Consumable Monitoring Method, Device, Equipment and Storage Medium for Printing Equipment

Abstract

The present invention relates to the technical field of data analysis, and discloses a method, device, equipment and storage medium for monitoring consumables of a printing device, which are used to improve the accuracy of monitoring consumables of a printing device. The method includes: dividing communication data into multiple sub-data, performing data implicit coding to obtain multiple coded data; performing data signal interference recognition to obtain interference signal data, performing data cleaning to obtain a target data set and performing abnormal state analysis to obtain a state analysis result, and performing a first vector transformation on the state analysis result to obtain a first state vector; performing a second vector transformation on the consumable monitoring data to obtain a second state vector, performing vector fusion on the first state vector and the second state vector to obtain a target fusion vector, performing data format conversion to obtain target feature data, performing device and consumable state recognition on the target feature data to obtain a target device and consumable state, and sending the target device and consumable state to a remote monitoring terminal.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a method, device, equipment and storage medium for monitoring consumables of printing equipment. Background Art

[0002] In modern industrial and commercial environments, printing equipment is an indispensable tool, widely used in various printing tasks, including book publishing, advertising production, packaging printing, etc. To ensure the efficient operation of printing equipment and printing quality, the management of consumables (such as ink, paper, print heads, etc.) is crucial. Traditional consumable monitoring methods are usually based on schedule maintenance or experience, lacking real-time performance and accuracy, and easily leading to resource waste and production interruptions. With the development of Internet of Things (IoT) technology, the intelligence level of printing equipment has been continuously improved, and the equipment can monitor various parameters in real time, including the consumption of consumables. Therefore, researchers have begun to explore data-based consumable monitoring methods to better manage and maintain printing equipment.

[0003] Existing data monitoring methods still face some challenges. For example, the communication data sent from printing equipment contains a large amount of information, and effective data processing and analysis methods are required to extract useful information. That is, the accuracy rate of existing solutions is low. Summary of the Invention

[0004] The present invention provides a method, device, equipment and storage medium for monitoring consumables of printing equipment, which is used to improve the accuracy rate of consumable monitoring of printing equipment.

[0005] In the first aspect of the present invention, a method for monitoring consumables of printing equipment is provided. The method for monitoring consumables of printing equipment includes:

[0006] Construct a communication network for a preset target printing equipment to obtain a target communication network. At the same time, connect the target printing equipment to the target communication network, and collect data through a consumable monitoring sensor in the target printing equipment to obtain consumable monitoring data corresponding to the target printing equipment;

[0007] Collect communication data sent by the target printing equipment through a preset signal acquisition device, and perform signal band identification on the communication data to obtain a signal band corresponding to the communication data;

[0008] Divide the communication data through the signal band to obtain multiple sub-data, and perform data coding segment mapping on each sub-data to obtain a data coding segment corresponding to each sub-data;

[0009] Perform data implicit coding on each sub-data through the data coding segment corresponding to each sub-data to obtain multiple coded data;

[0010] Identify data signal interference for each of the encoded data to obtain corresponding interference signal data, and perform data cleaning on each of the encoded data through the interference signal data to obtain a target data set;

[0011] Input the target data set into a preset abnormal communication recognition model for abnormal state analysis to obtain a state analysis result, and perform a first vector conversion on the state analysis result to obtain a first state vector;

[0012] Perform a second vector conversion on the consumable monitoring data to obtain a second state vector. At the same time, perform vector fusion on the first state vector and the second state vector to obtain a target fusion vector and perform data format conversion to obtain target feature data, and perform device and consumable state recognition on the target feature data to obtain a target device and consumable state, and send the target device and consumable state to a preset remote monitoring terminal.

[0013] Combined with the first aspect, in the first embodiment of the first aspect of the present invention, the method of collecting communication data sent by the target printing device through a preset signal collection device and performing signal band identification on the communication data to obtain the signal band corresponding to the communication data includes:

[0014] Analyze the data interface parameters of the signal collection device to obtain the first interface parameters corresponding to the signal collection device;

[0015] Analyze the data interface parameters of the target printing device to obtain the second interface parameters corresponding to the target printing device;

[0016] Match the data transmission parameters of the first interface parameters and the second interface parameters to obtain corresponding adapted interface parameters;

[0017] Based on the adapted interface parameters, respectively correct the first interface parameters and the second interface parameters to obtain the first target parameters corresponding to the first interface parameters and the second target parameters corresponding to the second interface parameters;

[0018] Correct the interface parameters of the signal collection device through the first target parameters. At the same time, correct the interface parameters of the target printing device through the second target parameters, and collect the communication data sent by the target printing device through the signal collection device;

[0019] Perform signal band identification on the communication data to obtain the signal band corresponding to the communication data.

[0020] Combined with the first embodiment of the first aspect, in the second embodiment of the first aspect of the present invention, the identifying the signal band of the communication data to obtain the signal band corresponding to the communication data includes:

[0021] Performing frequency-domain conversion on the communication data to obtain a corresponding frequency-domain data set;

[0022] Calculating the data frequency of the frequency-domain data set through the Fourier transform algorithm to obtain corresponding frequency data;

[0023] Based on the frequency data, extracting the spectral features of the frequency-domain data to obtain a spectral feature set corresponding to the communication data, where the spectral feature set includes modulation depth data and frequency offset data;

[0024] Constructing a standard band data table based on the frequency data to obtain a corresponding standard band data table;

[0025] Through the standard band data table, performing standard band matching on the spectral feature set to obtain the signal band corresponding to the communication data.

[0026] Combined with the second embodiment of the first aspect, in the third embodiment of the first aspect of the present invention, the dividing the communication data through the signal band to obtain multiple sub-data and performing data coding segment mapping on each sub-data to obtain the data coding segment corresponding to each sub-data includes:

[0027] Identifying the band type of the signal band to obtain the band type corresponding to the signal band;

[0028] Constructing a division time window for the band type to obtain multiple different time window data;

[0029] Performing first data division on the communication data through multiple different time window data to obtain multiple candidate division data;

[0030] Identifying the frequency components of the frequency data to obtain at least one frequency component data;

[0031] Performing second data division on multiple candidate division data through at least one frequency component data to obtain multiple sub-data;

[0032] Performing data coding segment mapping on each sub-data to obtain the data coding segment corresponding to each sub-data.

[0033] Combined with the third embodiment of the first aspect, in the fourth embodiment of the first aspect of the present invention, the mapping of data coding segments to each sub-data to obtain the data coding segments corresponding to each sub-data includes:

[0034] Perform data block division on each sub-data respectively to obtain a set of data blocks corresponding to each sub-data;

[0035] Perform multi-layer data fusion on the set of data blocks corresponding to each sub-data respectively to obtain multi-layer fusion data corresponding to each sub-data;

[0036] Perform data coding segment mapping through the multi-layer fusion data corresponding to each sub-data to obtain the data coding segments corresponding to each sub-data.

[0037] Combined with the first aspect, in the fifth embodiment of the first aspect of the present invention, the data implicit coding of each sub-data through the data coding segments corresponding to each sub-data to obtain a plurality of coded data includes:

[0038] Perform data desensitization processing on each sub-data respectively through the data coding segments corresponding to each sub-data to obtain a plurality of desensitized data;

[0039] Analyze the data truncation positions of each desensitized data to obtain a plurality of data truncation positions;

[0040] Perform data value truncation on each desensitized data through the plurality of data truncation positions to obtain a plurality of truncated data;

[0041] Create a data fuzzification mapping table and perform data fuzzification processing on the plurality of truncated data according to the data fuzzification mapping table to obtain the plurality of coded data.

