Cold-chain logistics digital management and control system and method based on Internet of Things

Abstract

The invention discloses a cold-chain logistics digital management and control system and method based on the Internet of Things, relates to the technical field of industrial Internet of Things and intelligent control, and solves the problems of data islands, equipment monitoring and maintenance lag, dependence on artificial experience, difficulty in remote cooperation and lack of full-life-cycle management in existing cold-chain logistics. The data acquisition module acquires full-process operation parameters in real time; the platform processes, stores and analyzes the data; the quality closed-loop management module is used for comparing equipment performance data with a preset standard to realize full-life-cycle quality digital evaluation and tracing from construction to operation; the remote cooperation and diagnosis module supports remote monitoring, diagnosis and program debugging. According to the method, a data island can be broken through, full-process transparent management is realized, the equipment fault risk is reduced through predictive maintenance, and the operation and maintenance efficiency and the response speed are improved by means of remote collaboration, so that a digital management and control closed loop covering the full life cycle of the cold chain facility is constructed.

Description

Technical Field

[0001] This application relates to the field of industrial Internet of Things (IoT) and intelligent control technology, and in particular to an IoT-based digital management and control system and method for cold chain logistics. Background Technology

[0002] In the cold chain logistics industry, maintaining a stable storage environment, reducing operational energy consumption, and ensuring product quality are core objectives. Currently, while the industry widely utilizes IoT technology for remote monitoring of key equipment such as compressors, condensers, and evaporators, achieving some data collection and over-limit alarm functions, this traditional model, primarily based on data collection and status monitoring, has significant limitations.

[0003] On the one hand, throughout the entire process from pre-cooling, warehousing, transportation to distribution, key data such as temperature, humidity, and equipment status are scattered across various independent systems, lacking effective connectivity and integration, forming "data silos" that hinder transparent traceability and collaborative management throughout the entire process. On the other hand, existing systems mostly focus on single-point monitoring and post-event alarms, failing to conduct in-depth analysis and predictive maintenance of the operating efficiency, aging trends, and potential faults of core equipment such as refrigeration compressors, condensers, and evaporators. This results in a high risk of unplanned equipment downtime, high energy consumption, and the quality of the project heavily relies on the experience of on-site construction and commissioning personnel, lacking objective digital evaluation methods. Furthermore, system maintenance and program debugging often require technicians to be physically present on-site, leading to slow response times and high costs, especially for multinational or remote projects.

[0004] Therefore, there is an urgent need for a digital management and control system and method for cold chain logistics based on the Internet of Things (IoT). This system should be designed to cover the entire lifecycle of cold chain facility construction and operation, and integrate IoT data collection, intelligent analysis, and remote collaboration capabilities. This will enable closed-loop management from equipment procurement quality control and construction process supervision to operation and maintenance optimization, thereby improving the reliability, efficiency, and economy of the overall supply chain. Summary of the Invention

[0005] To address the problems of "data silos," lagging equipment monitoring and maintenance, reliance on manual experience, difficulties in remote collaboration, and lack of full lifecycle management in existing cold chain logistics, this application provides a digital control system and method for cold chain logistics based on the Internet of Things.

[0006] This application provides a digital management and control system and method for cold chain logistics based on the Internet of Things, adopting the following technical solution:

[0007] A digital management and control system for cold chain logistics based on the Internet of Things, comprising:

[0008] The data acquisition module collects real-time operating parameters of the entire process, including compressors, condensers, evaporators, expansion valves, and pipelines, from various sensors and controllers deployed in the cold chain logistics facilities.

[0009] The data aggregation and analysis platform receives and stores all process operation parameters, cleans, correlates, and calculates the data based on a preset algorithm model, and stores it as structured data with timestamps to form a facility operation database.

[0010] The quality closed-loop management module is built into the data aggregation and analysis platform and, based on the facility operation database, compares the collected equipment performance data with preset standards to digitally assess and trace the quality of cold chain facilities throughout their entire lifecycle, from construction to operation, thus realizing a closed-loop correlation between construction quality and operational performance.

