An intelligent washing equipment integrated management method based on internet of things

Abstract

The application discloses an intelligent washing equipment integrated management method based on an Internet of Things, which comprises the following steps: S1, equipment classification and adaptation preparation; S2, multi-protocol unified adaptation and access; S3, data standardization processing; S4, cross-system seamless docking; and S5, equipment integrated management and dynamic optimization. Through technical means such as multi-protocol unified access, low-cost transformation of old equipment, cross-system seamless docking and plug-in hot update adaptation, the application breaks data islands, reduces transformation and operation and maintenance costs, shortens docking cycles, improves system adaptation flexibility, expansibility and collaborative management efficiency, realizes unified integrated management of different brands, different models, new and old washing equipment and full-process data intercommunication, adapts to application requirements of multiple scenes such as hotels, factories and hospitals, simultaneously guarantees system operation stability and data interaction safety, fully utilizes existing equipment resources, and provides an efficient, universal and low-cost solution for intelligent washing equipment integrated management.

Description

Technical Field

[0001] This invention relates to the field of intelligent washing equipment technology, and in particular to an integrated management method for intelligent washing equipment based on the Internet of Things. Background Technology

[0002] With the rapid development of IoT technology, smart washing equipment has been widely used in hotels, hospitals, textile factories, laundry plants and other scenarios. In order to achieve unified monitoring, scheduling and management of multiple and various types of smart washing equipment, IoT-based integrated management methods for smart washing equipment have emerged. Existing IoT-based integrated management methods for smart laundry equipment suffer from significant shortcomings in terms of technical compatibility and system integration, severely hindering the improvement of management efficiency and the expansion of application scenarios. Specifically: 1. Fragmented protocols and standards: Different brands and models of smart laundry equipment use their own proprietary communication protocols, resulting in inconsistent data formats. This makes it difficult for multiple devices to be uniformly connected to the same management platform, creating data silos and preventing global collaborative management. 2. Difficulty in compatibility between new and old equipment: Older laundry equipment lacks IoT modules and has closed interfaces. To connect it to the existing management system, a dedicated gateway must be deployed or large-scale hardware modifications are required, leading to high costs and issues such as interface incompatibility and unstable data transmission. 3. Complex cross-system integration: The interface standards between the smart laundry equipment management platform and the supporting scenario-based systems are inconsistent, requiring extensive customized development for integration. This results in long development cycles, high maintenance costs, and a high risk of integration failures, making it difficult to achieve data interoperability and closed-loop processes. 4. Poor protocol adaptation flexibility: When adding new laundry equipment or upgrading equipment protocols, the management system needs to be completely restructured or its code modified on a large scale, resulting in poor scalability and an inability to quickly adapt to new equipment and protocols. In summary, it is clear that developing an intelligent washing equipment integration management method that can effectively solve the aforementioned compatibility and integration problems has become an urgent technical need in this field. To this end, this application proposes an intelligent washing equipment integration management method based on the Internet of Things. Summary of the Invention

[0003] Based on the technical problems existing in the background technology, the present invention proposes an integrated management method for intelligent washing equipment based on the Internet of Things.

[0004] The present invention proposes an integrated management method for intelligent washing equipment based on the Internet of Things, comprising the following steps: S1: Equipment Classification and Adaptation Preparation: Classify and identify the washing equipment to be connected as smart and old types, collect the core information of the two types of equipment respectively, store the information of smart equipment in the cloud equipment archive, and determine the IoT transformation and adaptation plan for old equipment. S2: Unified adaptation and access of multiple protocols: The edge gateway cluster deploys a multi-protocol parsing engine and accelerates protocol conversion through FPGA. Smart devices automatically match protocols and connect to the cloud. Private protocol devices import parsing rules for adaptation. Old devices are connected through IoT transformation modules. The device adaptation module adopts plug-in and hot-update configuration to adapt to new devices and protocol upgrades. S3: Data Standardization Processing: The data standardization module cleans, unifies, encrypts, encapsulates, and verifies the raw data from the device to ensure that the data transmitted to the cloud is reliable and valid. S4: Seamless cross-system integration: Through standardized interfaces and dynamic routing configurations of cross-system integration modules, data interoperability and process collaboration between the cloud platform and supporting systems are achieved, and data interaction security is ensured through a unified security framework; S5: Device Integration Management and Dynamic Optimization: The cloud platform manages all devices in a unified manner, monitors and schedules tasks in real time, and the edge gateway cluster collects status and dynamically optimizes and adapts parameters through AI algorithms to achieve full lifecycle management and remote operation and maintenance of devices.

[0005] Preferably, the specific logical steps of S1 are as follows: S101: Preliminary Equipment Screening: Traverse all washing equipment that needs to be connected to the management system, collect the basic hardware parameters of each device, and label the device number as follows. , Let n be the total number of devices to be connected, and establish an initial device information set. ,in This represents the nth washing machine; S102: Equipment Classification and Identification: Based on the equipment hardware parameters, the equipment type is determined using a classification formula, as follows: ,in This is the type identifier for the i-th device. This indicates that the device is an intelligent washing machine. This indicates that the equipment is an old-fashioned washing machine. Let be the number of IoT modules in the i-th device. This indicates that the device has at least one built-in IoT communication module. This indicates that the device does not have an IoT module. The interface open identifier for the i-th device. This indicates that the device has an open control interface. This indicates that the device has no open control interface; S103: Core Information Collection: Core parameters are collected for the two categories of classified devices. The completeness of the collected data is verified using the following formula: ,in Let be the core information collection completeness rate of the i-th device, with a value range of [0, 100%]. At that time, the collection was deemed qualified. Let represent the number of core parameters actually collected by the i-th device. The number of core parameter standards that need to be collected by the i-th device is [number] for intelligent devices. =4, including device model, communication protocol type, interface parameters, and data output format; older devices. =3, including equipment model, operating parameters, and control interface type; S104: Information Storage and Adaptation Scheme Determination: For The intelligent devices will collect qualified core information and store it in the device archive on the cloud management platform. The archive index formula is: ;for For older equipment, based on the core parameters collected, the IoT transformation module model and interface adaptation scheme are determined using an adaptation matching formula, which is as follows: ,in Let be the matching degree between the i-th old device and the candidate IoT transformation module, with a value range of [0,3]. Timely determination of suitability The weights of the k-th class of core parameters, Let k be the actual value of the k-th type of core parameter for the i-th old equipment. The standard values ​​for the k-th type of adaptation parameters for candidate IoT transformation modules.