[0042] Combined with the first aspect, in the sixth embodiment of the first aspect of the present invention, the second vector conversion of the consumable monitoring data to obtain a second state vector, and at the same time, the vector fusion of the first state vector and the second state vector to obtain a target fusion vector and perform data format conversion to obtain target feature data, and perform device and consumable status recognition on the target feature data to obtain a target device and consumable status, and send the target device and consumable status to a preset remote monitoring terminal, includes:

[0043] Perform data weighted fusion on the consumable monitoring data to obtain the second state vector;

[0044] Perform semantic information analysis on the first state vector to obtain corresponding first semantic information;

[0045] Perform semantic information analysis on the second state vector to obtain corresponding second semantic information;

[0046] Perform a first weight calculation on the first state vector through the first semantic information to obtain first weight data;

[0047] Perform a second weight calculation on the second state vector through the second semantic information to obtain second weight data;

[0048] Perform vector fusion on the first state vector and the second state vector based on the first weight data and the second weight data to obtain the target fusion vector;

[0049] Perform vector feature mapping on the target fusion vector based on a preset vector feature mapping table to obtain the target feature data;

[0050] Perform state relationship matching on the target feature vector based on a preset feature state relationship library to obtain corresponding target device and consumable states, and send the target device and consumable states to the remote monitoring terminal.

[0051] The second aspect of the present invention provides a consumable monitoring device for a printing device, and the consumable monitoring device for the printing device includes:

[0052] A construction module, configured to construct a target communication network for a preset target printing device, obtain the target communication network, and at the same time, connect the target printing device to the target communication network, and collect data through a consumable monitoring sensor in the target printing device to obtain consumable monitoring data corresponding to the target printing device;

[0053] An identification module, configured to collect communication data sent by the target printing device through a preset signal collection device, and perform signal band identification on the communication data to obtain a signal band corresponding to the communication data;

[0054] A partitioning module, configured to partition the communication data through the signal band to obtain a plurality of sub-data, and perform data coding segment mapping on each sub-data to obtain a data coding segment corresponding to each sub-data;

[0055] An encoding module, configured to perform data implicit encoding on each sub-data through the data coding segment corresponding to each sub-data to obtain a plurality of encoded data;

[0056] A cleaning module, configured to perform data signal interference identification on each encoded data to obtain corresponding interference signal data, and perform data cleaning on each encoded data through the interference signal data to obtain a target data set;

[0057] An analysis module for inputting the target data set into a pre-set abnormal communication recognition model for abnormal state analysis to obtain a state analysis result, and performing a first vector transformation on the state analysis result to obtain a first state vector;

[0058] A conversion module for performing a second vector transformation on the consumable monitoring data to obtain a second state vector. At the same time, performing vector fusion on the first state vector and the second state vector to obtain a target fusion vector, performing data format conversion to obtain target feature data, and performing device and consumable state recognition on the target feature data to obtain a target device and consumable state, and sending the target device and consumable state to a pre-set remote monitoring terminal.

[0059] The third aspect of the present invention provides a consumable monitoring device for a printing device, including: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor calls the instructions in the memory so that the consumable monitoring device of the printing device executes the above-mentioned consumable monitoring method for the printing device.

[0060] The fourth aspect of the present invention provides a computer-readable storage medium, in which instructions are stored, and when it runs on a computer, it causes the computer to execute the above-mentioned consumable monitoring method for the printing device.

[0061] In the technical solution provided by the present invention, communication data is divided into multiple sub-data, and multiple encoded data are obtained through data implicit encoding; interference signal data is obtained through data signal interference recognition, and the target data set is obtained through data cleaning and abnormal state analysis is performed to obtain a state analysis result, and the first state vector is obtained through the first vector conversion of the state analysis result; the consumable monitoring data is subjected to the second vector conversion to obtain the second state vector, the first state vector and the second state vector are vector-fused to obtain the target fusion vector, and data format conversion is performed to obtain the target feature data. The target device and consumable state are identified from the target feature data, and the target device and consumable state are sent to the remote monitoring terminal. By monitoring the consumable state and performance of the printing device in real time, and through the collection and analysis of sensor data and communication data, the present invention can immediately detect any potential problems or abnormal situations. Through the accurate monitoring of the consumable consumption, predictive maintenance can be carried out, problems leading to equipment failure or downtime can be identified and solved in advance, thereby reducing production interruptions and maintenance costs. It helps to optimize the use of consumables and avoid unnecessary waste. Through real-time data, production managers can better plan the replenishment and replacement of consumables to ensure the effective utilization of resources. The data-based method provides more accurate consumable consumption information, and compared with the traditional schedule maintenance method, it can more accurately predict the life and replacement time of consumables, thereby improving the equipment performance. The use of data analysis and machine learning technologies makes the monitoring and maintenance process more automated and intelligent. By accurately monitoring the device state and consumable consumption, the efficiency of the production process can be significantly improved. This helps to reduce production time, increase production, and reduce production costs, thereby improving the accuracy and efficiency of the consumable monitoring of the printing device. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 FIG. is a schematic diagram of an embodiment of the method for monitoring consumables of a printing device in an embodiment of the present invention;

[0063] Figure 2 FIG. is a flowchart of identifying the signal band of communication data in an embodiment of the present invention;

[0064] Figure 3 FIG. is a flowchart of dividing communication data through the signal band in an embodiment of the present invention;

[0065] Figure 4 FIG. is a flowchart of mapping data encoding segments for each sub-data in an embodiment of the present invention;

[0066] Figure 5 FIG. is a schematic diagram of an embodiment of the device for monitoring consumables of a printing device in an embodiment of the present invention;

[0067] Figure 6Schematic diagram of an embodiment of the consumable monitoring device of the printing device in the embodiment of the present invention. Specific implementation manner

[0068] The embodiment of the present invention provides a method, device, equipment and storage medium for monitoring consumables of a printing device, which is used to improve the accuracy of monitoring consumables of the printing device.

[0069] The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and drawings of the present invention are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or equipment comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or equipment.

[0070] For ease of understanding, the specific process of the embodiment of the present invention will be described below. Please refer to Figure 1 An embodiment of the consumable monitoring method of the printing device in the embodiment of the present invention includes:

[0071] S101. Construct a communication network for a preset target printing device to obtain a target communication network. At the same time, connect the target printing device to the target communication network, and collect data through a consumable monitoring sensor in the target printing device to obtain consumable monitoring data corresponding to the target printing device;

[0072] It can be understood that the execution subject of the present invention can be a consumable monitoring device of a printing device, or a terminal or a server. Specifically, it is not limited here. The embodiment of the present invention will be described by taking the server as the execution subject as an example.

[0073] Specifically, select target printing devices that should have communication capabilities. Perform preset settings on these devices to ensure their compatibility with the communication network. Determine the topology of the communication network, including the connection methods between devices, routing, and network topology diagrams. This helps ensure effective communication between devices. Select an appropriate communication protocol to ensure the security and reliability of data transmission. For example, protocols such as TLS / SSL can be used to encrypt communication data to prevent data leakage and interference. Install communication hardware, such as sensors, communication modules, or network interfaces, on the printing devices so that the devices can connect to the communication network. Configure the network for the devices, including assigning IP addresses, setting network parameters, etc. Connect the devices to the target communication network to ensure that the devices can successfully join the network. Once the target communication network is established and the devices are successfully connected, the next step is to collect data through the consumable monitoring sensors inside the devices. These sensors can monitor various parameters of the printing device, including consumable consumption, temperature, humidity, speed, etc. For example, consider a printing factory that uses multiple digital printing presses to produce various printed materials. Each printing press is equipped with consumable monitoring sensors to monitor the consumption of ink and paper. These sensors are connected to the central data management system of the factory through the target communication network. The network interface of the printing press is configured to connect to the communication network of the factory. This involves assigning a unique IP address and port number to each printing press. The consumable monitoring sensors inside the printing press start to monitor the consumption of ink and paper in real time. These sensors can measure parameters such as the weight of the ink tank and the diameter of the paper roll. The sensors convert the monitored data into digital format and transmit it through the communication network to the central data management system. This data can be timestamped consumption records. The central data management system receives and stores the sensor data and analyzes the data simultaneously.