[0011] The remote collaboration and diagnostic module is also built into the data aggregation and analysis platform, supporting authorized users to remotely access system data, receive early warnings, and remotely monitor, diagnose, debug programs, and optimize parameters for on-site programmable logic controllers and human-machine interfaces.

[0012] Preferably, the quality closed-loop management module specifically includes:

[0013] The equipment performance digital analysis unit continuously compares and analyzes the actual operating parameters of the purchased refrigeration equipment with its factory standard parameters or contractually agreed parameters, and generates a visualized equipment quality compliance report.

[0014] The construction process quality traceability unit, based on the abnormal patterns stored in the facility operation database, combined with timestamps and construction records, helps to locate engineering quality problems caused by poor installation, defective pipe insulation, improper system debugging, or cold bridge effect.

[0015] Preferably, the data aggregation and analysis platform also includes a predictive maintenance module. Based on historical and real-time trend analysis of compressor running time, oil pressure, oil temperature, differential pressure and refrigerant parameters, the predictive maintenance module predicts the optimal time for refrigeration oil replacement, filter replacement and refrigerant replenishment; and establishes a normal equipment operation model. Through deviation analysis between real-time data and the model, it provides early warning of potential fault risks such as compressor overload, abnormal evaporator frosting, decreased condensation efficiency and pipeline blockage.

[0016] Preferably, the remote collaboration and diagnostic module supports remote monitoring, online modification, and download updates of the on-site PLC program and touch screen configuration program through secure remote transmission technology; records all operation logs of manual modifications to key operating parameters; and alarms and blocks unauthorized or non-compliant modifications.

[0017] Preferably, the data acquisition module collects at least the following full-process operating parameters:

[0018] Compressor unit parameters: operating status, operating current, suction / discharge temperature and pressure, power supply voltage, oil supply pressure / temperature / differential pressure, cumulative operating time;

[0019] Evaporator parameters: air cooler operating status, return / outlet air temperature, operating and defrosting current, superheat;

[0020] Condenser parameters: water pump and fan operating status, condensing pressure / temperature, subcooling;

[0021] Expansion valve parameters: operating status, evaporation pressure / temperature, opening degree, superheat;

[0022] Piping parameters: pressure drop and temperature rise at key nodes.

[0023] Preferably, the data aggregation and analysis platform is connected to a visualization terminal to display the real-time status of the cold chain facility throughout the entire process, historical performance curves, quality assessment results, and maintenance early warning information in the form of digital twins, trend charts, and topology diagrams.

[0024] Preferably, it also includes edge computing nodes deployed at the cold chain facility site, used to perform local preprocessing, protocol conversion and real-time edge analysis of the data from the data acquisition module, and to perform local caching and basic early warning when the network is interrupted.

[0025] A digital management and control method for cold chain logistics based on the Internet of Things includes the following steps:

[0026] Step 1: Continuously acquire multi-source operational data throughout the entire lifecycle of cold chain facilities, from construction to operation, through the data acquisition module;

[0027] Step 2: Upload the multi-source operational data to the data aggregation and analysis platform for standardized processing, associated storage, and modeling analysis;

[0028] Step 3: Utilize the quality closed-loop management module to evaluate equipment and engineering quality during the construction phase based on real-time and historical data analysis, and trace the source of failures back to the construction phase during the operation phase, thus forming a quality control closed loop;

[0029] Step 4: Based on the results of in-depth data analysis, generate a predictive maintenance task list and early warning instructions for abnormal equipment operation, and push them to maintenance personnel;

[0030] Step 5: Through the remote collaboration and diagnostic module, respond to maintenance needs or optimization commands to perform non-contact program debugging, parameter optimization, and fault diagnosis on remote facilities.

[0031] In summary, this application includes at least one of the following beneficial effects:

[0032] 1. This application uses a "quality closed-loop management module" to digitally link and continuously compare real-time performance data of equipment during operation (such as compressor efficiency and pipeline pressure drop) with its factory standards and construction and commissioning records. When abnormal energy consumption or refrigeration failure occurs during operation, the system can not only alarm, but also automatically link historical data to accurately trace back to specific stages during the construction period (such as incorrect installation torque of a specific valve or insufficient insulation thickness of a certain section of pipeline). This completely breaks down the traditional data silos between "operation and maintenance" and "engineering construction", forming a digital evaluation closed loop where "construction quality determines operational performance and operational data provides feedback on construction quality", fundamentally changing the vague management model that relies on manual experience for backtracking.

