Whole life cycle traceability system based on intelligent washing and method thereof

Abstract

The invention relates to the technical field of life cycle traceability, in particular to a full life cycle traceability system and method based on intelligent washing. The method comprises the following steps: acquiring washing clothes and user information, generating an RFID identification code, binding the RFID identification code with the user information, creating a user-clothes binding relationship, performing digital mirror image copying on the binding relationship, generating a clothes data file in combination with a database chain technology, and predicting the service life of the clothes by analyzing clothes state data and predicting the service life of the clothes. And making a personalized washing plan. And executing the washing plan and monitoring the washing result, and finally updating the clothes data file according to the washing effect to generate a washing traceability file. According to the invention, safer and more accurate full-life-cycle traceability operation is realized.

Description

Technical Field

[0001] The present invention relates to the field of life cycle traceability technology, and in particular to a full life cycle traceability system and method based on smart washing. Background Art

[0002] Traditional washing methods often lack accurate management of the entire life cycle of clothing, especially in the washing process, they cannot fully consider the material, state and individual needs of clothing. This method not only leads to differences in washing effects, but also fails to achieve accurate management and traceability of clothing, making it difficult to effectively extend the service life of clothing, and unable to provide users with personalized and customized services. In the existing technology, many washing management systems only rely on simple rules and static data analysis, ignoring the actual needs of clothing in different states. This method cannot monitor and adjust the dynamic changes in the washing process in real time, and it is difficult to provide effective clothing state prediction and life analysis before and after washing. Therefore, the clothing washing process often has no scientific basis, resulting in waste of resources and clothing damage. At the same time, the lack of effective user information and clothing state association leads to the lack of personalized services and cannot meet users' expectations for smart washing. Summary of the invention

[0003] Based on this, it is necessary to provide a full life cycle traceability system and method based on smart washing to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a full life cycle traceability method based on smart washing includes the following steps: Step S1: Acquire laundry information and user personal information; perform digital identity mapping on the laundry information to generate an RFID identification code; perform rights binding on the user personal information based on the RFID identification code to generate a user-clothing binding relationship; Step S2: Digitally mirror the user-clothing binding relationship to obtain mirror copy data; perform traceability link combination on the mirror copy data to generate a clothing data archive; Step S3: performing clothing status analysis on the washed clothing information according to the clothing data file to obtain clothing status data; performing life prediction based on the clothing status data to generate predicted clothing life data; Step S4: Analyze the washing demand of the laundry information according to the laundry data file to generate the laundry demand; design a washing plan based on the predicted laundry life data and the laundry demand to obtain a laundry plan; Step S5: Control execution based on the clothing washing plan and monitor the clothing washing results; update the clothing data file according to the clothing washing plan and the clothing washing results to generate a washing traceability file.

[0005] The present invention generates an RFID identification code by binding the laundry information with the user's personal information, thereby achieving an accurate match between the laundry and the user's identity, ensuring that each piece of clothing can be bound to the corresponding user, and combining and storing the clothing data based on database chain technology, thereby providing an unalterable clothing data archive, ensuring that all information about each piece of clothing from washing to use can be traced and verified, and by analyzing the status of the clothing, the health status and degree of wear of the clothing can be grasped in real time, personalized processing and management can be performed for the different states of the clothing, and a washing plan can be designed according to the washing needs and life prediction data of the clothing, thereby accurately optimizing the washing frequency and method, thereby extending the service life of the clothing, and The washing results of clothes can be monitored and adjusted to ensure that each link in the washing process can be executed according to the established standards, the clothing data file can be updated in real time, and the washing traceability file can be generated. A complete washing history can be provided for each piece of clothing, and users can query the historical washing records of clothes at any time. The technical means based on the combination of RFID identification and database chain can realize the information flow and closed-loop management of various links such as clothing and users, washing needs, and washing plans, thereby improving the intelligence and automation level of the entire washing process. On the basis of realizing the management of the entire life cycle of clothing, this method can provide personalized washing solutions, optimize the use and management of clothing, enhance users' care and experience of clothing, and promote the widespread application of smart washing systems.

[0006] Preferably, step S1 comprises the following steps: Step S11: obtaining laundry information and user personal information; performing coding conversion on the laundry information to obtain laundry information coding; Step S12: Perform digital identity mapping on the clothing information code to generate an RFID identification code; Step S13: standardizing the user's personal information to obtain user data in a unified format; performing association analysis on the user data in the unified format based on the RFID identification code to obtain a user-clothing association relationship; Step S14: Mapping and binding are performed according to the user-clothing association relationship to generate a user-clothing binding relationship.

[0007] The present invention can ensure that the unique identifier of each piece of clothing is uniformly managed and efficiently stored by encoding and converting the washed clothing information, thereby improving the accuracy and standardization of clothing information processing, and using RFID identification codes for digital identity mapping to achieve efficient binding of clothing and user identity, thereby enhancing the accurate matching of clothing information and user data during the washing process, and performing standardized processing of user personal information, thereby being able to unify user data from different sources or formats into a standardized format, thereby reducing processing problems caused by inconsistent data formats, and performing association analysis on standardized user data based on RFID identification codes to accurately analyze the relationship between users and clothing, and by mapping and binding the user-clothing association relationship, ensuring that the detailed binding relationship between each piece of clothing and the corresponding user can be quickly and accurately generated, so that the system can track the washing status, usage and historical records of each piece of clothing during the life cycle of the clothing, and by efficiently associating RFID identification codes with user information, a traceable clothing management system is established, which can update data in real time and track its history when any status of the user or clothing changes.

[0008] Preferably, step S2 comprises the following steps: Step S21: establishing a data template for the user-clothing binding relationship to obtain a relationship data template; mapping and converting the user-clothing binding relationship according to the relationship data template to generate a digital image of the binding relationship; Step S22: redundantly copy the binding relationship digital image to obtain image copy data; Step S23: evenly divide the mirror copy data into blocks to obtain block copy data; Step S24: Connect the block copy data with a database chain structure to generate a copy database chain structure; construct a traceability link for the mirror copy data based on the copy database chain structure to obtain a traceability link; Step S25: integrating the traceability links to generate clothing data files.

[0009] The present invention establishes a data template for the user-clothing binding relationship, and the generated relationship data template provides a structured basis for subsequent data management. The binding relationship digital mirror generated by mapping conversion ensures that the association information between the user and the clothing can be clearly recorded and tracked. The formation of the digital mirror makes data storage and retrieval more efficient, and improves the system's response speed to user needs. The design of the data template can flexibly adapt to the needs of different users and clothing types, and enhances the applicability and scalability of the system. The binding relationship digital mirror is redundantly replicated, and the generated mirror copy data ensures the security and reliability of the data. The implementation of redundant replication reduces the risk of data loss. The consistency of the mirror copy data with the original data provides a guarantee for subsequent data processing, ensuring that rapid recovery can be performed in the event of a failure or data damage, and improving the stability of the system. The setting of redundant replication can support multiple backup strategies and meet data protection requirements in different scenarios. The mirror copy data is evenly divided into blocks, and the generated block copy data provides flexibility for subsequent database processing. The implementation of even block division ensures that data can be efficiently managed and accessed during storage, and block processing reduces the time complexity of data retrieval, making It can still maintain good performance when the amount of data is large. The structured management of block copy data lays the foundation for the expansion and maintenance of the system, ensuring that specific data blocks can be quickly located and operated in the subsequent processing process, improving the processing efficiency of the overall system. The block copy data is connected by a database chain structure. The generated copy database chain structure provides an efficient access path for data management. The implementation of the chain structure makes the relationship between each block copy data clearer. The construction of the traceability link can quickly locate the source of a specific data block and its change history, enhancing the traceability and transparency of the data. The traceability function based on the database chain structure provides support for subsequent data analysis and decision-making, and improves the intelligence level of the system. The traceability link is integrated, and the generated clothing data archive provides users with comprehensive clothing life cycle information. The integrated archive can record the use, maintenance and cleaning history of each piece of clothing in detail, so that users can clearly understand the status and value of clothing. The implementation of archive integration improves the availability of data, enabling quick access to required information when needed, enhancing users' control over clothing management, and overall improving the efficiency and intelligence level of the smart washing system in full life cycle management.

