A furniture monitoring system and method based on cloud service

Abstract

The present invention discloses a furniture monitoring system and method based on cloud service, which specifically relates to the field of furniture monitoring, including: integrating smart furniture into the cloud for unified management; connecting the instruction receiving module of the smart furniture to the cloud, and tracking the life cycle of local control instructions based on the cloud instruction synchronization and feedback upload mechanism; establishing a cloud multi-dimensional instruction processing channel to realize cloud synchronization instruction processing; setting a feedback signal monitoring time window, and defining an instruction timer for cloud copy instructions; real-time monitoring of smart furniture feedback signals received by the cloud, counting and analyzing instruction recovery and instruction execution deviation; monitoring newly uploaded copy instructions of smart furniture received by the cloud, analyzing the possibility of loss and execution deviation of copy instructions, and when the possibility of loss or execution deviation of copy instructions is high, the cloud sends target parameters and correction instructions to the local instruction processing module to adjust the device parameters of the smart furniture.

Description

Technical Field

[0001] The present invention relates to the field of furniture monitoring technology, and more specifically, to a furniture monitoring system and method based on cloud services. Background Art

[0002] In the application of smart furniture, the effectiveness of control instructions is crucial to ensure the normal operation of the equipment and user experience. Smart furniture usually has multiple control methods, such as voice, gesture, remote APP control, etc. The multi-dimensional control instructions generated by these methods are transmitted to the local device for execution.

[0003] However, with the increasing complexity of smart furniture equipment systems, the increasing complexity of control instructions, and changes in the network environment, the failure of instructions has gradually become a key challenge affecting the performance and reliability of smart furniture systems. The failure of multi-dimensional control instructions of smart furniture is usually manifested as inaccurate instruction execution, instruction loss, or mismatch between instructions and the actual status of the device. If this problem cannot be solved, it will not only affect the user experience, but may also lead to equipment failure, energy waste, and even safety hazards. Long-term instruction failure may also lead to a decline in the trust of the smart furniture system, affecting market promotion and application.

[0004] With the popularization of cloud services in the field of smart furniture, how to use cloud services to compensate for the failure of local commands has become an urgent problem that needs to be solved in the field of smart furniture.

[0005] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a furniture monitoring system and method based on cloud service to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions: S1: Label the smart furniture in the set monitoring scene, integrate the command processing engine of the labeled smart furniture into the cloud, and establish a cloud command processing virtual engine image; S2: Connect the command receiving module of smart furniture to the cloud, and track the life cycle of local control commands based on the cloud command synchronization and feedback upload mechanism; S3: Based on the available control methods of the smart furniture registered in the cloud, a cloud multi-dimensional command processing channel is established to execute cloud synchronization command processing; S4: according to the feedback data size and network configuration of different smart furniture, set the feedback signal monitoring time window and define the command timer of the cloud copy command; S5: monitor the smart furniture feedback signals received by the cloud in real time, match the feedback data with the cloud-copied instructions, and count the uncollected instructions and the instructions with execution offsets; S6: Monitor the newly uploaded copy instructions of the smart furniture received by the cloud, analyze the possibility of loss and execution deviation of the copy instructions, and when the possibility of loss or execution deviation of the copy instructions is high, the cloud adjusts the device parameters of the smart furniture by sending device target parameters and correction instructions to the local instruction processing module.

[0008] In a preferred embodiment, in S1, the smart furniture in the set monitoring scene is marked, and the command processing engine of the marked smart furniture is integrated into the cloud. The establishment of the cloud command processing virtual engine image specifically includes: Obtain the hardware unique identification code of all smart furniture to be connected to the cloud monitoring in the set monitoring scene, and generate a device identification based on the unique identification code; Customize the communication protocol and send the device ID of each smart furniture to the cloud for storage. When the device goes online for the first time, the cloud will match the ID and verify the registration of the smart furniture. Integrate the command processing engine of registered smart furniture into the cloud and establish a cloud command processing virtual engine image.