[0006] Preferably, the specific logical steps of S2 are as follows: S201: Edge Gateway Cluster and Multi-Protocol Parsing Engine Deployment: Deploy a distributed edge gateway cluster, labeling the gateway number as j. m represents the number of edge gateways. Each gateway deploys a multi-protocol parsing engine, integrating mainstream IoT communication protocol parsing modules, while reserving a private protocol import interface to establish a set of correspondences between gateways and devices. ,in This represents the set of devices that the m-th edge gateway is responsible for accessing; S202: FPGA Protocol Conversion Acceleration Configuration: Configure an FPGA acceleration module for the multi-protocol parsing engine of each edge gateway. Optimize configuration parameters using an acceleration efficiency calculation formula to ensure real-time protocol conversion. The acceleration efficiency formula is as follows: ,in Let be the protocol conversion acceleration efficiency of the j-th edge gateway, with a value ranging from [100%, +∞). The acceleration configuration was deemed qualified at that time. The average protocol conversion time when the j-th gateway does not have FPGA acceleration enabled. The average time for protocol conversion after enabling FPGA acceleration for the j-th gateway; S203: Automatic Protocol Matching for Smart Devices: For smart devices categorized in S1, the device adaptation module calls the protocol matching algorithm to automatically match the corresponding parsing module in the multi-protocol parsing engine. The matching accuracy is verified using the following formula: ,in The accuracy rate for automatic matching of smart device protocols is set, with a value range of [0, 100%]. The match is deemed successful at that time. The number of smart devices that were successfully matched by the protocol; The total number of smart devices participating in the protocol matching; after a successful match, bidirectional communication between the device and the cloud management platform is established through the edge gateway. S204: Proprietary Protocol Device Adaptation: For smart devices using proprietary protocols, import the proprietary protocol parsing rules through the protocol import interface. The compatibility of the parsing rules is determined by the following formula: ,in This represents the adaptation rate for private protocol parsing rules, with a value range of [0, 100%]. Timely determination of suitability The length of instructions / data that can be correctly parsed by the private protocol parsing rules. The total instruction / data length of the private protocol, after fitting, completes the conversion between the private protocol and the unified protocol, enabling device-cloud interface; S205: Integration and Adaptation of Legacy Equipment: For legacy equipment categorized in S1, install the established IoT upgrade modules and verify the interface compatibility between the modules and the equipment using the interface adaptation formula, which is as follows: ,in The interface compatibility score between the i-th old device and the IoT transformation module is given, with a value range of [0,2]. When the compatibility is deemed acceptable, To adapt weights for the interface, This is the matching identifier for the p-th type interface parameter of the i-th old device and the retrofit module. (During matching...) =1, when there is no match =0, after passing compatibility tests, the modified module collects device data and completes protocol conversion through the edge gateway to achieve connection with the cloud; S206: Plug-in Hot-Update Configuration: The device adaptation module adopts a plug-in architecture. For new devices or protocol upgrade requirements, corresponding protocol parsing plug-ins are added. The validity of plug-in updates is determined by the following formula: ;in To optimize plugin update efficiency, the value range is [0, 100%]. The update is deemed acceptable at that time. The update time for the protocol parsing plugin, The time spent reconstructing and adapting traditional systems to new protocols is reduced. Once the update is successful, the adaptation of new devices or protocol upgrades can be completed without reconstructing the system.

[0007] Preferably, the specific logical steps of S4 are as follows: S401: Supporting System Screening and Interface Initialization: Identify all supporting systems for the intelligent washing management scenario and label each system with the number k. Let p be the total number of supporting systems, and establish a set of supporting systems. ,in Representing the p-th supporting system, the cross-system interface module loads the preset standardized interface interface and initializes the interface parameters; S402: Dynamic Routing Rule Configuration: Based on the interface parameters of the supporting system, dynamic routes are generated through routing rule configuration formulas to ensure optimal data transmission paths. The routing configuration formula is as follows: ,in The routing adaptation score for the k-th supporting system is given, with a value ranging from [0, 100]. The routing configuration is deemed valid at that time. For transmission delay weight, For transmission link length weight, The average transmission latency between the cloud platform and the k-th supporting system. This is the length of the transmission link between the cloud platform and the kth supporting system. S403: Interface Parameter Configuration and Verification: Based on the routing configuration results, configure the interface parameters between the cloud management platform and each supporting system. Ensure the configuration is reasonable using the interface parameter verification formula, which is as follows: ,in This is the verification value for the docking parameters of the k-th supporting system, with a value range of [0, 3]. The verification is deemed successful at that time. To connect the parameter weights, This is a qualification indicator for the configuration of the k-th supporting system's q-th type of docking parameter. When qualified... =1, when unqualified =0; S404: Data Interoperability and Process Collaboration Implementation: After successful verification, data interaction between the cloud platform and supporting systems is initiated. The interaction status is monitored in real time using the data interoperability success rate formula, which is as follows: ,in This represents the data interoperability success rate between the cloud platform and the k-th supporting system, with a value ranging from [0, 100%]. The system was determined to be functioning normally. The number of data frames for successful interaction is the total number of data interaction frames. At the same time, process collaboration is achieved, and the data of the washing equipment is synchronized to the supporting system, and the instructions of the supporting system are received and forwarded to the corresponding equipment; S405: Deployment of unified security framework and security verification: Deploy a unified security framework in the docking link, and use encryption, key management and zero-trust architecture to ensure data security. Evaluate the security of data interaction through a security verification formula, and the security verification formula is as follows: , where is the security coefficient of the kth supporting system data interaction, and the value range is [0,1], it is determined that the security is qualified when is the number of data frames tampered with during the data interaction process, is the total number of data interaction frames; S406: Dynamic routing hot update and maintenance: Real-time monitor the docking status of the supporting system. When the interface of the supporting system is upgraded or a new supporting system is added, optimize the update process through the routing hot update efficiency formula without stopping the overall docking service. The routing hot update efficiency formula is as follows: , where is the routing hot update efficiency, and the value range is [0,100%], it is determined that the update is qualified when is the time-consuming for routing hot update, is the time-consuming for reconfiguring the routing after stopping the docking service.

