Remote monitoring and fault diagnosis system of thermo-sensitive paper printing equipment

Abstract

The invention discloses a remote monitoring and fault diagnosis system for thermo-sensitive paper printing equipment, which relates to the field of thermo-sensitive paper printing systems, and comprises a remote monitoring center module which comprises a monitoring server and 3-8 clients, and is characterized in that the server has double stock data, and the clients display equipment parameters; the equipment end data acquisition module is provided with six interfaces matched with six types of sensors and used for acquiring parameters such as temperature and rotating speed; the remote data transmission module transmits data with the Ethernet and MQTT through 4G / 5G and is cached through an SD card; the intelligent fault diagnosis module comprises a BP neural network and a fault knowledge base; the remote control module is used for issuing 20 instructions in a three-way handshake manner; the environment adaptation module and the temperature and humidity sensor control preheating and parameters. According to the invention, remote monitoring and intelligent fault diagnosis of equipment are realized, the printing quality and the consumable management efficiency are improved, the cluster load is balanced, the energy consumption is reduced, the operation safety is guaranteed, the operation and maintenance cost is reduced, and the system is suitable for multi-scene large-scale management.

Description

Technical Field

[0001] This invention relates to the field of thermal paper printing system technology, and more particularly to a remote monitoring and fault diagnosis system for thermal paper printing equipment. Background Technology

[0002] Thermal paper printing equipment serves as a core terminal in scenarios such as retail POS, logistics waybill printing, and medical invoice output, and its operational stability is directly linked to business efficiency. With the expansion of chain stores and the large-scale construction of logistics parks, the number of deployed thermal paper printing equipment has surged and their distribution has become increasingly dispersed, highlighting the shortcomings of traditional operation and maintenance models. Currently, most equipment still relies on local control panels for status monitoring, displaying only basic information such as printhead temperature and power on / off status. They lack the ability to collect real-time data on key parameters such as roller speed fluctuations, print density uniformity, and consumable balance changes. Maintenance personnel must conduct regular on-site inspections to troubleshoot potential faults. In scenarios where equipment is distributed across dozens or even hundreds of locations, manual inspections are not only time-consuming and labor-intensive but also prone to delays in fault detection due to inspection intervals. For example, if latent overheating of the printhead is not addressed promptly, it may lead to equipment burnout, causing business interruptions of several hours to several days.

[0003] Inefficiency in the fault diagnosis process further exacerbates maintenance pressure. Existing equipment fault indications largely rely on built-in error codes, which can only identify explicit faults such as printhead open circuits and motor overloads, lacking effective diagnostic tools for progressive faults such as blurry printing and skewed paper feed. Upon arrival, maintenance personnel must systematically check mechanical components, circuit connections, and parameter settings, often resulting in multiple trips to retrieve spare parts due to inaccurate fault location, extending downtime. Furthermore, the fault code systems of different brands of equipment are inconsistent, requiring maintenance personnel to master the fault logic of multiple devices, leading to high learning costs and difficulty in establishing standardized fault handling procedures. In addition, the lack of systematic storage and analysis of equipment operating data makes it impossible to uncover patterns in high-frequency problems from historical faults, resulting in recurring similar faults and persistently high maintenance costs.

[0004] There are also significant limitations in equipment management and optimization. Consumable balance monitoring often uses mechanical contact designs, which are inaccurate and easily affected by paper wrinkles. This frequently results in situations where "the display shows remaining consumables, but they suddenly run out," especially during peak retail hours or logistics sorting seasons. Paper shortages and downtime directly impact checkout efficiency or package sorting progress. Energy management lacks refined monitoring, leaving equipment running at high loads undetected and causing unnecessary energy waste. Furthermore, the impact of environmental factors on print quality is not effectively considered. In high-temperature and high-humidity environments, uneven printing and paper sticking are common, and the equipment cannot automatically adapt and adjust, requiring repeated manual parameter adjustments. This not only reduces operational efficiency but may also exacerbate equipment wear and tear due to improper parameter settings. These problems collectively lead to low maintenance efficiency and long downtime for thermal paper printing equipment, making it difficult to meet the high demands of modern business for equipment reliability and refined management. Summary of the Invention

[0005] The present invention proposes a remote monitoring and fault diagnosis system for thermal paper printing equipment to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a remote monitoring and fault diagnosis system for thermal paper printing equipment, comprising: The remote monitoring center module includes one monitoring server and 3-8 monitoring clients. The monitoring server communicates with each monitoring client via Ethernet. The monitoring server is equipped with an Intel Xeon E5 processor, 32GB DDR4 memory, and a 4TB RAID5 disk array, supporting simultaneous access to data from 50-200 thermal paper printing devices. The data storage adopts a "real-time database + historical database" architecture, with the real-time database retaining 24 hours of data and the historical database retaining 1-3 years of data. Each monitoring client is equipped with a 19-inch LCD screen with a resolution of 1920×1080 and built-in fault management software, supporting multi-window display of different device operating parameters and fault information. The equipment-side data acquisition module integrates six sensor interfaces, each supporting 4-20mA analog signal and RS485 digital signal input; it is equipped with a PT1000 platinum resistance temperature sensor with a measurement range of 0-200℃, accuracy of ±0.05℃, and a sampling period of 500ms; an incremental encoder speed sensor with a resolution of 500 lines / revolution, outputting A / B phase pulse signals, and converting the roller speed through pulse counting; a CMOS image density sensor with a frame rate of 30fps, equipped with an 850nm infrared supplement light to ensure clear imaging of printed samples in low-light environments; a laser displacement consumable sensor with a measurement distance of 20-100mm and a sampling frequency of 20Hz, used to detect the remaining thermal paper; a closed-loop Hall current sensor with a bandwidth of 2kHz, a measurement range of 0-10A, and an accuracy of ±0.05A, used to monitor the equipment's operating current; and a triaxial acceleration vibration sensor with a range of ±10g and a sampling rate of 1kHz, used to detect abnormal mechanical vibrations of the equipment. The remote data transmission module integrates an industrial-grade 4G / 5G module and a gigabit Ethernet chip. The 4G / 5G module supports the B1 / B3 / B5 / B8 / B38 / B41 frequency bands. It uses the MQTT protocol to transmit data with QoS level 2 to ensure reliable message delivery. The data frame structure includes an 8-byte device ID, a 4-byte timestamp, a 1-byte parameter type, a 4-byte parameter value, and a 2-byte CRC16 checksum. The module is equipped with a 128GB SD card as a local cache and has power-off protection. It caches data when the network is down and automatically retransmits it in chronological order after reconnection. The heartbeat interval is set to 30 seconds, and the reconnection delay doubles from 1 second to 30 seconds each time. The intelligent fault diagnosis module includes an improved BP neural network model and a fault knowledge base. The BP neural network model consists of an input layer, two hidden layers, and an output layer. The input layer has 12 neurons corresponding to 12 equipment operating parameters, each hidden layer has 32 neurons with ReLU activation function, and the output layer has 8 neurons corresponding to 8 common faults. The model has a training sample size of 5000+. The fault knowledge base is stored in a MySQL database and contains 200-500 fault cases. Each case includes a fault code, triggering conditions, troubleshooting steps, and spare part model. After receiving the collected parameters, the model matches features to identify the fault type and locate the cause. The remote control module contains a library of 20 basic instructions, coded from 0x01 to 0x20, corresponding to functions such as device start, stop, and temperature adjustment. Instruction issuance employs a "three-way handshake" mechanism: after the monitoring center sends an instruction, the device must return a confirmation signal, and the monitoring center then returns an execution confirmation signal. It supports a 10-instruction queue buffer with a configurable execution timeout of 30 seconds by default. It can issue instructions to adjust printing parameters, including printhead temperature adjustment within ±5℃, roller speed adjustment within ±5r / min, and print density adjustment within ±10%. It also supports device start / stop and emergency shutdown instructions, and the device must provide confirmation of the instruction execution results. The environment adaptation module is equipped with temperature and humidity sensors. The sensors are deployed at the air inlet of the device to avoid heat sources affecting measurement accuracy. The temperature measurement range is 0-40℃ with an accuracy of ±0.5℃, and the humidity measurement range is 20%-90%RH with an accuracy of ±3%RH. The sampling period is 1 minute. The device automatically adjusts its operating parameters according to the ambient temperature and humidity. In high humidity environments, it increases the printhead temperature and speeds up the roller rotation. In low temperature environments, it activates the printhead preheating function with a preheating temperature of 35-40℃ and a preheating time of ≤30 seconds, ensuring stable printing quality under different environments.