[0009] In a preferred embodiment, in S2, the command receiving module of the smart furniture is connected to the cloud, and based on the cloud command synchronization and feedback upload mechanism, tracking the life cycle of the local control command specifically includes: The specific execution process of the cloud command synchronization and feedback upload mechanism is as follows: When the local command receiving module of the smart furniture receives a local control command, a copy command is generated, and a command sequence number is generated according to the command type and the receiving time; Integrate the copy instruction, instruction serial number, instruction upload device ID, and real-time device parameters to create a copy instruction data set and upload it to the cloud; At the same time, the local command processing module of the smart furniture device performs synchronous analysis and processing of local control commands and device parameter control; After the local control instruction is executed, the local control instruction is destroyed and the instruction survival time is recorded. At the same time, the execution result feedback data carrying the instruction serial number is uploaded to the cloud.

[0010] In a preferred embodiment, in S3, based on the available control modes of the smart furniture registered in the cloud, establishing a cloud multi-dimensional instruction processing channel to execute cloud synchronization instruction processing specifically includes: Based on the total number of available control methods of the smart furniture devices registered in the cloud, an equal number of cloud multi-dimensional command processing channels are established, and the cloud command processing engine image is deployed in the corresponding processing channel; Provide a unified processing and distribution interface. After receiving the replication instruction data set, the processing and distribution interface distributes the instructions received in the cloud to the corresponding processing channel based on the instruction type. Obtain the device identification number in the copy instruction data set, perform identification number matching search in the processing channel, and select the corresponding instruction processing virtual engine image to parse and process the copy instruction; Based on the copy instruction processing result and in combination with the uploaded real-time device parameters, the device target parameters after the copy instruction is executed are obtained.

[0011] In a preferred embodiment, in S4, according to the feedback data size and network configuration of different smart furniture, the feedback signal monitoring time window is set, and the command timer of the cloud copy command is defined, which specifically includes: The corresponding feedback data format is set based on the device parameter form of different smart furniture, and the data packet size of a single feedback data upload is calculated according to the set feedback data format; Obtain the bandwidth of the transmission network device, and calculate the theoretical waiting time for receiving feedback signals from different smart furniture by combining the feedback data packet size of different smart furniture and the actual network delay; Based on the average survival time of local control instructions of smart furniture and the theoretical waiting time of feedback signals, a dynamic monitoring time window of feedback signals of smart furniture is set; The dynamic monitoring time window size of the feedback signal corresponding to each smart furniture is marked as the maximum waiting time for the smart furniture command recovery; Based on the smart furniture identifier to which the copy instruction in the cloud-based multi-dimensional instruction processing channel belongs, a timer is created for the copy instructions in all channels, and the maximum waiting time for recycling the smart furniture instruction corresponding to the copy instruction is used as the end time of the timer.

[0012] In a preferred embodiment, in S5, the smart furniture feedback signal received by the cloud is monitored in real time, the feedback data is matched with the cloud copy instruction, and the unrecovered instructions and the execution offset instructions are counted, specifically including: Open up two storage spaces on the cloud server, marked as the unrecovered instruction pool and the untrusted instruction pool; Monitor all copy instructions on the cloud. When the timer of a copy instruction expires, trigger the instruction execution result clearing event, delete all device target parameter data after the copy instruction is executed, and mark the copy instruction as an unrecovered instruction and add it to the unrecovered instruction pool. Monitor the feedback signal of smart furniture received by the cloud in real time, obtain the instruction serial number in the feedback data, and compare and screen the instruction serial number with the instruction serial numbers of all copied instructions in the cloud; If there is an identical instruction sequence number, the device target parameter data after the execution of the copy instruction holding the instruction sequence number is compared with the actual device parameter data in the feedback data, and the specific ratio of the parameter value deviation to the adjustable value range of the parameter is calculated, which is marked as the control parameter deviation rate; A confidence threshold of the control parameter deviation rate is set, and the target parameter data of the device whose control parameter deviation rate is higher than the confidence threshold is deleted. At the same time, the copy instructions corresponding to the target parameters are marked as untrusted instructions and added to the untrusted instruction pool.