[0008] Preferably, the specific logical steps of S5 are as follows: S501: Aggregation of device access status: The cloud management platform receives the data standardized in step S3, aggregates the access status of all connected devices, and marks the device number as i, , n is the total number of connected devices, and a device integration management set is established , where represents the integration management information of the nth device; S502: Real-time device status monitoring: The cloud platform collects the running status parameters of each device in real time, and verifies the monitoring effect through the status monitoring accuracy formula. The status monitoring accuracy formula is as follows: , where is the status monitoring accuracy of the ith device, and the value range is [0,100%], it is determined that the monitoring is qualified when is the number of correct status times for real-time monitoring of the ith device, is the total number of monitoring times of the ith device. During the monitoring process, the running status of the device is displayed in real time, and an alarm is triggered for abnormal status; S503: Unified scheduling of washing tasks: Based on the supporting system instructions received from S4 and the real-time operating status of the equipment, the scheduling scheme is optimized through the task scheduling efficiency formula to achieve efficient task allocation. The scheduling efficiency formula is as follows: ,in To optimize the scheduling efficiency of the washing tasks, the value ranges from [0, 100%]. The scheduling is deemed qualified at that time. The standard completion time for the task. To determine the actual task completion time, during the scheduling process, a hybrid framework based on genetic algorithms and Petri nets is used to dynamically allocate cross-protocol task resources and optimize task execution paths. S504: Dynamic Optimization of Adaptation Parameters: The edge gateway cluster collects real-time data on device access status, protocol operation, and cross-system interoperability. Adaptation issues are analyzed using AI algorithms, and the rationality of optimization parameters is evaluated using an optimization effect formula, as follows: ,in To accommodate the optimized parameter settings, the value range is [0, 100%]. The optimization was deemed successful at that time. To optimize the equipment compatibility failure rate, To optimize the device compatibility failure rate before optimization, the compatibility parameters are automatically updated after optimization to ensure system stability; S505: Device Lifecycle Management: The cloud platform records the entire lifecycle information of each device and verifies the management effectiveness through a lifecycle management integrity formula, as follows: ,in To ensure the integrity of equipment lifecycle management, the value range is [0, 100%]. The management was deemed qualified at that time. This refers to the actual number of recorded device lifecycle information entries. The number of lifecycle information entries required by the standard; S506: Remote Operation and Maintenance and Fault Prediction: The cloud platform supports remote configuration, upgrades, and diagnostics of devices. The effectiveness of operation and maintenance is evaluated using an operation and maintenance response efficiency formula, as follows: ,in For remote operation and maintenance response efficiency, the value range is [0, 100%]. The actual response time for remote operation and maintenance. To establish a standard response time for remote operation and maintenance, and based on equipment operation data and historical fault records, predict equipment failures and push maintenance plans to achieve predictive maintenance.

[0009] Preferably, in S2, the edge gateway cluster adopts a distributed deployment method, with each edge gateway responsible for the access of washing equipment in a certain area. The edge gateways communicate with each other through wireless or wired communication. When an edge gateway fails, the equipment it is responsible for automatically switches to the adjacent edge gateway. The edge gateway adopts an IP65-level waterproof, moisture-proof, and dustproof design, and uses a rust-resistant metal shell to adapt to the harsh working environment of the laundry room. The IoT upgrade module features a pluggable design, supporting interface docking with different types of old washing equipment. It has a built-in power management unit that can automatically adjust power consumption and supports LoRa, ZigBee and Bluetooth wireless expansion methods. It can be connected to an external concentrator to achieve centralized management of non-smart washing equipment.

[0010] Preferably, in S3, the preset unified data standard can be customized according to different application scenarios such as hotels, factories, and hospitals. The data standardization module uses a dynamic parameter mapping engine to dynamically extract and transform the requested data according to routing rules to adapt to the data requirements of different scenarios. Furthermore, the data is encrypted using the national cryptographic SM4 encryption algorithm during data encapsulation.

[0011] Preferably, in S4, the cross-system docking module supports multi-center data transmission, can simultaneously synchronize washing equipment data to multiple supporting systems, and supports secondary development, providing Python and C language development environments and complete development documentation to adapt to the differentiated docking needs of different projects.

[0012] Compared with existing technologies, the beneficial effects of this invention are: 1. By leveraging the multi-protocol parsing engine and unified data standards of the edge gateway cluster, data unification and interconnection between washing equipment of different brands and protocols are achieved, breaking down data silos and enabling global collaborative management of equipment; 2. Low-cost and lightweight transformation of old washing equipment can be achieved through pluggable IoT transformation modules, without the need for additional deployment of dedicated gateways or large-scale hardware modifications, avoiding problems such as interface incompatibility and unstable data transmission, and enabling the collaborative operation of new and old equipment; 3. By using pre-defined standardized interfaces and dynamic routing adaptation technology, seamless integration with scenario-based supporting systems can be achieved without extensive customized development, shortening the integration cycle, reducing maintenance costs, and realizing data interoperability and process closure. 4. Through plug-in protocol compatibility, hot-update configuration and AI dynamic optimization, the entire management system does not need to be reconstructed when adding new devices or upgrading protocols, and adaptation can be completed quickly, greatly improving system scalability and adaptation efficiency, and adapting to different needs in multiple scenarios.