[0074] S102. Collect the communication data sent by the target printing device through the preset signal acquisition device, and identify the signal band of the communication data to obtain the signal band corresponding to the communication data;

[0075] Specifically, a preset signal acquisition device is used to collect communication data sent by the target printing device. This communication data includes information such as device status and consumable consumption. The signal band of these communication data is identified to determine the specific signal band to which the data belongs. The data interface parameters of the signal acquisition device are analyzed to obtain the first interface parameters of the device. These parameters include communication rate, data format, communication protocol, etc. This is to ensure the matching of data transmission parameters between the acquisition device and the printing device. At the same time, the data interface parameters of the target printing device are analyzed to obtain the second interface parameters of the device, which also include communication rate, data format, communication protocol, etc. This step is to understand the communication characteristics of the device itself. The first interface parameters of the signal acquisition device are matched with the second interface parameters of the target printing device to ensure the compatibility of data transmission parameters between the two. Based on the adapted interface parameters, the first interface parameters and the second interface parameters are respectively corrected. This involves adjusting the communication rate, data format or other communication parameters to ensure the accurate transmission of data. Using the corrected interface parameters, the signal band of the communication data is identified again. This time, the identification of the signal band will be more accurate because the interface parameters have been adjusted according to the target device. For example, assume a printing company has multiple digital printers, and each printer has different communication protocols and data interfaces. The server monitors the ink consumption of each printer. The server installs a dedicated signal acquisition device and connects it to each printer. The device regularly collects the communication data sent by the printer, including the ink consumption report. The data interface parameters of the signal acquisition device are analyzed to determine its first interface parameters, including communication rate and data format. The data interface parameters of each printer are analyzed to obtain its second interface parameters, which vary depending on the device model. By matching the first interface parameters and the second interface parameters, the compatibility of data transmission parameters between the signal acquisition device and the printer is ensured. According to the adapted interface parameters, the first interface parameters and the second interface parameters are adjusted. For example, if a printer requires a lower communication rate, the interface parameters will be adjusted accordingly. Using the corrected interface parameters, the signal band of the communication data is identified again. This can help the server accurately identify the signal band of the communication data, such as the specific signal band of the ink consumption report.

[0076] Among them, the collected communication data is subjected to frequency-domain conversion to convert time-domain data into frequency-domain data. This process usually involves using Fourier transform or similar techniques to analyze the components of the data at different frequencies. The frequency-domain data set is processed through the Fourier transform algorithm to calculate the frequency components of the data. This step helps to determine the different frequency components in the communication data. Based on the frequency data, spectrum feature extraction is performed. This includes extracting spectrum information about the signal from the frequency-domain data, such as modulation depth data and frequency offset data, etc. Based on the frequency data, a standard band data table is constructed. This table lists the frequency ranges and characteristics of various standard signal bands. The standard band data table is used to map the communication data to specific signal bands. Using the standard band data table, the spectrum feature set is subjected to standard band matching to determine the signal band to which the communication data belongs. This step compares the frequencies and characteristics of the signal band with the standard band for matching. For example, assume that a printing company uses digital printing presses for printing operations. The server monitors the ink consumption of each printing press and automatically orders more ink when needed. The signal acquisition device of the server collects the communication data sent by the printing press, including the ink consumption report. These data are first subjected to frequency-domain conversion to convert the time-domain data into frequency-domain data. Through the Fourier transform algorithm, the frequency-domain data is analyzed to calculate the frequency components of the data. This step helps to determine the frequency components related to ink consumption in the communication data. Based on the frequency data, the server extracts spectrum features from the frequency-domain data. This includes modulation depth data (describing the intensity change of the signal) and frequency offset data (describing the offset of the signal frequency). The server constructs a standard band data table listing the frequency ranges and characteristics of the standard signal bands related to ink consumption. Using the standard band data table, the server performs standard band matching on the spectrum feature set collected from the printing press. This step helps the server to determine the signal band to which the communication data belongs, so as to understand the ink consumption situation.

[0077] S103. Divide the communication data through the signal band to obtain multiple sub-data, and perform data coding segment mapping on each sub-data to obtain the data coding segment corresponding to each sub-data;

[0078] Specifically, identify the signal bands in the collected communication data. This step helps to determine the communication characteristics in different bands, enabling better division and understanding of the data. Based on the band types, divide time windows to better analyze the data. Different band types require different time windows to capture their characteristics. Use data from multiple different time windows to perform the first division of the communication data, obtaining multiple candidate division data. This step helps to divide the communication data into smaller parts for further processing. Identify the frequency components in the communication data. This can help determine the frequency characteristics of different parts, which are associated with different data coding segments. Use at least one frequency component data to perform the second division of the multiple candidate division data. This step helps to divide the communication data more precisely and divide it into smaller sub-data. Map the data coding segments for each sub-data to obtain the corresponding data coding segments for each sub-data. This step can map the communication data to the corresponding coding segments according to the characteristics of the communication data. For example, assume a printing company uses digital printers for printing operations and wants to monitor the status of the print heads of each printer. The status of the print head includes the ink jetting frequency, which directly affects the printing quality. The signal acquisition device of the server collects the communication data sent by the printer, including the print head status report. By identifying the characteristics of different bands, such as different ink jetting frequencies, the communication data can be divided into different band types. Different band types require different time windows to capture their characteristics. For example, high-frequency ink jetting requires a shorter time window. Use data from different time windows to perform the first division of the communication data, obtaining multiple candidate division data. Each candidate division data corresponds to the status of the print head within a different time period. Identify the frequency components in each candidate division data to determine the ink jetting frequency characteristics therein. Use the identified frequency component data to perform the second division of the candidate division data. This can help to more accurately capture the print head status information related to different ink jetting frequencies. Map the data coding segments for each sub-data to map it to the corresponding data coding segments. This step helps to organize and manage the print head status information for subsequent analysis and monitoring.

[0079] Among them, each sub-data is divided into data blocks, which are split into smaller data block sets. This process helps to better manage and process large amounts of data, making it easier to analyze and store. Multilayer data fusion is performed on the data block sets corresponding to each sub-data. The data in the data block sets are fused multiple times to generate a higher-level data representation. This helps to extract more information and features of the data. By performing data encoding segment mapping on the multilayer fusion data corresponding to each sub-data, it is mapped to the corresponding data encoding segment. This step helps to organize and manage the data and associate it with a specific encoding segment. For example, assume a printing company uses digital printing presses for printing operations and monitors the status of the print heads of each printing press, including the ink jet frequency. Each sub-data (the print head status report of each printing press) is divided into data blocks. This can split different time periods or specific events in the report into smaller data blocks, such as hourly data blocks or data blocks for each ink jet event. Multilayer data fusion is performed on each data block set. The data within each data block is fused to generate a higher-level data representation, such as the average ink jet frequency per hour. The fused data of different data blocks are fused again to obtain a data representation over a longer time range, such as the average ink jet frequency per day. By performing data encoding segment mapping on the multilayer fusion data corresponding to each sub-data, it is mapped to the corresponding data encoding segment. This helps to organize and manage the data and associate it with a specific encoding segment (for example, the unique identifier of each printing press).