[0013] In a preferred embodiment, in S6, the newly uploaded copy instructions of the smart furniture received by the cloud are monitored, and the possibility of loss and execution deviation of the copy instructions is analyzed. When the possibility of loss or execution deviation of the copy instructions is high, the cloud sends the device target parameters and correction instructions to the local instruction processing module to adjust the device parameters of the smart furniture, which specifically includes: Get the newly uploaded copy instructions of the smart furniture received by the cloud, extract the operation code and numerical data in the copy instruction, match the operation code and numerical data with all the instructions in the unrecovered instruction pool and the untrusted instruction pool, use the embedded word vector analysis model to convert the keywords in the instruction into vectors, calculate the cosine similarity between the two, and then judge whether the copy instruction hits based on the threshold comparison method of similarity; If the copy command hits a command in the unrecovered command pool, it is judged that the command is likely to be lost. At this time, the cloud processes the command and sends the processed device target parameters to the local command processing module of the smart furniture for direct control of the device parameters. If the copied instruction hits an instruction in the untrusted instruction pool, it is judged that the possibility of control deviation of the instruction is high. At this time, the control parameter deviation rate of the instruction is obtained, and a control correction instruction is generated based on the control parameter deviation rate. The correction instruction is sent to the instruction processing module of the smart furniture device for control parameter correction.

[0014] In another aspect, the present invention provides a furniture monitoring system based on cloud services, comprising: A local command receiving module, used for receiving real-time local control commands; Local command processing module, used for parsing and processing local control commands and controlling device parameters; A cloud command receiving module is used to integrate and upload to the cloud the copy command generated by the local control command received by the command receiving module based on the smart furniture and the relevant parameter device data; The cloud instruction processing module is used to establish a cloud multi-dimensional instruction processing channel and the allocation and use of the virtual engine image, analyze and process the uploaded copy instruction, and obtain the device target parameters after the copy instruction is executed; The feedback signal monitoring module is used to set the dynamic monitoring time window of each replication instruction on the cloud, and at the same time, count and analyze the instruction recovery loss and execution deviation through the instruction matching between the feedback data and the cloud replication instruction; The cloud-based intelligent control module is used to monitor the newly uploaded replication instructions of smart furniture received by the cloud, analyze the possibility of loss and execution deviation of replication instructions, and initiate intelligent control of the device execution target parameters of smart furniture from the cloud.

[0015] Technical effects and advantages of a furniture monitoring system and method based on cloud services of the present invention: Smart furniture is managed uniformly in the cloud and integrated into the cloud, providing a basis for the accurate transmission and execution of subsequent instructions. The cloud access of the instruction receiving module enables the life cycle of local control instructions to be tracked in real time. By establishing a multi-dimensional instruction processing channel in the cloud, unified control of different types of equipment can be achieved to meet the processing requirements of different modal data. At the same time, the monitoring time window of the feedback signal is combined with the network configuration to ensure the accurate reception and analysis of the feedback signal, effectively avoiding the problem of instruction loss or execution deviation caused by network fluctuations. At the same time, through instruction matching and execution deviation analysis, the cloud can adjust the target parameters in real time and correct the deviation in execution.

[0016] It realizes closed-loop management of cloud-based intelligent regulation and device feedback, improves the success rate and accuracy of command execution, enhances the overall stability and user experience of smart furniture, solves the problems of multi-dimensional control command failure and execution deviation in traditional smart furniture systems, and provides more reliable technical support for the smart furniture industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a furniture monitoring method based on cloud services according to the present invention; Figure 2 This is a structural schematic diagram of a furniture monitoring system based on cloud services of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention. Embodiment 1:

[0019] Figure 1 The present invention provides a furniture monitoring method based on cloud service, which comprises the following steps: S1: Label the smart furniture in the set monitoring scene, integrate the command processing engine of the labeled smart furniture into the cloud, and establish a cloud command processing virtual engine image; S2: Connect the command receiving module of smart furniture to the cloud, and track the life cycle of local control commands based on the cloud command synchronization and feedback upload mechanism; S3: Based on the available control methods of the smart furniture registered in the cloud, a cloud multi-dimensional command processing channel is established to execute cloud synchronization command processing; S4: according to the feedback data size and network configuration of different smart furniture, set the feedback signal monitoring time window and define the command timer of the cloud copy command; S5: monitor the smart furniture feedback signals received by the cloud in real time, match the feedback data with the cloud-copied instructions, and count the uncollected instructions and the instructions with execution offsets; S6: Monitor the newly uploaded copy instructions of the smart furniture received by the cloud, analyze the possibility of loss and execution deviation of the copy instructions, and when the possibility of loss or execution deviation of the copy instructions is high, the cloud adjusts the device parameters of the smart furniture by sending device target parameters and correction instructions to the local instruction processing module.