[0010] Preferably, step S3 comprises the following steps: Step S31: performing clothing matching on the washed clothing information according to the clothing data file to obtain the current clothing washing history record; performing clothing change progress analysis on the current clothing washing history record to generate the clothing wear process; Step S32: analyzing the status of the washed clothes based on the wear process of the clothes to obtain clothing status data; Step S33: positioning the clothing life node of the clothing data file according to the clothing state, and generating the current clothing life node; Step S34: performing life prediction on the clothing state data based on the current clothing life node to generate predicted clothing life data.

[0011] Through clothing matching and historical record analysis, the system of the present invention can accurately track the washing history of each piece of clothing, timely evaluate its wear process, and provide accurate basic data for subsequent clothing status analysis and life prediction. Through clothing wear process analysis, the system can monitor the usage status of clothing in real time, generate detailed clothing status data, and ensure real-time monitoring and evaluation of clothing. Through clothing status data, the system can accurately locate the life nodes of clothing, clarify the current life cycle stage of clothing, and provide data support for subsequent washing plans and life predictions. Through analysis of clothing life nodes, the system can accurately predict the remaining service life of clothing, warn of clothing loss and aging in advance, and help formulate more scientific and reasonable washing and maintenance strategies, extend the service life of clothing and reduce unnecessary waste.

[0012] Preferably, step S31 includes the following steps: Extracting feature identifiers from laundry information to obtain laundry features; Matching the characteristics of the washed clothes with the clothes data files to obtain matching clothes file information; Integrate and process the matching clothing archive information to generate the current clothing washing history record; Sort the current laundry history records by timestamp to obtain laundry time series data; Performing washing interval analysis on the laundry washing time series data to obtain the washing interval range; According to the washing interval, the clothing washing time series data is evaluated for the degree of change of the clothing at the interval, and the degree of change of the clothing at the interval is generated; The change process mapping is performed based on the degree of clothing change in the intervals to generate the clothing wear process.

[0013] By extracting clothing features and matching information, the present invention enables the system to accurately identify the specific characteristics of each piece of clothing, accurately integrate the historical washing records of clothing, and ensure the comprehensiveness and consistency of data; through timestamp sorting and washing interval analysis, the system can clearly track the washing time interval of each piece of clothing, providing a scientific basis for subsequent change evaluation and wear analysis; by evaluating the degree of change in the washing interval, the system can accurately measure the wear of clothing in different washing intervals, and provide more targeted washing suggestions and wear predictions; through change process mapping, the system can depict the entire process of clothing wear, help monitor the wear trend of clothing, predict the loss of clothing in advance, and provide decision support for optimizing washing plans and extending the service life of clothing.

[0014] Preferably, step S4 comprises the following steps: Step S41: extracting the reference frames before and after washing from the clothing data archive to generate a historical washing reference frame; Step S42: performing washing demand analysis on the laundry information based on the historical washing reference system to generate laundry washing demand; Step S43: mapping washing parameters of the laundry washing requirements to obtain required washing parameters; Step S44: Design a washing plan based on the required washing parameters and the predicted clothing life data to obtain a clothing washing plan.

[0015] By extracting the reference system before and after washing, the present invention can establish a comparison of the washing status of clothes and accurately record the changes before and after each washing. Based on the historical washing reference system, the system can analyze the washing needs of clothes, evaluate the actual status and care requirements of clothes, and ensure the personalization and accuracy of the washing plan. Through the mapping of washing needs and parameters, the system can convert the washing needs into specific washing parameters to ensure that each washing operation conforms to the characteristics and status of clothes, and avoid damage to clothes caused by improper operation. By integrating washing parameters and predicting the life of clothes, the system can design a scientific and reasonable washing plan to ensure that washing is performed at a reasonable time and frequency, maximize the use cycle of clothes and optimize the utilization of washing resources.

[0016] Preferably, step S42 includes the following steps: Extract key features of the historical washing reference system and obtain the reference system feature vector; Perform historical washing reference matching on the washing clothes information based on the reference system feature vector to generate current clothes effect reference data; Compare the laundry information and the current laundry effect reference data to generate laundry difference data; Cleanable stains are identified on the clothing difference data to obtain cleanable difference data; washing demand mapping is performed based on the cleanable difference data to generate clothing washing demand.

[0017] By extracting the key features of the historical washing reference system, the present invention enables the system to extract core information from a large amount of washing data and accurately grasp the changes in clothes before and after washing. Through historical washing reference matching, the system can compare the washing effect of the current clothes with the historical data, and accurately evaluate the washing effect and status of the clothes. Through difference comparison, the system can identify the difference between the current washing effect and the historical effect of the clothes, and accurately locate the changes in the clothes. Through cleanable stain identification, the system can filter out the parts that need to be cleaned from the difference data, effectively improving the washing efficiency and effect. By mapping the washing requirements of the cleanable difference data, the system can accurately customize the washing plan for each piece of clothing, ensuring the high efficiency of the washing process and the best protection of the clothes.

[0018] Preferably, step S44 includes the following steps: Perform demand configuration mapping on the demand washing parameters to obtain candidate parameter configuration solutions; Performing a washing intensity evaluation on the candidate parameter configuration schemes to obtain candidate washing intensity data; Perform washing intensity impact analysis on predicted clothing life data to generate life impact data; Conduct wear acceleration evaluation on the life impact data to obtain wear acceleration data; Perform correlation analysis on candidate washing intensity data based on wear acceleration data to generate a wear-intensity correlation curve; The wear-intensity correlation curve is screened for low wear intensity to obtain appropriate washing intensity data; A washing plan is designed based on the appropriate washing intensity data to obtain a clothing washing plan.

[0019] Through demand configuration mapping, the present invention can generate multiple candidate solutions according to the washing needs of clothes, and provide multiple options for subsequent washing intensity evaluation. Through the washing intensity evaluation of the candidate parameter configuration solutions, the system can screen out the most suitable washing intensity from multiple solutions, and provide a scientific basis for ensuring the washing effect and life of clothes. Through the analysis of the impact of washing intensity and predicted clothing life, the system can evaluate the impact of washing with different intensities on the life of clothes, and ensure that the selection of washing intensity can both meet the cleaning needs and extend the service life of clothes. Through wear acceleration evaluation, the system can accurately predict the accelerated effect of different washing intensities on clothing wear, and provide targeted washing solutions to avoid excessive wear. Through wear-intensity correlation analysis, the system can generate a specific wear intensity relationship diagram to provide data support for the formulation of a more refined washing intensity control plan. By screening low wear intensity, the system can ensure that while meeting the washing needs, the clothes are protected to the greatest extent and unnecessary wear is reduced. By combining appropriate washing intensity data, the system can formulate the most suitable washing plan to ensure that the clothes are efficiently and gently washed.

[0020] Preferably, step S5 comprises the following steps: Step S51: adjusting washing parameters based on the laundry washing plan to obtain adjustment parameters; performing intelligent washing according to the adjustment parameters, and monitoring the laundry washing process at the same time; Step S52: recording the operation parameters of the laundry process to obtain the laundry operation parameters; integrating the data of the laundry process and the laundry operation parameters to generate the laundry washing results; Step S53: Repeatedly bind the laundry plan and the laundry result based on the user-clothing binding relationship to obtain single-washing global data; Step S54: updating the clothing data file according to the global data of a single wash, and generating a washing traceability file.