[0080] S104. Perform data implicit encoding on each sub-data through the data encoding segments corresponding to each sub-data to obtain multiple encoded data;

[0081] Specifically, perform data implicit encoding on each sub-data according to its corresponding data encoding segment. Data implicit encoding is a technique that converts the original data into another form in order to retain key features while protecting data privacy. This can be accomplished through encryption, hashing, or other encoding methods. At the same time, perform data desensitization processing on each sub-data. Data desensitization is a data protection technique that reduces the risk of data leakage by eliminating or replacing sensitive information. Perform data truncation position analysis on each desensitized data. This step helps to determine where to truncate or cut the data for further processing. The truncation position is usually determined based on specific rules or algorithms of the data. According to multiple data truncation positions, perform data value truncation on each desensitized data. Delete or replace some parts of the data with other values. Create a data obfuscation mapping table that includes the data truncation positions and the corresponding obfuscation rules. This mapping table is used to record the data truncation positions and truncation rules so that the encoded data can be restored or processed subsequently. According to the data obfuscation mapping table, perform data obfuscation processing on multiple truncated data. This step can be a reverse operation to restore the truncated data into partially obfuscated or fully desensitized data according to the mapping table. For example, a printing company uses printing equipment to monitor the consumable consumption of each printer, including ink consumption data. The server needs to transmit this data to a remote monitoring terminal for analysis, but does not want to disclose the specific data of each printer to protect its privacy. Perform data implicit encoding on the ink consumption data of each printer. For example, encrypt the consumption amount into a specific encoded form. At the same time, perform data desensitization processing on the ink consumption data. For example, replace the specific printer model and serial number with an anonymous number. According to the characteristics of the desensitized data, determine the data truncation position. For example, determine where to truncate the decimal part of the consumption amount. According to the truncation position, truncate the decimal part of the consumption amount data to retain the integer part and replace the decimal part with "X". Create a data obfuscation mapping table that records the data truncation position and truncation rules. For example, the mapping table can indicate to truncate at the third decimal place and replace with "X". According to the data obfuscation mapping table, restore the truncated data into partially obfuscated data for analysis on the remote monitoring terminal.

[0082] S105. Identify data signal interference for each encoded data to obtain the corresponding interference signal data, and perform data cleaning on each encoded data through the interference signal data to obtain a target data set;

[0083] Specifically, data signal interference recognition is performed on each encoded data. In the data preprocessing stage, the server processes the encoded data transmitted from the printing device, removing existing noise and outliers. Then, interference detection algorithms, such as frequency domain analysis, are used to detect potential signal interference. This step helps identify problems existing in the data. Next, interference features are extracted, including the frequency, amplitude, time domain features, etc. of the interference. This feature information will be used for further interference recognition. Using machine learning algorithms or rule engines, the server can classify or label the identified interference. Subsequently, interference signal data is obtained. This stage involves intercepting data segments containing interference to form interference signal data. At the same time, detailed information about the interference is recorded, including the type, time, duration, etc. of the interference, for subsequent analysis and data cleaning. Data cleaning is performed on each encoded data through the interference signal data. By using the interference signal data, the server removes or corrects the interfered parts in the encoded data. This can be achieved by methods such as interpolation, substitution, or deletion of damaged data. In addition, the server can also attempt to recover the original data from the interference signal data, using difference, interpolation, or reconstruction algorithms, and selecting appropriate methods according to the type and degree of the interference. Finally, the cleaned data is verified to ensure its integrity and accuracy. This can be done by techniques such as data checksum, CRC (Cyclic Redundancy Check) to check the consistency of the data. Through this complete process, the server can cope with different types of signal interference while ensuring the accuracy of the data, ensuring that the monitoring system obtains reliable consumable monitoring data.

[0084] S106. Input the target data set into a preset abnormal communication recognition model for abnormal state analysis to obtain a state analysis result, and perform a first vector transformation on the state analysis result to obtain a first state vector;

[0085] Specifically, prepare the processed target dataset for input. This dataset contains the monitoring data collected from the printing equipment, including information such as consumable consumption and equipment performance parameters. Develop and train an abnormal communication recognition model in advance, which can automatically detect the abnormal communication patterns of the equipment. This model can be constructed based on technologies such as machine learning, deep learning, or rule engines. Input the target dataset into the abnormal communication recognition model for analysis. The model will evaluate the data to detect any abnormal communication patterns or abnormal events. The model identifies abnormal situations by comparing with known normal communication patterns. These abnormal situations include equipment failures, abnormal consumable consumption, performance degradation, etc. The model outputs the status analysis results, including detailed information such as the type of abnormality, the timestamp of the abnormality, and the reason. These results can help operators better understand the operating conditions of the equipment. Encode the status analysis results into digital or symbolic forms for further processing. The encoding can be discrete labels, such as "1" indicating normal equipment, "2" indicating abnormal consumable consumption, "3" indicating performance degradation, etc. Based on the encoded results, generate the first status vector. This vector is a data structure containing status information for subsequent analysis and decision-making. For example, assume a printing company uses a printing equipment monitoring system to track the operating conditions of multiple printers. The system collects the consumable consumption data and performance parameters of each printer and inputs them into a pre-set abnormal communication recognition model as the target dataset. The system organizes the consumable consumption situation and performance parameters of each printer into a dataset, including the number of printed pages per hour, ink consumption, paper consumption, etc. The server has developed an abnormal communication recognition model in previous research, which has been trained to identify various equipment communication abnormal patterns, such as network disconnection, packet loss, etc. The dataset is input into the model, and the model analyzes the communication patterns of each printer. For example, it detects that the communication of a certain printer is frequently interrupted during a certain period and marks this situation as abnormal. The model can also analyze the consumable data. If the ink consumption of a certain printer is abnormally high, it will also be identified as abnormal. The model outputs a status analysis result. The status analysis result is encoded into a digital representation, such as "1" indicating normal, "2" indicating communication abnormality, "3" indicating consumable abnormality. These encodings are combined into the first status vector so that the system can more easily perform subsequent analysis and alarm processing.

[0086] S107. Perform a second vector transformation on the consumable monitoring data to obtain a second status vector. At the same time, fuse the first status vector and the second status vector to obtain a target fusion vector, perform data format conversion on the target fusion vector to obtain target feature data, perform device and consumable status recognition on the target feature data to obtain the target device and consumable status, and send the target device and consumable status to a pre-set remote monitoring terminal.

[0087] It should be noted that the first state vector and the second state vector are subjected to data weighted fusion to obtain the second state vector. The weighted fusion can assign weights according to the importance of different parameters. Semantic information analysis is performed on the first state vector and the second state vector to extract the semantic information of each vector. This can involve natural language processing techniques or domain knowledge. Based on the semantic information, the first weight data and the second weight data are calculated. These weights reflect the importance of each vector and can be adjusted according to specific circumstances. The first state vector and the second state vector are subjected to vector fusion using the first weight data and the second weight data to obtain the target fusion vector. The target fusion vector is converted into target feature data using a preset vector feature mapping table. This step can involve techniques such as feature extraction and dimensionality reduction to obtain a more informative data representation. The target feature data is matched with the state relationships using a preset feature state relationship library. This library contains the corresponding relationships between various states and features to quickly and accurately identify the device and consumable states. According to the matching results, the states of the device and consumables are determined. This includes various states such as normal device operation and consumable replacement required. The target device and consumable state information is sent to a remote monitoring terminal through a preset communication module. This can be accomplished through network connections, API calls, or other communication methods. For example, assume a printing company uses this consumable monitoring method to manage its multiple printers. The monitoring system collects data such as ink consumption, paper consumption, and printing speed of each printer every hour and generates the first state vector based on this data. At the same time, the system also collects sensor data such as device temperature and voltage and generates the second state vector. The sensor data is vectorized, and then the first state vector and the second state vector are subjected to data weighted fusion to obtain the second state vector. Through semantic information analysis, the system understands that ink consumption has a greater impact on printer performance, so a higher weight is assigned to the first state vector. The two vectors are fused using the weights to obtain the target fusion vector. The target fusion vector is converted into target feature data using a preset vector feature mapping table, which represents the overall state of the printer. The system matches the target feature data with the state relationship library to determine the state of each printer. For example, if the target feature data of a certain printer indicates excessive ink consumption, the state is identified as "ink replacement required". The system uses the communication module to send the device and consumable state information to the remote monitoring terminal for operators to view in real time and take necessary maintenance measures.