[0020] In S1, the smart furniture in the set monitoring scene is labeled, and the command processing engine of the labeled smart furniture is integrated into the cloud. A cloud command processing virtual engine image is established, and the monitoring scene can be flexibly set. By labeling the smart furniture, it has a unique identification mark to ensure that each device can be accurately distinguished and managed in the monitoring scene. The labeled device information is integrated into the cloud for centralized management, data storage and real-time tracking. This process ensures that the status, command execution and feedback information of the device can be updated and monitored in real time in the cloud, providing a platform foundation for subsequent intelligent control and data analysis.

[0021] Furthermore, the system will automatically scan all smart furniture devices within the set monitoring scene by reading the hardware unique identification code of each device (such as MAC address, device serial number or UUID). The hardware unique identification code is the unique identifier of the device at the hardware level, ensuring that each device can be uniquely identified in the network. Then, the system converts the hardware unique identification code into a device identification and generates a digital identity for the device, ensuring that each device can be accurately tracked and managed, and can independently record data and process instructions.

[0022] To ensure that the device can communicate with the cloud system efficiently and securely, the MQTT protocol can be selected according to the actual application scenario. The MQTT client on the device side will publish a message, including information such as device identification, device type, device model, and device status. The message will be sent to the MQTT proxy server on the cloud. After receiving this message, the cloud will further process the data and send the device identification of each smart furniture to the cloud for storage. When the device goes online for the first time, the cloud will match the identification and verify the registration of the smart furniture.

[0023] After completing the identification and registration of the device, the system integrates the command processing engine of each registered smart furniture and generates a corresponding cloud virtual engine image. The command processing engine of each device includes the device's control logic, execution rules, and command response mechanism. By generating a cloud virtual engine image, the cloud can efficiently simulate the device's command processing process and provide alternative solutions or continue to execute device commands in special circumstances.

[0024] In S2, the command receiving module of the smart furniture is connected to the cloud, and based on the cloud command synchronization and feedback upload mechanism, the life cycle of the local control command is tracked.

[0025] When the local command receiving module (e.g., microcontroller or local gateway) of smart furniture receives a control command from a user or an external system, the module first parses the command type. The command type can be a device switch, temperature adjustment, mode switching, etc. On this basis, the local receiving module generates a unique command serial number based on the command type and reception time. This serial number is a unique identifier in the life cycle of the command and is used for subsequent tracking and management.

[0026] The generated replication instructions are integrated with the instruction serial number, as well as the device identification and real-time device parameters (such as current temperature, humidity, brightness, etc.) of the smart furniture to form a complete replication instruction data set, which contains detailed information about the instructions and the current status of the device when executed.

[0027] At the same time, the local command processing module of the smart furniture device synchronously parses and processes the local control commands and controls the device parameters, such as adjusting the room temperature to a specified value, or raising the temperature by 5 degrees.

[0028] After the local control command is executed, the local control command is destroyed and the command survival time is recorded. At the same time, the execution result feedback data carrying the command sequence number is uploaded to the cloud. Among them, the destruction operation is to clear the completed command data to prevent waste of system resources. At the same time, the command survival time is recorded. The command survival time represents the time from receiving the command to completing the execution.

[0029] In S3, based on the available control methods of smart furniture registered in the cloud, a cloud multi-dimensional command processing channel is established to perform cloud synchronous command processing. Among them, the multi-dimensional command processing channel can ensure that each command can be correctly routed and processed according to its type and priority, thereby achieving real-time response of cloud commands and efficient coordination of device control, and improving the response speed and accuracy of the overall smart furniture system.

[0030] Based on the total number of available control methods for smart furniture devices registered in the cloud, an equal number of cloud-based multi-dimensional command processing channels are established. Through cloud containerization technology, each channel runs specific processing instances, and these instances achieve load balancing and resource scheduling through virtualization technology.

[0031] Provide a unified processing and distribution interface. After receiving the replication instruction data set, the processing and distribution interface distributes the instructions received in the cloud to the corresponding processing channel based on the instruction type. Each instruction will go through a classification algorithm based on data pattern matching to determine the type of instruction, and then route the instruction to the target channel.