[0013] This invention breaks down data silos, reduces transformation and maintenance costs, shortens integration cycles, and improves system adaptability, scalability, and collaborative management efficiency through multi-protocol unified access, low-cost retrofitting of old equipment, seamless cross-system integration, and plug-in hot-updateability. It achieves unified integrated management and full-process data interoperability for different brands, models, and both new and old laundry equipment, adapting to the application needs of hotels, factories, hospitals, and other scenarios. At the same time, it ensures system stability and data interaction security, making full use of existing equipment resources and providing an efficient, universal, and low-cost solution for the integrated management of intelligent laundry equipment. Attached Figure Description

[0014] Figure 1 This is a flowchart of an integrated management method for intelligent washing equipment based on the Internet of Things proposed in this invention. Detailed Implementation

[0015] The present invention will be further explained below with reference to specific embodiments.

[0016] Example Reference Figure 1 This embodiment proposes an integrated management method for intelligent washing equipment based on the Internet of Things, including the following steps: S1: Equipment Classification and Adaptation Preparation: Classify and identify the washing equipment to be connected as smart and old types, collect the core information of the two types of equipment respectively, store the information of smart equipment in the cloud equipment archive, and determine the IoT transformation and adaptation plan for old equipment. The specific logical steps are as follows: S101: Preliminary Equipment Screening: Traverse all washing equipment that needs to be connected to the management system, collect the basic hardware parameters of each device, and label the device number as follows. , Let n be the total number of devices to be connected, and establish an initial device information set. ,in This represents the nth washing machine; S102: Equipment Classification and Identification: Based on the equipment hardware parameters, the equipment type is determined using a classification formula, as follows: ,in This is the type identifier for the i-th device. This indicates that the device is an intelligent washing machine. This indicates that the equipment is an old-fashioned washing machine. Let be the number of IoT modules in the i-th device. This indicates that the device has at least one built-in IoT communication module. This indicates that the device does not have an IoT module. The interface open identifier for the i-th device. This indicates that the device has an open control interface. This indicates that the device has no open control interface; S103: Core Information Collection: Core parameters are collected for the two categories of classified devices. The completeness of the collected data is verified using the following formula: ,in Let be the core information collection completeness rate of the i-th device, with a value range of [0, 100%]. At that time, the collection was deemed qualified. Let represent the number of core parameters actually collected by the i-th device. The number of core parameter standards that need to be collected by the i-th device is [number] for intelligent devices. =4, including device model, communication protocol type, interface parameters, and data output format; older devices. =3, including equipment model, operating parameters, and control interface type; S104: Information Storage and Adaptation Scheme Determination: For The intelligent devices will collect qualified core information and store it in the device archive on the cloud management platform. The archive index formula is: ;for For older equipment, based on the core parameters collected, the IoT transformation module model and interface adaptation scheme are determined using an adaptation matching formula, which is as follows: ,in Let be the matching degree between the i-th old device and the candidate IoT transformation module, with a value range of [0,3]. Timely determination of suitability The weights of the k-th class of core parameters, Let k be the actual value of the k-th type of core parameter for the i-th old equipment. The standard values ​​for the k-th type of adaptation parameters for candidate IoT transformation modules; S2: Unified adaptation and access of multiple protocols: The edge gateway cluster deploys a multi-protocol parsing engine and accelerates protocol conversion through FPGA. Smart devices automatically match protocols and connect to the cloud. Private protocol devices import parsing rules for adaptation. Old devices are connected through IoT transformation modules. The device adaptation module adopts plug-in and hot-update configuration to adapt to new devices and protocol upgrades. The specific logical steps are as follows: S201: Edge Gateway Cluster and Multi-Protocol Parsing Engine Deployment: Deploy a distributed edge gateway cluster, labeling the gateway number as j. m represents the number of edge gateways. Each gateway deploys a multi-protocol parsing engine, integrating mainstream IoT communication protocol parsing modules, while reserving a private protocol import interface to establish a set of correspondences between gateways and devices. ,in This represents the set of devices that the m-th edge gateway is responsible for accessing; S202: FPGA Protocol Conversion Acceleration Configuration: Configure an FPGA acceleration module for the multi-protocol parsing engine of each edge gateway. Optimize configuration parameters using an acceleration efficiency calculation formula to ensure real-time protocol conversion. The acceleration efficiency formula is as follows: ,in Let be the protocol conversion acceleration efficiency of the j-th edge gateway, with a value ranging from [100%, +∞). The acceleration configuration was deemed qualified at that time. The average protocol conversion time when the j-th gateway does not have FPGA acceleration enabled. The average time for protocol conversion after enabling FPGA acceleration for the j-th gateway; S203: Automatic Protocol Matching for Smart Devices: For smart devices categorized in S1, the device adaptation module calls the protocol matching algorithm to automatically match the corresponding parsing module in the multi-protocol parsing engine. The matching accuracy is verified using the following formula: ,in The accuracy rate for automatic matching of smart device protocols is set, with a value range of [0, 100%]. The match is deemed successful at that time. The number of smart devices that were successfully matched by the protocol; The total number of smart devices participating in the protocol matching; after a successful match, bidirectional communication between the device and the cloud management platform is established through the edge gateway. S204: Proprietary Protocol Device Adaptation: For smart devices using proprietary protocols, import the proprietary protocol parsing rules through the protocol import interface. The compatibility of the parsing rules is determined by the following formula: ,in This represents the adaptation rate for private protocol parsing rules, with a value range of [0, 100%]. Timely determination of suitability The length of instructions / data that can be correctly parsed by the private protocol parsing rules. The total instruction / data length of the private protocol, after fitting, completes the conversion between the private protocol and the unified protocol, enabling device-cloud interface; S205: Integration and Adaptation of Legacy Equipment: For legacy equipment categorized in S1, install the established IoT upgrade modules and verify the interface compatibility between the modules and the equipment using the interface adaptation formula, which is as follows: ,in The interface compatibility score between the i-th old device and the IoT transformation module is given, with a value range of [0,2]. When the compatibility is deemed acceptable, To adapt weights for the interface, This is the matching identifier for the p-th type interface parameter of the i-th old device and the retrofit module. (During matching...) =1, when there is no match =0, after passing compatibility tests, the modified module collects device data and completes protocol conversion through the edge gateway to achieve connection with the cloud; S206: Plug-in Hot-Update Configuration: The device adaptation module adopts a plug-in architecture. For new devices or protocol upgrade requirements, corresponding protocol parsing plug-ins are added. The validity of plug-in updates is determined by the following formula: ;in To optimize plugin update efficiency, the value range is [0, 100%]. The update is deemed acceptable at that time. The update time for the protocol parsing plugin, The time spent reconstructing and adapting traditional systems to new protocols is reduced; once the update is successful, adaptation for new devices or protocol upgrades can be completed without reconstructing the system. In addition, the edge gateway cluster adopts a distributed deployment method. Each edge gateway is responsible for the access of washing equipment in a certain area. The edge gateways communicate with each other through wireless or wired communication. When an edge gateway fails, the equipment it is responsible for will automatically switch to the adjacent edge gateway. The edge gateway adopts IP65 level waterproof, moisture-proof and dustproof design, and uses rust-resistant metal shell to adapt to the harsh working environment of the laundry room. The IoT transformation module adopts a pluggable design, supports interface docking with