Remote monitoring and fault diagnosis system for thermal paper printing equipment

Abstract

The application discloses a kind of remote monitoring and fault diagnosis system of thermal paper printing equipment, it is related to thermal paper printing system field, including: remote monitoring center module contains 1 monitoring server and 3-8 client, server double warehouse inventory data, client shows equipment parameter;Equipment end data acquisition module, 6-way interface is equipped with 6 kinds of sensors, temperature, rotational speed and other parameters are collected;Remote data transmission module, with 4G / 5G and Ethernet, data is transmitted by MQTT, and SD card is cached;Intelligent fault diagnosis module contains BP neural network and fault knowledge base;Remote control module, 20 instructions, issue under "three times handshaking";Environment adaptation module, temperature and humidity sensor controls preheating and parameter.The application realizes equipment remote monitoring and intelligent fault diagnosis, improves printing quality and consumable management efficiency, balances cluster load, reduces energy consumption, guarantees operation safety, reduces operation and maintenance cost, and is suitable for multi-scene large-scale management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of thermal paper printing system, and particularly relates to a remote monitoring and fault diagnosis system of thermal paper printing equipment. BACKGROUND

[0002] As the core terminal of retail cash register, logistics slip printing, medical bill output and other scenes, the running stability and business efficiency of thermal paper printing equipment are directly related. With the expansion of chain stores, the large-scale construction of logistics parks, the number of thermal paper printing equipment deployment has increased rapidly and the distribution has become more dispersed, and the short board of traditional operation and maintenance mode has become increasingly prominent. At present, most of the equipment still relies on the local operation panel for state monitoring, and only the basic information such as print head temperature, power on-off can be displayed, and there is a lack of real-time collection capability of key parameters such as roller speed fluctuation, print density uniformity, and consumable quantity change, and the maintenance personnel need to conduct regular on-site inspection to troubleshoot potential faults. In the scene where the equipment is distributed in dozens or even hundreds of network points, manual inspection not only consumes time and effort, but also is prone to cause fault discovery lag due to inspection interval, for example, the hidden overheating of the print head is not handled in time, which may cause the equipment to burn out and cause business interruption for several hours to several days.

[0003] The inefficiency of the fault diagnosis link further aggravates the maintenance pressure. The existing equipment fault prompt relies on the built-in error code, which can only identify the explicit faults such as print head open circuit and motor overload, and lacks effective diagnosis means for progressive faults such as print blur and paper skew. After the maintenance personnel arrive at the scene, they need to check mechanical parts, circuit connection and parameter setting one by one, and often need to make several round trips to adjust spare parts due to inaccurate fault positioning, which prolongs the downtime. At the same time, the fault code system of different brand equipment is not unified, and the maintenance personnel need to master the fault logic of multiple equipment, which has high learning cost and is difficult to form a standardized fault handling process. In addition, the equipment operation data lacks systematic storage and analysis, and it is impossible to mine the rules of high-frequency problems from historical faults, and the same type of fault occurs repeatedly, resulting in high maintenance cost.

[0004] There are obvious limitations in the device management and optimization level. The consumable level monitoring mostly adopts mechanical contact type design, which has low precision and is easily affected by paper creases, often resulting in "displaying remaining amount but suddenly running out". Especially during the retail peak period or the peak season of logistics sorting, paper shortage will directly affect the cash register efficiency or the package sorting progress. Energy consumption management lacks fine monitoring, and the device is in high load operation state for a long time without being detected, causing unnecessary energy waste. At the same time, the influence of environmental factors on printing quality is not effectively considered, and uneven printing color and paper sticking are prone to occur in high temperature and humidity environment. The device cannot automatically adapt and adjust, and manual parameter adjustment is required, which not only reduces the operation efficiency, but also may exacerbate the device wear and tear due to improper parameter setting. These problems together lead to low operation efficiency and long downtime of the thermal paper printing device, which is difficult to meet the high requirements of modern business on device reliability and fine management. SUMMARY

[0005] The remote monitoring and fault diagnosis system of the thermal paper printing device provided by the present application solves the problems mentioned in the prior art.

[0006] In order to achieve the above purpose, the present application adopts the following technical scheme: a remote monitoring and fault diagnosis system of a thermal paper printing device, comprising:

[0007] A remote monitoring center module, the remote monitoring center module comprises 1 monitoring server and 3-8 monitoring clients, the monitoring server and each monitoring client communicate through Ethernet; the monitoring server is configured with Intel Xeon E5 processor, 32GB DDR4 memory and 4TB RAID5 disk array, and can support simultaneous access of 50-200 thermal paper printing device data, the data storage adopts "real-time library + historical library" architecture, the real-time library retains 24 hours of data, and the historical library retains 1-3 years of data; each monitoring client is equipped with a 19-inch liquid crystal display screen with a resolution of 1920x1080, and is provided with built-in fault management software to support multi-window display of different device running parameters and fault information;

[0008] Device end data acquisition module, the device end data acquisition module integrates 6 way sensor interface, each interface supports 4-20mA analog signal and RS485 digital signal input;Equipped with PT1000 platinum resistance temperature sensor, measurement range 0-200 ℃, accuracy ± 0.05 ℃, sampling period 500 ms;Incremental encoder speed sensor, resolution 500 lines / coil, output A / B phase pulse signal, through pulse counting conversion roller speed;CMOS image density sensor, frame rate 30 fps, carry 850 nm infrared fill light, ensure that the dark environment under the printing sample imaging clear;Laser displacement consumable sensor, measurement distance 20-100 mm, sampling frequency 20 Hz, for detecting thermal paper margin;Closed loop hall current sensor, bandwidth 2kHz, measurement range 0-10A, accuracy ± 0.05A, monitor device working current;Three-axis acceleration vibration sensor, range ± 10g, sampling rate 1kHz, capture device mechanical vibration anomaly;