[0007] Furthermore, the present invention also includes a print quality assessment module, which preprocesses the printed sample image acquired by the CMOS image sensor. The preprocessing process includes grayscale conversion, Gaussian denoising, and edge cropping; it extracts pixel data from three key regions—the top 1 / 4, middle 1 / 2, and bottom 1 / 4 of the printed sample—and uses a formula... Calculate the print density index C; where C is the print density index, with a value ranging from 0 to 255, and the larger the value, the higher the print density; a is the area number, with a value of 1 to 3, corresponding to the top, middle and bottom areas respectively. denoted as the number of effective pixels in region a, where the number of effective pixels is the total number of pixels after removing blurred pixels at the edges. The grayscale value of the i-th valid pixel in region a ranges from 0 to 255. The module classifies the print quality level according to the C value: C ≥ 220 is excellent, 180 ≤ C < 220 is acceptable, 150 ≤ C < 180 is to be optimized, and C < 150 is unacceptable. When it is to be optimized, the module pushes the parameter adjustment combination scheme. When it is unacceptable, the module triggers the reprint instruction and records the reason for the unacceptability.

[0008] Furthermore, the present invention also includes a consumable balance prediction module. This module obtains the current thermal paper roll radius *r* (in mm) using a laser displacement sensor; combined with the thermal paper core radius *r0*, where *r0* is a fixed value ranging from 10-20 mm and is entered during device initialization; and the thermal paper thickness *d* (in mm), which is automatically imported based on the consumable model and ranges from 0.08-0.12 mm; first, it uses a formula... Calculate the remaining paper length L in mm; then use the formula Calculate the remaining available time R; where R is the remaining available time in hours; v is the average printing speed of the device in mm / h, which is the weighted average of the last 4 hours, with the weight decreasing over time, 0.4 for the first hour and 0.2 for hours 2-4; k is the printing task coefficient, k=1.2 for continuous printing and k=1.0 for intermittent printing; when R≤4h, the module connects to the ERP system to query the latest consumable warehouse inventory, pushes a replenishment list including warehouse location, inventory quantity and estimated delivery time, and displays a paper roll replacement countdown on the monitoring client.

[0009] Furthermore, the present invention also includes a multi-parameter linkage early warning module. This module presets a matrix of equipment operating parameter thresholds: printhead temperature normal range 30-60℃, early warning range 60-70℃, fault range >70℃; roller speed normal range 30-70 r / min, early warning range 20-30 r / min or 70-80 r / min, fault range <20 r / min or >80 r / min; operating current normal range 0.5-5A, early warning range 5-6A, fault range >6A; and establishes 12 sets of parameter linkage rules, with rule 1 being for the printhead. If the temperature is >65℃ and the operating current is >5A for 5 seconds, and Rule 2 is if the roller speed fluctuation is >±15% and the vibration acceleration is >3g for 3 seconds; after the warning is triggered, the module automatically retrieves the parameter curve of the equipment in the past hour and marks the abnormal starting point, and generates a detailed sheet containing the abnormal parameter trend chart, related parameter changes and historical records of similar warnings; pushes the warning to the maintenance personnel according to the priority of "maintenance APP push (10s unread) → SMS (5min unread) → telephone voice (10min unread)", and records the warning trigger time and duration. After the warning is lifted, it is automatically marked as "processed".

[0010] Furthermore, the present invention also includes a multi-device cluster management module, which supports grouping according to a three-level architecture of "region-outlet-device", supporting up to 10 regions, with a maximum of 50 outlets per region and a maximum of 20 devices per outlet; each group can be configured with independent parameter templates, with a default printhead temperature of 50℃ for convenience store devices and 55℃ for warehouse devices; the device load value L is calculated using the formula: L = (current number of tasks / maximum number of tasks) × 0.6 + (continuous running time / rated continuous running time) × 0.4 The current number of tasks is the number of printing tasks that the device is currently executing, and the maximum number of tasks is the device's rated number of tasks that it can process simultaneously, ranging from 5 to 10. The continuous running time is the length of time the device has been running since its last shutdown, and the rated continuous running time is the device's designed continuous working time, ranging from 8 to 24 hours. New tasks are preferentially assigned to devices with L < 0.3. When all devices have L ≥ 0.7, a task queuing mechanism is activated, sorting tasks according to priority 1-5, with level 1 tasks being executed first. At the same time, a "device overload" reminder is pushed to the branch administrator.