different types of old washing equipment, has a built-in power management unit that can automatically adjust power consumption, and supports LoRa, ZigBee and Bluetooth wireless expansion methods. It can be connected to an external concentrator to achieve centralized management of non-smart washing equipment. S3: Data Standardization Processing: The data standardization module cleans, unifies, encrypts, encapsulates, and verifies the raw data from the device to ensure that the data transmitted to the cloud is reliable and valid. The preset unified data standard can be customized according to different application scenarios such as hotels, factories, and hospitals. The data standardization module uses a dynamic parameter mapping engine to dynamically extract and transform the requested data according to routing rules to adapt to the data requirements of different scenarios. The data is also encrypted using the national cryptographic SM4 encryption algorithm during data encapsulation. S4: Seamless cross-system integration: Through standardized interfaces and dynamic routing configurations of cross-system integration modules, data interoperability and process collaboration between the cloud platform and supporting systems are achieved, and data interaction security is ensured through a unified security framework; The specific logical steps are as follows: S401: Supporting System Screening and Interface Initialization: Identify all supporting systems for the intelligent washing management scenario and label each system with the number k. Let p be the total number of supporting systems, and establish a set of supporting systems. ,in Representing the p-th supporting system, the cross-system interface module loads the preset standardized interface interface and initializes the interface parameters; S402: Dynamic Routing Rule Configuration: Based on the interface parameters of the supporting system, dynamic routes are generated through routing rule configuration formulas to ensure optimal data transmission paths. The routing configuration formula is as follows: ,in The routing adaptation score for the k-th supporting system is given, with a value ranging from [0, 100]. The routing configuration is deemed valid at that time. For transmission delay weight, For transmission link length weight, The average transmission latency between the cloud platform and the k-th supporting system. This is the length of the transmission link between the cloud platform and the kth supporting system. S403: Interface Parameter Configuration and Verification: Based on the routing configuration results, configure the interface parameters between the cloud management platform and each supporting system. Ensure the configuration is reasonable using the interface parameter verification formula, which is as follows: ,in This is the verification value for the docking parameters of the k-th supporting system, with a value range of [0, 3]. The verification is deemed successful at that time. To connect the parameter weights, This is a qualification indicator for the configuration of the k-th supporting system's q-th type of docking parameter. When qualified... =1, when unqualified =0; S404: Data Interoperability and Process Collaboration Implementation: After successful verification, data interaction between the cloud platform and supporting systems is initiated. The interaction status is monitored in real time using the data interoperability success rate formula, which is as follows: ,in This represents the data interoperability success rate between the cloud platform and the k-th supporting system, with a value ranging from [0, 100%]. The system was determined to be functioning normally. The number of data frames for successful interaction. The total number of data interaction frames is used to simultaneously achieve process collaboration, synchronize washing equipment data to the supporting system, receive instructions from the supporting system and forward them to the corresponding devices; S405: Unified Security Framework Deployment and Security Verification: A unified security framework is deployed in the interoperability link, employing encryption, key management, and a zero-trust architecture to ensure data security. The security of data interaction is evaluated using a security verification formula, as follows: ,in is the safety factor for data interaction of the k-th supporting system, and its value range is [0, 1]. It is determined that the safety is qualified when is the number of data frames tampered during the data interaction process, is the total number of data interaction frames; S406: Dynamic routing hot update and maintenance: Real-time monitor the docking status of the supporting system. When the supporting system interface is upgraded or a new supporting system is added, optimize the update process through the routing hot update efficiency formula without stopping the overall docking service. The routing hot update efficiency formula is as follows: , where is the routing hot update efficiency, and its value range is [0, 100%]. It is determined that the update is qualified when is the time-consuming for routing hot update, is the time-consuming for reconfiguring the routing after stopping the docking service; In addition, the cross-system docking module supports multi-center data transmission at the same time, can synchronize the washing equipment data to multiple supporting systems simultaneously, and supports secondary development, providing Python and C language development environments and complete development documents to adapt to the differentiated docking requirements of different projects; S5: Equipment integration management and dynamic optimization: The cloud platform uniformly manages all equipment, monitors and schedules tasks in real time. The edge gateway cluster collects the status and dynamically optimizes and adapts parameters through AI algorithms to achieve full life cycle management and remote operation and maintenance of the equipment; The specific logical steps are as follows: S501: Aggregation of equipment access status: The cloud management platform receives the data processed by standardization in step S3, aggregates the access status of all accessed equipment, marks the equipment number as i, , n is the total number of accessed equipment, and establish an equipment integration management set , where represents the integration management information of the nth equipment; S502: Real-time monitoring of equipment status: The cloud platform collects the operation status parameters of each equipment in real time, and verifies the monitoring effect through the status monitoring accuracy formula. The status monitoring accuracy formula is as follows: , where is the status monitoring accuracy of the i-th equipment, and its value range is [0, 100%]. It is determined that the monitoring is qualified when is the number of correct status times for real-time monitoring of the i-th equipment, is the total number of monitoring times of the i-th equipment. During the monitoring process, the operation status of the equipment is displayed in real time, and an alarm is triggered for abnormal status; S503: Unified scheduling of washing tasks: Based on the supporting system instructions received from S4 and the real-time operating status of the equipment, the scheduling scheme is optimized through the task scheduling efficiency formula to achieve efficient task allocation. The scheduling efficiency formula is as follows: ,in To optimize the scheduling efficiency of the washing tasks, the value ranges from [0, 100%]. The scheduling is deemed qualified at that time. The standard completion time for the task. To determine the actual task completion time, during the scheduling process, a hybrid framework based on genetic algorithms and Petri nets is used to dynamically allocate cross-protocol task resources and optimize task execution paths. S504: Dynamic Optimization of Adaptation Parameters: The edge gateway cluster collects real-time data on device access status, protocol operation, and cross-system interoperability. Adaptation issues are analyzed using AI algorithms, and the rationality of optimization parameters is evaluated using an optimization effect formula, as follows: ,in To accommodate the optimized parameter settings, the value range is [0, 100%]. The optimization was deemed successful at that time. To optimize the equipment compatibility failure rate, To optimize the device compatibility failure rate before optimization, the compatibility parameters are automatically updated after optimization to ensure system stability; S505: Device Lifecycle Management: The cloud platform records the entire lifecycle information of each device and verifies the management effectiveness through a lifecycle management integrity formula, as follows: ,in To ensure the integrity of equipment lifecycle management, the value range is [0, 100%]. The management was deemed qualified at that time. This refers to the actual number of recorded device lifecycle information entries. The number of lifecycle information entries required by the standard; S506: Remote Operation and Maintenance and Fault Prediction: The cloud platform supports remote configuration, upgrades, and diagnostics of devices. The effectiveness of operation and maintenance is evaluated using an operation and maintenance response efficiency formula, as follows: ,in For remote operation and maintenance response efficiency, the value range is [0, 100%]. The actual response time for remote operation and maintenance. To establish a standard response time for remote operation and maintenance, and based on equipment operation data and historical fault records, predict equipment failures and push maintenance plans to achieve predictive maintenance.