[0033] 2. By using the "predictive maintenance module" to perform machine learning analysis on equipment operating trends, health models such as compressor efficiency decline and evaporator frosting rate are established. This allows for the predictive assessment of the evolution of hidden problems such as refrigeration oil performance degradation, filter clogging, and refrigerant micro-leakage. Maintenance work orders are generated weeks or months before a failure occurs. This capability minimizes the risk of unplanned downtime. At the same time, by optimizing maintenance timing (such as replacing consumables during off-peak electricity seasons), equipment maintenance costs and overall system energy consumption are significantly reduced, achieving a fundamental shift from an "after-the-fact" to an "early-the-fact" operation and maintenance model.

[0034] 3. Through the "remote collaboration and diagnosis module," combined with secure remote transmission and virtualization technology, PLC programs and touch screen interfaces in cold chain facilities anywhere in the world can be monitored online, logic debugged, parameter optimized, and even program updates and downloads can be performed. For newly completed cold storage facilities or cold chain trucks that experience temperature control abnormalities during transportation, experts can complete complex system debugging or troubleshooting without traveling, saving costs. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the cloud layer, edge layer, and terminal layer collaborative architecture of the control system in this embodiment of the present application;

[0036] Figure 2 This is a schematic diagram of the workflow of the field perception and control layer (end layer) in this embodiment of the present application;

[0037] Figure 3 This is a flowchart of the data processing and logic judgment of the edge computing layer (edge ​​layer) in this embodiment of the present application;

[0038] Figure 4 This is a schematic diagram of the core business modules and data flow of the cloud platform layer (cloud layer) in this embodiment of the application. Detailed Implementation

[0039] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0040] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0041] In this application, the terms "upper," "lower," "left," "right," "front," "rear," "top," "bottom," "inner," "outer," "middle," "vertical," "horizontal," "lateral," and "longitudinal" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are primarily for the purpose of better describing this application and its embodiments, and are not intended to limit the indicated device, element, or component to having a specific orientation, or to be constructed and operated in a specific orientation.

[0042] Furthermore, some of the aforementioned terms, besides indicating location or positional relationships, may also have other meanings. For example, the term "above" may, in certain circumstances, indicate a dependency or connection. Those skilled in the art can understand the specific meaning of these terms in this application based on the specific circumstances.

[0043] In addition, the term "multiple" should mean two or more.

[0044] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0045] This application discloses an IoT-based digital management and control system and method for cold chain logistics, aiming to build a digital and intelligent management and control system covering the entire life cycle of cold chain facility construction, commissioning, operation, and maintenance.

[0046] Example 1

[0047] This application discloses an IoT-based digital management and control system for cold chain logistics, which adopts a collaborative architecture of cloud, edge, and terminal layers, and includes the following components: on-site perception and control layer, data acquisition module, data aggregation and analysis platform, quality closed-loop management module, and remote collaboration and diagnosis module.

[0048] The field perception and control layer (end layer) is deployed at cold chain facilities such as cold storage, refrigerated trucks, and distribution stations, and is the data source and execution end of the system.

[0049] The data acquisition module includes various smart sensors, data loggers, and edge gateways. The sensor network covers the core equipment and critical paths of the refrigeration system, collecting real-time operational parameters throughout the entire process. The collected parameters include:

[0050] Compressor unit parameters: running, stopped, or fault status; three-phase operating current; suction temperature and pressure; discharge temperature and pressure; power supply voltage; oil supply pressure or temperature; oil filter differential pressure; cumulative operating time.

[0051] Evaporator parameters: air cooler operating status, return air temperature, outlet air temperature, operating current, defrosting current, electronic expansion valve opening, and superheat.

[0052] Condenser parameters: Cooling water pump and fan operating status, condensing pressure and temperature, cooling water inlet or outlet temperature (water-cooled type), ambient temperature (air-cooled type), subcooling degree.