[0009] Remote data transmission module, the remote data transmission module is built-in industrial grade 4G / 5G module and gigabit Ethernet chip, 4G / 5G module supports B1 / B3 / B5 / B8 / B38 / B41 band;Adopt MQTT protocol transmission data, QoS level 2, ensure that the message can be reliably delivered;Data frame structure contains 8 bytes device ID, 4 bytes timestamp, 1 byte parameter type, 4 bytes parameter value and 2 bytes CRC16 check bit;Module equipped with 128GB SD card as local cache, with power failure protection function, cache data when network is disconnected, after networking, automatically supplement transmission in time sequence, heartbeat packet interval is set to 30s, disconnection reconnect delay from 1s to 30s gradually doubled;

[0010] Intelligent fault diagnosis module, the intelligent fault diagnosis module contains improved BP neural network model and fault knowledge base;BP neural network model contains input layer, 2 layers of hidden layer and output layer, 12 neurons of input layer correspond to 12 items of device running parameters, 32 neurons of each layer of hidden layer, activation function is ReLU, 8 neurons of output layer correspond to 8 kinds of common faults, model training sample size 5000+;Fault knowledge base is stored in MySQL database, contains 200-500 fault cases, each case contains fault code, trigger condition, troubleshooting steps and spare parts model, receives collection parameters to realize fault type identification and cause positioning through model matching characteristics;

[0011] The remote control module comprises a 20-base instruction library, instruction codes are 0x01-0x20, corresponding to device start, stop, temperature adjustment and other functions respectively; the instruction issuing adopts a "three-way handshake" mechanism, after the monitoring center sends an instruction, the device needs to return a receiving confirmation signal, and the monitoring center returns an execution confirmation signal; 10 instruction queue buffers are supported, the execution timeout time is configurable, and the default is 30s; a print parameter adjustment instruction can be issued, the print head temperature adjustment range is ±5℃, the rubber roller speed adjustment range is ±5r / min, and the print density adjustment range is ±10%, meanwhile, device start and stop and emergency shutdown instructions for faults are supported, and the instruction execution result needs to be fed back by the device for confirmation;

[0012] The environment adaptation module is provided with a temperature and humidity sensor, the sensor is arranged at the air inlet position of the device to avoid the influence of heat source on the measurement accuracy; the temperature measurement range is 0-40℃, the accuracy is ±0.5℃, the humidity measurement range is 20%-90%RH, the accuracy is ±3%RH, and the sampling period is 1min; the device operation parameters are automatically adjusted according to the environmental temperature and humidity, the print head temperature is increased and the rubber roller speed is accelerated in a high-humidity environment, the print head preheating function is started in a low-temperature environment, the preheating temperature is set to 35-40℃, the preheating time is ≤30s, and the print quality is ensured to be stable in different environments.

[0013] Further, the application also comprises a print quality evaluation module, which pre-processes the print sample image collected by the CMOS image sensor, and the pre-processing process comprises greying, Gaussian denoising and edge clipping; pixel data of three key regions of the top 1 / 4, the middle 1 / 2 and the bottom 1 / 4 of the print sample are extracted, and the print density index C is calculated through the formula C is the print density index, the value range is 0-255, and the larger the value is, the higher the print density is; a is the region number, the value is 1-3, and the top, middle and bottom regions are respectively corresponding to the region number 1, 2 and 3; N is the effective pixel number of the a-th region, and the effective pixel number is the total pixel amount after the edge blur pixels are removed; G is the gray value of the i-th effective pixel of the a-th region, and the value range is 0-255; the module divides the print quality level according to the C value, C≥220 is high quality, 180≤C<220 is qualified, 150≤C<180 is to be optimized, and C<150 is unqualified; when to be optimized, a parameter adjustment combination scheme is pushed, and when unqualified, a reprint instruction is triggered and the unqualified reason is recorded.

[0014] Further, the application also includes a consumable remaining amount prediction module, which obtains a current thermal paper roll radius r by a laser displacement sensor, in mm; combines a thermal paper roll core radius r0, r0 is a fixed value, with a value range of 10-20 mm, which is inputted during device initialization; a thermal paper thickness d, in mm, which is automatically imported according to a consumable model, with a value range of 0.08-0.12 mm; calculates a remaining paper length L, in mm, by a formula first; calculates a remaining available duration R by a formula second; wherein R is the remaining available duration, in h; v is an average printing speed of the device, in mm / h, with a weighted average value of the last 4 hours, with a weight decreasing over time, with a weight of 0.4 for the first hour, and a weight of 0.2 for the 2-4 hours; k is a printing task coefficient, with k=1.2 for continuous printing, and k=1.0 for intermittent printing; when R≤4h, the module queries a nearest consumable warehouse inventory from an ERP system, pushes a replenishment list containing a warehouse location, an inventory quantity and an estimated delivery time, and displays a paper roll replacement countdown on a monitoring client.