[0021] By regulating washing parameters, the system of the present invention can make personalized adjustments for different clothing types and washing needs, and monitor the washing process at the same time. By recording and integrating the operating parameters during the washing process, the system can track each washing of the clothing in detail and generate washing results. By repeatedly binding the user-clothing relationship, the system can ensure that the washing plan and washing results are associated with the clothing data of a specific user. By updating the clothing data file, the system can generate a complete washing traceability file to ensure that the historical information of each washing is accurately recorded, thereby providing data support for the full life cycle management of clothing.

[0022] The present invention also provides a full life cycle traceability system based on smart washing, which is used to execute the full life cycle traceability method based on smart washing as described above. The full life cycle traceability system based on smart washing includes: An information binding module is used to obtain laundry information and user personal information; perform digital identity mapping on the laundry information to generate an RFID identification code; perform rights binding on the user's personal information based on the RFID identification code to generate a user-clothing binding relationship; The archive construction module is used to digitally mirror the user-clothing binding relationship to obtain mirror copy data; trace the mirror copy data to generate a clothing data archive; The life prediction module is used to analyze the status of washed clothes according to the clothing data file to obtain clothing status data; perform life prediction based on the clothing status data to generate predicted clothing life data; A washing plan design module is used to analyze the washing demand of the washing clothes according to the clothing data file, generate the washing demand of the clothes; design the washing plan based on the predicted clothing life data and the washing demand of the clothes, so as to obtain the washing plan of the clothes; The traceability file update module is used to control execution based on the clothing washing plan and monitor the clothing washing results; the clothing data file is updated according to the clothing washing plan and the clothing washing results to generate a washing traceability file.

[0023] By mapping the user's personal information to a digital identity and generating a unique RFID identification code, the present invention can ensure the precise association between each user and his / her washed clothes, enhance data security and privacy protection, and provide personalized services for subsequent washing processes. Through digital mirror replication and combined with database chain technology, the system realizes the immutability and traceability of clothing data. The data archive of each piece of clothing can be permanently preserved and verified, ensuring the transparency and credibility of the washing service. By combining clothing status data with life prediction, the system can accurately evaluate the current usage status of the clothing and predict its life in advance, providing a scientific basis to extend the use cycle of the clothing and reduce damage caused by improper washing. By analyzing washing needs and life prediction data, the system can generate a personalized washing plan to ensure that each piece of clothing is optimized according to its characteristics and needs to avoid unnecessary damage. By real-time monitoring of washing results and updating clothing data archives, the system can record detailed information of each washing process, ensure the transparency and traceability of the washing process, enhance users' trust in washing services, and provide data support for future washing decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the steps of a full life cycle traceability method based on smart washing; Figure 2 Detailed implementation flow chart of step S2; Figure 3 is a schematic diagram of a detailed implementation process of step S3; The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0025] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 3 , a full life cycle traceability method based on smart washing, including the following steps: Step S1: Acquire laundry information and user personal information; perform digital identity mapping on the laundry information to generate an RFID identification code; perform rights binding on the user personal information based on the RFID identification code to generate a user-clothing binding relationship; Step S2: Digitally mirror the user-clothing binding relationship to obtain mirror copy data; perform traceability link combination on the mirror copy data to generate a clothing data archive; Step S3: performing clothing status analysis on the washed clothing information according to the clothing data file to obtain clothing status data; performing life prediction based on the clothing status data to generate predicted clothing life data; Step S4: Analyze the washing demand of the laundry information according to the laundry data file to generate the laundry demand; design a washing plan based on the predicted laundry life data and the laundry demand to obtain a laundry plan; Step S5: Control execution based on the clothing washing plan and monitor the clothing washing results; update the clothing data file according to the clothing washing plan and the clothing washing results to generate a washing traceability file.

[0029] The present invention generates an RFID identification code by binding the laundry information with the user's personal information, thereby achieving an accurate match between the laundry and the user's identity, ensuring that each piece of clothing can be bound to the corresponding user, and combining and storing the clothing data based on database chain technology, thereby providing an unalterable clothing data archive, ensuring that all information about each piece of clothing from washing to use can be traced and verified, and by analyzing the status of the clothing, the health status and degree of wear of the clothing can be grasped in real time, personalized processing and management can be performed for the different states of the clothing, and a washing plan can be designed according to the washing needs and life prediction data of the clothing, thereby accurately optimizing the washing frequency and method, thereby extending the service life of the clothing, and The washing results of clothes can be monitored and adjusted to ensure that each link in the washing process can be executed according to the established standards, the clothing data file can be updated in real time, and the washing traceability file can be generated. A complete washing history can be provided for each piece of clothing, and users can query the historical washing records of clothes at any time. The technical means based on the combination of RFID identification and database chain can realize the information flow and closed-loop management of various links such as clothing and users, washing needs, and washing plans, thereby improving the intelligence and automation level of the entire washing process. On the basis of realizing the management of the entire life cycle of clothing, this method can provide personalized washing solutions, optimize the use and management of clothing, enhance users' care and experience of clothing, and promote the widespread application of smart washing systems.

[0030] In the embodiment of the present invention, refer to Figure 1 , is a schematic diagram of the steps of a full life cycle traceability method based on smart washing of the present invention. In this example, the full life cycle traceability method based on smart washing includes the following steps: Step S1: Acquire laundry information and user personal information; perform digital identity mapping on the laundry information to generate an RFID identification code; perform rights binding on the user personal information based on the RFID identification code to generate a user-clothing binding relationship; In this embodiment, the basic information of the washed clothes, including the type, brand, color, material and other data of the clothes, is obtained through the washing device or scanning device, and the barcode or QR code on the clothing label is read by the sensor device to obtain the unique identification information of the clothes. Then, the system obtains the user's personal information from the user database, including the user's name, contact information, account information, etc. The system uses data processing technology to perform digital identity mapping on the washed clothes information, that is, each piece of clothing is matched with the user's identity information, and a set of unique RFID identification codes (radio frequency identification identification codes) are generated by the RFID tag generator according to the characteristics of the clothes and the user information. Each RFID identification code is bound to the user's personal information to generate a binding relationship between the user and the clothes, that is, the user's personal information and the specific information of the clothes are connected through the RFID identification code.

[0031] Step S2: Digitally mirror the user-clothing binding relationship to obtain mirror copy data; perform traceability link combination on the mirror copy data to generate a clothing data archive; In this embodiment, the binding relationship data between each user and all his / her clothes are extracted. The binding information includes the user's identity, the unique identification of the clothes, the washing record, usage history and maintenance record of the clothes, etc. All data are stored in a database in a digital format. The relationship between each piece of clothing and the user is uniquely numbered using a timestamp-based identification to ensure that each updated binding information can be traced back through the timestamp. Subsequently, these binding information are digitally mirrored. The mirroring process is to independently copy the metadata of each binding relationship between the user and the clothes to ensure that the copy of each binding relationship can be consistent with the original data in the mirror storage. Differential storage technology is adopted in the copying process to ensure that only changed data is stored. Incremental replication is used to ensure that each update of the mirror copy data does not affect the normal use of the original data, while ensuring redundant backup of the data. The data storage format used is a structured data table, which includes fields such as user ID, clothing ID, washing history, and maintenance history. The mirror copy data includes all historical changes of this information, and the history of each clothing in the user's life cycle is extracted. All historical operation data of the clothes, including the time, equipment, and operator information of each washing, drying, storage and other operations, are collected and used to establish a time chain of the clothes. Each operation step is connected into a link in chronological order. In the process of generating the link, the specific parameter information of each operation, such as washing temperature, washing method, type of detergent used, etc., is combined to associate these parameters with each operation, so that the link not only contains the time sequence of the operation, but also can be traced back to the specific operation details. In the process of link combination, all operation data are associated through a unified identifier (RFID identification code) to ensure that the information between each link corresponds accurately. After the link combination is completed, all the data of the clothes are integrated together in chronological order to form a complete clothing data file. The data file includes the basic information of the clothes, all operation history, usage status, etc., and uses encryption algorithms to encrypt and store sensitive data to ensure the privacy of the data. The generated clothing data file has a complete traceability chain. The information of any link can be queried through the link to obtain specific operation records, forming a traceable and verifiable clothing life cycle data file.