[0032] The device identification number in the copy instruction data set is obtained, the identification number matching search is performed in the processing channel, and the corresponding instruction processing virtual engine image is selected to parse and process the copy instruction.

[0033] Based on the copy instruction processing result and in combination with the uploaded real-time device parameters, the device target parameters after the copy instruction is executed are obtained.

[0034] In S4, according to the feedback data size and network configuration of different smart furniture, the feedback signal monitoring time window is set and the command timer of the cloud copy command is defined.

[0035] Different smart furniture device parameters have different data formats and structures. For example, a temperature control device may output a floating temperature value (such as degrees Celsius), while the feedback signal of a lighting device may only be an on / off state. A unified feedback data format is set according to the feedback form of different devices, such as: Assuming the feedback format of the temperature control device is: {"device_id": "A123", "temperature": 22.5, "status": "on"}, and the feedback format of the lighting device is: {"device_id": "B456", "status": "off"}, calculate the size of a single feedback data packet. Assuming that each field is encoded in UTF-8, the specific size of each field is: device_id: 24 bytes, temperature (assuming a floating double-precision floating point number, 8 bytes), status (assuming a string type, "on" or "off" 2 bytes).

[0036] Then when setting the data packet for a single feedback data upload, the feedback data packet size of the temperature control device is equal to 24 (device_id) + 8 (temperature) + 2 (status) = 34 bytes, and the feedback data packet size of the lighting device is equal to 24 (device_id) + 2 (status) = 26 bytes.

[0037] Obtain the bandwidth of the transmission network equipment, and calculate the theoretical waiting time for receiving feedback signals from different smart furniture by combining the feedback data packet size of different smart furniture and the actual network delay. Assuming that there is currently a data packet size D (unit: byte), bandwidth B (unit: bit per second), network delay L (unit: millisecond), the theoretical waiting time for its feedback signal is The calculation expression should be: ; The multiplication by 8 is to convert bytes into bits. Assuming the bandwidth B = 50Mbps, the delay L = 2ms, and the feedback data packet size of the temperature control device 34 bytes is taken into the calculation, the specific calculation process is: , that is, the theoretical waiting time of the feedback signal is .

[0038] Based on the average survival time of the local control instructions of smart furniture and the theoretical waiting time of the feedback signal, the dynamic monitoring time window of the feedback signal of smart furniture is set. The calculation method of the dynamic time window size is: ; In the formula, is the dynamic time window size, and The destruction and generation time of the i-th local control instruction, is the total number of received local execution instructions.

[0039] The dynamic monitoring time window size of the feedback signal corresponding to each smart furniture is marked as the maximum waiting time for the smart furniture command recovery.

[0040] Based on the smart furniture identifier to which the copy instruction in the cloud-based multi-dimensional instruction processing channel belongs, a timer is created for the copy instructions in all channels, and the maximum waiting time for recycling the smart furniture instruction corresponding to the copy instruction is used as the end time of the timer.

[0041] In S5, the smart furniture feedback signal received from the cloud is monitored in real time, the feedback data is matched with the cloud copy instructions, and the unrecovered instructions and the instructions with execution offset are counted.

[0042] Two storage spaces are opened up on the cloud server, marked as the unrecovered instruction pool and the untrusted instruction pool. The unrecovered instruction pool is used to store the replication instructions that have not been successfully recovered (i.e., not executed or failed to execute) after the timer expires, and the untrusted instruction pool is used to store the replication instructions that have untrustworthy behaviors during the execution of the instructions (the deviation between the feedback data and the expected target data exceeds the set threshold).

[0043] Monitor all copy instructions on the cloud. When the timer of a copy instruction expires, trigger the instruction execution result clearing event, delete all device target parameter data after the copy instruction is executed, and mark the copy instruction as an unrecovered instruction and add it to the unrecovered instruction pool.

[0044] Monitor the smart furniture feedback signal received from the cloud in real time, obtain the instruction serial number in the feedback data, and compare and screen the instruction serial number with the instruction serial numbers of all copied instructions in the cloud.