[0015] Further, the application also includes a multi-parameter linkage early warning module, which presets a device running parameter threshold matrix, with a normal range of a print head temperature of 30-60℃, a warning range of 60-70℃, and a fault range of >70℃; a normal range of a rubber roller rotating speed of 30-70r / min, a warning range of 20-30r / min or 70-80r / min, and a fault range of <20r / min or >80r / min; a normal range of a working current of 0.5-5A, a warning range of 5-6A, and a fault range of >6A; establishes 12 groups of parameter linkage rules, with rule 1 being a print head temperature >65℃ and a working current >5A for 5s, and rule 2 being a rubber roller rotating speed fluctuation >±15% and a vibration acceleration >3g for 3s; after early warning triggering, the module automatically retrieves a parameter curve of the device in the last 1 hour and marks an abnormal starting point, generates a detail sheet containing an abnormal parameter trend graph, associated parameter changes and historical similar early warning processing records; pushes to a maintenance personnel according to a priority of “maintenance APP push (10s unread)→SMS (5min unread)→telephone voice (10min unread)”, and records an early warning triggering time and a duration, and automatically marks a “processed” state after early warning removal.

[0016] Further, the application also includes a multi-device cluster management module, which supports grouping according to a three-level architecture of "region-branch-device", supports up to 10 regions, up to 50 branches in each region, and up to 20 devices in each branch; each group can be configured with an independent parameter template, and the default print head temperature of a convenience store scenario device is 50℃, and the default print head temperature of a warehouse scenario device is 55℃; a device load value L is calculated, and the formula is L=(current task number / maximum task number)×0.6+(continuous running time / required continuous running time)×0.4; wherein the current task number is the number of print tasks being executed by the device, the maximum task number is the rated simultaneous task processing number of the device, and the value range is 5-10; the continuous running time is the running time of the device from the last shutdown to the present, and the required continuous running time is the design continuous working time of the device, and the value range is 8-24h; new tasks are preferentially assigned to devices with L<0.3, and when all devices have L≥0.7, a task queuing mechanism is started, tasks are sorted according to priority 1-5, and 1st-level tasks are preferentially executed, and a "device load too high" reminder is pushed to the branch manager.

[0017] Further, the application also includes a remote fault debugging module, which has a built-in fault code analysis library and supports custom fault code analysis of 200+ manufacturers; within 10s after a fault occurs, a parameter snapshot of the previous 3 minutes is automatically captured, the sampling frequency is 1Hz, and fault waveform graphs such as temperature-time curve and current-speed scatter plot are generated; a parameter adjustment slider is provided, the print head temperature adjustment step is 1℃, the rubber roller speed adjustment step is 1r / min, and the print density adjustment step is 1%; an action test button is provided, including functions such as "rubber roller alone rotation", "print head heating test", and "paper feeding test"; fault log export is supported, the export format is CSV, and fields such as fault occurrence time, parameter value, and diagnosis result are included; the test print test page contains 10-level gray bars, horizontal / vertical / slanted lines, and different font sizes of text, and after debugging, the gray bar recognition rate and line continuity are calculated through image analysis, the gray bar recognition rate is ≥90%, the line breakpoint number is ≤2, and it is determined that the debugging is qualified, and after qualification, the debugging parameters are saved to the "device exclusive parameter library".

[0018] Further, the application also includes an energy consumption monitoring optimization module, which calculates the energy consumption E of the device through the formula ; wherein E is the total energy consumption of the device in the statistical period, the unit is kW・h; U is the rated voltage of the device, and the fixed value is 220V; The device real-time current value of the tth sampling period is A, the sampling period is a fixed value 0.5h, n is the sampling number in the statistical period, the module identifies three types of high energy consumption scenes, which are standby energy consumption > 0.05kW·h / 2h, peak power > 1.2kW for 5min, and printing single-page energy consumption > 0.001kW·h; for different scenes, the optimization strategy is pushed, the print head heating module is turned off during standby, the printing task is dispersed during high load, and the concentration is adjusted to the economic mode during daily printing; an energy consumption comparison report is generated every month, the energy consumption data of this month, last month and the same period last year are compared, and the energy saving effect is quantified.

[0019] Further, the present application also includes a fault log audit module, which records complete information of each fault, and the log field includes device SN number, fault occurrence time (accurate to ms), 12 running parameter values (with units) at the time of fault, diagnosis model output probability, processing personnel ID, processing time length, spare parts replacement record (including spare parts SN number); a fault audit report is generated every month, the occurrence frequency, average repair time and repair success rate of various faults are counted; the high-fault device is analyzed, the device with monthly fault times > 3 times is marked as a high-fault device, and the device service life and maintenance record are associated; the fault knowledge base is updated based on the audit data, the optimal processing scheme of high-frequency faults is supplemented, and the subsequent fault diagnosis accuracy is improved.

[0020] Further, the present application also includes a system security protection module, which adopts a three-level protection mechanism; the first-level protection is identity authentication, which adopts OAuth2.0 protocol, supports account password + mobile phone verification code + fingerprint / face biometric identification, the password requires to contain uppercase and lowercase letters, numbers and special symbols, the length is greater than or equal to 12 bits, and the password is forced to change every 90 days; the second-level protection is data security, data transmission adopts AES-256 encryption + SSL / TLS protocol, data storage adopts multi-node sharding encryption, and each frame of data is attached with a device unique digital certificate signature; the third-level protection is abnormal behavior monitoring, which contains 15 abnormal behavior rules, such as "5 times of login failure within 1 hour" and "issuing stop command outside working hours"; after triggering the abnormal rule, the "alarm + permission freezing (24 hours) + log recording" response is executed; the security log is kept for 180 days, and multi-dimensional query can be supported according to time, device and behavior type.