[0032] Step S3: performing clothing status analysis on the washed clothing information according to the clothing data file to obtain clothing status data; performing life prediction based on the clothing status data to generate predicted clothing life data; In this embodiment, according to the clothing data file, the system inputs the extracted washing clothing information into the clothing status analysis module. The clothing status analysis module analyzes the current status of the clothing through the set algorithm model, including the degree of wear, color change, damage and other information of the clothing. During the analysis process, the system combines image processing technology to recognize the image of the clothing, uses deep learning technology to classify and distinguish the image, identifies the specific state of the clothing, and generates clothing status data. The clothing status data includes detailed information such as various damage conditions and color changes. Based on the generated clothing status data, the system continues to perform life prediction. The life prediction is calculated using a regression model. The model input is the degree of wear, material information and historical washing data of the clothing. Through regression analysis, the predicted life data of each piece of clothing is output. The generated predicted clothing life data will be used for subsequent washing demand analysis and plan design.

[0033] Step S4: Analyze the washing demand of the laundry information according to the laundry data file to generate the laundry demand; design a washing plan based on the predicted laundry life data and the laundry demand to obtain a laundry plan; In this embodiment, a washing demand analysis is performed based on historical washing records in the clothing data file and combined with clothing status data. The washing demand analysis module automatically generates clothing washing demand by calculating factors such as clothing wear, pollution degree, fiber damage, etc. During the analysis process, the system predicts the clothing's requirements for washing temperature, washing intensity and detergent type through a model, and generates corresponding washing demand parameters. Subsequently, based on the predicted clothing life data and the generated clothing washing demand, the system further designs a washing plan. The washing plan design module uses an optimization algorithm to balance the washing intensity and life loss of the clothing. The algorithm designs the best washing process based on the specific washing needs of the clothing, ensuring that the washing process can both effectively remove dirt and maximize the service life of the clothing. Finally, a complete clothing washing plan is output for use by a subsequent washing execution module.

[0034] Step S5: Control execution based on the clothing washing plan and monitor the clothing washing results; update the clothing data file according to the clothing washing plan and the clothing washing results to generate a washing traceability file.

[0035] In this embodiment, washing parameters are regulated according to the laundry washing plan, and the regulated parameters include washing temperature, washing time, washing speed, etc. The operation parameters of the washing machine are automatically adjusted according to the preset washing plan through the smart control module to ensure that each link in the washing process meets the design requirements. After the washing execution starts, the system monitors the laundry washing process, records the washing operation parameters in real time through sensors (such as temperature sensors, humidity sensors, etc.), and uploads these data to the data integration module. The system compares the washing operation parameters with the initial state data of the laundry to generate the laundry washing results. The washing results include the state changes of the laundry during the washing process, such as the removal of stains, the change in the degree of wear, etc. Subsequently, according to the user-clothing binding relationship, the laundry washing plan and the washing results are repeatedly bound to generate single washing global data. Finally, the system updates the laundry data file according to the single washing global data. The updated data file will contain all key information in the washing process, including washing intensity, temperature, time, etc., and a washing traceability file is generated at the same time. The data in the traceability file can be used for subsequent quality monitoring and problem tracking.

[0036] Preferably, step S1 comprises the following steps: Step S11: obtaining laundry information and user personal information; performing coding conversion on the laundry information to obtain laundry information coding; Step S12: Perform digital identity mapping on the clothing information code to generate an RFID identification code; Step S13: standardizing the user's personal information to obtain user data in a unified format; performing association analysis on the user data in the unified format based on the RFID identification code to obtain a user-clothing association relationship; Step S14: Mapping and binding are performed according to the user-clothing association relationship to generate a user-clothing binding relationship.

[0037] In this embodiment, basic information of clothing, such as clothing brand, model, material, color, washing requirements, etc., is obtained by scanning equipment, and the information is uploaded to the central data management system. Then, the system performs coding conversion on the collected clothing information. Specifically, the system uses a hash algorithm (such as SHA-256) to encode the basic attributes of the clothing and converts them into a set of unique coding strings. These codes include clothing type, size, color and other characteristics. The coding is completed by a data conversion tool to generate clothing information codes. Finally, each piece of clothing will be assigned a unique code to identify the clothing to ensure the uniqueness and traceability of the information. The clothing information code will be used as key data for subsequent processing. According to the generated clothing information code, each piece of clothing is associated with a set of unique RFID identification codes (radio frequency identification codes) through a digital identity mapping algorithm. The specific operation is that the system takes the clothing information code as input and calculates a corresponding RFID identification code through a mapping algorithm. The RFID identification code is generated by an RFID tag generator. The RFID tag contains encrypted information and can contain basic information of the clothing, washing records and user information. After the tag is generated, it is embedded into the clothing through a radio frequency device. The RFID identification code will serve as a unique digital identifier of the clothing and can be used in It is used to identify and track the status of clothing in real time in the subsequent washing process and management. The generation process of RFID identification code depends on the algorithm model built into the system, which unifies user data (such as user name, contact information, address, etc.) from different sources into a predetermined format, and uses data standardization tools to convert all user information fields (such as name, phone number, address) into a unified format to ensure the consistency and standardization of user data. After that, the system performs association analysis based on the mapping relationship between user personal information and RFID identification code. The analysis process uses the algorithm model to identify the binding relationship between users and clothing according to RFID identification code. The system establishes the association relationship between users and clothing by comparing user ID and RFID identification code data, and generates a user-clothing association data table. The association analysis process uses big data technology, user-clothing association data table, and accurately binds user personal information to the RFID identification code of clothing through mapping binding algorithm. The specific operation is that the system identifies the user identity of each piece of clothing according to the RFID identification code, and uses the mapping algorithm to bind each user to the corresponding clothing one-to-one, generating a user-clothing binding relationship. The system integrates user information and corresponding clothing information through database operations to ensure that each user's clothing information can be accurately recorded and updated in real time.

[0038] Preferably, step S2 comprises the following steps: Extract the binding relationship data between each user and all their clothes. The binding information includes the user's identity, the unique identification of the clothes, the washing records, usage history and maintenance records of the clothes, etc. All data are stored in the database in a digital format. Use a timestamp-based identification to uniquely number the relationship between each piece of clothing and the user, ensuring that each updated binding information can be traced back through the timestamp. Then, these binding information are digitally mirrored. The mirror replication process is to independently copy the metadata of each binding relationship between the user and the clothes, ensuring that the copy of each binding relationship can be consistent with the original data in the mirror storage. Differential storage technology is adopted in the replication process to ensure that only changed data is stored. Incremental replication is used to ensure that each update of the mirror copy data does not affect the normal use of the original data, while ensuring redundant backup of the data. The data storage format used is a structured data table, which contains fields such as user ID, clothing ID, washing history, maintenance history, etc. The mirror copy data includes all historical changes of this information, extracting all Historical operation data, including the time, equipment, and operator information of each washing, drying, storage and other operations, are used to establish a time chain of clothing. Each operation step is connected into a link in chronological order. In the process of generating the link, the specific parameter information of each operation, such as washing temperature, washing method, type of detergent used, etc., is combined to associate these parameters with each operation, so that the link not only contains the time sequence of the operation, but also can be traced back to the specific operation details. In the process of link combination, all operation data are associated through a unified identifier (RFID identification code) to ensure that the information between each link is accurately corresponding. After the link combination is completed, all data of the clothing are integrated together in chronological order to form a complete clothing data file. The data file includes basic information of the clothing, all operation history, usage status, etc., and encryption algorithms are used to encrypt and store sensitive data to ensure data privacy. The generated clothing data file has a complete traceability chain. Information at any link can be obtained through link query to obtain specific operation records, forming a traceable and verifiable clothing life cycle data file.