[0011] Furthermore, the present invention also includes a remote fault debugging module, which has a built-in fault code parsing library and supports the parsing of over 200 custom fault codes from various manufacturers. Within 10 seconds of a fault occurring, it automatically captures a parameter snapshot of the three minutes preceding the fault, with a sampling frequency of 1Hz, generating fault waveforms such as temperature-time curves and current-speed scatter plots. It provides parameter adjustment sliders with step sizes of 1℃ for printhead temperature, 1r / min for roller speed, and 1% for print density. It is equipped with action test buttons, including functions such as "roller rotation alone," "printhead heating test," and "paper feed test." It supports fault log export in CSV format, containing fields such as fault occurrence time, parameter values, and diagnostic results. The test print page includes 10 levels of grayscale bars, horizontal / vertical / diagonal lines, and text of different sizes. After debugging, image analysis is used to calculate the grayscale bar recognition rate and line continuity. A grayscale bar recognition rate ≥90% and ≤2 line breakpoints are considered a successful debugging result. After successful debugging, the debugging parameters are saved to the "device-specific parameter library."

[0012] Furthermore, the present invention also includes an energy consumption monitoring and optimization module, which uses a formula... Calculate the equipment energy consumption E; where E is the total energy consumption of the equipment during the statistical period, in kW·h; U is the rated voltage of the equipment, a fixed value of 220V; Δt represents the real-time current value of the device in the t-th sampling period, in A; Δt is the sampling period, a fixed value of 0.5h; n is the number of samples within the statistical period; the module identifies three types of high-energy-consumption scenarios: standby power consumption > 0.05kW·h / 2h, peak power > 1.2kW for 5min, and single-page printing power consumption > 0.001kW·h; optimization strategies are pushed for different scenarios: the printhead heating module is turned off during standby, printing tasks are distributed during high load, and the density is adjusted to economy mode during daily printing; a monthly energy consumption comparison report is generated, comparing the energy consumption data of the current month with the previous month and the same period last year to quantify the energy-saving effect.

[0013] Furthermore, the present invention also includes a fault log auditing module, which records complete information for each fault. The log fields include the device SN number, fault occurrence time (accurate to milliseconds), 12 operating parameter values ​​(with units) at the time of the fault, diagnostic model output probability, personnel ID, processing time, and spare parts replacement records (including spare parts SN number). A fault audit report is generated monthly, which statistically analyzes the frequency of various faults, average repair time, and repair success rate. High-fault equipment is analyzed, and equipment with more than 3 faults per month is marked as high-fault equipment. The equipment's service life and maintenance records are also linked. The fault knowledge base is updated based on the audit data to supplement the optimal handling solutions for high-frequency faults and improve the accuracy of subsequent fault diagnosis.

[0014] Furthermore, the present invention also includes a system security protection module, which adopts a three-level protection mechanism. The first level of protection is identity authentication, which adopts the OAuth2.0 protocol and supports account password + mobile phone verification code + fingerprint / facial biometric recognition. The password must contain uppercase and lowercase letters, numbers and special symbols, and be at least 12 characters long. It must be changed every 90 days. The second level of protection is data security. Data transmission adopts AES-256 encryption + SSL / TLS protocol, and data storage adopts multi-node fragmented encryption. Each frame of data is signed with a unique digital certificate of the device. The third level of protection is abnormal behavior monitoring, which includes 15 abnormal behavior rules, such as "5 failed login attempts within 1 hour" and "issuing a shutdown command outside of working hours". After an abnormal rule is triggered, an "alarm + permission freeze (24 hours) + log recording" response is executed. The security log is retained for 180 days and supports multi-dimensional query by time, device and behavior type.

[0015] Furthermore, the present invention also includes a fault statistical analysis module, which uses formulas... Calculate the equipment failure rate F; where F is the equipment failure rate, expressed as a percentage (%). The total number of equipment failures during the statistical period, expressed in times. The module calculates the total runtime of the equipment within the statistical period, in hours. It also categorizes and calculates F-values ​​by equipment model, deployment area, and service life, and plots a trend chart of failure rate. The time granularity supports daily, weekly, and monthly calculations. Based on the statistical data, it identifies high-incidence types and time periods of failures, establishes a failure expert database, and updates the database quarterly to provide experience support for failure diagnosis and reduce the recurrence rate of similar failures.

[0016] Compared with existing technologies, the beneficial effects of this invention are: In terms of remote monitoring and fault handling, the system overcomes the limitations of local monitoring by utilizing multiple types of high-precision sensors to comprehensively collect equipment operating parameters. From printhead temperature and roller speed to print density and energy consumption data, all data can be uploaded to the monitoring center in real time. Maintenance personnel can remotely monitor the operating status of multiple devices without frequent on-site inspections, significantly reducing labor costs. The intelligent fault diagnosis module combines a neural network model and a fault knowledge base to accurately identify explicit faults as well as progressive problems such as blurry printing and speed fluctuations. It automatically locates the cause of the fault and pushes troubleshooting solutions, avoiding blind troubleshooting by maintenance personnel, shortening the fault handling cycle, and reducing the impact of equipment downtime on business. At the same time, the fault log auditing and statistical analysis functions can accumulate fault data, form standardized processing procedures, reduce the recurrence rate of similar faults, and improve the standardization level of operation and maintenance.

[0017] In terms of print quality and consumable management optimization, the system analyzes printed samples in real time through a print quality assessment module, proactively identifying issues such as uneven density and broken lines, and pushing parameter adjustment solutions to prevent substandard documents from reaching downstream processes and ensure service quality. The consumables remaining quantity prediction module accurately calculates remaining paper length and usable time, automatically connecting to the inventory system to push replenishment lists when supplies are low, completely solving the problem of "suddenly running out of consumables" and ensuring continuous business operations. The environmental adaptation module automatically adjusts equipment parameters based on temperature and humidity, ensuring printing stability in different environments without manual intervention, reducing quality fluctuations caused by environmental factors, and improving ease of use.

[0018] In terms of multi-device clustering and energy management, the system supports hierarchical grouping management by "region-site-device," and intelligently allocates printing tasks using load balancing algorithms to avoid overloading some devices while leaving others idle, thus improving the overall operating efficiency of the cluster. This is particularly suitable for large-scale deployment scenarios such as chain stores and logistics parks. The energy consumption monitoring and optimization module can identify issues such as high standby energy consumption and abnormal peak power, and push targeted energy-saving strategies to achieve efficient energy utilization and reduce long-term operating costs. In addition, the system's security protection mechanism ensures the security of device operation and data transmission through multi-level authentication, data encryption, and abnormal behavior monitoring, avoiding the risk of malicious operation or data leakage.