[0088] In an embodiment of the present invention, communication data is divided into multiple sub - data, and multiple encoded data are obtained through implicit data encoding; interference signal data is obtained through data signal interference recognition, and a target data set is obtained through data cleaning and abnormal state analysis is performed to obtain a state analysis result. The state analysis result is subjected to a first vector transformation to obtain a first state vector; the consumable monitoring data is subjected to a second vector transformation to obtain a second state vector, and the first state vector and the second state vector are vector - fused to obtain a target fusion vector, which is subjected to data format conversion to obtain target feature data. The target device and consumable state are identified from the target feature data, and the target device and consumable state are sent to a remote monitoring terminal. The present invention can immediately detect any potential problems or abnormal situations by monitoring the consumable state and performance of a printing device in real - time through the collection and analysis of sensor data and communication data. Through the accurate monitoring of the consumable consumption, predictive maintenance can be carried out, problems leading to equipment failures or downtimes can be identified and solved in advance, thereby reducing production interruptions and maintenance costs. It helps to optimize the use of consumables and avoid unnecessary waste. Through real - time data, production managers can better plan the replenishment and replacement of consumables to ensure the effective utilization of resources. The data - based method provides more accurate consumable consumption information. Compared with traditional schedule - based maintenance methods, it can more accurately predict the lifespan and replacement time of consumables, thereby improving equipment performance. It utilizes data analysis and machine learning technologies to make the monitoring and maintenance processes more automated and intelligent. By accurately monitoring the equipment state and consumable consumption, the efficiency of the production process can be significantly improved. This helps to reduce production time, increase production volume, and lower production costs, thereby improving the accuracy and efficiency of the consumable monitoring of the printing device.

[0089] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0090] (1) Analyze the data interface parameters of the signal acquisition device to obtain the first interface parameters corresponding to the signal acquisition device;

[0091] (2) Analyze the data interface parameters of the target printing device to obtain the second interface parameters corresponding to the target printing device;

[0092] (3) Match the data transmission parameters of the first interface parameters and the second interface parameters to obtain the corresponding adapted interface parameters;

[0093] (4) Based on the adapted interface parameters, respectively correct the first interface parameters and the second interface parameters to obtain the first target parameters corresponding to the first interface parameters and the second target parameters corresponding to the second interface parameters;

[0094] (5) Modify the interface parameters of the signal acquisition device through the first target parameter. Meanwhile, modify the interface parameters of the target printing device through the second target parameter, and collect the communication data sent by the target printing device through the signal acquisition device;

[0095] (6) Identify the signal band of the communication data to obtain the signal band corresponding to the communication data.

[0096] Specifically, the signal acquisition device needs to establish a communication connection with the target printing device to obtain the device's monitoring data. Different printing devices have different communication interfaces and parameter requirements, so parameter analysis and matching are needed. First, obtain the interface parameter data from the signal acquisition device, which describe the communication characteristics of the acquisition device itself, such as the rate of USB connection, communication protocol, etc. Then, obtain the interface parameter data from the target printing device, which describe the communication requirements of the device, such as the supported protocol and rate. Match these two sets of parameter data to determine the adaptation relationship between them. The matching process can involve protocol compatibility, rate matching, and comparison of other communication parameters. Once the matching is successful, the adapted interface parameters can be obtained, which describe how to configure the communication parameters of the signal acquisition device to match the target printing device. Use the adapted interface parameters to modify the interface parameters of the signal acquisition device and the target printing device. This ensures that the communication between the two is based on the matching interface parameters, thus reducing the risk of communication errors and data loss. Once the interface parameter modification is completed, the signal acquisition device can establish an effective communication connection with the target printing device and start collecting the monitoring data sent by the device. These data can include information such as ink consumption, printing speed, device status, etc., for subsequent data processing and analysis. After collecting the monitoring data, the signal band where the communication data is located can be determined by the method of signal band identification. This helps to classify and analyze the data to further understand the status and performance of the printing device. For example, consider a printing company with multiple printers models of different types. These printers use different communication protocols and rates. By performing interface parameter analysis and matching, the signal acquisition device can establish the correct communication connection with each printer, ensuring accurate data collection. In this way, the server can monitor the status of each printer in real time, take maintenance measures in a timely manner, improve production efficiency and resource utilization. This process also helps to reduce errors and failures, improving the reliability of the monitoring system.

[0097] In a specific embodiment, as Figure 2 shown, the process of performing the step of identifying the signal band of the communication data may specifically include the following steps:

[0098] S201. Perform frequency domain conversion on the communication data to obtain the corresponding frequency domain data set;

[0099] S202. Calculate the data frequency of the frequency-domain data set through the Fourier transform algorithm to obtain the corresponding frequency data;

[0100] S203. Based on the frequency data, extract the spectral features of the frequency-domain data to obtain the spectral feature set corresponding to the communication data, where the spectral feature set includes modulation depth data and frequency offset data;

[0101] S204. Construct a standard band data table based on the frequency data to obtain the corresponding standard band data table;

[0102] S205. Through the standard band data table, perform standard band matching on the spectral feature set to obtain the signal band corresponding to the communication data.

[0103] It should be noted that the communication data is subjected to frequency-domain conversion to obtain the corresponding frequency-domain data set. The original signal data is extracted from the communication data and is converted into a frequency-domain data set by applying the Fourier transform algorithm. The Fourier transform is a key technology for converting a signal from the time domain to the frequency domain, which represents the data as a combination of frequency components. The data frequency of the frequency-domain data set is calculated through the Fourier transform algorithm to obtain the data related to the signal frequency, that is, the frequency data. The frequency data is a quantitative representation of the signal frequency and plays a key role in subsequent analysis. Based on the frequency data, spectral feature extraction is performed, which is a key step in signal band identification. In this step, the extracted spectral features usually include modulation depth data and frequency offset data. The modulation depth data describes the modulation amplitude of the signal, while the frequency offset data describes the degree of deviation of the signal frequency from the reference frequency. In order to better compare with the known standard signal bands, a standard band data table needs to be constructed. This table lists the frequency ranges and feature descriptions of various signal bands, such as the Wi-Fi band or the Bluetooth band. Through the standard band data table, standard band matching is performed on the previously extracted spectral feature set. This matching process usually involves comparing features such as frequency range, modulation depth, and frequency offset to determine the specific signal band to which the communication data belongs. For example, assume that a printing company monitors the printing equipment on its production line. By applying the signal band identification technology, the server can accurately identify the signal bands where different devices are located from the communication data transmitted by the printing equipment. For example, if a certain printer uses Wi-Fi communication, this monitoring method can determine that its communication data is in the Wi-Fi band of 2.4 GHz or 5 GHz. This helps the server to understand the communication status of each device in real time for better management and maintenance.

[0104] In a specific embodiment, as Figure 3 shown, the process of executing step S103 may specifically include the following steps:

[0105] S301. Identify the band type of the signal band to obtain the band type corresponding to the signal band;

[0106] S302. Construct time windows for the band type division to obtain multiple different time window data;

[0107] S303. Perform the first data division on the communication data through multiple different time window data to obtain multiple candidate division data;

[0108] S304. Identify the frequency components of the frequency data to obtain at least one frequency component data;

[0109] S305. Perform the second data division on the multiple candidate division data through at least one frequency component data to obtain multiple sub-data;

[0110] S306. Map the data coding segments for each sub-data to obtain the data coding segments corresponding to each sub-data.