[0021] Further, the present application also includes a fault statistical analysis module, which calculates the device fault occurrence rate F by the formula F = n / T Wherein F is the device fault occurrence rate, unit %; n is the total number of times of device faults in the statistical period, unit times; In order to count the total running time of the equipment in the statistical period, the unit is h; the module classifies and counts F value according to equipment model, deployment area and service life, draws a failure rate trend graph, and the time granularity supports day, week and month; based on the statistical data, the high-incidence type and period of failure are identified, the failure expert library is established, the expert library is updated once a quarter, the experience support is provided for failure diagnosis, and the recurrence rate of similar failures is reduced.

[0022] Compared with the existing technology, the beneficial effects of the present application are:

[0023] In terms of remote monitoring and fault handling, the system breaks through the limitation of local monitoring, realizes comprehensive collection of equipment operation parameters with the help of multiple types of high-precision sensors, can upload all real-time data such as print head temperature, rubber roller speed, print density and energy consumption data to the monitoring center, and the operation and maintenance personnel can remotely master the running state of multiple devices without frequent on-site inspection, which greatly reduces the labor cost. The intelligent fault diagnosis module combines neural network model and fault knowledge base, which can not only accurately identify dominant faults, but also capture progressive problems such as printing blur and speed fluctuation, automatically locate the fault cause and push the troubleshooting scheme, avoid blind troubleshooting of maintenance personnel, shorten the fault handling cycle, and reduce the impact of equipment downtime on business. At the same time, the fault log audit and statistical analysis function can accumulate fault data, form a standardized processing flow, reduce the recurrence rate of similar faults, and improve the standardization level of operation and maintenance.

[0024] In terms of print quality and consumable management optimization, the system analyzes the print sample in real time through the print quality evaluation module, finds problems such as uneven density and line breakpoint in advance and pushes the parameter adjustment scheme, avoids unqualified tickets flowing into the downstream link, and guarantees the service quality. The consumable remaining amount prediction module accurately calculates the remaining paper length and available time, automatically interfaces with the inventory system to push the replenishment list when the remaining amount is low, completely solves the problem of "sudden depletion of consumables", and ensures the continuous operation of business. The environment adaptation module can automatically adjust the equipment parameters according to temperature and humidity, which can guarantee the printing stability in different environments without manual intervention, reduce the quality fluctuation caused by environmental factors, and improve the convenience of equipment use.

[0025] In terms of multi-device cluster and energy consumption management, the system supports hierarchical and grouped management according to "region-branch-device", intelligently allocates print tasks combined with load balancing algorithm, avoids the situation that some devices are overloaded and some devices are idle, improves the overall operation efficiency of the cluster, and is especially suitable for large-scale deployment scenarios such as chain stores and logistics parks. The energy consumption monitoring optimization module can identify problems such as standby high energy consumption and peak power anomaly, push targeted energy-saving strategies, realize efficient use of energy, and reduce long-term operation cost. In addition, the system security protection mechanism protects the safety of device operation and data transmission through multi-level authentication, data encryption and abnormal behavior monitoring, avoids the risk of malicious operation or data leakage.

[0026] 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

[0027] 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;

[0028] Figure 2 A bar chart showing the distribution of fault types in thermal paper printing equipment;

[0029] Figure 3 Line graph showing the time taken to troubleshoot thermal paper printing equipment malfunctions;

[0030] Figure 4 Multi-dimensional performance radar chart for thermal paper printing equipment

[0031] Figure 5 A bar chart comparing the energy consumption of thermal paper printing equipment. Detailed Implementation

[0032] 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.

[0033] 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.

[0034] Furthermore, the terms "first", "second", "third", "fourth", "fifth", "sixth", "seventh" and "eight" are only used to describe purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined as "first", "second", "third", "fourth", "fifth", "sixth", "seventh" and "eight" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "a plurality of" is two or more than two, unless otherwise specifically limited. In addition, the terms "mounting", "connecting", "connecting" should be broadly understood, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be connected inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances, and the present application will be further described in detail below with reference to the drawings.

[0035] Reference Figures 1 to 5 A remote monitoring and fault diagnosis system of a thermal paper printing device, comprising:

[0036] A remote monitoring center module, the remote monitoring center module comprises one monitoring server and 3-8 monitoring clients, the monitoring server and each monitoring client communicate through Ethernet; the monitoring server is configured with Intel Xeon E5 processor, 32GB DDR4 memory and 4TB RAID5 disk array, supporting simultaneous access to 50-200 thermal paper printing device data, data storage adopts "real-time library + historical library" architecture, real-time library retains 24 hours data, historical library retains 1-3 years data; each monitoring client is equipped with a 19-inch liquid crystal display screen with a resolution of 1920x1080, and a built-in fault management software supporting multi-window display of different device running parameters and fault information;

[0037] A device-side data acquisition module, the device-side data acquisition module integrates 6 sensor interfaces, each interface supporting 4-20mA analog signal and RS485 digital signal input; equipped with PT1000 platinum resistance temperature sensor, measurement range 0-200℃, accuracy ±0.05℃, sampling period 500ms; incremental encoder speed sensor, resolution 500 lines / coil, output A / B phase pulse signal, convert roller speed through pulse counting; CMOS image density sensor, frame rate 30fps, equipped with 850nm infrared fill light to ensure clear imaging of printing samples in dark environment; laser displacement consumable sensor, measurement distance 20-100mm, sampling frequency 20Hz, used for detecting thermal paper remaining amount; closed-loop Hall current sensor, bandwidth 2kHz, measurement range 0-10A, accuracy ±0.05A, monitoring device working current; three-axis acceleration vibration sensor, range ±10g, sampling rate 1kHz, capturing device mechanical vibration abnormalities;