[0053] Expansion valve parameters: operating status, evaporation pressure and temperature, valve head temperature, opening percentage, and superheat.

[0054] Piping parameters: Piping pressure and temperature at key nodes (such as before and after the dryer filter, at the sight glass, and at the distributor) are used to calculate pressure drop and temperature rise, and to evaluate insulation effect and smoothness.

[0055] Environmental parameters: temperature, humidity, carbon dioxide concentration (for fruit and vegetable storage) at multiple points in each warehouse or carriage.

[0056] Furthermore, edge computing nodes (edge ​​layers) are typically industrial gateways or edge servers with a certain computing power, deployed in the field. Their functions include:

[0057] Data preprocessing: The raw data is initially cleaned by filtering, removing outlier values, and unifying units.

[0058] Protocol conversion: Convert device data from different manufacturers and using different protocols (such as Modbus, BACnet, CAN, etc.) into a standard format (such as MQTT, JSON) and upload it to the cloud.

[0059] Edge real-time analysis: Performs high-frequency real-time logic judgments, such as over-limit alarms (temperature exceeding the standard), linkage control (automatic activation of standby units when the temperature is too high), and local data caching.

[0060] Resume data transmission after network interruption: When the network is interrupted, the data is cached locally and automatically resumed when the network is restored, ensuring data continuity.

[0061] The data aggregation and analysis platform (cloud) serves as the core hub, responsible for receiving, storing, processing, and analyzing all operational parameters. Based on preset algorithm models, it cleans, correlates (e.g., correlates the temperature of the same evaporator, fan status, and expansion valve opening), aggregates and calculates (e.g., calculates daily average energy consumption and equipment comprehensive energy efficiency ratio COP), and stores it as structured data with timestamps to form a facility operation database.

[0062] The data aggregation and analysis platform is connected to a visualization terminal, which displays the real-time status of the entire cold chain facility, historical performance curves, quality assessment results, and maintenance early warning information in the form of digital twins, trend charts, and topology maps.

[0063] The quality closed-loop management module is built into the data aggregation and analysis platform. After the equipment is put into operation, the system automatically retrieves the performance commitments (such as rated cooling capacity, COP value, rated current, etc.) in its factory technical specifications or contract, and continuously compares them with its real-time or historical operating data (such as actual cooling capacity, operating energy efficiency, current curve), generating a visualized equipment quality compliance report. The report displays performance deviations in chart form, providing an objective basis for equipment acceptance and supplier evaluation.

[0064] In addition, the system includes a database of typical abnormal operating patterns caused by installation issues (such as "pressure drop in a specific pipeline consistently higher than the design value," which may indicate an undersized pipe diameter or too many bends; and "excessive periodic fluctuations in localized temperature," which may indicate gaps or cold bridges in the insulation layer). When such anomalies occur during operation, the system automatically matches the pattern database and combines it with the timestamp of the first occurrence of the problem in the facility operation database, along with the construction and commissioning records at that time (such as pipeline welding records, airtightness test reports, and insulation material acceptance forms). This enables digital traceability from "operational failure" to "construction root cause," thereby achieving digital assessment and traceability of the entire lifecycle quality of cold chain facilities from construction to operation.

[0065] The predictive maintenance module, based on historical and real-time trend analysis of compressor operating time, oil pressure, oil temperature, differential pressure, and refrigerant parameters, predicts the optimal timing for refrigeration oil replacement, filter replacement, and refrigerant replenishment, establishing a normal operating status model for each core piece of equipment (such as the compressor). Model parameters include operating current, suction and discharge pressure differential, oil temperature-oil pressure relationship, and vibration spectrum (e.g., with vibration sensors). Machine learning algorithms analyze the deviation trends between real-time data and the health model. For example, by monitoring the slow upward trend of compressor operating current and combining it with an oil analysis model (based on operating time and oil temperature), the module predicts the degree of refrigeration oil deterioration and generates recommended oil change work orders 2 to 4 weeks in advance. Similarly, by analyzing the correlation between evaporator fan current and inlet / outlet air temperature difference, the module predicts filter blockage or frost abnormalities, providing early warnings. Thus, through deviation analysis of real-time data and the model, it provides early warnings of potential fault risks such as compressor overload, abnormal evaporator frost, decreased condensing efficiency, and pipe blockage.