[0045] If there is an identical instruction sequence number, the device target parameter data after the execution of the copy instruction holding the instruction sequence number is compared with the actual device parameter data in the feedback data, and the specific ratio of the parameter value deviation to the adjustable value range of the parameter is calculated, which is marked as the control parameter deviation rate. The specific calculation expression is: ; In the formula, is the control parameter offset rate, are the actual parameters and the target parameters, It is the adjustable value range of the parameter. Set the confidence threshold of the control parameter deviation rate (the default setting in this solution is 0.1, i.e. 10%), and delete the device target parameter data whose control parameter deviation rate is higher than the confidence threshold. Assume that the target parameter target_device_param = 30, the actual parameter actual_device_param in the feedback data = 15, and the adjustable range of the device is [0, 100]. The calculated result is 0.15, which is greater than the default setting value of 0.1 in this scheme. Therefore, the copy instruction corresponding to this target parameter is marked as an untrusted instruction and added to the untrusted instruction pool.

[0046] In S6, the newly uploaded copy instructions of the smart furniture received by the cloud are monitored, and the possibility of loss and execution deviation of the copy instructions is analyzed. When the possibility of loss or execution deviation of the copy instructions is high, the cloud adjusts the device parameters of the smart furniture by sending device target parameters and correction instructions to the local instruction processing module. The newly uploaded copy instructions of the smart furniture received by the cloud are obtained, and the operation code and numerical data in the copy instructions are extracted. The operation code and numerical data are matched with all instructions in the unrecovered instruction pool and the untrusted instruction pool for keywords. Since the instructions of the same smart furniture device have the same structure, this process can use an embedded word vector analysis model (such as Word2Vec or GloVe) to convert the keywords in the instruction into vectors, and then calculate the cosine similarity between the two. Then, according to the threshold comparison method of similarity, when the instruction similarity reaches the threshold, the copy instruction hit is marked.

[0047] If the copy command hits a command in the unrecovered command pool, it is judged that the command is likely to be lost. At this time, the cloud processes the command and sends the processed device target parameters to the local command processing module of the smart furniture for direct control of the device parameters. If the copied instruction hits an instruction in the untrusted instruction pool, it is judged that the possibility of control deviation of the instruction is high. At this time, the control parameter deviation rate of the instruction is obtained, and a control correction instruction is generated based on the control parameter deviation rate. Assuming that the cloud algorithm judges based on historical data or device feedback, such a deviation rate (6.25%) indicates that the device usually needs an additional adjustment step to compensate for the deviation. The correction instruction can be: "Increase the brightness of the living room light by 6.25%" or "Adjust the brightness of the living room light to 85.5%" (i.e. 80% + 6.25% correction). The specific selection needs to be consistent with the instruction control method of the smart furniture. The correction instruction is sent to the instruction processing module of the smart furniture device for control parameter correction. Embodiment 2:

[0048] The difference between Example 2 of the present invention and Example 1 is that this example introduces a furniture monitoring system based on cloud services.

[0049] Figure 2 A structural schematic diagram of a furniture monitoring system based on cloud services of the present invention is given. A furniture monitoring system based on cloud services includes: A local command receiving module, used for receiving real-time local control commands; Local command processing module, used for parsing and processing local control commands and controlling device parameters; A cloud command receiving module is used to integrate and upload to the cloud the copy command generated by the local control command received by the command receiving module based on the smart furniture and the relevant parameter device data; The cloud instruction processing module is used to establish a cloud multi-dimensional instruction processing channel and the allocation and use of the virtual engine image, analyze and process the uploaded copy instruction, and obtain the device target parameters after the copy instruction is executed; The feedback signal monitoring module is used to set the dynamic monitoring time window of each replication instruction on the cloud, and at the same time, count and analyze the instruction recovery loss and execution deviation through the instruction matching between the feedback data and the cloud replication instruction; The cloud-based intelligent control module is used to monitor the newly uploaded replication instructions of smart furniture received by the cloud, analyze the possibility of loss and execution deviation of replication instructions, and initiate intelligent control of the device execution target parameters of smart furniture from the cloud.