[0038] Remote data transmission module, which is built-in industrial-grade 4G / 5G module and gigabit Ethernet chip, 4G / 5G module supports B1 / B3 / B5 / B8 / B38 / B41 frequency band; adopts MQTT protocol to transmit data, QoS level 2, ensures that the message can be reliably delivered; data frame structure contains 8-byte device ID, 4-byte timestamp, 1-byte parameter type, 4-byte parameter value and 2-byte CRC16 check bit; the module is equipped with 128GB SD card as local cache, with power failure protection function, cache data when network is disconnected, automatically supplement transmission in time sequence after networking, heartbeat packet interval is set to 30s, disconnection reconnection delay is doubled from 1s to 30s;

[0039] Intelligent fault diagnosis module, which contains improved BP neural network model and fault knowledge base; BP neural network model contains input layer, 2 layers of hidden layer and output layer, 12 neurons of input layer correspond to 12 device operating parameters, 32 neurons of each hidden layer, activation function is ReLU, 8 neurons of output layer correspond to 8 common faults, model training sample size is 5000+; fault knowledge base uses MySQL database to store, contains 200-500 fault cases, each case contains fault code, trigger condition, troubleshooting steps and spare parts model, after receiving the collected parameters, the model matches the characteristics to realize fault type identification and cause positioning;

[0040] Remote control module, which contains 20 basic instruction library, instruction code is 0x01-0x20, respectively corresponding to device start, stop, temperature adjustment and other functions; instruction issuing adopts "three-way handshake" mechanism, after the monitoring center sends the instruction, the device needs to return the receiving confirmation signal, and the monitoring center returns the execution confirmation signal again; supports 10 instruction queue cache, execution timeout time can be configured, default is 30s; can issue print parameter adjustment instruction, print head temperature adjustment range ±5℃, glue roller speed adjustment range ±5r / min, print density adjustment range ±10%, at the same time supports device start and stop and fault emergency shutdown instruction, the instruction execution result needs to be fed back by the device;

[0041] Environment adaptation module, which carries temperature and humidity sensor, the sensor is deployed at the device air inlet position to avoid the influence of heat source on measurement accuracy; temperature measurement range is 0-40℃, accuracy is ±0.5℃, humidity measurement range is 20%-90%RH, accuracy is ±3%RH, sampling period is 1min; automatically adjusts device operating parameters according to environmental temperature and humidity, increases print head temperature and speeds up glue roller speed in high humidity environment, starts print head preheating function in low temperature environment, preheating temperature is set to 35-40℃, preheating time is ≤30s, ensures stable print quality in different environments.

[0042] In the application, a printing quality evaluation module is further included, which pre-processes the printing sample image collected by the CMOS image sensor, and the pre-processing procedure includes graying, Gaussian denoising and edge clipping; pixel data of three key regions of the top 1 / 4, the middle 1 / 2 and the bottom 1 / 4 of the printing sample are extracted, and the printing density index C is calculated through the formula C; wherein C is the printing density index, the value range is 0-255, and the larger the value is, the higher the printing density is; a is the region number, the value is 1-3, and respectively corresponds to the top, middle and bottom regions; is the effective pixel number of the a-th region, and the effective pixel number is the total pixel quantity after removing the edge blur pixels; is the gray value of the i-th effective pixel of the a-th region, and the value range is 0-255; the module divides the printing quality level according to the C value, C≥220 is high quality, 180≤C<220 is qualified, 150≤C<180 is to be optimized, and C<150 is unqualified; the parameter adjustment combination scheme is pushed when to be optimized, and the reprint instruction is triggered and the unqualified reason is recorded when unqualified.

[0043] In the application, a consumable remaining amount prediction module is further included, the consumable remaining amount prediction module obtains the current thermal paper roll radius r through a laser displacement sensor, and the unit is mm; the thermal paper roll core radius r0 is combined, r0 is a fixed value, the value range is 10-20 mm, and is input during device initialization; the thermal paper thickness d is mm, is automatically imported according to the consumable model, and the value range is 0.08-0.12 mm; first, the residual paper length L is calculated through the formula ; and then the remaining available duration R is calculated through the formula ; wherein R is the remaining available duration, the unit is h; v is the average printing speed of the device, the unit is mm / h, and the weighted average value of the last 4 hours is taken, the weight decreases with time, the weight of the first hour is 0.4, and the weight of the 2-4 hours is 0.2; k is the printing task coefficient, k=1.2 when continuously printing, and k=1.0 when intermittently printing; when R≤4h, the module queries the nearest consumable warehouse inventory through the ERP system, pushes the replenishment list containing the warehouse location, the inventory quantity and the expected arrival time, and displays the paper roll replacement countdown on the monitoring client.

[0044] In the application, a multi-parameter linkage early warning module is also included, which presets a device operation parameter threshold matrix, the normal range of the print head temperature is 30-60 DEG C, the early warning range is 60-70 DEG C, and the fault range is > 70 DEG C; the normal range of the glue roller rotating speed is 30-70 r / min, the early warning range is 20-30 r / min or 70-80 r / min, and the fault range is < 20 r / min or > 80 r / min; the normal range of the working current is 0.5-5 A, the early warning range is 5-6 A, and the fault range is > 6 A; 12 groups of parameter linkage rules are established, rule 1 is that the print head temperature > 65 DEG C and the working current > 5 A for 5 s, and rule 2 is that the glue roller rotating speed fluctuation > ± 15% and the vibration acceleration > 3 g for 3 s; after the early warning is triggered, the module automatically retrieves the parameter curve of the device in the last one hour and marks the abnormal starting point, generates a detail sheet containing an abnormal parameter trend chart, associated parameter changes and historical similar early warning processing records; the early warning is pushed to the operation and maintenance personnel according to the priority of ''operation and maintenance APP push (10 s unread) -> short message (5 min unread) -> telephone voice (10 min unread)'', and the early warning triggering time and duration are recorded, and the early warning is automatically marked as ''processed'' after the early warning is removed.