[0017] This embodiment breaks down data silos, reduces transformation and maintenance costs, shortens the integration cycle, and improves system adaptability, scalability, and collaborative management efficiency by using technologies such as unified access via multiple protocols, low-cost retrofitting of old equipment, seamless cross-system integration, and plug-in hot-updateability. It achieves unified integrated management and full-process data interoperability for different brands, models, and both new and old laundry equipment, adapting to the application needs of hotels, factories, hospitals, and other scenarios. At the same time, it ensures system stability and data interaction security, making full use of existing equipment resources and providing an efficient, universal, and low-cost solution for the integrated management of intelligent laundry equipment.

[0018] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for integrated management of intelligent washing equipment based on the Internet of Things, characterized in that, Includes the following steps: S1: Equipment Classification and Adaptation Preparation: Classify and identify the washing equipment to be connected as smart and old types, collect the core information of the two types of equipment respectively, store the information of smart equipment in the cloud equipment archive, and determine the IoT transformation and adaptation plan for old equipment. S2: Unified adaptation and access of multiple protocols: The edge gateway cluster deploys a multi-protocol parsing engine and accelerates protocol conversion through FPGA. Smart devices automatically match protocols and connect to the cloud. Private protocol devices import parsing rules for adaptation. Old devices are connected through IoT transformation modules. The device adaptation module adopts plug-in and hot-update configuration to adapt to new devices and protocol upgrades. S3: Data Standardization Processing: The data standardization module cleans, unifies, encrypts, encapsulates, and verifies the raw data from the device to ensure that the data transmitted to the cloud is reliable and valid. S4: Seamless cross-system integration: Through standardized interfaces and dynamic routing configurations of cross-system integration modules, data interoperability and process collaboration between the cloud platform and supporting systems are achieved, and data interaction security is ensured through a unified security framework; S5: Device Integration Management and Dynamic Optimization: The cloud platform manages all devices in a unified manner, monitors and schedules tasks in real time, and the edge gateway cluster collects status and dynamically optimizes and adapts parameters through AI algorithms to achieve full lifecycle management and remote operation and maintenance of devices.

2. The integrated management method for intelligent washing equipment based on the Internet of Things according to claim 1, characterized in that, The specific logical steps of S1 are as follows: S101: Preliminary Equipment Screening: Traverse all washing equipment that needs to be connected to the management system, collect the basic hardware parameters of each device, and label the device number as follows. , Let n be the total number of devices to be connected, and establish an initial device information set. ,in This represents the nth washing machine; S102: Equipment Classification and Identification: Based on the equipment hardware parameters, the equipment type is determined using a classification formula, as follows: ,in This is the type identifier for the i-th device. This indicates that the device is an intelligent washing machine. This indicates that the equipment is an old-fashioned washing machine. Let be the number of IoT modules in the i-th device. This indicates that the device has at least one built-in IoT communication module. This indicates that the device does not have an IoT module. The interface open identifier for the i-th device. This indicates that the device has an open control interface. This indicates that the device has no open control interface; S103: Core Information Collection: Core parameters are collected for the two categories of classified devices. The completeness of the collected data is verified using the following formula: ,in Let be the core information collection completeness rate of the i-th device, with a value range of [0, 100%]. At that time, the collection was deemed qualified. Let represent the number of core parameters actually collected by the i-th device. The number of core parameter standards that need to be collected by the i-th device is [number] for intelligent devices. =4, including device model, communication protocol type, interface parameters, and data output format; older devices. =3, including equipment model, operating parameters, and control interface type; S104: Information Storage and Adaptation Scheme Determination: For The intelligent devices will collect qualified core information and store it in the device archive on the cloud management platform. The archive index formula is: ;for For older equipment, based on the core parameters collected, the IoT transformation module model and interface adaptation scheme are determined using an adaptation matching formula, which is as follows: ,in Let be the matching degree between the i-th old device and the candidate IoT transformation module, with a value range of [0,3]. Timely determination of suitability The weights of the k-th class of core parameters, Let k be the actual value of the k-th type of core parameter for the i-th old equipment. The standard values ​​for the k-th type of adaptation parameters for candidate IoT transformation modules.