[0019] Overall, the system enables thermal paper printing equipment to shift from "passive maintenance" to "proactive prevention" and from "decentralized management" to "centralized control." It not only improves equipment reliability and reduces operation and maintenance costs, but also continuously improves equipment operating efficiency and service quality through data-driven optimization strategies, providing comprehensive support for the management of thermal paper printing equipment in retail, logistics, medical and other fields. Attached Figure Description

[0020] Figure 1 This is a schematic block diagram of the remote monitoring and fault diagnosis system for thermal paper printing equipment proposed in this invention; Figure 2 A bar chart showing the distribution of fault types in thermal paper printing equipment; Figure 3 Line graph showing the time taken to troubleshoot thermal paper printing equipment malfunctions; Figure 4 Multi-dimensional performance radar chart for thermal paper printing equipment Figure 5 A bar chart comparing the energy consumption of thermal paper printing equipment. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0023] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0024] Reference Figures 1 to 5 A remote monitoring and fault diagnosis system for thermal paper printing equipment, comprising: The remote monitoring center module includes one monitoring server and 3-8 monitoring clients. The monitoring server communicates with each monitoring client via Ethernet. The monitoring server is equipped with an Intel Xeon E5 processor, 32GB DDR4 memory, and a 4TB RAID5 disk array, supporting simultaneous access to data from 50-200 thermal paper printing devices. The data storage adopts a "real-time database + historical database" architecture, with the real-time database retaining 24 hours of data and the historical database retaining 1-3 years of data. Each monitoring client is equipped with a 19-inch LCD screen with a resolution of 1920×1080 and built-in fault management software, supporting multi-window display of different device operating parameters and fault information. The equipment-side data acquisition module integrates six sensor interfaces, each supporting 4-20mA analog signal and RS485 digital signal input; it is equipped with a PT1000 platinum resistance temperature sensor with a measurement range of 0-200℃, accuracy of ±0.05℃, and a sampling period of 500ms; an incremental encoder speed sensor with a resolution of 500 lines / revolution, outputting A / B phase pulse signals, and converting the roller speed through pulse counting; a CMOS image density sensor with a frame rate of 30fps, equipped with an 850nm infrared supplement light to ensure clear imaging of printed samples in low-light environments; a laser displacement consumable sensor with a measurement distance of 20-100mm and a sampling frequency of 20Hz, used to detect the remaining thermal paper; a closed-loop Hall current sensor with a bandwidth of 2kHz, a measurement range of 0-10A, and an accuracy of ±0.05A, used to monitor the equipment's operating current; and a triaxial acceleration vibration sensor with a range of ±10g and a sampling rate of 1kHz, used to detect abnormal mechanical vibrations of the equipment. The remote data transmission module integrates an industrial-grade 4G / 5G module and a gigabit Ethernet chip. The 4G / 5G module supports the B1 / B3 / B5 / B8 / B38 / B41 frequency bands. It uses the MQTT protocol to transmit data with QoS level 2 to ensure reliable message delivery. The data frame structure includes an 8-byte device ID, a 4-byte timestamp, a 1-byte parameter type, a 4-byte parameter value, and a 2-byte CRC16 checksum. The module is equipped with a 128GB SD card as a local cache and has power-off protection. It caches data when the network is down and automatically retransmits it in chronological order after reconnection. The heartbeat interval is set to 30 seconds, and the reconnection delay doubles from 1 second to 30 seconds each time. The intelligent fault diagnosis module includes an improved BP neural network model and a fault knowledge base. The BP neural network model consists of an input layer, two hidden layers, and an output layer. The input layer has 12 neurons corresponding to 12 equipment operating parameters, each hidden layer has 32 neurons with ReLU activation function, and the output layer has 8 neurons corresponding to 8 common faults. The model has a training sample size of 5000+. The fault knowledge base is stored in a MySQL database and contains 200-500 fault cases. Each case includes a fault code, triggering conditions, troubleshooting steps, and spare part model. After receiving the collected parameters, the model matches features to identify the fault type and locate the cause. The remote control module contains a library of 20 basic instructions, coded from 0x01 to 0x20, corresponding to functions such as device start, stop, and temperature adjustment. Instruction issuance employs a "three-way handshake" mechanism: after the monitoring center sends an instruction, the device must return a confirmation signal, and the monitoring center then returns an execution confirmation signal. It supports a 10-instruction queue buffer with a configurable execution timeout of 30 seconds by default. It can issue instructions to adjust printing parameters, including printhead temperature adjustment within ±5℃, roller speed adjustment within ±5r / min, and print density adjustment within ±10%. It also supports device start / stop and emergency shutdown instructions, and the device must provide confirmation of the instruction execution results. The environment adaptation module is equipped with temperature and humidity sensors. The sensors are deployed at the air inlet of the device to avoid heat sources affecting measurement accuracy. The temperature measurement range is 0-40℃ with an accuracy of ±0.5℃, and the humidity measurement range is 20%-90%RH with an accuracy of ±3%RH. The sampling period is 1 minute. The device automatically adjusts its operating parameters according to the ambient temperature and humidity. In high humidity environments, it increases the printhead temperature and speeds up the roller rotation. In low temperature environments, it activates the printhead preheating function with a preheating temperature of 35-40℃ and a preheating time of ≤30 seconds, ensuring stable printing quality under different environments.

[0025] This invention also includes a print quality assessment module, which preprocesses the printed sample image acquired by the CMOS image sensor. The preprocessing process includes grayscale conversion, Gaussian denoising, and edge cropping; it extracts pixel data from three key regions—the top 1 / 4, middle 1 / 2, and bottom 1 / 4 of the printed sample—and uses a formula... Calculate the print density index C; where C is the print density index, with a value ranging from 0 to 255, and the larger the value, the higher the print density; a is the area number, with a value of 1 to 3, corresponding to the top, middle and bottom areas respectively. denoted as the number of effective pixels in region a, where the number of effective pixels is the total number of pixels after removing blurred pixels at the edges. The grayscale value of the i-th valid pixel in region a ranges from 0 to 255. The module classifies the print quality level according to the C value: C ≥ 220 is excellent, 180 ≤ C < 220 is acceptable, 150 ≤ C < 180 is to be optimized, and C < 150 is unacceptable. When it is to be optimized, the module pushes the parameter adjustment combination scheme. When it is unacceptable, the module triggers the reprint instruction and records the reason for the unacceptability.

