Protocol data processing method and system for lighting system control

Abstract

The invention discloses a protocol data processing method and system for lighting system control. The method comprises the following steps: acquiring a plurality of pieces of lighting communication data sent by a plurality of pieces of intelligent lighting equipment to be processed in a target lighting system; based on a classifier model, identifying and classifying equipment parameters corresponding to each piece of illumination communication data to obtain a plurality of equipment data sets; identifying a transmission parameter corresponding to each equipment data set based on a neural network algorithm; and forwarding and processing each piece of illumination communication data according to the equipment parameters and the transmission parameters. Therefore, the accuracy and efficiency of equipment communication in the large lighting system can be improved, communication errors are reduced, and the communication cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a protocol data processing method and system for lighting system control. Background Art

[0002] As users' lighting scenes become more diverse and their requirements for lighting quality become higher, existing lighting systems begin to introduce more and more complex lighting equipment. The control and scheduling of these large numbers of lighting equipment has become an important technical challenge. In the existing lighting system equipment control technology, there is no effective consideration of combining intelligent algorithms to effectively forward and schedule data under different protocols of a large number of lighting equipment. The control effect is poor, the efficiency is low, and it is prone to errors. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a protocol data processing method and system for lighting system control, which can improve the accuracy and efficiency of equipment communication in large lighting systems, reduce communication errors, and reduce communication costs.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a protocol data processing method for lighting system control, the method comprising: Acquire multiple lighting communication data sent by multiple intelligent lighting devices to be processed in the target lighting system; Based on the classifier model, identifying and classifying the device parameters corresponding to each lighting communication data to obtain multiple device data sets; Based on a neural network algorithm, identifying transmission parameters corresponding to each of the device data sets; Each lighting communication data is forwarded and processed according to the device parameters and the transmission parameters.

[0005] As an optional implementation, in the first aspect of the present invention, the lighting communication data includes a data type and data content; the data type is switch control data, parameter adjustment data, notification data or connection establishment data.

[0006] As an optional implementation, in the first aspect of the present invention, the device parameters include device type, device location, device computing capability, device interface transmission speed and device status.

[0007] As an optional implementation, in the first aspect of the present invention, the device parameters corresponding to each lighting communication data are identified and classified based on the classifier model to obtain multiple device data sets, including: Input each lighting communication data into a trained classifier model to obtain a device parameter corresponding to each lighting communication data; the classifier model is trained by a training data set including a plurality of training lighting communication data and corresponding device parameter annotations; All the lighting communication data are grouped according to the similarities between the device parameters to obtain multiple device data sets.

[0008] As an optional implementation, in the first aspect of the present invention, all the lighting communication data are grouped according to the similarity between the device parameters to obtain multiple device data sets, including: For any two pieces of lighting communication data, calculating a first similarity between the device parameters of the two pieces of lighting communication data; Calculating a second similarity between the data contents of the two lighting communication data; Based on a dynamic programming algorithm and preset objective functions and restriction conditions, all the lighting communication data are grouped according to the first similarity and the second similarity to obtain a plurality of device data sets.

[0009] As an optional implementation, in the first aspect of the present invention, the objective function includes minimizing the number of all the device data sets and maximizing the amount of the lighting communication data in any one of the device data sets; The restriction conditions include that the similarity parameter between any two lighting communication data that are not in the same device data set is less than the average of the similarity parameters of the two with all other lighting communication data in their respective device data sets, and the first similarity corresponding to any two lighting communication data in each device data set is greater than a first threshold and the second similarity is greater than a second threshold; the first threshold is greater than the second threshold; the similarity parameter is the weighted sum average of the first similarity and the second similarity; wherein the weight of the first similarity is greater than the weight of the second similarity.

[0010] As an optional implementation, in the first aspect of the present invention, the transmission parameters include the transmission protocol type, the transmission purpose, the transmission data encryption and decryption information and the transmission target device.

[0011] As an optional implementation, in the first aspect of the present invention, the identifying the transmission parameter corresponding to each of the device data sets based on a neural network algorithm includes: Counting a device parameter statistical set corresponding to each device data set; the device parameter statistical set includes all the device parameters whose statistical values ​​rank in the first preset number of places among the device parameters corresponding to all the lighting communication data in the device data set; the statistical value is a weighted sum average of the data volume of all the lighting communication data corresponding to the device parameter and the number of occurrences of the device parameter; Each of the device data sets and the corresponding device parameter statistical set are input into a trained neural network model to obtain the transmission parameters corresponding to each of the device data sets; the neural network model is trained by a training data set including multiple training device data sets and corresponding device parameter statistical set annotations and transmission parameter annotations.