[0045] In the application, a multi-device cluster management module is also included, which supports grouping according to a ''region-network point-device'' three-level architecture, supports a maximum of 10 regions, a maximum of 50 network points in each region, and a maximum of 20 devices in each network point; each group can be configured with an independent parameter template, and the default print head temperature of a convenience store scene device is 50 DEG C, and the default print head temperature of a warehouse scene device is 55 DEG C; the device load value L is calculated, and the formula is L = (current task number / maximum task number) * 0.6 + (continuous running time / required continuous running time) * 0.4; wherein the current task number is the number of print tasks being executed by the device, the maximum task number is the rated simultaneous task processing quantity of the device, and the value range is 5-10; the continuous running time is the running time of the device from the last shutdown to the present, and the required continuous running time is the design continuous working time of the device, and the value range is 8-24 h; the new task is preferentially assigned to the device with L < 0.3, and when all devices L >= 0.7, the task queuing mechanism is started, the tasks are sorted according to the task priority 1-5, the level 1 task is preferentially executed, and the ''device load is too high'' reminder is pushed to the network point administrator.

[0046] In the application, a remote fault debugging module is also included, the remote fault debugging module is built-in fault code analysis library, supports 200+ manufacturers custom fault code analysis;After the fault occurs, the parameter snapshot of 3 minutes before the fault is automatically captured within 10s, the sampling frequency is 1Hz, the temperature-time curve, current-speed scatter plot and other fault waveform graphs are generated;The parameter adjustment slider is provided, the print head temperature adjustment step is 1℃, the glue roller speed adjustment step is 1r / min, the print density adjustment step is 1%;The action test button is equipped, including functions such as 'glue roller rotation alone', 'print head heating test', 'paper feeding test' and the like;Support fault log export, the export format is CSV, including fault occurrence time, parameter value, diagnosis result and other fields;The test print test page includes 10 gray scale bars, horizontal lines / vertical lines / sloping lines and different font texts, after debugging, the gray scale bar recognition rate and line continuity are calculated through image analysis, the gray scale bar recognition rate is greater than or equal to 90%, and the line breakpoint number is less than or equal to 2, which is judged as debugging qualified, and the debugging parameters are saved to the 'device exclusive parameter library' after being qualified.

[0047] In the application, an energy consumption monitoring optimization module is also included, the energy consumption monitoring optimization module calculates the equipment energy consumption E through the formula The equipment energy consumption E;Wherein E is the total energy consumption of the equipment in the statistical period, the unit is kW・h;U is the rated voltage of the equipment, the fixed value is 220V; The real-time current value of the equipment in the tth sampling period, the unit is A;Δt is the sampling period, the fixed value is 0.5h;N is the sampling number in the statistical period;The module identifies three kinds of high energy consumption scenes, which are standby energy consumption > 0.05kW・h / 2h, peak power > 1.2kW for 5min, and single-page printing energy consumption > 0.001kW・h;Optimization strategies are pushed for different scenes, the print head heating module is turned off during standby, the printing task is dispersed during high load, and the density is adjusted to the economic mode during daily printing;Energy consumption comparison report is generated every month, the energy consumption data of this month, last month and the same period last year are compared, and the energy saving effect is quantified.

[0048] In the application, a fault log audit module is also included, the fault log audit module records complete information of each fault, the log field includes device SN number, fault occurrence time (accurate to ms), 12 running parameter values (with unit) when the fault occurs, diagnosis model output probability, processing personnel ID, processing time length, spare parts replacement record (including spare parts SN number);Fault audit report is generated every month, the occurrence frequency, average repair time and repair success rate of various faults are counted;The fault-prone equipment is analyzed, the equipment with monthly fault times > 3 times is marked as fault-prone equipment, and the equipment service life and maintenance record are associated;Based on the audit data, the fault knowledge base is updated, the optimal processing scheme of high-frequency fault is supplemented, and the subsequent fault diagnosis accuracy is improved.

[0049] In the application, the system security protection module adopts a three-level protection mechanism; the first-level protection is identity authentication, which adopts OAuth2.0 protocol, supports account password + mobile phone verification code + fingerprint / face biometric identification, the password requires to contain uppercase and lowercase letters, numbers and special symbols, the length is greater than or equal to 12 bits, and the password is forced to be changed every 90 days; the second-level protection is data security, data transmission adopts AES-256 encryption + SSL / TLS protocol, data storage adopts multi-node sharding encryption, and each frame of data is attached with a unique digital certificate signature of the equipment; the third-level protection is abnormal behavior monitoring, which contains 15 abnormal behavior rules, such as “5 times of login failure within 1 hour” and “issuing a shutdown instruction outside working hours”; after triggering the abnormal rules, the response of “alarm + permission freezing (24 hours) + log recording” is executed; the security log is kept for 180 days, and multi-dimensional query is supported according to time, equipment and behavior type.