[0026] This invention also includes a consumables remaining quantity prediction module. This module obtains the current thermal paper roll radius *r* (in mm) using a laser displacement sensor; combined with the thermal paper core radius *r0*, where *r0* is a fixed value ranging from 10-20 mm and is entered during device initialization; and the thermal paper thickness *d* (in mm), which is automatically imported based on the consumable model and ranges from 0.08-0.12 mm; first, it uses a formula... Calculate the remaining paper length L in mm; then use the formula Calculate the remaining available time R; where R is the remaining available time in hours; v is the average printing speed of the device in mm / h, which is the weighted average of the last 4 hours, with the weight decreasing over time, 0.4 for the first hour and 0.2 for hours 2-4; k is the printing task coefficient, k=1.2 for continuous printing and k=1.0 for intermittent printing; when R≤4h, the module connects to the ERP system to query the latest consumable warehouse inventory, pushes a replenishment list including warehouse location, inventory quantity and estimated delivery time, and displays a paper roll replacement countdown on the monitoring client.

[0027] This invention also includes a multi-parameter linkage early warning module. This module presets a matrix of equipment operating parameter thresholds: printhead temperature normal range 30-60℃, early warning range 60-70℃, fault range >70℃; roller speed normal range 30-70 r / min, early warning range 20-30 r / min or 70-80 r / min, fault range <20 r / min or >80 r / min; operating current normal range 0.5-5A, early warning range 5-6A, fault range >6A; and establishes 12 sets of parameter linkage rules, with rule 1 being printhead temperature. The warning is triggered when the temperature is >65℃ and the operating current is >5A for 5 seconds, and when the roller speed fluctuation is >±15% and the vibration acceleration is >3g for 3 seconds. After the warning is triggered, the module automatically retrieves the parameter curve of the equipment in the past hour and marks the abnormal starting point, and generates a detailed list containing the abnormal parameter trend chart, related parameter changes and historical records of similar warnings. The warning is pushed to the maintenance personnel according to the priority of "maintenance APP push (10s unread) → SMS (5min unread) → telephone voice (10min unread)", and the warning trigger time and duration are recorded. After the warning is lifted, it is automatically marked as "processed".

[0028] This invention also includes a multi-device cluster management module, which supports grouping according to a three-level architecture of "region-outlet-device", supporting up to 10 regions, up to 50 outlets in each region, and up to 20 devices in each outlet; each group can be configured with an independent parameter template, with a default printhead temperature of 50°C for convenience store devices and 55°C for warehouse devices; the device load value L is calculated using the formula L = (current number of tasks / maximum number of tasks) × 0.6 + (continuous running time / rated continuous running time) × 0.4; The current number of tasks refers to the number of printing tasks currently being executed by the device, and the maximum number of tasks is the device's rated capacity to process tasks simultaneously, ranging from 5 to 10. The continuous running time is the device's running time since its last shutdown, and the rated continuous running time is the device's designed continuous working time, ranging from 8 to 24 hours. New tasks are preferentially assigned to devices with L < 0.3. When all devices have L ≥ 0.7, a task queuing mechanism is activated, sorting tasks by priority from 1 to 5, with level 1 tasks being executed first. At the same time, a "device overload" reminder is sent to the branch administrator.

[0029] This invention also includes a remote fault debugging module. This module has a built-in fault code parsing library that supports parsing custom fault codes from over 200 manufacturers. Within 10 seconds of a fault occurring, it automatically captures a parameter snapshot of the three minutes preceding the fault, with a sampling frequency of 1Hz, generating fault waveforms such as temperature-time curves and current-speed scatter plots. It provides parameter adjustment sliders with step sizes of 1℃ for printhead temperature, 1r / min for roller speed, and 1% for print density. It is equipped with action test buttons, including functions such as "roller rotation alone," "printhead heating test," and "paper feed test." It supports fault log export in CSV format, containing fields such as fault occurrence time, parameter values, and diagnostic results. The test print page includes 10 levels of grayscale bars, horizontal / vertical / diagonal lines, and text of different sizes. After debugging, image analysis is used to calculate the grayscale bar recognition rate and line continuity. A grayscale bar recognition rate ≥90% and ≤2 line breakpoints are considered successful. After successful debugging, the debugging parameters are saved to the "device-specific parameter library."

[0030] This invention also includes an energy consumption monitoring and optimization module, which uses a formula... Calculate the equipment energy consumption E; where E is the total energy consumption of the equipment during the statistical period, in kW·h; U is the rated voltage of the equipment, a fixed value of 220V; Δt represents the real-time current value of the device in the t-th sampling period, in A; Δt is the sampling period, a fixed value of 0.5h; n is the number of samples within the statistical period; the module identifies three types of high-energy-consumption scenarios: standby power consumption > 0.05kW·h / 2h, peak power > 1.2kW for 5min, and single-page printing power consumption > 0.001kW·h; optimization strategies are pushed for different scenarios: the printhead heating module is turned off during standby, printing tasks are distributed during high load, and the density is adjusted to economy mode during daily printing; a monthly energy consumption comparison report is generated, comparing the energy consumption data of the current month with the previous month and the same period last year to quantify the energy-saving effect.

[0031] This invention also includes a fault log auditing module, which records complete information for each fault. Log fields include the device serial number (SN), fault occurrence time (accurate to milliseconds), 12 operating parameter values ​​(with units) at the time of the fault, diagnostic model output probability, personnel ID, processing time, and spare parts replacement records (including spare parts SN). A monthly fault audit report is generated, statistically analyzing the frequency of various faults, average repair time, and repair success rate. High-fault-prone equipment is analyzed, with equipment experiencing more than 3 faults per month marked as high-fault-prone, and the data is correlated with the equipment's service life and maintenance records. The fault knowledge base is updated based on the audit data, supplementing optimal handling solutions for high-frequency faults and improving the accuracy of subsequent fault diagnosis.