3. The integrated management method for intelligent washing equipment based on the Internet of Things according to claim 2, characterized in that, The specific logical steps of S2 are as follows: S201: Edge Gateway Cluster and Multi-Protocol Parsing Engine Deployment: Deploy a distributed edge gateway cluster, labeling the gateway number as j. m represents the number of edge gateways. Each gateway deploys a multi-protocol parsing engine, integrating mainstream IoT communication protocol parsing modules, while reserving a private protocol import interface to establish a set of correspondences between gateways and devices. ,in This represents the set of devices that the m-th edge gateway is responsible for accessing; S202: FPGA Protocol Conversion Acceleration Configuration: Configure an FPGA acceleration module for the multi-protocol parsing engine of each edge gateway. Optimize configuration parameters using an acceleration efficiency calculation formula to ensure real-time protocol conversion. The acceleration efficiency formula is as follows: ,in Let be the protocol conversion acceleration efficiency of the j-th edge gateway, with a value ranging from [100%, +∞). The acceleration configuration was deemed qualified at that time. The average protocol conversion time when the j-th gateway does not have FPGA acceleration enabled. The average time for protocol conversion after enabling FPGA acceleration for the j-th gateway; S203: Automatic Protocol Matching for Smart Devices: For smart devices categorized in S1, the device adaptation module calls the protocol matching algorithm to automatically match the corresponding parsing module in the multi-protocol parsing engine. The matching accuracy is verified using the following formula: ,in The accuracy rate for automatic matching of smart device protocols is set, with a value range of [0, 100%]. The match is deemed successful at that time. The number of smart devices that were successfully matched by the protocol; The total number of smart devices participating in the protocol matching; after a successful match, bidirectional communication between the device and the cloud management platform is established through the edge gateway. S204: Proprietary Protocol Device Adaptation: For smart devices using proprietary protocols, import the proprietary protocol parsing rules through the protocol import interface. The compatibility of the parsing rules is determined by the following formula: ,in This represents the adaptation rate for private protocol parsing rules, with a value range of [0, 100%]. Timely determination of suitability The length of instructions / data that can be correctly parsed by the private protocol parsing rules. The total instruction / data length of the private protocol, after fitting, completes the conversion between the private protocol and the unified protocol, enabling device-cloud interface; S205: Integration and Adaptation of Legacy Equipment: For legacy equipment categorized in S1, install the established IoT upgrade modules and verify the interface compatibility between the modules and the equipment using the interface adaptation formula, which is as follows: ,in The interface compatibility score between the i-th old device and the IoT transformation module is given, with a value range of [0,2]. When the compatibility is deemed acceptable, To adapt weights for the interface, This is the matching identifier for the p-th type interface parameter of the i-th old device and the retrofit module. (During matching...) =1, when there is no match =0, after passing compatibility tests, the modified module collects device data and completes protocol conversion through the edge gateway to achieve connection with the cloud; S206: Plug-in Hot-Update Configuration: The device adaptation module adopts a plug-in architecture. For new devices or protocol upgrade requirements, corresponding protocol parsing plug-ins are added. The validity of plug-in updates is determined by the following formula: ;in To optimize plugin update efficiency, the value range is [0, 100%]. The update is deemed acceptable at that time. The update time for the protocol parsing plugin, The time spent reconstructing and adapting traditional systems to new protocols is reduced. Once the update is successful, the adaptation of new devices or protocol upgrades can be completed without reconstructing the system.

4. The integrated management method for intelligent washing equipment based on the Internet of Things according to claim 3, characterized in that, The specific logical steps of S4 are as follows: S401: Supporting System Screening and Interface Initialization: Identify all supporting systems for the intelligent washing management scenario and label each system with the number k. Let p be the total number of supporting systems, and establish a set of supporting systems. ,in Representing the p-th supporting system, the cross-system interface module loads the preset standardized interface interface and initializes the interface parameters; S402: Dynamic Routing Rule Configuration: Based on the interface parameters of the supporting system, dynamic routes are generated through routing rule configuration formulas to ensure optimal data transmission paths. The routing configuration formula is as follows: ,in The routing adaptation score for the k-th supporting system is given, with a value ranging from [0, 100]. The routing configuration is deemed valid at that time. For transmission delay weight, For transmission link length weight, The average transmission latency between the cloud platform and the k-th supporting system. This is the length of the transmission link between the cloud platform and the kth supporting system. S403: Interface Parameter Configuration and Verification: Based on the routing configuration results, configure the interface parameters between the cloud management platform and each supporting system. Ensure the configuration is reasonable using the interface parameter verification formula, which is as follows: ,in This is the verification value for the docking parameters of the k-th supporting system, with a value range of [0, 3]. The verification is deemed successful at that time. To connect the parameter weights, This is a qualification indicator for the configuration of the k-th supporting system's q-th type of docking parameter. When qualified... =1, when unqualified =0; S404: Data Interoperability and Process Collaboration Implementation: After successful verification, data interaction between the cloud platform and supporting systems is initiated. The interaction status is monitored in real time using the data interoperability success rate formula, which is as follows: ,in This represents the data interoperability success rate between the cloud platform and the k-th supporting system, with a value ranging from [0, 100%]. The system was determined to be functioning normally. The number of data frames for successful interaction. The total number of data interaction frames is used to simultaneously achieve process collaboration, synchronize washing equipment data to the supporting system, receive instructions from the supporting system and forward them to the corresponding devices; S405: Unified Security Framework Deployment and Security Verification: A unified security framework is deployed in the interoperability link, employing encryption, key management, and a zero-trust architecture to ensure data security. The security of data interaction is evaluated using a security verification formula, as follows: ,in The security coefficient for data interaction of the k-th supporting system is defined, with a value ranging from [0,1]. When the safety is deemed acceptable, This represents the number of data frames that were tampered with during the data exchange process. This represents the total number of data interaction frames. S406: Dynamic Routing Hot Update and Maintenance: Real-time monitoring of the integration status of supporting systems. When supporting system interfaces are upgraded or new supporting systems are added, the update process is optimized through a routing hot update efficiency formula, without stopping the overall integration service. The hot update efficiency formula is as follows: ,in To optimize hot route updates, the value ranges from [0, 100%]. The update is deemed acceptable at that time. For the time taken to hot update the route, The time required to reconfigure the route after stopping the connection service.