[0050] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0051] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0052] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0053] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0054] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0055] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0056] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0057] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0058] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0059] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A furniture monitoring method based on cloud service, characterized in that: The steps include: S1: Label the smart furniture in the set monitoring scene, integrate the command processing engine of the labeled smart furniture into the cloud, and establish a cloud command processing virtual engine image; S2: Connect the command receiving module of smart furniture to the cloud, and track the life cycle of local control commands based on the cloud command synchronization and feedback upload mechanism; S3: Based on the available control methods of the smart furniture registered in the cloud, a cloud multi-dimensional command processing channel is established to execute cloud synchronization command processing; S4: according to the feedback data size and network configuration of different smart furniture, set the feedback signal monitoring time window and define the command timer of the cloud copy command; S5: monitor the smart furniture feedback signals received by the cloud in real time, match the feedback data with the cloud-copied instructions, and count the uncollected instructions and the instructions with execution offsets; S6: Monitor the newly uploaded copy instructions of the smart furniture received by the cloud, analyze the possibility of loss and execution deviation of the copy instructions, and when the possibility of loss or execution deviation of the copy instructions is high, the cloud adjusts the device parameters of the smart furniture by sending device target parameters and correction instructions to the local instruction processing module.

2. The furniture monitoring method based on cloud service according to claim 1, characterized in that: In S1, the smart furniture in the set monitoring scene is labeled, and the command processing engine of the labeled smart furniture is integrated into the cloud. The cloud command processing virtual engine image is established, which specifically includes: Obtain the hardware unique identification code of all smart furniture to be connected to the cloud monitoring in the set monitoring scene, and generate a device identification based on the unique identification code; Customize the communication protocol and send the device ID of each smart furniture to the cloud for storage. When the device goes online for the first time, the cloud will match the ID and verify the registration of the smart furniture. Integrate the command processing engine of registered smart furniture into the cloud and establish a cloud command processing virtual engine image.

3. The furniture monitoring method based on cloud service according to claim 2, characterized in that: In S2, the command receiving module of the smart furniture is connected to the cloud. Based on the cloud command synchronization and feedback upload mechanism, the life cycle of the local control command is tracked, including: The specific execution process of the cloud command synchronization and feedback upload mechanism is as follows: When the local command receiving module of the smart furniture receives a local control command, a copy command is generated, and a command sequence number is generated according to the command type and the receiving time; Integrate the copy instruction, instruction serial number, instruction upload device ID, and real-time device parameters to create a copy instruction data set and upload it to the cloud; At the same time, the local command processing module of the smart furniture device performs synchronous analysis and processing of local control commands and device parameter control; After the local control instruction is executed, the local control instruction is destroyed and the instruction survival time is recorded. At the same time, the execution result feedback data carrying the instruction serial number is uploaded to the cloud.

4. The furniture monitoring method based on cloud service according to claim 3, characterized in that: In S3, based on the available control methods of the smart furniture registered in the cloud, a cloud multi-dimensional command processing channel is established to execute cloud synchronization command processing, which specifically includes: Based on the total number of available control methods of the smart furniture devices registered in the cloud, an equal number of cloud multi-dimensional command processing channels are established, and the cloud command processing engine image is deployed in the corresponding processing channel; Provide a unified processing and distribution interface. After receiving the replication instruction data set, the processing and distribution interface distributes the instructions received in the cloud to the corresponding processing channel based on the instruction type. Obtain the device identification number in the copy instruction data set, perform identification number matching search in the processing channel, and select the corresponding instruction processing virtual engine image to parse and process the copy instruction; Based on the copy instruction processing result and in combination with the uploaded real-time device parameters, the device target parameters after the copy instruction is executed are obtained.

5. The furniture monitoring method based on cloud service according to claim 4, characterized in that: In S4, according to the feedback data size and network configuration of different smart furniture, the feedback signal monitoring time window is set, and the command timer of the cloud copy command is defined, which specifically includes: The corresponding feedback data format is set based on the device parameter form of different smart furniture, and the data packet size of a single feedback data upload is calculated according to the set feedback data format; Obtain the bandwidth of the transmission network device, and calculate the theoretical waiting time for receiving feedback signals from different smart furniture by combining the feedback data packet size of different smart furniture and the actual network delay; Based on the average survival time of local control instructions of smart furniture and the theoretical waiting time of feedback signals, a dynamic monitoring time window of feedback signals of smart furniture is set; The dynamic monitoring time window size of the feedback signal corresponding to each smart furniture is marked as the maximum waiting time for the smart furniture command recovery; Based on the smart furniture identifier to which the copy instruction in the cloud-based multi-dimensional instruction processing channel belongs, a timer is created for the copy instructions in all channels, and the maximum waiting time for recycling the smart furniture instruction corresponding to the copy instruction is used as the end time of the timer.