[0050] In the application, the fault statistical analysis module calculates the equipment failure rate F through the formula The equipment failure rate F is calculated, wherein F is the equipment failure rate, and the unit is %; is the total number of equipment failures in the statistical period, and the unit is times; is the total running time of the equipment in the statistical period, and the unit is h; the module classifies and statistically calculates the F value according to the equipment model, deployment area and service life, draws a failure rate trend graph, and supports day, week and month in time granularity; based on the statistical data, the high-incidence type and period of faults are identified, a fault expert library is established, the expert library is updated once every quarter, experience support is provided for fault diagnosis, and the recurrence rate of similar faults is reduced.

[0051] Embodiment 1: Cluster management application of 100 heat-sensitive paper printing equipment of chain convenience stores

[0052] System configuration and deployment

[0053] In this embodiment, 10 convenience stores (10 cash register heat-sensitive paper printers in each store) of a certain chain brand are monitored, a remote monitoring center is deployed in the headquarters computer room, an Intel Xeon E5-2640 dual processor, 64 GB memory and 8 TB RAID5 array are used for the monitoring server, a CentOS7 operating system is installed, an InfluxDB (24-hour data is retained) is used for a real-time database, and a MySQL (2-year data is retained) is used for a historical database; five monitoring clients are respectively deployed in the operation and maintenance department and the regional management department, the client software supports split-screen display of the real-time states of 10 equipment, and the response delay is less than or equal to 300 ms.

[0054] The device end data acquisition module is integrated in each printer, and is configured with a PT1000 temperature sensor (installed 2 cm below the print head), a 600 line encoder (installed at the shaft end of the rubber roller), a 1.3 million pixel CMOS camera (installed 10 cm above the paper outlet), a laser displacement sensor (installed 5 cm to the side of the paper roll), an ACS712 current sensor (installed at the power input end), and an ADXL345 vibration sensor (installed at the bottom of the machine body). The sampling period is uniformly set to 500 ms, and the data is output after being preprocessed by an STM32F103 single-chip microcomputer.

[0055] 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, the data frame CRC16 check uses the MODBUS algorithm, and when the SD card buffer is full, the data 7 days ago is automatically overwritten. The reconnection interval is 1s→2s→4s→8s→16s→30s in a cycle.

[0056] Running process and function implementation

[0057] 1. Daily monitoring and parameter adjustment system After starting, real-time acquisition of print head temperature (30-60℃), rubber roller speed (40-60r / min), print density (converted from the gray value acquired by the CMOS camera), thermal paper reserve (laser ranging value converted to radius), working current (0.8-2.5A), and machine body vibration (≤2g) are performed. The parameters are updated to the monitoring center every 30s. When the print head temperature of No. 3 machine in No. 7 convenience store exceeds 55℃ for 3 consecutive times, the system automatically issues an instruction to reduce the temperature by 3℃, and displays a "temperature warning-automatically adjusted" prompt on the client.

[0058] 2. Fault diagnosis and processing When the No. 5 machine in No. 8 convenience store appears "print fuzzy" alarm, the intelligent fault diagnosis module calls the BP neural network model (input layer 12 parameters: temperature average value in the last 10s, speed standard deviation, current peak value, etc.), and the output layer 8 fault probability "print head aging" (89%), "concentration setting too low" (10%), and other (1%). The 128th case in the fault knowledge base is matched, and the "replace the print head of TH320 type, check whether the concentration parameter is 65%" solution is pushed. The operation and maintenance personnel remotely call the test printing function, output a test page containing 10-level gray bars, and the CMOS image analysis shows that there is no difference between gray bars 1-3, confirming that the print head is aging. The replacement is completed within 2 hours, and the system records the whole process of fault handling.

[0059] 3. Print quality evaluation and consumable management The concentration index is calculated using the formula where a=1 (top), 2 (middle), 3 (bottom), =5000 (effective pixels per area), The gray value is 165 (to be optimized). The system pushes the combination scheme of "temperature +2℃ and concentration +5%", and the adjusted C=192 (qualified). The consumable remaining amount prediction is passed The remaining paper length is calculated, and then The available duration is obtained, and when R=3.5h, the replenishment order is automatically pushed to the nearest warehouse, including "3 rolls of thermal paper of model TP-57 are needed, and it is expected to arrive in 1.5 hours".

[0060] Effect verification and table analysis

[0061] Table 1: Comparison of traditional operation and maintenance and the system operation and maintenance (100 devices / month)

[0062] Indicators Traditional operation and maintenance System operation and maintenance Average length of fault discovery 8.5 hours 12 minutes Average length of fault repair 4.2 hours 1.1 hours Number of paper shortage downtime 23 times 0 times Number of printing quality complaints 15 times 2 times Monthly operation and maintenance labor cost 8500 yuan 3200 yuan

[0063] The traditional operation and maintenance relies on the repair of the clerk, and the fault is found more than 8 hours later. The repair needs to go back and forth to adjust the parts, which takes 4.2 hours. The lack of paper causes 23 times of downtime, and the printing quality problem causes 15 times of complaints. The labor cost is high. The system can find the fault within 12 minutes through real-time monitoring, and the intelligent diagnosis can reduce the repair time to 1.1 hours. Precise prediction of consumable demand realizes zero downtime due to lack of paper, quality pre-control reduces complaints to 2 times, labor cost is reduced by 62%, and the operation and maintenance efficiency and service quality of the printing equipment of the chain convenience store are significantly improved.