[0012] A second aspect of an embodiment of the present invention discloses a protocol data processing system for lighting system control, the system comprising: An acquisition module, used for acquiring a plurality of lighting communication data sent by a plurality of intelligent lighting devices to be processed in a target lighting system; A classification module, used for identifying and classifying device parameters corresponding to each lighting communication data based on a classifier model to obtain multiple device data sets; An identification module, used for identifying the transmission parameters corresponding to each of the device data sets based on a neural network algorithm; A processing module is used to forward and process each lighting communication data according to the device parameters and the transmission parameters.

[0013] As an optional implementation, in the second aspect of the present invention, the lighting communication data includes a data type and data content; the data type is switch control data, parameter adjustment data, notification data or connection establishment data.

[0014] As an optional implementation, in the second aspect of the present invention, the device parameters include device type, device location, device computing capability, device interface transmission speed and device status.

[0015] As an optional implementation, in the second aspect of the present invention, the classification module identifies and classifies the device parameters corresponding to each lighting communication data based on the classifier model to obtain a specific method of multiple device data sets, including: Input each lighting communication data into a trained classifier model to obtain a device parameter corresponding to each lighting communication data; the classifier model is trained by a training data set including a plurality of training lighting communication data and corresponding device parameter annotations; All the lighting communication data are grouped according to the similarities between the device parameters to obtain multiple device data sets.

[0016] As an optional implementation, in the second aspect of the present invention, the classification module groups all the lighting communication data according to the similarity between the device parameters to obtain a specific manner of multiple device data sets, including: For any two pieces of the lighting communication data, calculating a first similarity between the device parameters of the two pieces of the lighting communication data; Calculating a second similarity between the data contents of the two lighting communication data; Based on a dynamic programming algorithm and preset objective functions and restriction conditions, all the lighting communication data are grouped according to the first similarity and the second similarity to obtain a plurality of device data sets.

[0017] As an optional implementation, in the second aspect of the present invention, the objective function includes minimizing the number of all the device data sets and maximizing the amount of the lighting communication data in any one of the device data sets; The restriction conditions include that the similarity parameter between any two lighting communication data that are not in the same device data set is less than the average of the similarity parameters of the two with all other lighting communication data in their respective device data sets, and the first similarity corresponding to any two lighting communication data in each device data set is greater than a first threshold and the second similarity is greater than a second threshold; the first threshold is greater than the second threshold; the similarity parameter is the weighted sum average of the first similarity and the second similarity; wherein the weight of the first similarity is greater than the weight of the second similarity.

[0018] As an optional implementation, in the second aspect of the present invention, the transmission parameters include the transmission protocol type, the transmission purpose, the transmission data encryption and decryption information and the transmission target device.

[0019] As an optional implementation, in the second aspect of the present invention, the identification module identifies the specific manner of the transmission parameter corresponding to each of the device data sets based on a neural network algorithm, including: Counting a device parameter statistical set corresponding to each device data set; the device parameter statistical set includes all the device parameters whose statistical values ​​rank in the first preset number of places among the device parameters corresponding to all the lighting communication data in the device data set; the statistical value is a weighted sum average of the data volume of all the lighting communication data corresponding to the device parameter and the number of occurrences of the device parameter; Each of the device data sets and the corresponding device parameter statistical set are input into a trained neural network model to obtain the transmission parameters corresponding to each of the device data sets; the neural network model is trained by a training data set including multiple training device data sets and corresponding device parameter statistical set annotations and transmission parameter annotations.

[0020] A third aspect of the present invention discloses another protocol data processing system for lighting system control, the system comprising: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute part or all of the steps in the protocol data processing method for lighting system control disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the protocol data processing method for lighting system control disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention can classify communication data based on a classifier model, and then identify different sets of transmission parameters based on a neural network, so as to realize automatic identification and precise forwarding processing of communication data of multiple lighting devices in a big data scenario, thereby improving the accuracy and efficiency of device communication in large-scale lighting systems, reducing communication errors, and reducing communication costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a flow chart of a protocol data processing method for lighting system control disclosed in an embodiment of the present invention.

[0025] Figure 2 It is a structural schematic diagram of a protocol data processing system for lighting system control disclosed in an embodiment of the present invention.

[0026] Figure 3 It is a structural schematic diagram of another protocol data processing system for lighting system control disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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.