[0039] Preferably, step S3 comprises the following steps: Step S31: performing clothing matching on the washed clothing information according to the clothing data file to obtain the current clothing washing history record; performing clothing change progress analysis on the current clothing washing history record to generate the clothing wear process; Step S32: analyzing the status of the washed clothes based on the wear process of the clothes to obtain clothing status data; Step S33: positioning the clothing life node of the clothing data file according to the clothing state, and generating the current clothing life node; Step S34: performing life prediction on the clothing state data based on the current clothing life node to generate predicted clothing life data.

[0040] In this embodiment, the washing clothes information is matched according to the clothes data file. The washing history records related to the current clothes are queried from the database using the clothes unique identifier (RFID tag) through the clothes information matching module in the system. The history records contain the washing data of each time of the clothes, washing parameters, the type of detergent used, the washing temperature, the washing time and other information. The matching process is performed through the data retrieval engine. When matching, the system will filter the historical data according to the characteristic information of the clothes (such as fabric type, color, size, etc.) to ensure that the matched record is an accurate historical record of the current clothes. Then, the washing history records of the current clothes are analyzed for clothing change progress. The change progress analysis module calculates the washing history records according to the number of washes, the detergent used, the washing temperature, the washing time and other information in the historical records. The progress of clothing wear is calculated based on the data of clothing conditions and washing intensity, combined with the durability data of clothing fabrics. The progress analysis is performed using a regression model (such as linear regression, support vector machine, etc.). The wear rate and change trend of clothing are calculated through the model, and then the wear progress data of clothing are generated. The wear progress data includes information such as the wear time period, wear degree and damage type. The wear progress data is extracted from the clothing data archive, and then compared with the friction coefficient, strength and durability parameters of the clothing material. A comprehensive analysis of the clothing status is performed using algorithms based on data mining (such as cluster analysis, decision trees, etc.). The status analysis module combines the wear progress with the actual usage of clothing (such as wearing frequency, washing intensity, etc.) , evaluate the current state of the clothing, and generate clothing state data based on the analysis results. The clothing state data includes information such as the wear level of the clothing, whether there is a risk of damage or deformation, and whether it is suitable for continued use. The tools used for analysis include dedicated machine learning models and state assessment algorithms. The life nodes of the clothing are located according to the clothing state data and the preset life cycle model. The life node positioning module first identifies the current clothing life cycle node in the life cycle model based on factors such as the material, frequency of use, and number of washes of the clothing. The life cycle model is based on historical data and industry standards, and includes multiple life cycle stages of clothing, such as initial state, wear state, and near-scrap state. The positioning process compares the current clothing state data According to the node standard of the life cycle model, the life node of the current clothing is accurately located. The life node includes the current stage of the clothing (such as newly purchased, daily use, near scrapping, etc.). The located life node can be used for subsequent life cycle management and life prediction. The system will mark the life cycle node of each clothing and record the specific time and related data of the node. The prediction model (such as linear regression, support vector regression, neural network, etc.) is used to predict the life of the clothing. The prediction model is based on the multi-dimensional data such as the life node data, wear process, frequency of use and washing conditions of the current clothing. The regression model obtained through training is used for calculation to generate the remaining life data of the clothing. The life prediction module combines the wear and tear of the clothing with the life cycle nodes.The future usage status of clothing is estimated. The prediction process takes into account the decay characteristics of different materials, the impact of different washing intensities on lifespan, and the frequency of use. Finally, the expected lifespan data of clothing is obtained. The lifespan prediction results include not only the expected usage time, but also the specific clothing damage risk, such as the probability of clothing damage after a certain time point. The prediction algorithm regularly optimizes itself through feedback from historical data.

[0041] Preferably, step S31 includes the following steps: Extracting feature identifiers from laundry information to obtain laundry features; Matching the characteristics of the washed clothes with the clothes data files to obtain matching clothes file information; Integrate and process the matching clothing archive information to generate the current clothing washing history record; Sort the current laundry history records by timestamp to obtain laundry time series data; Performing washing interval analysis on the laundry washing time series data to obtain the washing interval range; According to the washing interval, the clothing washing time series data is evaluated for the degree of change of the clothing at the interval, and the degree of change of the clothing at the interval is generated; The change process mapping is performed based on the degree of clothing change in the intervals to generate the clothing wear process.

[0042] In this embodiment, feature identification is extracted from the laundry information. The system reads the identification information (RFID tag) of each piece of clothing through the clothing recognition module. These tags contain information such as the material, brand, size, color, and washing requirements of the clothing. The system extracts relevant features from the physical tags of the clothing using image processing technology or barcode scanning technology, and further obtains data such as the temperature, humidity, and weight of the clothing through sensors. These feature identifications are extracted and converted into digital information through the data acquisition module to generate a set of structured feature data sets as laundry feature data. The laundry features and clothing data archives are matched to the laundry information. The system compares the laundry feature data with the historical information in the clothing data archives. The data archive contains detailed information about each piece of clothing, including historical washing records, cleaning intensity, material type, etc. The database is searched for clothing archives that are most similar to the current clothing features through feature matching algorithms (such as similarity calculation based on Euclidean distance). The system uses clustering algorithms or classification algorithms to group clothing to ensure that the most relevant historical archives are matched. The matching results generate matching clothing archive information, including the clothing's historical washing conditions, wearing frequency, washing methods, and any repair or replacement records. The matching clothing archive information is integrated and processed to generate the current clothing washing history record. The integration processing module summarizes all relevant washing records in the matching archives of clothing, including washing date, type of detergent used, temperature, time, Washing mode and other information, through data cleaning and formatting operations, remove irrelevant or redundant information, and normalize the data to ensure that all historical record data are in a unified format. The integrated historical record information is saved in the digital archive of the clothes and arranged in chronological order. The current clothing washing history record is sorted by timestamp. The system sorts all washing records according to the time identifier in the historical record (that is, the timestamp of each wash), and uses a time sorting algorithm (such as quick sort, merge sort, etc.) to arrange all historical washing data from the earliest to the latest in time to ensure the time sequence of the washing records and generate washing time series data. Each record in the washing time series data contains information such as washing time, detergent type, washing intensity, etc. The washing interval analysis module calculates the washing interval based on the time difference between each wash, and uses the time difference calculation method (such as subtracting the timestamps of two washes) to determine the duration of each washing cycle. The mean, standard deviation and other statistics of the washing cycle are calculated for each set of data to obtain the time interval of each washing cycle. During the analysis process, the system batches the data through the time window, and reasonably distinguishes and classifies the washing intervals according to different washing frequencies and clothing materials. Finally, the washing interval interval is obtained, and the degree of change of the clothing interval is evaluated based on the washing interval interval. The degree of change evaluation module evaluates the wear of the clothing based on the washing interval data and the wear model of the clothing.The wear model takes into account factors such as washing frequency, washing intensity, and clothing material. Through data regression analysis methods, combined with historical data and washing intervals, the wear degree of clothing in each washing cycle is calculated. During the evaluation process, the system will use machine learning algorithms such as linear regression and support vector machine (SVM) for training, and modeling based on historical wear data and actual washing intervals, and finally generate the degree of change of clothing in the interval. The degree of change data reflects the impact of different washing intervals on the wear of clothing. Based on the degree of change of clothing in the interval, the change process is mapped. The process mapping module uses a mapping algorithm to convert the wear data of clothing into change process data. During the mapping process, the system uses a curve fitting algorithm (such as least squares method, B-spline curve, etc.) to fit the degree of change data with time to generate a wear process diagram of clothing. The process mapping process takes into account the aging speed, material durability and actual use conditions of different types of clothing. The wear process data obtained after mapping represents the wear change trend of clothing in each time period, and finally generates a complete clothing wear process. ,

[0043] Preferably, step S4 comprises the following steps: Step S41: extracting the reference frames before and after washing from the clothing data file to generate a historical washing reference frame; Step S42: performing washing demand analysis on the laundry information based on the historical washing reference system to generate laundry washing demand; Step S43: mapping washing parameters of the laundry washing requirements to obtain required washing parameters; Step S44: Design a washing plan based on the required washing parameters and the predicted clothing life data to obtain a clothing washing plan.