[0111] It should be noted that the type recognition of the signal band is performed. The purpose is to determine the type of the signal band where the communication data is located. The signal band type recognition is achieved by analyzing the characteristics of the communication data such as frequency, modulation depth, frequency offset, etc. For example, assume that the server has a printing device, and its communication data is included in the Wi-Fi band of 2.4 GHz. By analyzing the characteristics of the communication data, the server determines that the band type is Wi-Fi. According to the recognized signal band type, it is necessary to divide time windows for more detailed analysis of the communication data. Different bands of communication require different time window sizes and positions. For example, Wi-Fi communication requires a time window of 1 second, while Bluetooth communication requires a time window of 0.5 second. These time windows are used to divide the communication data into different time periods for subsequent data processing. After dividing the time windows, the first data division can be started. This step divides the communication data within each time window into multiple candidate division data, and each candidate division data represents a communication data segment within a time period. This helps to analyze the communication data in a finer granularity. The frequency component identification is performed. This is an important step aimed at identifying the frequency components in the communication signal. By applying frequency domain analysis techniques such as Fourier transform, the frequency components of the communication signal can be determined. For example, if the server identifies a specific frequency component in Wi-Fi communication, then this component corresponds to the fundamental frequency of the Wi-Fi signal. After identifying at least one frequency component, the second data division can be performed. This step further divides the candidate division data using the identified frequency component data, and decomposes the communication data within each time window into smaller sub-data segments. These sub-data segments will more specifically reflect different communication events or activities. The data coding segment mapping is performed for each sub-data. This step associates each sub-data with a specific data coding segment for further analysis and identification. The data coding segment is usually used to represent specific characteristics or attributes of the data, which helps to better understand the communication data.

[0112] In a specific embodiment, as Figure 4 shown, the process of performing the data coding segment mapping step for each sub-data may specifically include the following steps:

[0113] S401. Divide each sub-data into data blocks respectively to obtain a set of data blocks corresponding to each sub-data;

[0114] S402. Perform data multi-layer fusion on the set of data blocks corresponding to each sub-data respectively to obtain multi-layer fusion data corresponding to each sub-data;

[0115] S403. Perform data coding segment mapping through the multi-layer fusion data corresponding to each sub-data to obtain a data coding segment corresponding to each sub-data.

[0116] Specifically, for each sub-data, data block division is performed. The goal of this step is to decompose each sub-data into smaller data blocks for further analysis and processing. The size of the data blocks can be adjusted according to specific requirements, usually determined based on the characteristics of the data and the application scenario. For example, for a segment of audio data, it can be divided into audio clips of several hundred milliseconds or several seconds. Data multi-layer fusion is performed on the set of data blocks corresponding to each sub-data. Multi-layer fusion is the process of merging or fusing multiple data blocks into a larger data unit. This process can include data fusion at different levels, from simple block-level fusion to more advanced feature fusion. For example, if the server is processing a series of image data blocks, they can be merged into a complete image, and at a higher level, their features can be fused to obtain more comprehensive information. Data coding segment mapping is performed through the multi-layer fusion data corresponding to each sub-data. The goal of this step is to associate the multi-layer fusion data with specific data coding segments for further analysis, identification, or storage. Data coding segments are usually predefined data blocks or feature sets that are used to represent different aspects or attributes of the data. For example, in image processing, data coding segments can represent different image regions or objects. For example, assume that the server is monitoring the performance of a high-speed digital printing press that prints newspapers at extremely high speeds. The server collects a series of printing data through sensors, generating one data per second. The printing data per second is divided into sub-data. In this case, the sub-data can be a data snapshot per second, including the status information of various parts of the printing press, such as the inkjet heads, paper delivery system, printing speed, etc. For each sub-data, data block division is performed. Taking the status information of the inkjet heads as an example, it can be divided into multiple data blocks, each block representing a different area or unit of the inkjet heads. These data blocks contain information such as the ink jetting situation and ink flow rate. Data multi-layer fusion is performed on the set of data blocks corresponding to each sub-data. In the case of the status information of the inkjet heads, the information of different data blocks can be merged into a more comprehensive status report, including an overview of the performance of the entire inkjet heads, such as ink uniformity and jetting pressure. Data coding segment mapping is performed through the multi-layer fusion data corresponding to each sub-data. Here, the data coding segments can represent different printing press statuses, such as normal operation, ink problems, paper jams, etc. By associating the multi-layer fusion data with these data coding segments, the server can identify the status of the printing press in real time and take timely measures to ensure high-quality printing operations.

[0117] In a specific embodiment, the process of performing step S104 may specifically include the following steps:

[0118] (1) Perform data desensitization processing on each sub-data through the data coding segments corresponding to each sub-data to obtain multiple desensitized data;

[0119] (2) Analyze the data truncation positions for each desensitized data to obtain multiple data truncation positions;

[0120] (3) Truncate the data values of each desensitized data through multiple data truncation positions to obtain multiple truncated data;

[0121] (4) Create a data fuzzification mapping table, and perform data fuzzification processing on multiple truncated data according to the data fuzzification mapping table to obtain multiple encoded data.

[0122] Specifically, perform data desensitization processing on each sub-data through the corresponding data encoding segment of each sub-data to obtain multiple desensitized data. For each sub-data block, perform data desensitization processing. The purpose of data desensitization is to hide or blur sensitive information to protect privacy. Desensitization can adopt various methods, such as replacement, adding noise, data fuzzification, etc. For example, if a sub-data block represents the ink consumption, the specific value can be replaced with a range value. For example, 100ml - 200ml can be desensitized to 100ml - 500ml. For each desensitized data block, perform data truncation position analysis. The purpose of this step is to determine which bits or values need to be retained and which can be truncated. The truncation position analysis is based on the importance and context of the data. For example, for data representing temperature, the digits after the decimal point can be truncated because the precision after the decimal point is not critical information. According to the result of the data truncation position analysis, perform data value truncation on the desensitized data block. Limit the data value within a certain range or truncate it to a specific number of digits. For example, truncate the temperature data from 27.356°C to 27°C. Create a data fuzzification mapping table, which specifies how to fuzzify different types of data blocks. The mapping table contains desensitization and truncation rules, as well as the mapping method of data values. For example, the mapping table can specify how to map temperature values to integers and map specific ranges of ink consumption to range values. According to the rules of the data fuzzification mapping table, perform data fuzzification processing on the truncated data block. This step will generate fuzzified encoded data, which contains information for protecting privacy. For example, fuzzify the temperature of 27°C to "25°C - 30°C" and fuzzify the ink consumption to "100ml - 500ml".

[0123] In a specific embodiment, the process of executing step S107 may specifically include the following steps:

[0124] (1) Perform data weighted fusion on the consumable monitoring data to obtain a second state vector;

[0125] (2) Perform semantic information analysis on the first state vector to obtain the corresponding first semantic information;

[0126] (3) Analyze the semantic information of the second state vector to obtain the corresponding second semantic information;

[0127] (4) Perform a first weight calculation on the first state vector through the first semantic information to obtain first weight data;

[0128] (5) Perform a second weight calculation on the second state vector through the second semantic information to obtain second weight data;

[0129] (6) Perform vector fusion on the first state vector and the second state vector based on the first weight data and the second weight data to obtain a target fusion vector;

[0130] (7) Perform vector feature mapping on the target fusion vector based on a pre-set vector feature mapping table to obtain target feature data;

[0131] (8) Perform state relationship matching on the target feature vector based on a pre-set feature state relationship library to obtain the corresponding target device and consumable state, and send the target device and consumable state to a remote monitoring terminal.