[0064] Example 2: Application of intelligent management of 50 single-sided printers in logistics park

[0065] I. System configuration and deployment

[0066] This embodiment is applied to 50 high-speed single-sided printers in a logistics park (10 in each of the 5 sorting areas). The remote monitoring center server uses dual hot standby (Intel Xeon Silver4210 processor, 128GB memory), which can support 50 channels of 1080P printing sample images at the same time. The multi-device cluster management module is grouped in three levels of "park-sorting area-equipment". The default printing head temperature of the devices in the first sorting area is 60℃, the roller speed is 70r / min, and it is suitable for thick single-sided paper.

[0067] The environmental adaptation module installs SHT30 temperature and humidity sensors (accuracy ±0.3℃ / ±2%RH) in each sorting area, which are deployed 30cm above the equipment air inlet, with a sampling period of 1min. The energy consumption monitoring and optimization module accesses the park intelligent electric meter, and calculates the energy consumption according to the formula , U=220V, Δt=0.5h, and identifies the devices with standby energy consumption>0.05kW・h / 2h.

[0068] II. Running process and function implementation

[0069] 1. Multi-device load balancing

[0070] The multi-parameter linkage early warning module presets 12 groups of rules. When the rule 1 is triggered by the "print head temperature 68℃ + current 3.2A for 5s" of the No. 8 machine in the No. 3 sorting area, the system calls the parameter curve in the past 1 hour, displays the linear rise of temperature from 60℃, and synchronously pushes to the operation and maintenance APP (10s unread triggers SMS). The multi-device cluster management module calculates the load value L=(current task number / maximum task number)×0.6+(continuous running time / rated time)×0.4. When the devices with L>0.7 reach 3, the new task is automatically allocated to the No. 5 machine with L=0.2 to balance the load.

[0071] 2. Environmental adaptation and energy consumption optimization

[0072] In the plum rain season, the humidity in the park reaches 85%RH. The environmental adaptation module automatically increases the print head temperature of all devices to 65℃ and increases the roller speed by 5r / min to avoid paper sticking. In low temperature weather (<15℃), the preheating function (38℃, 20s) is started to ensure normal printing and color development. The energy consumption monitoring finds that the standby energy consumption of the device in the No. 2 area at night is 0.08kW・h / 2h, and the "22:00 after automatic closing of print head heating" strategy is pushed. After the implementation, the monthly average power saving is 120kW・h.

[0073] 3. Fault statistics and safety protection

[0074] The fault statistics analysis module calculates the occurrence rate according to the formula F=(N_f / N_t)×100%. In May, the total running of 50 devices is 14400h, and the fault is 18 times, F=0.125%. The trend chart shows that "roller jam" accounts for 45%, and the fault knowledge base is updated to supplement the "clean the roller every week" prevention scheme. The system security protection module uses OAuth2.0 authentication (password + fingerprint), data transmission AES-256 encryption, and monitors "5 times of login failure at non-working time" to trigger the abnormal rule, freezes the account for 24h and records the log.

[0075] III. Effect verification and table analysis

[0076] Table 2: Comparison of device running indicators in the logistics park before and after the system is applied

[0077] Indicators Before application After application Daily average running time of equipment 16 hours 20 hours Daily average printing volume of a single device 800 copies 1200 copies Total number of monthly faults 42 times 18 times Printing volume per unit energy consumption 120 copies / kW·h 180 copies / kW·h Data transmission security incidents 3 times 0 times

[0078] Before the application, the device is not evenly loaded, and malfunctions frequently, with daily average operation of only 16 hours, and daily average printing of 800 single units per single device; the insufficient environmental adaptation leads to 42 malfunctions per month, high energy consumption (120 single units / kW·h), and 3 data transmission abnormalities. After the application, the operation is extended to 20 hours through load balancing, and the printing capacity of a single device is increased by 50% to 1200 single units; the environmental adaptation and fault pre-control reduce the malfunctions to 18 per month; after the energy consumption optimization, the printing capacity per unit energy consumption reaches 180 single units / kW·h; the security protection realizes zero security incidents, and fully meets the high-efficiency, stable and safe requirements of high-speed single sheet printing in the logistics park.

[0079] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can make equivalent replacement or change according to the technical scheme and the inventive concept of the present application within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

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. The consumables remaining quantity prediction module obtains the current thermal paper roll radius r (in mm) through a laser displacement sensor; combined with the thermal paper core radius r0 (a fixed value ranging from 10-20 mm, entered during device initialization); and the thermal paper thickness d (in mm), automatically imported according to the consumable model, ranging from 0.08-0.12 mm; firstly, 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 equipment 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 the 2nd to 4th hours; 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. A multi-parameter linkage early warning module is configured with a preset threshold matrix for equipment operating parameters. The normal range for printhead temperature is 30-60℃, the warning range is 60-70℃, and the fault range is >70℃. The normal range for roller speed is 30-70 r / min, the warning range is 20-30 r / min or 70-80 r / min, and the fault range is <20 r / min or >80 r / min. The normal range for operating current is 0.5-5A, the warning range is 5-6A, and the fault range is >6A. Twelve sets of parameter linkage rules are established: Rule 1 is when printhead temperature >65℃ and operating current >5A for 5 seconds; Rule 2 is when roller speed fluctuation >±15% and vibration acceleration >3g for 3 seconds. After an early warning is triggered, the module automatically retrieves the equipment's parameter curves for the past hour and marks the abnormal starting point, generating a detailed report including an abnormal parameter trend chart, changes in related parameters, and historical records of similar early warnings. Energy consumption monitoring and optimization module, the energy consumption monitoring and optimization module uses the 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 power consumption > 0.05kW·h / 2h, peak power > 1.2kW for 5 minutes, 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; The fault log auditing module 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.

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 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.

4. 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.

5. 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.

6. 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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