponding preventive or maintenance measures need to be taken. The safety threshold is set according to the working specifications of the equipment, and the various parameters in the predicted results are compared with the safety threshold. When the predicted result exceeds the safe range, the system will automatically generate an early warning notification to remind relevant personnel to take measures.

[0183] The judgment unit 303 is used to obtain the comparison result and judge whether it is abnormal according to the comparison result. If the prediction result is greater than a threshold, it is judged to be abnormal.

[0184] In this module, the judgment unit 303 obtains the comparison result. By comparing the prediction result with the safety threshold, the system can quickly determine whether the status of the device is abnormal. If the prediction result is greater than the safety threshold, the system determines that the device is in an abnormal state, thereby triggering an early warning notification or an automated maintenance process;

[0185] If the prediction result is within the threshold range, that is, less than or equal to the threshold, the system will determine it as a normal state and the device can continue to operate. If the prediction result exceeds the safety threshold, that is, greater than the threshold, the system will determine it as an abnormal state. At this time, the device may face risks such as overload, overheating, and excessive vibration.

[0186] When the system determines that the equipment is in an abnormal state, it will automatically trigger the early warning and maintenance process. Based on the comparison results, the system can take a variety of treatment measures, such as notifying maintenance personnel, scheduling equipment to reduce load or shut down, and even arranging spare parts replacement. Abnormal state processing can reduce downtime losses caused by failures and prevent major safety hazards.

[0187] like Figure 8 As shown, as a preferred embodiment of the present invention, the visualization module 400 includes:

[0188] The visualization unit 401 is used to perform visualization processing based on the operation data, prediction results and maintenance plan, make the operation data, prediction results and maintenance plan into icons, and display the real-time status and trend of the equipment through the dashboard.

[0189] In this module, the visualization unit 401 performs visualization processing according to the operation data, prediction results and maintenance plan, and visualizes the operation data, prediction results and maintenance plan into charts respectively, so as to more clearly display various indicators of the equipment, mainly including temperature, pressure, vibration, current and operation time, etc.

[0190] The dashboard integrates operating data, forecast results and maintenance plan charts into one interface, allowing managers to quickly view the comprehensive status of equipment. The focus of dashboard design is on reasonable information layout, real-time updates and highlighting key indicators.

[0191] The result sending unit 402 is used to display the usage, failure rate and maintenance plan of the equipment through visualization processing, and send the visualization result to the terminal.

[0192] In this module, the result sending unit 402 displays the usage, failure rate and maintenance plan of the equipment through visualization. The usage of the equipment displays the operating status of the equipment, including real-time operating parameters (such as temperature, vibration, current, etc.) and historical usage data (such as operating time, load conditions, etc.). The visualization of usage can help managers determine the current health status of the equipment and identify potential problems;

[0193] Failure rate shows the equipment's failure history and current failure trend, including the frequency, cause, and duration of failures. Visualization of failure rates helps managers identify high-failure components and possible risk points, providing support for maintenance decisions;

[0194] The maintenance plan displays the historical maintenance records and future maintenance arrangements of the equipment, helping managers understand the health management of the equipment. Through the visualization of the maintenance plan, managers can reasonably arrange the use and maintenance of the equipment to ensure the continued normal operation of the equipment.

[0195] The visualization modification unit 403 is used to obtain information uploaded by the terminal, wherein the uploaded information includes improvement suggestions and processing methods, and modify the visualization processing information according to the uploaded information.

[0196] In this module, the visualization modification unit 403 obtains the terminal upload information. In the device management system, users upload improvement suggestions and processing methods through terminal devices. These feedback contents usually contain problems and improvement suggestions encountered by users during actual use. For example, some data display may not be intuitive enough, or some important information is not highlighted. Managers can put forward opinions and specific processing methods.

[0197] Based on the improvement suggestions and processing methods uploaded by users, the system will adjust the visualization processing. This may include changing the chart type, adjusting the refresh frequency, adding new charts, rearranging the layout, etc., to optimize the data display effect and make it more in line with user needs.

[0198] In one embodiment, a computer device is provided, the computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are implemented:

[0199] Obtain device information, including device type and management program, and obtain device identification technology, including barcode, QR code, RFID and NFC;

[0200] Acquire scene information according to device information, the scene information includes device transportation and working scenes, select device identification according to the scene information, upload data to the device identification through middleware, the middleware includes an API interface, and realize sharing of device information through the middleware;

[0201] Acquire operation data, the operation data including operation records, fault records and maintenance records, build a prediction model based on the operation data, the prediction model adopts the ARIMA model, obtain prediction results, judge subsequent work conditions based on the prediction results, compare the prediction results with the threshold, and judge whether there is an abnormality based on the comparison results;

[0202] The operation data is visualized, including charts and dashboards, which display the equipment usage, failure rate and maintenance plan through visualization, and send the visualization results to the terminal.

[0203] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the processor performs the following steps:

[0204] Obtain device information, including device type and management program, and obtain device identification technology, including barcode, QR code, RFID and NFC;

[0205] Acquire scene information according to device information, the scene information includes device transportation and working scenes, select device identification according to the scene information, upload data to the device identification through middleware, the middleware includes an API interface, and realize sharing of device information through the middleware;

[0206] Acquire operation data, the operation data including operation records, fault records and maintenance records, build a prediction model based on the operation data, the prediction model adopts the ARIMA model, obtain prediction results, judge subsequent work conditions based on the prediction results, compare the prediction results with the threshold, and judge whether there is an abnormality based on the comparison results;

[0207] The operation data is visualized, including charts and dashboards, which display the equipment usage, failure rate and maintenance plan through visualization, and send the visualization results to the terminal.

[0208] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0209] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0210] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described 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.