[0066] The remote collaboration and diagnostic module employs secure remote transmission technologies such as VPN and end-to-end encryption to establish a secure channel between authorized users (e.g., equipment experts, commissioning engineers) and on-site PLCs and touchscreens. Authorized users can view the on-site PLC's operating status, variable values, and ladder logic diagrams, as well as the current screen display, in real-time via a web interface in a virtualized manner. Program variables can be modified and logic debugged online, and the modified program can be securely downloaded to the on-site equipment. All remote and local modifications to key parameters and program downloads are fully logged, including the operator, time, and modified content. Real-time alarms are issued and execution is blocked for unauthorized operations (such as exceeding safety threshold settings) or modifications that do not conform to operating procedures.

[0067] Example 2

[0068] A digital management and control method for cold chain logistics based on the Internet of Things includes the following steps:

[0069] Step 1: During the construction phase (single equipment commissioning, system integration), operation phase (daily operation), and maintenance phase (planned / unplanned maintenance) of the cold chain facility, the data acquisition module continuously acquires multi-source heterogeneous data such as equipment performance parameters, environmental parameters, energy consumption data, and manually entered construction and maintenance records. Through the data acquisition module, the entire life cycle of the cold chain facility from construction to operation is continuously acquired through multi-source operation data.

[0070] Step Two: After initial data processing by edge computing nodes, the multi-source operational data is uploaded to a data aggregation and analysis platform for standardized cleaning, spatiotemporal correlation, storage, and in-depth modeling analysis. For example, this includes calculating the real-time energy efficiency of the computing system, analyzing the uniformity of the temperature field, and identifying the operating modes of the equipment.

[0071] Step 3: During the construction phase, the quality closed-loop management module uses data from the commissioning period to generate an initial performance baseline report, assessing whether the equipment and installation quality meet standards. During the operation phase, when the system detects performance degradation (e.g., slower cooling in a certain area) or abnormal alarms (e.g., frequent defrosting of an evaporator), the module automatically initiates root cause analysis. It compares the current abnormal pattern with historical commissioning data and construction records. For example, if the analysis indicates that the problem may stem from an unreasonable piping design, the system will automatically retrieve the construction drawings and installation inspection records for that piping, helping managers quickly pinpoint whether the issue is a design flaw or a failure to follow the drawings during construction. This closed-loop feedback of operational issues to the construction phase forms a quality control closed loop.

[0072] Step 4: The predictive maintenance module continuously trains and optimizes the equipment health model based on historical data in the facility operation database, and monitors the deviation between the current operating data and the model in real time. When it detects indicative signs such as "the compressor exhaust temperature has a slow but continuous upward trend" or "the condenser heat exchange temperature difference gradually increases", it automatically generates early warning information and maintenance suggestions (such as "it is recommended to clean the condenser fins within one week") and pushes the task to the mobile terminals of relevant maintenance personnel.

[0073] Step 5: Through the remote collaboration and diagnostic module, view the PLC program logic online, monitor real-time data trends, and perform remote diagnostics. After confirming the problem, control parameters can be directly modified online, control logic optimized, and even the control program updated, completing efficient non-contact maintenance. The entire process is recorded and archived, forming a knowledge base case study, so that in the future, even when responding to maintenance needs or optimization commands, non-contact program debugging, parameter optimization, and fault diagnosis can be performed on remote facilities.

[0074] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. An Internet of Things-based cold chain logistics digital management and control system, characterized in that, include: The data acquisition module collects real-time operating parameters of the entire process, including compressors, condensers, evaporators, expansion valves, and pipelines, from various sensors and controllers deployed in the cold chain logistics facilities. The data aggregation and analysis platform receives and stores all process operation parameters, cleans, correlates, and calculates the data based on a preset algorithm model, and stores it as structured data with timestamps to form a facility operation database. The quality closed-loop management module is built into the data aggregation and analysis platform and, based on the facility operation database, compares the collected equipment performance data with preset standards to digitally assess and trace the quality of cold chain facilities throughout their entire lifecycle, from construction to operation, thus realizing a closed-loop correlation between construction quality and operational performance. The remote collaboration and diagnostic module is also built into the data aggregation and analysis platform, supporting authorized users to remotely access system data, receive early warnings, and remotely monitor, diagnose, debug programs, and optimize parameters for on-site programmable logic controllers and human-machine interfaces.