[0032] This invention also includes a system security protection module, which adopts a three-level protection mechanism. The first level of protection is identity authentication, using the OAuth2.0 protocol, supporting account password + mobile phone verification code + fingerprint / facial biometric recognition. Passwords must contain uppercase and lowercase letters, numbers, and special symbols, be at least 12 characters long, and be forcibly changed every 90 days. The second level of protection is data security, with data transmission using AES-256 encryption + SSL / TLS protocol, and data storage using multi-node fragmented encryption. Each frame of data is signed with a unique digital certificate attached to the device. The third level of protection is abnormal behavior monitoring, which includes 15 abnormal behavior rules, such as "5 failed login attempts within 1 hour" and "issuing a shutdown command outside of working hours". After an abnormal rule is triggered, an "alarm + permission freeze (24 hours) + log recording" response is executed. Security logs are retained for 180 days and support multi-dimensional queries by time, device, and behavior type.

[0033] This invention also includes a fault statistical analysis module, which uses formulas... Calculate the equipment failure rate F; where F is the equipment failure rate, expressed as a percentage (%). The total number of equipment failures during the statistical period, expressed in times. The module calculates the total runtime of the equipment within the statistical period, in hours. It also categorizes and calculates F-values ​​by equipment model, deployment area, and service life, and plots a trend chart of failure rate. The time granularity supports daily, weekly, and monthly calculations. Based on the statistical data, it identifies high-incidence types and time periods of failures, establishes a failure expert database, and updates the database quarterly to provide experience support for failure diagnosis and reduce the recurrence rate of similar failures.

[0034] Example 1: Cluster Management Application of 100 Thermal Paper Printing Devices in a Chain Convenience Store System Configuration and Deployment This example focuses on 10 convenience stores of a chain brand (each with 10 thermal paper POS printers). The remote monitoring center is deployed in the headquarters server room. The monitoring server uses an Intel Xeon E5-2640 dual processor, 64GB of memory, an 8TB RAID5 array, and runs CentOS 7. The real-time database uses InfluxDB (retaining 24 hours of data), and the historical database uses MySQL (retaining 2 years of data). Five monitoring clients are deployed in the operations and maintenance department and each regional management office. The client software supports split-screen display of the real-time status of 10 devices, with a response latency of ≤300ms.

[0035] The device-side data acquisition module is integrated into each printer, equipped with a PT1000 temperature sensor (installed 2cm below the printhead), a 600-line encoder (on the roller shaft), a 1.3-megapixel CMOS camera (10cm above the paper output), a laser displacement sensor (5cm to the side of the paper roll), an ACS712 current sensor (power input), and an ADXL345 vibration sensor (bottom of the printer). The sampling period is uniformly set to 500ms, and the data is output after preprocessing by an STM32F103 microcontroller.

[0036] The remote data transmission module uses a Huawei ME909s-8214G module (supporting B1 / B3 / B5 / B8 frequency bands) and a Realtek RTL8111F gigabit network card. The MQTT protocol client ID is bound to the device MAC address, QoS=2, and the data frame CRC16 check uses the MODBUS algorithm. When the SD card cache is full, it automatically overwrites data older than 7 days. The reconnection interval cycles from 1s to 30s.

[0037] Operation process and function implementation 1. After the daily monitoring and parameter adjustment system is started, it collects parameters in real time, including printhead temperature (30-60℃), roller speed (40-60r / min), print density (converted from grayscale values ​​collected by a CMOS camera), thermal paper balance (converted from laser ranging value to radius), operating current (0.8-2.5A), and machine vibration (≤2g), updating the data to the monitoring center every 30 seconds. When the printhead temperature of the No. 3 machine at No. 7 Convenience Store exceeds 55℃ three times consecutively, the system automatically issues a command to lower the temperature by 3℃, and simultaneously displays a "Temperature Warning - Automatic Adjustment Already" message on the client.

[0038] 2. Fault Diagnosis and Handling: A "blurry printing" alarm appeared on printer #5 at Convenience Store #8. The intelligent fault diagnosis module invoked a BP neural network model (input layer with 12 parameters: average temperature over the past 10 seconds, standard deviation of rotation speed, peak current, etc.). The output layer showed "printhead aging" (89%), "density setting too low" (10%), and other (1%) among the 8 types of fault probabilities. Matching this with case number 128 in the fault knowledge base, the system recommended "replace the TH320 printhead and check if the density parameter is 65%". Maintenance personnel remotely used the test print function to output a test page with 10 levels of grayscale bars. CMOS image analysis showed no difference between grayscale levels 1-3, confirming printhead aging. The printhead was replaced within 2 hours, and the entire fault handling process was recorded by the system.

[0039] 3. Print quality assessment and consumables management use formulas. Calculate the concentration index, where a = 1 (top), 2 (middle), and 3 (bottom). =5000 (effective pixels per region) This is a grayscale value. When C=165 (needs optimization) for machine #7 in store #10, the system recommends a combination of "temperature +2℃ and concentration +5%", after which C=192 (qualified). Consumables remaining quantity prediction passed. Calculate the remaining paper length, then... The available time is determined. When R=3.5h, a replenishment order is automatically sent to the nearest warehouse, which includes "3 rolls of thermal paper of model TP-57 are needed, and the paper is expected to be delivered in 1.5 hours".

[0040] Effect verification and table analysis Table 1: Comparison of Traditional Operation and Maintenance with This System (100 devices / month) index Traditional Operations and Maintenance System maintenance Average time to fault detection 8.5 hours 12 minutes Average time to repair faults 4.2 hours 1.1 hours Number of machine downtimes due to paper shortages 23 times 0 times Print quality complaints 15 times 2 times Monthly maintenance labor costs 8500 yuan 3200 yuan Traditional maintenance relies on manual troubleshooting after store staff report problems, resulting in a delay of over 8 hours in fault detection, 4.2 hours of travel time for parts requisition, 23 downtimes due to paper shortages, and 15 complaints about print quality issues, leading to high labor costs. This system detects faults within 12 minutes through real-time monitoring, reduces repair time to 1.1 hours through intelligent diagnostics, achieves zero downtime due to paper shortages by accurately predicting consumable needs, reduces complaints to 2 per complaint through pre-control of quality, and lowers labor costs by 62%, significantly improving the maintenance efficiency and service quality of printing equipment in chain convenience stores.

[0041] Example 2: Intelligent Management Application of 50 Waybill Printers in a Logistics Park I. System Configuration and Deployment This embodiment is applied to 50 high-speed label printers in a logistics park (divided into 5 sorting areas, 10 printers in each area). The remote monitoring center server adopts dual-machine hot standby (Intel Xeon Silver 4210 processor, 128GB memory) and supports simultaneous processing of 50 1080P printed sample images. The multi-device cluster management module is grouped into three levels: "park-sorting area-device". The default printhead temperature of the equipment in sorting area 1 is 60℃ and the rubber roller speed is 70r / min, which is suitable for thick label paper.