5. The integrated management method for intelligent washing equipment based on the Internet of Things according to claim 4, characterized in that, The specific logical steps of S5 are as follows: S501: Device Access Status Summary: The cloud management platform receives the data after standardized processing in step S3, summarizes the access status of all connected devices, and marks the device number as i. Let n be the total number of connected devices, and establish a device integration management set. ,in This represents the integrated management information for the nth device; S502: Real-time device status monitoring: The cloud platform collects the operation status parameters of each device in real time, and verifies the monitoring effect through the status monitoring accuracy formula, which is as follows: , where is the status monitoring accuracy of the i-th device, and the value range is [0, 100%]. It is determined that the monitoring is qualified when is the number of correct status times of the real-time monitoring of the i-th device. is the total number of monitoring times of the i-th device. During the monitoring process, the operation status of the device is displayed in real time, and an alarm is triggered for abnormal status; S503: Unified scheduling of washing tasks: Based on the supporting system instructions received from S4 and the real-time operating status of the equipment, the scheduling scheme is optimized through the task scheduling efficiency formula to achieve efficient task allocation. The scheduling efficiency formula is as follows: ,in To optimize the scheduling efficiency of the washing tasks, the value ranges from [0, 100%]. The scheduling is deemed qualified at that time. The standard completion time for the task. To determine the actual task completion time, during the scheduling process, a hybrid framework based on genetic algorithms and Petri nets is used to dynamically allocate cross-protocol task resources and optimize task execution paths. S504: Dynamic Optimization of Adaptation Parameters: The edge gateway cluster collects real-time data on device access status, protocol operation, and cross-system interoperability. Adaptation issues are analyzed using AI algorithms, and the rationality of optimization parameters is evaluated using an optimization effect formula, as follows: ,in To accommodate the optimized parameter settings, the value range is [0, 100%]. The optimization was deemed successful at that time. To optimize the equipment compatibility failure rate, To optimize the device compatibility failure rate before optimization, the compatibility parameters are automatically updated after optimization to ensure system stability; S505: Device Lifecycle Management: The cloud platform records the entire lifecycle information of each device and verifies the management effectiveness through a lifecycle management integrity formula, as follows: ,in To ensure the integrity of equipment lifecycle management, the value range is [0, 100%]. The management was deemed qualified at that time. This refers to the actual number of recorded device lifecycle information entries. The number of lifecycle information entries required by the standard; S506: Remote Operation and Maintenance and Fault Prediction: The cloud platform supports remote configuration, upgrades, and diagnostics of devices. The effectiveness of operation and maintenance is evaluated using an operation and maintenance response efficiency formula, as follows: ,in For remote operation and maintenance response efficiency, the value range is [0, 100%]. The actual response time for remote operation and maintenance. To establish a standard response time for remote operation and maintenance, and based on equipment operation data and historical fault records, predict equipment failures and push maintenance plans to achieve predictive maintenance.

6. The integrated management method for intelligent washing equipment based on the Internet of Things according to claim 1, characterized in that, In S2, the edge gateway cluster adopts a distributed deployment method. Each edge gateway is responsible for the access of washing equipment in a certain area. The edge gateways communicate with each other through wireless or wired communication. When an edge gateway fails, the equipment it is responsible for automatically switches to the adjacent edge gateway. The edge gateway adopts IP65 level waterproof, moisture-proof and dustproof design, and uses rust-resistant metal shell to adapt to the harsh working environment of the laundry room. The IoT upgrade module features a pluggable design, supporting interface docking with different types of old washing equipment. It has a built-in power management unit that can automatically adjust power consumption and supports LoRa, ZigBee and Bluetooth wireless expansion methods. It can be connected to an external concentrator to achieve centralized management of non-smart washing equipment.

7. The integrated management method for intelligent washing equipment based on the Internet of Things according to claim 1, characterized in that, In S3, the preset unified data standard can be customized according to different application scenarios such as hotels, factories, and hospitals. The data standardization module uses a dynamic parameter mapping engine to dynamically extract and transform the requested data according to routing rules to adapt to the data requirements of different scenarios. The data is also encrypted using the national cryptographic SM4 encryption algorithm during data encapsulation.

8. The integrated management method for intelligent washing equipment based on the Internet of Things according to claim 1, characterized in that, In S4, the cross-system docking module supports multi-center data transmission, which can simultaneously synchronize washing equipment data to multiple supporting systems. It also supports secondary development, providing Python and C language development environments and complete development documentation to adapt to the differentiated docking needs of different projects.

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