[0132] Specifically, the first state vector and the second state vector that have undergone the aforementioned processing are subjected to data weighted fusion. This step aims to combine the information of the two state vectors to obtain more comprehensive information. The fusion can adopt various methods, such as weighted average, weighted summation, etc. Semantic information analysis is performed on the first state vector and the second state vector. This includes understanding the data in the state vectors and their meanings. For example, for the first state vector, it contains information about the performance of the printing device, while the second state vector contains information about the consumables. Semantic information analysis helps to determine the importance of each vector and how to calculate the weights. Based on the semantic information, a first weight calculation is performed on the first state vector to obtain the first weight data. Similarly, a second weight calculation is performed on the second state vector to obtain the second weight data. The weight calculation involves machine learning algorithms to determine the relative importance of each state vector. Using the first weight data and the second weight data, vector fusion is performed on the first state vector and the second state vector. This can be achieved through weighted average or other linear combination methods. The fused vector is the target fusion vector, which combines the printing device performance and consumable information. A quantity feature mapping table is used to perform feature mapping on the target fusion vector. This step can reduce the dimension of the vector or map it to a higher-level feature space for further analysis and processing. Based on a preset feature state relationship library, state relationship matching is performed on the target feature vector. This step involves comparing and matching the target feature data with the known device and consumable states. The matching process can be implemented using fuzzy logic, rule engines, or machine learning algorithms. The obtained target device and consumable states are sent to a preset remote monitoring terminal. This ensures real-time monitoring and remote management, as well as timely measures to maintain and manage the printing device and consumables. For example, assume that a printing device monitoring system needs to monitor the ink consumption and printing speed of a printer. The first state vector contains printing speed information, and the second state vector contains ink consumption information. Through semantic information analysis, the system determines that the printing speed is more critical for the monitoring task. Therefore, the weight of the first state vector is higher. Through vector fusion, feature mapping, and state relationship matching, the system can obtain the operating state of the printer, such as normal, low ink, high speed, etc., and send this state information to the remote monitoring terminal for real-time monitoring and management.

[0133] The consumable monitoring method for a printing device in the embodiment of the present invention has been described above. Next, the consumable monitoring device for a printing device in the embodiment of the present invention will be described. Please refer to Figure 5 One embodiment of the consumable monitoring device for a printing device in the embodiment of the present invention includes:

[0134] A building module 501 is configured to build a communication network for a preset target printing device to obtain a target communication network. Meanwhile, the target printing device is connected to the target communication network, and data collection is performed through a consumable monitoring sensor in the target printing device to obtain consumable monitoring data corresponding to the target printing device;

[0135] An identification module 502 is configured to collect communication data sent by the target printing device through a preset signal collection device, and perform signal band identification on the communication data to obtain a signal band corresponding to the communication data;

[0136] A partitioning module 503 is configured to partition the communication data through the signal band to obtain a plurality of sub-data, and perform data coding segment mapping on each sub-data to obtain a data coding segment corresponding to each sub-data;

[0137] An encoding module 504 is configured to perform data implicit encoding on each sub-data through a data coding segment corresponding to each sub-data to obtain a plurality of encoded data;

[0138] A cleaning module 505 is configured to identify data signal interference for each encoded data to obtain corresponding interference signal data, and perform data cleaning on each encoded data through the interference signal data to obtain a target data set;

[0139] An analysis module 506 is configured to input the target data set into a preset abnormal communication identification model for abnormal state analysis to obtain a state analysis result, and perform a first vector conversion on the state analysis result to obtain a first state vector;

[0140] A conversion module 507 is configured to perform a second vector conversion on the consumable monitoring data to obtain a second state vector. Meanwhile, the first state vector and the second state vector are fused to obtain a target fusion vector and perform data format conversion to obtain target feature data, and perform device and consumable state identification on the target feature data to obtain a target device and consumable state, and send the target device and consumable state to a preset remote monitoring terminal.

[0141] Through the collaborative cooperation of the above-mentioned various components, the communication data is divided into multiple sub-data, and multiple encoded data are obtained through data implicit encoding; the interference signal data is obtained by identifying data signal interference, the data is cleaned to obtain the target data set and the abnormal state analysis is carried out to obtain the state analysis result, and the first vector conversion is carried out on the state analysis result to obtain the first state vector; the second vector conversion is carried out on the consumable monitoring data to obtain the second state vector, the first state vector and the second state vector are vector-fused to obtain the target fusion vector for data format conversion to obtain the target feature data, the target device and consumable state are identified from the target feature data, and the target device and consumable state are sent to the remote monitoring terminal. By monitoring the consumable state and performance of the printing device in real time, and through the collection and analysis of sensor data and communication data, the present invention can immediately detect any potential problems or abnormal situations. Through the accurate monitoring of the consumable consumption, predictive maintenance can be carried out, problems leading to equipment failure or downtime can be identified and solved in advance, thereby reducing production interruptions and maintenance costs. It helps to optimize the use of consumables and avoid unnecessary waste. Through real-time data, production managers can better plan the replenishment and replacement of consumables to ensure the effective utilization of resources. The data-based method provides more accurate consumable consumption information, and compared with the traditional time-schedule maintenance method, the life and replacement time of consumables can be predicted more accurately, thereby improving the equipment performance. The data analysis and machine learning technologies are utilized to make the monitoring and maintenance process more automated and intelligent. By accurately monitoring the equipment state and consumable consumption, the efficiency of the production process can be significantly improved. This helps to reduce the production time, increase the output, and lower the production cost, and further improves the accuracy and efficiency of the consumable monitoring of the printing device.

[0142] Above Figure 5 The consumable monitoring device of the printing device in the embodiment of the present invention is described in detail from the perspective of modular functional entities. Next, the consumable monitoring equipment of the printing device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0143] Figure 6It is a schematic structural diagram of a consumable monitoring device for a printing device provided by an embodiment of the present invention. The consumable monitoring device 600 of the printing device may vary greatly due to different configurations or performances, and may include one or more processors (central processing units, CPUs) 610 (for example, one or more processors) and a memory 620, and one or more storage media 630 (for example, one or more mass storage devices) for storing application programs 633 or data 632. Among them, the memory 620 and the storage media 630 may be transient storage or persistent storage. The program stored in the storage media 630 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations for the consumable monitoring device 600 of the printing device. Further, the processor 610 may be configured to communicate with the storage media 630 and execute a series of instruction operations in the storage media 630 on the consumable monitoring device 600 of the printing device.

[0144] The consumable monitoring device 600 of the printing device may further include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input / output interfaces 660, and / or one or more operating systems 631, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art can understand that Figure 6 The shown structural diagram of the consumable monitoring device of the printing device does not constitute a limitation on the consumable monitoring device of the printing device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0145] The present invention also provides a consumable monitoring device for a printing device. The consumable monitoring device of the printing device includes a memory and a processor. When the computer-readable instructions stored in the memory are executed by the processor, the processor executes the steps of the consumable monitoring method for the printing device in the above embodiments.

[0146] The present invention also provides a computer-readable storage medium. The computer-readable storage medium may be a non-volatile computer-readable storage medium, or may also be a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer executes the steps of the consumable monitoring method for the printing device.

[0147] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0148] If the integrated unit is implemented in the form of a software functional unit and sold or passed as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0149] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of various embodiments of the present invention.