[0044] In this embodiment, key information before and after each washing is extracted from the clothing data archive, including clothing status data before washing (such as color, texture, degree of wear, size, etc.) and change data after washing (such as color restoration, fiber damage, etc.). These data are arranged in chronological order to form a comparison data set before and after washing. Data cleaning and preprocessing algorithms (such as outlier removal, data standardization, etc.) are used to ensure data accuracy, and a historical washing reference system is generated. This reference system is based on the characteristic changes of each piece of clothing before and after washing. The washing demand of each piece of clothing is analyzed using the data in the historical washing reference system. First, by analyzing multiple dimensions such as the wear, color change, and cleanliness of the clothing, combined with factors such as the material and frequency of use of the clothing, a multivariate regression analysis model is used to establish a washing demand prediction model. According to the historical washing information of each piece of clothing, the future washing demand of the clothing is predicted, such as cleanliness requirements, washing intensity, temperature, washing frequency, etc. The washing demand analysis results generate a clothing washing demand data set, and washing parameter mapping is performed on the clothing washing demand to obtain the required washing parameters. The mapping module uses a mapping algorithm to map the demand according to the washing demand data of the clothing. The data is converted into specific washing parameters. The key parameters mapped include the required water temperature, washing time, detergent type and dosage, washing mode, etc. Through the weighted average method and fuzzy logic algorithm (such as fuzzy rule-based reasoning engine), different washing demand parameters are reasonably combined to generate demand washing parameters. The specific process is to select the appropriate washing intensity and time according to the cleanliness and wear degree in the washing demand. The system provides customized washing parameters for each piece of clothing according to the material characteristics of different clothes. Combining the required washing parameters of each piece of clothing and the predicted clothing life data (such as predicting the remaining life of clothing through historical wear data), the optimization algorithm (such as particle swarm optimization algorithm) is used to design a washing plan for each piece of clothing. The design process takes into account the life prediction of each piece of clothing to ensure that the washing plan can extend the service life of the clothing and optimize the wear of the clothing. The system formulates the most appropriate washing cycle and washing intensity for each piece of clothing based on the washing demand and life prediction of the clothing. The washing plan includes specific parameters for each washing, such as washing frequency, washing temperature, washing time, detergent dosage, etc. Through the multi-objective optimization model, a personalized and efficient clothing washing plan is generated.

[0045] Preferably, step S42 includes the following steps: Extract key features of the historical washing reference system and obtain the reference system feature vector; Perform historical washing reference matching on the washing clothes information based on the reference system feature vector to generate current clothes effect reference data; Compare the laundry information and the current laundry effect reference data to generate laundry difference data; Cleanable stains are identified on the clothing difference data to obtain cleanable difference data; washing demand mapping is performed based on the cleanable difference data to generate clothing washing demand.

[0046] In this embodiment, the data of the clothing state before and after each washing in the historical washing reference system is subjected to dimensionality reduction processing by a data processing algorithm (such as PCA principal component analysis or LDA linear discriminant analysis), and the clothing features before and after washing (such as color change, fiber wear, size change, type and degree of stains before washing, etc.) are converted into representative feature vectors. In the process of feature extraction, the dimensional difference of each feature dimension is eliminated by normalization processing, and the change of the state before and after washing is converted into a multidimensional feature vector. The feature vector includes the change of the clothing during the washing process, such as the wear resistance and color difference change after each washing, and the feature information of the current laundry (such as fabric type, stain type, color, fiber material, etc.) is converted into a multidimensional feature vector. ) is compared with the feature vector extracted from the historical washing reference system, and the historical washing reference data similar to the current clothing features is selected using distance measurement methods such as Euclidean distance or cosine similarity. Through this matching method, the washing instance in the historical washing process that is most similar to the current clothing features is found to generate the current clothing effect reference data. The current clothing effect reference data contains clothing effect information related to the historical washing results, such as color difference after washing, degree of wear, stain removal effect, etc., and extracts the data before and after washing of the current clothing, such as the type of stains, the clothing state before washing and the state after washing. Through feature comparison algorithms (such as difference image processing, image contrast calculation, etc.), the differences between the clothing state after washing and the historical state are identified. The difference between the washing reference data, the difference data includes the changes in the cleanliness, color difference, stain removal of the clothes, etc. The difference comparison results generate clothing difference data, which records the difference in washing effects, such as whether different stain types are completely removed, whether the color of the clothes is restored, whether the degree of wear is increased, etc. The clothing difference data is used to identify cleanable stains, and the image processing technology based on machine learning (such as convolutional neural network CNN) is used to analyze the clothing difference data, especially to classify the stains, and identify which stains are within the cleanable range. Usually, the model is trained (using a large number of labeled stain samples for training) to identify the stains on the clothes that can still be removed by conventional washing. The system obtains cleanable difference data based on the stain type (such as oil stains, juice stains, etc.). The system extracts the coordinates and type information of the cleanable area based on the image recognition algorithm, and uses it as the basis for mapping the washing needs. Through the analysis of the cleanable difference data, the system identifies the type, degree and washing method requirements of the stains, and combines the material, color and other information of the clothes. The mapping algorithm (such as a rule engine based on a decision tree) is used to map the cleanable stains to specific washing needs. The mapping process includes determining the required water temperature, washing time, detergent type and dosage, washing intensity and other parameters, and adjusting the parameters of the washing process according to the wear of the clothes and the washing effect requirements, such as low-temperature water washing, strong washing, dry cleaning or delicate washing.

[0047] Preferably, step S44 includes the following steps: Perform demand configuration mapping on the demand washing parameters to obtain candidate parameter configuration solutions; Performing a washing intensity evaluation on the candidate parameter configuration schemes to obtain candidate washing intensity data; Perform washing intensity impact analysis on predicted clothing life data to generate life impact data; Conduct wear acceleration evaluation on the life impact data to obtain wear acceleration data; Perform correlation analysis on candidate washing intensity data based on wear acceleration data to generate a wear-intensity correlation curve; The wear-intensity correlation curve is screened for low wear intensity to obtain appropriate washing intensity data; A washing plan is designed based on the appropriate washing intensity data to obtain a clothing washing plan.