[0028] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0029] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] The present invention discloses a protocol data processing method and system for lighting system control, which can classify communication data based on a classifier model, and then identify different sets of transmission parameters based on a neural network, so as to realize automatic identification and accurate forwarding processing of communication data of multiple lighting devices in a big data scenario, thereby improving the accuracy and efficiency of device communication in a large lighting system, reducing communication errors and reducing communication costs. The following are detailed descriptions.

[0031] Embodiment 1 See also Figure 1 , Figure 1 1 is a flow chart of a protocol data processing method for lighting system control disclosed in an embodiment of the present invention. Figure 1 The described protocol data processing method for lighting system control can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the protocol data processing method for lighting system control may include the following operations: 101. Acquire multiple lighting communication data sent by multiple intelligent lighting devices to be processed in a target lighting system.

[0032] 102. Based on the classifier model, identify and classify the device parameters corresponding to each lighting communication data to obtain multiple device data sets. 103. Based on the neural network algorithm, identify the transmission parameters corresponding to each device data set. 104. Forward and process each lighting communication data according to the device parameters and transmission parameters.

[0033] It can be seen that the above-mentioned embodiments of the invention can classify communication data based on the classifier model, and then identify different sets of transmission parameters based on the neural network, so as to realize automatic identification and precise forwarding processing of communication data of multiple lighting devices in big data scenarios, thereby improving the accuracy and efficiency of device communication in large-scale lighting systems, reducing communication errors, and reducing communication costs.

[0034] As an optional embodiment, in the above steps, the lighting communication data includes a data type and data content; the data type is switch control data, parameter adjustment data, notification data or connection establishment data.

[0035] It can be seen that through the above optional embodiments, the content and type of lighting communication data are clarified, thereby helping to improve the accuracy and efficiency of device communication in large-scale lighting systems, reduce communication errors, and reduce communication costs.

[0036] As an optional embodiment, in the above steps, the device parameters include device type, device location, device computing capability, device interface transmission speed and device status.

[0037] It can be seen that through the above optional embodiments, the content and type of device parameters are clarified, thereby helping to improve the accuracy and efficiency of device communication in large-scale lighting systems, reduce communication errors, and reduce communication costs.

[0038] As an optional embodiment, in the above steps, based on the classifier model, the device parameters corresponding to each lighting communication data are identified and classified to obtain multiple device data sets, including: Input each lighting communication data into the trained classifier model to obtain the device parameter corresponding to each lighting communication data; optionally, the classifier model is trained by a training data set including a plurality of training lighting communication data and corresponding device parameter annotations; According to the similarity between device parameters, all lighting communication data are grouped to obtain multiple device data sets.

[0039] It can be seen that through the above-mentioned optional embodiments, the device parameters of the lighting communication data can be identified based on the trained classifier model, and then grouped based on the similarity algorithm to obtain multiple device data sets, thereby helping to improve the accuracy and efficiency of device communication in large-scale lighting systems, reduce communication errors, and reduce communication costs.

[0040] As an optional embodiment, in the above steps, all lighting communication data are grouped according to the similarity between device parameters to obtain multiple device data sets, including: For any two lighting communication data, calculating a first similarity between device parameters of the two lighting communication data; Calculating a second similarity between the data contents of the two lighting communication data; Based on a dynamic programming algorithm and preset objective functions and constraints, all lighting communication data are grouped according to a first similarity and a second similarity to obtain a plurality of device data sets.

[0041] It can be seen that through the above-mentioned optional embodiments, it is possible to reasonably group multiple device data sets with higher mutual similarity based on the similarity algorithm and the dynamic programming algorithm, so as to reduce errors in the subsequent identification of transmission parameters, thereby helping to improve the accuracy and efficiency of device communication in large-scale lighting systems, reduce communication errors, and reduce communication costs.

[0042] As an optional embodiment, in the above steps, the objective function includes minimizing the number of all device data sets and maximizing the amount of lighting communication data in any device data set; The restriction conditions include that the similarity parameter between any two lighting communication data that are not in the same device data set is less than the average of the similarity parameters of the two with all other lighting communication data in their respective device data sets, and the first similarity corresponding to any two lighting communication data in each device data set is greater than a first threshold and the second similarity is greater than a second threshold; optionally, the first threshold is greater than the second threshold; the similarity parameter is the weighted sum average of the first similarity and the second similarity; wherein the weight of the first similarity is greater than the weight of the second similarity.