Claims

1. A method for monitoring consumables of a printing device, characterized in that, The consumable monitoring method of the printing device includes: Construct a communication network for a preset target printing device to obtain a target communication network. At the same time, connect the target printing device to the target communication network, and collect data through a consumable monitoring sensor in the target printing device to obtain consumable monitoring data corresponding to the target printing device; Collect communication data sent by the target printing device through a preset signal acquisition device, and perform signal band identification on the communication data to obtain a signal band corresponding to the communication data; Divide the communication data through the signal band to obtain multiple sub-data, and perform data coding segment mapping on each sub-data to obtain a data coding segment corresponding to each sub-data; Perform data implicit coding on each sub-data through the data coding segment corresponding to each sub-data to obtain multiple coded data; Perform data signal interference identification on each coded data to obtain corresponding interference signal data, and perform data cleaning on each coded data through the interference signal data to obtain a target data set; Input the target data set into a preset abnormal communication recognition model for abnormal state analysis to obtain a state analysis result, and perform a first vector conversion on the state analysis result to obtain a first state vector; Perform a second vector conversion on the consumable monitoring data to obtain a second state vector. At the same time, perform vector fusion on the first state vector and the second state vector to obtain a target fusion vector and perform data format conversion to obtain target feature data, and perform device and consumable state identification on the target feature data to obtain a target device and consumable state, and send the target device and consumable state to a preset remote monitoring terminal; specifically including: performing data weighted fusion on the consumable monitoring data to obtain the second state vector; performing semantic information analysis on the first state vector to obtain corresponding first semantic information; performing semantic information analysis on the second state vector to obtain corresponding second semantic information; performing a first weight calculation on the first state vector through the first semantic information to obtain first weight data; performing a second weight calculation on the second state vector through the second semantic information to obtain second weight data; performing vector fusion on the first state vector and the second state vector based on the first weight data and the second weight data to obtain the target fusion vector; performing vector feature mapping on the target fusion vector based on a preset vector feature mapping table to obtain the target feature data; performing state relationship matching on the target feature vector based on a preset feature state relationship library to obtain a corresponding target device and consumable state, and sending the target device and consumable state to the remote monitoring terminal.

2. The consumable monitoring method of the printing device according to claim 1, characterized in that The step of collecting communication data sent by the target printing device through a preset signal acquisition device, and performing signal band identification on the communication data to obtain a signal band corresponding to the communication data includes: Analyze the data interface parameters of the signal acquisition device to obtain the first interface parameters corresponding to the signal acquisition device; Analyze the data interface parameters of the target printing device to obtain the second interface parameters corresponding to the target printing device; Match the data transmission parameters of the first interface parameters and the second interface parameters to obtain the corresponding adapted interface parameters; Based on the adapted interface parameters, respectively correct the first interface parameters and the second interface parameters to obtain the first target parameters corresponding to the first interface parameters and the second target parameters corresponding to the second interface parameters; Use the first target parameters to correct the interface parameters of the signal acquisition device. At the same time, use the second target parameters to correct the interface parameters of the target printing device, and use the signal acquisition device to collect the communication data sent by the target printing device; Identify the signal band of the communication data to obtain the signal band corresponding to the communication data.

3. The method for monitoring the consumables of the printing device according to claim 2, wherein, The identifying the signal band of the communication data to obtain the signal band corresponding to the communication data includes: Perform frequency domain conversion on the communication data to obtain the corresponding frequency domain data set; Calculate the data frequency of the frequency domain data set through the Fourier transform algorithm to obtain the corresponding frequency data; Based on the frequency data, extract the spectral features of the frequency domain data to obtain the spectral feature set corresponding to the communication data, where the spectral feature set includes modulation depth data and frequency offset data; Construct a standard band data table based on the frequency data to obtain the corresponding standard band data table; Through the standard band data table, perform standard band matching on the spectral feature set to obtain the signal band corresponding to the communication data.

4. The consumable monitoring method for a printing device according to claim 3, characterized in that, The dividing the communication data into multiple sub-data through the signal band and performing data coding segment mapping on each sub-data to obtain the data coding segment corresponding to each sub-data includes: Identify the band type of the signal band to obtain the band type corresponding to the signal band; Construct a divided time window for the band type to obtain multiple different time window data; Perform the first data division on the communication data through the multiple different time window data to obtain multiple candidate division data; Identify the frequency components of the frequency data to obtain at least one frequency component data; Perform the second data division on the multiple candidate division data through at least one frequency component data to obtain the multiple sub-data; Perform data coding segment mapping on each sub-data to obtain the data coding segment corresponding to each sub-data.

5. The consumable monitoring method of the printing device according to claim 4, characterized in that, The performing data coding segment mapping on each sub-data to obtain the data coding segment corresponding to each sub-data includes: Respectively perform data block division on each sub-data to obtain the data block set corresponding to each sub-data; Respectively perform data multi-layer fusion on the data block set corresponding to each sub-data to obtain the multi-layer fusion data corresponding to each sub-data; Perform data encoding segment mapping through the multi-layer fusion data corresponding to each of the sub-data to obtain the data encoding segment corresponding to each of the sub-data.

6. The consumable monitoring method of the printing device according to claim 1, wherein Perform data implicit encoding on each of the sub-data through the data encoding segment corresponding to each of the sub-data to obtain a plurality of encoded data, including: Perform data desensitization processing on each of the sub-data through the data encoding segment corresponding to each of the sub-data to obtain a plurality of desensitized data; Analyze the data truncation positions of each of the desensitized data to obtain a plurality of data truncation positions; Perform data value truncation on each of the desensitized data through the plurality of data truncation positions to obtain a plurality of truncated data; Create a data fuzzification mapping table, and perform data fuzzification processing on the plurality of truncated data according to the data fuzzification mapping table to obtain the plurality of encoded data.

7. A consumable monitoring device for a printing device, characterized in that, The consumable monitoring device of the printing device includes: A construction module, configured to construct a target communication network for a preset target printing device to obtain a target communication network. At the same time, connect the target printing device to the target communication network, and collect data through a consumable monitoring sensor in the target printing device to obtain the consumable monitoring data corresponding to the target printing device; An identification module, configured to collect communication data sent by the target printing device through a preset signal collection device, and perform signal band identification on the communication data to obtain the signal band corresponding to the communication data; A partitioning module, configured to partition the communication data through the signal band to obtain a plurality of sub-data, and perform data encoding segment mapping on each of the sub-data to obtain the data encoding segment corresponding to each of the sub-data; An encoding module, configured to perform data implicit encoding on each of the sub-data through the data encoding segment corresponding to each of the sub-data to obtain a plurality of encoded data; A cleaning module, configured to identify data signal interference for each of the encoded data to obtain corresponding interference signal data, and perform data cleaning on each of the encoded data through the interference signal data to obtain a target data set; An analysis module, configured to input the target data set into a preset abnormal communication identification model for abnormal state analysis to obtain a state analysis result, and perform a first vector conversion on the state analysis result to obtain a first state vector; A conversion module is configured to perform a second vector conversion on the consumable monitoring data to obtain a second state vector. Meanwhile, the first state vector and the second state vector are vector-fused to obtain a target fusion vector, and data format conversion is performed to obtain target feature data. Then, device and consumable status identification is performed on the target feature data to obtain a target device and consumable status, and the target device and consumable status are sent to a preset remote monitoring terminal. Specifically, it includes: performing data weighted fusion on the consumable monitoring data to obtain the second state vector; performing semantic information analysis on the first state vector to obtain corresponding first semantic information; performing semantic information analysis on the second state vector to obtain corresponding second semantic information; calculating a first weight on the first state vector through the first semantic information to obtain first weight data; calculating a second weight on the second state vector through the second semantic information to obtain second weight data; performing vector fusion on the first state vector and the second state vector based on the first weight data and the second weight data to obtain the target fusion vector; performing vector feature mapping on the target fusion vector based on a preset vector feature mapping table to obtain the target feature data; performing status relationship matching on the target feature vector based on a preset feature status relationship library to obtain the corresponding target device and consumable status, and sending the target device and consumable status to the remote monitoring terminal.

8. A consumable monitoring device for a printing device, characterized in that, The consumable monitoring device of the printing device includes: a memory and at least one processor, and instructions are stored in the memory; The at least one processor invokes the instructions in the memory to cause the consumable monitoring device of the printing device to execute the printing device consumable monitoring method according to any one of claims 1-6.

9. A computer-readable storage medium, on which instructions are stored, characterized in that, When the instructions are executed by the processor, the printing device consumable monitoring method according to any one of claims 1-6 is implemented.

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