2. The cold-chain logistics digital management and control system based on the Internet of Things according to claim 1, characterized in that, The quality closed-loop management module specifically includes: The equipment performance digital analysis unit continuously compares and analyzes the actual operating parameters of the purchased refrigeration equipment with its factory standard parameters or contractually agreed parameters, and generates a visualized equipment quality compliance report. The construction process quality traceability unit, based on the abnormal patterns stored in the facility operation database, combined with timestamps and construction records, helps to locate engineering quality problems caused by poor installation, defective pipe insulation, improper system debugging, or cold bridge effect.

3. The IoT-based digital management and control system for cold chain logistics according to claim 1, characterized in that: The data aggregation and analysis platform also includes a predictive maintenance module, which predicts the optimal timing for refrigeration oil replacement, filter replacement, and refrigerant replenishment based on historical and real-time trend analysis of compressor running time, oil pressure, oil temperature, differential pressure, and refrigerant parameters. Furthermore, a normal operating model for the equipment is established, and by analyzing the deviation between real-time data and the model, potential fault risks such as compressor overload, abnormal evaporator frosting, decreased condensation efficiency, and pipeline blockage are predicted in advance.

4. The IoT-based digital management and control system for cold chain logistics according to claim 1, characterized in that, The remote collaboration and diagnostic module supports remote monitoring, online modification, and download updates of on-site PLC programs and touchscreen configuration programs through secure remote transmission technology; it records all operation logs of manual modifications to key operating parameters and provides alarms and blocks unauthorized or non-compliant modifications.

5. The IoT-based digital management and control system for cold chain logistics according to claim 1, characterized in that, The data acquisition module collects at least the following full-process operating parameters: Compressor unit parameters: operating status, operating current, suction / discharge temperature and pressure, power supply voltage, oil supply pressure / temperature / differential pressure, cumulative operating time; Evaporator parameters: air cooler operating status, return / outlet air temperature, operating and defrosting current, superheat; Condenser parameters: water pump and fan operating status, condensing pressure / temperature, subcooling; Expansion valve parameters: operating status, evaporation pressure / temperature, opening degree, superheat; Piping parameters: pressure drop and temperature rise at key nodes.

6. The IoT-based digital management and control system for cold chain logistics according to claim 1, characterized in that: The data aggregation and analysis platform is connected to a visualization terminal, which displays the real-time status of the entire cold chain facility, historical performance curves, quality assessment results, and maintenance early warning information in the form of digital twins, trend charts, and topology maps.

7. The IoT-based digital management and control system for cold chain logistics according to claim 1, characterized in that: It also includes edge computing nodes, deployed on-site in cold chain facilities, used to perform local preprocessing, protocol conversion and real-time edge analysis of the data from the data acquisition module, and to perform local caching and basic early warning when the network is interrupted.

8. A digital management and control method for cold chain logistics based on the Internet of Things, applied to the system as described in any one of claims 1-7, characterized in that, Includes the following steps: Step 1: Continuously acquire multi-source operational data throughout the entire lifecycle of cold chain facilities, from construction to operation, through the data acquisition module; Step 2: Upload the multi-source operational data to the data aggregation and analysis platform for standardized processing, associated storage, and modeling analysis; Step 3: Utilize the quality closed-loop management module to evaluate equipment and engineering quality during the construction phase based on real-time and historical data analysis, and trace the source of failures back to the construction phase during the operation phase, thus forming a quality control closed loop; Step 4: Based on the results of in-depth data analysis, generate a predictive maintenance task list and early warning instructions for abnormal equipment operation, and push them to maintenance personnel; Step 5: Through the remote collaboration and diagnostic module, respond to maintenance needs or optimization commands to perform non-contact program debugging, parameter optimization, and fault diagnosis on remote facilities.

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