[0048] In this embodiment, the required washing parameters are mapped, and the input data of the mapping includes parameters such as stain type, washing material, and washing intensity in the clothing washing requirements. In combination with clothing characteristics and washing history data, the rule engine is used to map these washing requirements to a predefined parameter configuration set. The specific operation includes selecting a corresponding washing program (such as warm water washing, cold water washing, etc.) according to the type of clothing fabric (such as cotton, wool, synthetic fiber, etc.) and the type of stain (such as oil stains, juice stains, etc.), and generating multiple candidate solutions according to the washing requirements. Each candidate solution includes a specific water temperature, washing time, detergent type and dosage, etc. According to the parameters such as water temperature, washing time, detergent concentration, etc. in the candidate configuration solution, a multi-dimensional washing intensity evaluation model (for example, based on multiple regression analysis or support vector machine SVM (Support Vector The washing intensity of each solution is calculated by a washing machine (Machine Learning) model. The specific operation includes scoring according to the actual impact of different washing intensities on clothing (such as the degree of fiber damage and color fading of clothing after washing). The evaluation results generate candidate washing intensity data. The data includes the washing intensity evaluation results of each candidate solution. The washing intensity value is presented in the form of an index value or a grade value, indicating different intervals of washing intensity from low to high. The predicted clothing life data is compared with the candidate washing intensity data. Based on historical clothing wear data and a life prediction model (such as a regression analysis model based on machine learning), the wear of clothing under different washing intensities is simulated, and the impact of different washing intensities on clothing life is calculated. The operation specifically includes inputting parameters such as clothing material, type, and frequency of use, and generating life impact data based on the damage of clothing fibers, dyeing layers, etc. caused by different washing intensities. The life impact data describes the specific values ​​and trends of the shortening of clothing life due to different washing intensities, and reflects the degree of influence of washing intensity on the overall life of clothing. According to the data, by constructing a wear acceleration model (for example, a mathematical model based on accelerated life test), the accelerated effect of different washing intensities on clothing wear is evaluated. During operation, by analyzing the change rate of clothing under different washing intensities (such as the reduction of fabric thickness, the speed of color fading, etc.), wear acceleration data is generated. The wear acceleration data includes the acceleration of clothing loss under a specific intensity, which represents the accelerated wear effect of the washing process on clothing. Based on the wear acceleration data, the candidate washing intensity data are associated with each other. The relationship between wear and washing intensity is established using data mining techniques (such as association rule mining or K-means clustering analysis). By analyzing the relationship between wear acceleration data and washing intensity data, a wear-intensity association curve is generated. During operation, the system uses wear acceleration data as the dependent variable and washing intensity data as the independent variable to establish a regression model or other mathematical relationship model to generate curve data. The curve reflects the changing trend of clothing wear under different washing intensities. The shape of the curve is used to judge the actual wear effect of washing on clothing under different intensities.According to the shape of the wear-intensity correlation curve, the washing intensity range with less wear and better washing effect is selected. The system sets a threshold (for example, when the washing intensity is lower than a certain value, the wear rate is reduced to the minimum) to select the washing intensity with the least wear on the life of the clothes, and obtain the appropriate washing intensity data. The appropriate washing intensity data reflects the washing intensity value with the least wear on the clothes while ensuring the cleaning effect. The washing plan generation algorithm (such as optimization algorithm or dynamic programming method) is used to design a specific washing plan based on the appropriate washing intensity data. The plan includes parameters such as washing time, temperature, detergent type and dosage. In specific operations, the system combines the appropriate washing intensity with factors such as the material type and stain type of the clothes to generate a set of personalized washing plans. The washing plan can adjust various parameters according to actual washing needs to ensure that the clothes can be cleaned and the wear of the clothes can be minimized.

[0049] Preferably, step S5 comprises the following steps: Step S51: adjusting washing parameters based on the laundry washing plan to obtain adjustment parameters; performing intelligent washing according to the adjustment parameters, and monitoring the laundry washing process at the same time; Step S52: recording the operation parameters of the laundry process to obtain the laundry operation parameters; integrating the data of the laundry process and the laundry operation parameters to generate the laundry washing results; Step S53: Repeatedly bind the laundry plan and the laundry result based on the user-clothing binding relationship to obtain single-washing global data; Step S54: updating the clothing data file according to the global data of a single wash, and generating a washing traceability file.

[0050] In this embodiment, according to the contents of the laundry washing plan, such as washing intensity, water temperature, washing time and other parameters, a control algorithm (such as a PID controller or a fuzzy controller) is used for regulation. In specific implementation, the system dynamically adjusts by collecting real-time data (such as a water temperature sensor, a detergent concentration sensor, etc.). For example, if the current water temperature is higher than a predetermined value, the system will automatically adjust the temperature to maintain it within the target range, and dynamically adjust the washing time and the amount of detergent according to the type of clothing and the degree of stains. During the regulation process, the control strategy is adjusted using real-time feedback data. The final regulation parameters include accurate water temperature settings, detergent concentration, rotation speed, washing time, etc. The regulation parameters are actually executed by the washing machine, and the washing machine is equipped with a variety of sensors. , such as humidity sensors, speed sensors, temperature sensors, etc. These sensors monitor the status of clothes in the washing process in real time. The system controls the operation of the washing machine according to the control parameters, and ensures that the washing effect of clothes reaches the expected standard by adjusting the speed, temperature and water level. At the same time, the system continuously monitors various key data in the washing process, such as water temperature and humidity changes, and uses a data acquisition module to record all operating data in the washing process in real time, including water temperature, washing time, rotation speed, detergent dosage, clothing weight changes, etc. In specific operations, sensors integrated in the washing machine (such as temperature sensors, flow sensors, weight sensors, etc.) are used to obtain these data, which are transmitted to the data storage unit in real time for archiving, and all recorded operation data are recorded in real time. The running parameters constitute a complete washing operation parameter data set, integrating the washing operation parameters with other relevant information in the clothing washing process (such as clothing material, washing program, etc.). Through data processing and analysis algorithms (such as data fusion technology or multivariate linear regression model), all monitoring data (such as water temperature, washing time, humidity change, rotation rate, etc.) are correlated and analyzed with the washing effect. The generated clothing washing results include data such as clothing cleanliness evaluation, wear and tear, and color change. Through the binding relationship between users and clothing (through RFID tags), the clothing washing plan and washing results are associated. In specific operations, the user's clothing data (such as clothing type, washing needs, etc.) and the parameters involved in the washing plan (such as washing program) are correlated. , temperature, etc.), and then combined with the operating parameters generated during the washing process (such as washing intensity, humidity, time, etc.), all relevant data are associated and stored through the database system to generate global data for a single wash. The generated global data includes not only the specific washing information of the clothes, but also the user's usage habits, clothing characteristics and other information. By reading the historical data archive of the clothes, the initial information of the clothes (such as fabric type, washing frequency, cleaning effect, etc.) is obtained. Then, the global data generated during the washing process (such as washing time, temperature, cleaning effect, wear and tear, etc.) is merged and updated with the original archive. The updated archive forms the washing traceability archive of the clothes. The traceability archive records the detailed data of each wash for users to view or analyze.The final archive can be displayed through a visualization platform, including all the washing history, wear and tear, expected lifespan and other information of the clothes, which users can query at any time.

[0051] The present invention also provides a full life cycle traceability system based on smart washing, which is used to execute the full life cycle traceability method based on smart washing as described above. The full life cycle traceability system based on smart washing includes: An information binding module is used to obtain laundry information and user personal information; perform digital identity mapping on the laundry information to generate an RFID identification code; perform rights binding on the user's personal information based on the RFID identification code to generate a user-clothing binding relationship; The archive construction module is used to digitally mirror the user-clothing binding relationship to obtain mirror copy data; trace the mirror copy data to generate a clothing data archive; The life prediction module is used to analyze the status of washed clothes according to the clothing data file to obtain clothing status data; perform life prediction based on the clothing status data to generate predicted clothing life data; A washing plan design module is used to analyze the washing demand of the washing clothes according to the clothing data file, generate the washing demand of the clothes; design the washing plan based on the predicted clothing life data and the washing demand of the clothes, so as to obtain the washing plan of the clothes; The traceability file update module is used to control execution based on the clothing washing plan and monitor the clothing washing results; the clothing data file is updated according to the clothing washing plan and the clothing washing results to generate a washing traceability file.