[0042] The environmental adaptation module installs SHT30 temperature and humidity sensors (accuracy ±0.3℃ / ±2%RH) in each sorting area, positioned 30cm above the equipment's air inlet, with a sampling cycle of 1 minute; the energy consumption monitoring and optimization module connects to the park's smart meters, according to the formula... Given U=220V and Δt=0.5h, calculate energy consumption and identify devices with standby energy consumption >0.05kW・h / 2h.

[0043] II. Operation Flow and Function Implementation 1. Multi-device load balancing The multi-parameter linkage early warning module has 12 preset rules. When the printhead temperature of machine 8 in sorting area 3 reaches 68℃ and the current reaches 3.2A for 5 seconds, rule 1 is triggered. The system retrieves the parameter curve for the past hour, showing a linear increase in temperature from 60℃, and simultaneously pushes a notification to the maintenance APP (an SMS is triggered if the message is not read within 10 seconds). The multi-device cluster management module calculates the load value L = (current number of tasks / maximum number of tasks) × 0.6 + (continuous running time / rated time) × 0.4. When there are 3 devices with L > 0.7, the new task is automatically assigned to machine 5 with L = 0.2 to balance the load.

[0044] 2. Environmental Adaptation and Energy Consumption Optimization During the rainy season, the humidity in the park reaches 85%RH. The environmental adaptation module automatically raises the printhead temperature of all equipment to 65℃ and increases the roller speed by 5r / min to prevent paper sticking. In low-temperature weather (<15℃), the preheating function is activated (38℃, 20s) to ensure normal printing color development. Energy consumption monitoring found that the standby energy consumption of equipment in Zone 2 was 0.08kW・h / 2h at night. A strategy of "automatically turning off printhead heating after 22:00" was pushed, which saved an average of 120kW・h of electricity per month after implementation.

[0045] 3. Fault Statistics and Safety Protection The fault statistics and analysis module calculates the occurrence rate using the formula F=(N_f / N_t)×100%. In May, 50 devices operated for a total of 14,400 hours, with 18 faults, resulting in an F=0.125%. A trend chart shows that "rubber roller jamming" accounted for 45% of the faults. The fault knowledge base was updated to include a "weekly cleaning of rubber rollers" preventative measure. The system security module uses OAuth2.0 authentication (password + fingerprint) and AES-256 encryption for data transmission. Five failed login attempts outside of working hours triggered an anomaly rule, freezing the account for 24 hours and logging the error.

[0046] III. Effect Verification and Table Analysis Table 2: Comparison of Equipment Operation Indicators in Logistics Park Before and After System Application index Before application After application Average daily operating time of equipment 16 hours 20 hours Average daily printing volume per unit 800 orders 1200 orders Total number of monthly failures 42 times 18 times Printing volume per unit of energy consumption 120 units / kW·h 180 units / kW·h Data transmission security incidents 3 times 0 times Before implementation, the equipment suffered from uneven load distribution and frequent malfunctions, operating for only 16 hours per day with a single unit printing an average of 800 forms per day. Insufficient environmental adaptability resulted in 42 malfunctions per month, high energy consumption (120 forms / kW·h), and 3 data transmission anomalies. After implementation, load balancing extended operating hours to 20 hours, and the printing volume per unit increased by 50% to 1200 forms. Environmental adaptation and fault prevention reduced malfunctions to 18 per month. Energy consumption optimization achieved a printing volume of 180 forms / kW·h per unit of energy consumption. Security protection achieved zero security incidents, fully meeting the high-efficiency, stable, and secure requirements of high-speed waybill printing in logistics parks.

[0047] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A remote monitoring and fault diagnosis system for thermal paper printing equipment, characterized in that, include: The remote monitoring center module includes one monitoring server and three to eight monitoring clients, with the monitoring server communicating with each monitoring client via Ethernet. Data storage adopts a real-time database plus a historical database architecture. The real-time database retains 24 hours of data, while the historical database retains 1-3 years of data. The device-side data acquisition module integrates 6 sensor interfaces, each supporting 4-20mA analog signal and RS485 digital signal input; The remote data transmission module has a built-in industrial-grade module and a gigabit Ethernet chip. The industrial-grade module supports the B1 / B3 / B5 / B8 / B38 / B41 frequency bands. Data is transmitted using the MQTT protocol, with QoS level 2; the data frame structure includes an 8-byte device ID, a 4-byte timestamp, a 1-byte parameter type, a 4-byte parameter value, and a 2-byte CRC16 checksum. The intelligent fault diagnosis module includes an improved BP neural network model and a fault knowledge base. The BP neural network model consists of an input layer, two hidden layers, and an output layer. The input layer has 12 neurons corresponding to 12 equipment operating parameters, each hidden layer has 32 neurons with ReLU activation function, and the output layer has 8 neurons corresponding to 8 common faults. The fault knowledge base is stored in a MySQL database. After receiving the collected parameters, the model matches features to identify the fault type and locate the cause. The remote control module includes a basic instruction library with instruction codes from 0x01 to 0x20, corresponding to device start, stop, and temperature adjustment, respectively. Instruction issuance employs a three-way handshake mechanism: after the monitoring center sends an instruction, the device must return a reception confirmation signal, and the monitoring center then returns an execution confirmation signal. It supports a 10-instruction queue buffer with a configurable execution timeout of 30 seconds by default. It also issues instructions for adjusting print parameters. An environment adaptation module is equipped with a temperature and humidity sensor, which is deployed at the air inlet of the device; the device's operating parameters are automatically adjusted according to the ambient temperature and humidity.

2. The remote monitoring and fault diagnosis system for thermal paper printing equipment according to claim 1, characterized in that, It also includes a print quality assessment module, which preprocesses the printed sample images acquired by the CMOS image sensor. The preprocessing process includes grayscale conversion, Gaussian denoising, and edge cropping; it extracts pixel data from three key regions—the top 1 / 4, middle 1 / 2, and bottom 1 / 4 of the printed sample—and uses a formula... Calculate the print density index C; where C is the print density index, with a value ranging from 0 to 255, and the larger the value, the higher the print density; a is the area number, with a value of 1 to 3, corresponding to the top, middle and bottom areas respectively. denoted as the number of effective pixels in region a, where the number of effective pixels is the total number of pixels after removing blurred pixels at the edges. The grayscale value of the i-th valid pixel in region a ranges from 0 to 255. The module classifies the print quality level according to the C value: C ≥ 220 is excellent, 180 ≤ C < 220 is acceptable, 150 ≤ C < 180 is to be optimized, and C < 150 is unacceptable. When it is to be optimized, the module pushes the parameter adjustment combination scheme. When it is unacceptable, the module triggers the reprint instruction and records the reason for the unacceptability.