[0211] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0212] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A coding-based equipment life cycle management method, characterized in that: The method comprises: Obtain device information, including device type and management program, and obtain device identification technology, including barcode, QR code, RFID and NFC; Acquire scene information according to device information, the scene information includes device transportation and working scenes, select device identification according to the scene information, upload data to the device identification through middleware, the middleware includes an API interface, and realize sharing of device information through the middleware; Acquire operation data, the operation data including operation records, fault records and maintenance records, build a prediction model based on the operation data, the prediction model adopts the ARIMA model, obtain prediction results, judge subsequent work conditions based on the prediction results, compare the prediction results with the threshold, and judge whether there is an abnormality based on the comparison results; The operation data is visualized, including charts and dashboards, which display the equipment usage, failure rate and maintenance plan through visualization, and send the visualization results to the terminal.

2. According to the coding-based equipment life cycle management method of claim 1, it is characterized in that: The step of acquiring scene information according to device information, wherein the scene information includes device transportation and working scenes, selecting a device identifier according to the scene information, uploading data to the device identifier through a middleware, wherein the middleware includes an API interface, and realizing the sharing of device information through the middleware specifically includes: Acquire scene information according to device information, and select a device identifier according to the scene information, wherein the device identifier may be one or more types; Upload data to the device ID through the middleware, share device information through the middleware, and upload information to the blockchain; Obtain blockchain permissions, send permissions to user terminals, and obtain user terminal editing data on device information.

3. The method for managing the entire life cycle of equipment based on coding according to claim 2, characterized in that: The step of obtaining operation data, which includes operation records, fault records and maintenance records, building a prediction model based on the operation data, wherein the prediction model adopts an ARIMA model, obtaining prediction results, judging subsequent work conditions based on the prediction results, comparing the prediction results with a threshold, and judging whether there is an abnormality based on the comparison results specifically includes: Obtain operation data, obtain the operation records, fault records and maintenance records of the equipment through the operation data, and build a prediction model based on the operation data; Obtain the prediction results of the prediction model, judge the subsequent work status according to the prediction results, and compare the prediction results with the threshold value, where the threshold value is the safe working range; Obtain the comparison result and determine whether it is abnormal based on the comparison result. If the predicted result is greater than the threshold, it is determined to be abnormal.

4. The method for managing the entire life cycle of equipment based on coding according to claim 3, characterized in that: The step of performing visualization processing on the operation data, including charts and dashboards, displaying the use, failure rate and maintenance plan of the equipment through visualization processing, and sending the visualization results to the terminal specifically includes: Visualize the operation data, prediction results and maintenance plan, make them into icons, and display the real-time status and trend of the equipment through the dashboard; Display equipment usage, failure rate, and maintenance plan through visualization, and send visualization results to the terminal; The terminal upload information is obtained, wherein the uploaded information includes improvement suggestions and processing methods, and the visual processing information is modified according to the uploaded information.

5. The method for managing the entire life cycle of equipment based on coding according to claim 5, characterized in that: The management procedure includes a purchasing stage, a registration stage, a maintenance stage, an updating stage and a processing stage.

6. A coding-based equipment life cycle management system, characterized in that: The system comprises: The device information module obtains device information, including device type and management program, and obtains device identification technology, including barcode, QR code, RFID and NFC; An information uploading module obtains scene information according to device information, wherein the scene information includes device transportation and working scenes, selects a device identifier according to the scene information, and uploads data to the device identifier through a middleware, wherein the middleware includes an API interface, and sharing of device information is realized through the middleware; The prediction module obtains operation data, including operation records, fault records and maintenance records, builds a prediction model based on the operation data, and adopts the ARIMA model to obtain prediction results, judge the subsequent work status based on the prediction results, compare the prediction results with the threshold, and judge whether it is abnormal based on the comparison results; The visualization module performs visualization processing on the operation data. The visualization processing includes charts and dashboards. The usage, failure rate and maintenance plan of the equipment are displayed through visualization processing, and the visualization results are sent to the terminal.

7. The equipment life cycle management system based on coding according to claim 6 is characterized in that: The information uploading module includes: A device representation unit, which acquires scene information according to the device information and selects a device identifier according to the scene information, wherein the device identifier may be one or more types; The blockchain unit uploads data to the device identifier through the middleware, realizes the sharing of device information through the middleware, and uploads the information to the blockchain; The permission unit obtains blockchain permissions, sends permissions to user terminals, and obtains the editing data of device information by user terminals.

8. The equipment life cycle management system based on coding according to claim 7 is characterized in that: The prediction module comprises: A model building unit acquires operation data, obtains operation records, fault records and maintenance records of the equipment through the operation data, and builds a prediction model based on the operation data; A prediction result unit obtains the prediction result of the prediction model, determines the subsequent work status according to the prediction result, and compares the prediction result with a threshold value, wherein the threshold value is a safe working range; The judgment unit obtains the comparison result and judges whether it is abnormal according to the comparison result. If the prediction result is greater than a threshold, it is judged to be abnormal.

9. The equipment life cycle management system based on coding according to claim 8 is characterized in that: The visualization module comprises: The visualization unit performs visualization based on the operating data, prediction results and maintenance plan, makes icons out of the operating data, prediction results and maintenance plan, and displays the real-time status and trend of the equipment through the dashboard; A result sending unit displays the usage, failure rate and maintenance plan of the equipment through visual processing, and sends the visual results to the terminal; The visualization modification unit obtains information uploaded by the terminal, wherein the uploaded information includes improvement suggestions and processing methods, and modifies the visualization processing information according to the uploaded information.

10. The equipment life cycle management system based on coding according to claim 9 is characterized in that: The management procedure includes a purchasing stage, a registration stage, a maintenance stage, an updating stage and a processing stage.

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