6. The furniture monitoring method based on cloud service according to claim 5, characterized in that: In S5, the feedback signal of the smart furniture received by the cloud is monitored in real time, and the feedback data is matched with the cloud copy instruction. The uncollected instructions and the execution offset instructions are counted, including: Open up two storage spaces on the cloud server, marked as the unrecovered instruction pool and the untrusted instruction pool; Monitor all copy instructions on the cloud. When the timer of a copy instruction expires, trigger the instruction execution result clearing event, delete all device target parameter data after the copy instruction is executed, and mark the copy instruction as an unrecovered instruction and add it to the unrecovered instruction pool. Monitor the feedback signal of smart furniture received by the cloud in real time, obtain the instruction serial number in the feedback data, and compare and screen the instruction serial number with the instruction serial numbers of all copied instructions in the cloud; If there is an identical instruction sequence number, the device target parameter data after the execution of the copy instruction holding the instruction sequence number is compared with the actual device parameter data in the feedback data, and the specific ratio of the parameter value deviation to the adjustable value range of the parameter is calculated, which is marked as the control parameter deviation rate; A confidence threshold of the control parameter deviation rate is set, and the target parameter data of the device whose control parameter deviation rate is higher than the confidence threshold is deleted. At the same time, the copy instructions corresponding to the target parameters are marked as untrusted instructions and added to the untrusted instruction pool.

7. The furniture monitoring method based on cloud service according to claim 6, characterized in that: In S6, the cloud monitors the newly uploaded copy instructions of the smart furniture received, and analyzes the possibility of loss and execution deviation of the copy instructions. When the possibility of loss or execution deviation of the copy instructions is high, the cloud sends the device target parameters and correction instructions to the local instruction processing module to adjust the device parameters of the smart furniture. Specifically, the following steps are performed: Get the newly uploaded copy instructions of the smart furniture received by the cloud, extract the operation code and numerical data in the copy instruction, match the operation code and numerical data with all the instructions in the unrecovered instruction pool and the untrusted instruction pool, use the embedded word vector analysis model to convert the keywords in the instruction into vectors, calculate the cosine similarity between the two, and then judge whether the copy instruction hits based on the threshold comparison method of similarity; If the copy command hits a command in the unrecovered command pool, it is judged that the command is likely to be lost. At this time, the cloud processes the command and sends the processed device target parameters to the local command processing module of the smart furniture for direct control of the device parameters. If the copied instruction hits an instruction in the untrusted instruction pool, it is judged that the possibility of control deviation of the instruction is high. At this time, the control parameter deviation rate of the instruction is obtained, and a control correction instruction is generated based on the control parameter deviation rate. The correction instruction is sent to the instruction processing module of the smart furniture device for control parameter correction.

8. A furniture monitoring system based on cloud services, used to implement a furniture monitoring method based on cloud services as claimed in any one of claims 1 to 7, characterized in that: include: A local command receiving module, used for receiving real-time local control commands; Local command processing module, used for parsing and processing local control commands and controlling device parameters; A cloud command receiving module is used to integrate and upload to the cloud the copy command generated by the local control command received by the command receiving module based on the smart furniture and the relevant parameter device data; The cloud instruction processing module is used to establish a cloud multi-dimensional instruction processing channel and the allocation and use of the virtual engine image, analyze and process the uploaded copy instruction, and obtain the device target parameters after the copy instruction is executed; The feedback signal monitoring module is used to set the dynamic monitoring time window of each replication instruction on the cloud, and at the same time, count and analyze the instruction recovery loss and execution deviation through the instruction matching between the feedback data and the cloud replication instruction; The cloud-based intelligent control module is used to monitor the newly uploaded replication instructions of smart furniture received by the cloud, analyze the possibility of loss and execution deviation of replication instructions, and initiate intelligent control of the device execution target parameters of smart furniture from the cloud.

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