3. The remote monitoring and fault diagnosis system for thermal paper printing equipment according to claim 1, characterized in that, It also includes a consumables remaining quantity prediction module. This module obtains the current thermal paper roll radius *r* (in mm) through a laser displacement sensor; combined with the thermal paper core radius *r0*, where *r0* is a fixed value ranging from 10-20 mm and is entered during device initialization; and the thermal paper thickness *d* (in mm), which is automatically imported based on the consumable model and ranges from 0.08-0.12 mm; first, it uses a formula... Calculate the remaining paper length L in mm; then use the formula Calculate the remaining available time R; where R is the remaining available time in hours; v is the average printing speed of the device in mm / h, which is the weighted average of the last 4 hours, with the weight decreasing over time, 0.4 for the first hour and 0.2 for hours 2-4; k is the printing task coefficient, k=1.2 for continuous printing and k=1.0 for intermittent printing; when R≤4h, query the latest consumable warehouse inventory through the ERP system, push a replenishment list including warehouse location, inventory quantity and estimated delivery time, and display the paper roll replacement countdown on the monitoring client.

4. The remote monitoring and fault diagnosis system for thermal paper printing equipment according to claim 1, characterized in that, It also includes a multi-parameter linkage early warning module. The multi-parameter linkage early warning module presets a threshold matrix of equipment operating parameters. The normal range of printhead temperature is 30-60℃, the early warning range is 60-70℃, and the fault range is >70℃; the normal range of roller speed is 30-70r / min, the early warning range is 20-30r / min or 70-80r / min, and the fault range is <20r / min or >80r / min; the normal range of operating current is 0.5-5A, the early warning range is 5-6A, and the fault range is >6A. Twelve sets of parameter linkage rules are established. Rule 1 is that the printhead temperature is >65℃ and the operating current is >5A for 5 seconds. Rule 2 is that the roller speed fluctuation is >±15% and the vibration acceleration is >3g for 3 seconds. After the early warning is triggered, the module automatically retrieves the parameter curves of the equipment in the past hour and marks the abnormal starting point, generating a detailed sheet that includes an abnormal parameter trend chart, changes in related parameters, and historical records of similar early warning processing.

5. The remote monitoring and fault diagnosis system for thermal paper printing equipment according to claim 1, characterized in that, It also includes a multi-device cluster management module, which supports grouping by a three-level architecture, supports up to 10 regions, each region has up to 50 network points, and each network point has up to 20 devices; each group is configured with an independent parameter template.

6. The remote monitoring and fault diagnosis system for thermal paper printing equipment according to claim 1, characterized in that, It also includes a remote fault debugging module, which has a built-in fault code parsing library and supports manufacturer-defined fault code parsing; within 10 seconds of a fault occurring, it automatically captures a parameter snapshot of the 3 minutes prior to the fault, with a sampling frequency of 1Hz, and generates fault waveform diagrams such as temperature-time curves and current-speed scatter plots; it provides parameter adjustment sliders with printhead temperature adjustment steps of 1℃, roller speed adjustment steps of 1r / min, and print density adjustment steps of 1%; Equipped with action test buttons, including functions for individual roller rotation, printhead heating test, and paper feed test; Supports exporting fault logs.

7. The remote monitoring and fault diagnosis system for thermal paper printing equipment according to claim 1, characterized in that, It also includes an energy consumption monitoring and optimization module, which uses a formula... Calculate the equipment energy consumption E; where E is the total energy consumption of the equipment during the statistical period, in kW·h; U is the rated voltage of the equipment, a fixed value of 220V; Let be the real-time current value of the device in the t-th sampling period, in A; Δt is the sampling period, fixed at 0.5h; n is the number of samples within the statistical period; the module identifies three types of high-energy-consumption scenarios: standby energy consumption > 0.05kW・h / 2h, peak power > 1.2kW for 5min, and energy consumption per page > 0.001kW・h; optimization strategies are pushed for different scenarios: the printhead heating module is turned off during standby, printing tasks are distributed during high load, and the density is adjusted to economy mode during daily printing; a monthly energy consumption comparison report is generated, comparing the energy consumption data of the current month with the previous month and the same period last year to quantify the energy-saving effect.

8. The remote monitoring and fault diagnosis system for thermal paper printing equipment according to claim 1, characterized in that, It also includes a fault log auditing module, which records complete information for each fault. The log fields include the device SN number, fault occurrence time, 12 operating parameter values ​​at the time of the fault, diagnostic model output probability, personnel ID, processing time, and spare parts replacement records. Monthly fault audit reports are generated, which include statistics on the frequency of various faults, average repair time, and repair success rate. High-fault equipment is analyzed, and equipment with more than 3 faults per month is marked as high-fault equipment. The equipment's service life and maintenance records are also linked.

9. The remote monitoring and fault diagnosis system for thermal paper printing equipment according to claim 1, characterized in that, It also includes a system security protection module, which adopts a three-level protection mechanism. The first level of protection is identity authentication, which adopts the OAuth2.0 protocol and supports account password, mobile phone verification code, fingerprint or facial biometric recognition. Data transmission adopts AES-256 encryption + SSL / TLS protocol, data storage adopts multi-node fragmentation encryption, and each frame of data is attached with a unique digital certificate signature of the device. The third level of protection is abnormal behavior monitoring, which includes 15 abnormal behavior rules.

10. The remote monitoring and fault diagnosis system for thermal paper printing equipment according to claim 1, characterized in that, It also includes a fault statistics and analysis module, which uses formulas... Calculate the equipment failure rate F; where F is the equipment failure rate, expressed as a percentage (%). The total number of equipment failures during the statistical period, expressed in times. The module calculates the total runtime of the equipment within the statistical period, in hours; it also categorizes and statistically analyzes F-values ​​by equipment model, deployment area, and service life, and plots a trend chart of failure rate, with time granularity supporting daily, weekly, and monthly. Based on statistical data, identify the types and time periods of high-incidence faults and establish a fault expert database.

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