[0052] By mapping the user's personal information to a digital identity and generating a unique identification code, the present invention can ensure the precise association between each user and the clothes they wash, enhance data security and privacy protection, and provide personalized services for subsequent washing processes. Through digital mirror replication and combined with database chain technology, the system realizes the immutability and traceability of clothing data. The data archive of each piece of clothing can be permanently preserved and verified, ensuring the transparency and credibility of the washing service. Combined with clothing status data and life prediction, the system can accurately evaluate the current usage status of the clothing and predict its life in advance, providing a scientific basis to extend the use cycle of the clothing and reduce damage caused by improper washing. By analyzing washing needs and life prediction data, the system can generate a personalized washing plan to ensure that each piece of clothing is optimized for washing according to its characteristics and needs to avoid unnecessary damage. By real-time monitoring of washing results and updating clothing data archives, the system can record detailed information on each washing process, ensure the transparency and traceability of the washing process, enhance users' trust in washing services, and provide data support for future washing decisions.

[0053] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0054] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A full life cycle traceability method based on smart washing, characterized in that: The following steps are involved: Step S1: Acquire laundry information and user personal information; perform digital identity mapping on the laundry information and generate an RFID identification code; Bind user personal information to rights and interests based on RFID identification code to generate user-clothing binding relationship; Step S2: Digitally mirror the user-clothing binding relationship to obtain mirror copy data; perform traceability link combination on the mirror copy data to generate a clothing data archive; Step S3: performing clothing status analysis on the washed clothing information according to the clothing data file to obtain clothing status data; performing life prediction based on the clothing status data to generate predicted clothing life data; Step S4: Analyzing the laundry information according to the laundry data file to generate laundry demand; Designing a washing plan based on the predicted clothing life data and clothing washing needs to obtain a clothing washing plan; Step S5: Control execution based on the clothing washing plan and monitor the clothing washing results; update the clothing data file according to the clothing washing plan and the clothing washing results to generate a washing traceability file.

2. The whole life cycle traceability method based on smart washing according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: obtaining laundry information and user personal information; performing coding conversion on the laundry information to obtain laundry information coding; Step S12: Perform digital identity mapping on the clothing information code to generate an RFID identification code; Step S13: standardizing the user's personal information to obtain user data in a unified format; performing association analysis on the user data in the unified format based on the RFID identification code to obtain a user-clothing association relationship; Step S14: Mapping and binding are performed according to the user-clothing association relationship to generate a user-clothing binding relationship.

3. The whole life cycle traceability method based on smart washing according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: establishing a data template for the user-clothing binding relationship to obtain a relationship data template; mapping and converting the user-clothing binding relationship according to the relationship data template to generate a digital image of the binding relationship; Step S22: redundantly copy the binding relationship digital image to obtain image copy data; Step S23: evenly divide the mirror copy data into blocks to obtain block copy data; Step S24: Connect the block copy data in a database chain structure to generate a copy database chain structure; construct a traceability link for the mirror copy data based on the copy database chain structure to obtain a traceability link; Step S25: integrating the traceability links to generate clothing data files.

4. The whole life cycle traceability method based on smart washing according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: performing clothing matching on the washed clothing information according to the clothing data file to obtain the current clothing washing history record; performing clothing change progress analysis on the current clothing washing history record to generate the clothing wear process; Step S32: analyzing the status of the washed clothes based on the wear process of the clothes to obtain clothing status data; Step S33: positioning the clothing life node of the clothing data file according to the clothing state, and generating the current clothing life node; Step S34: performing life prediction on the clothing state data based on the current clothing life node to generate predicted clothing life data.

5. The whole life cycle traceability method based on smart washing according to claim 4 is characterized in that: Step S31 includes the following steps: Extracting feature identifiers from laundry information to obtain laundry features; Matching the characteristics of the washed clothes with the clothes data files to obtain matching clothes file information; Integrate and process the matching clothing archive information to generate the current clothing washing history record; Sort the current laundry history records by timestamp to obtain laundry time series data; Performing washing interval analysis on the laundry washing time series data to obtain the washing interval range; According to the washing interval, the clothing washing time series data is evaluated for the degree of change of the clothing at the interval, and the degree of change of the clothing at the interval is generated; The change process mapping is performed based on the degree of clothing change in the intervals to generate the clothing wear process.

6. The whole life cycle traceability method based on smart washing according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: extracting the reference frames before and after washing from the clothing data file to generate a historical washing reference frame; Step S42: performing washing demand analysis on the laundry information based on the historical washing reference system to generate laundry washing demand; Step S43: mapping washing parameters of the laundry washing requirements to obtain required washing parameters; Step S44: Design a washing plan based on the required washing parameters and the predicted clothing life data to obtain a clothing washing plan.

7. The whole life cycle traceability method based on smart washing according to claim 6 is characterized in that: Step S42 includes the following steps: Extract key features of the historical washing reference system and obtain the reference system feature vector; Perform historical washing reference matching on the washing clothes information based on the reference system feature vector to generate current clothes effect reference data; Compare the laundry information and the current laundry effect reference data to generate laundry difference data; Cleanable stains are identified on the clothing difference data to obtain cleanable difference data; washing demand mapping is performed based on the cleanable difference data to generate clothing washing demand.

8. The whole life cycle traceability method based on smart washing according to claim 6 is characterized in that: Step S44 includes the following steps: Perform demand configuration mapping on the demand washing parameters to obtain candidate parameter configuration solutions; Performing a washing intensity evaluation on the candidate parameter configuration schemes to obtain candidate washing intensity data; Perform washing intensity impact analysis on predicted clothing life data to generate life impact data; Conduct wear acceleration evaluation on the life impact data to obtain wear acceleration data; Perform correlation analysis on candidate washing intensity data based on wear acceleration data to generate a wear-intensity correlation curve; The wear-intensity correlation curve is screened for low wear intensity to obtain appropriate washing intensity data; A washing plan is designed based on the appropriate washing intensity data to obtain a clothing washing plan.

9. The whole life cycle traceability method based on smart washing according to claim 1 is characterized in that: Step S5 includes the following steps: Step S51: adjusting washing parameters based on the laundry washing plan to obtain adjustment parameters; performing intelligent washing according to the adjustment parameters, and monitoring the laundry washing process at the same time; Step S52: recording the operation parameters of the laundry process to obtain the laundry operation parameters; integrating the data of the laundry process and the laundry operation parameters to generate the laundry washing results; Step S53: Repeatedly bind the laundry plan and the laundry result based on the user-clothing binding relationship to obtain single-washing global data; Step S54: updating the clothing data file according to the global data of a single wash, and generating a washing traceability file.

10. A full life cycle traceability system based on smart washing, characterized in that: Used to execute the full life cycle traceability method based on smart washing as claimed in claim 1, the full life cycle traceability system based on smart washing includes: An information binding module is used to obtain laundry information and user personal information; perform digital identity mapping on the laundry information to generate an RFID identification code; perform rights binding on the user's personal information based on the RFID identification code to generate a user-clothing binding relationship; The archive construction module is used to digitally mirror the user-clothing binding relationship to obtain mirror copy data; trace the mirror copy data to generate a clothing data archive; The life prediction module is used to analyze the status of washed clothes according to the clothing data file to obtain clothing status data; perform life prediction based on the clothing status data to generate predicted clothing life data; A washing plan design module is used to analyze the washing demand of the washing clothes according to the clothing data file, generate the washing demand of the clothes; design the washing plan based on the predicted clothing life data and the washing demand of the clothes, so as to obtain the washing plan of the clothes; The traceability file update module is used to control execution based on the clothing washing plan and monitor the clothing washing results; the clothing data file is updated according to the clothing washing plan and the clothing washing results to generate a washing traceability file.

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