[0043] It can be seen that through the above-mentioned optional embodiments, the contents of the objective function and the restriction conditions are clarified, so that the present invention can reasonably group based on the similarity algorithm and the dynamic programming algorithm to obtain multiple device data sets with higher mutual similarity, so as to reduce errors in the subsequent identification of transmission parameters, thereby assisting in improving the accuracy and efficiency of device communication in large-scale lighting systems, reducing communication errors, and reducing communication costs.

[0044] As an optional embodiment, in the above steps, the transmission parameters include the transmission protocol type, the transmission purpose, the transmission data encryption and decryption information and the transmission target device.

[0045] It can be seen that through the above optional embodiments, the content and type of transmission parameters are clarified, thereby helping to improve the accuracy and efficiency of device communication in large-scale lighting systems, reduce communication errors, and reduce communication costs.

[0046] As an optional embodiment, in the above steps, identifying the transmission parameters corresponding to each device data set based on a neural network algorithm includes: Count the device parameter statistical set corresponding to each device data set; optionally, the device parameter statistical set includes all device parameters whose statistical values ​​are ranked in the first preset number of places among the device parameters corresponding to all lighting communication data in the device data set; the statistical value is the weighted sum average of the data volume of all lighting communication data corresponding to the device parameter and the number of occurrences of the device parameter; Each device data set and the corresponding device parameter statistical set are input into the trained neural network model to obtain the transmission parameters corresponding to each device data set; the neural network model is trained by a training data set including multiple training device data sets and corresponding device parameter statistical set annotations and transmission parameter annotations.

[0047] It can be seen that through the above-mentioned optional embodiments, it is possible to obtain a statistical set of device parameters corresponding to different data sets based on the statistical calculation of the device parameters, and then combine the data set and the device parameter set to identify more accurate transmission parameters through a trained neural network, thereby helping to improve the accuracy and efficiency of device communication in large-scale lighting systems, reduce communication errors, and reduce communication costs.

[0048] Embodiment 2 See also Figure 2 , Figure 2 1 is a schematic diagram of a protocol data processing system for lighting system control disclosed in an embodiment of the present invention. Figure 2 The described protocol data processing system for lighting system control can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the protocol data processing system for lighting system control may include: The acquisition module 201 is used to acquire a plurality of lighting communication data sent by a plurality of intelligent lighting devices to be processed in a target lighting system.

[0049] The classification module 202 is used to identify and classify the device parameters corresponding to each lighting communication data based on the classifier model to obtain multiple device data sets. The identification module 203 is used to identify the transmission parameters corresponding to each device data set based on a neural network algorithm. The processing module 204 is used to forward and process each lighting communication data according to the device parameters and transmission parameters.

[0050] It can be seen that the above-mentioned embodiments of the invention can classify communication data based on the classifier model, and then identify different sets of transmission parameters based on the neural network, so as to realize automatic identification and precise forwarding processing of communication data of multiple lighting devices in big data scenarios, thereby improving the accuracy and efficiency of device communication in large-scale lighting systems, reducing communication errors, and reducing communication costs.

[0051] As an optional embodiment, the lighting communication data includes a data type and data content; the data type is switch control data, parameter adjustment data, notification data or connection establishment data.

[0052] It can be seen that through the above optional embodiments, the content and type of lighting communication data are clarified, thereby helping to improve the accuracy and efficiency of device communication in large-scale lighting systems, reduce communication errors, and reduce communication costs.

[0053] As an optional embodiment, the device parameters include device type, device location, device computing capability, device interface transmission speed and device status.

[0054] It can be seen that through the above optional embodiments, the content and type of device parameters are clarified, thereby helping to improve the accuracy and efficiency of device communication in large-scale lighting systems, reduce communication errors, and reduce communication costs.

[0055] As an optional embodiment, the classification module identifies and classifies the device parameters corresponding to each lighting communication data based on the classifier model to obtain a specific method of multiple device data sets, including: Input each lighting communication data into the trained classifier model to obtain the device parameter corresponding to each lighting communication data; optionally, the classifier model is trained by a training data set including a plurality of training lighting communication data and corresponding device parameter annotations; According to the similarity between device parameters, all lighting communication data are grouped to obtain multiple device data sets.

[0056] It can be seen that through the above-mentioned optional embodiments, the device parameters of the lighting communication data can be identified based on the trained classifier model, and then grouped based on the similarity algorithm to obtain multiple device data sets, thereby helping to improve the accuracy and efficiency of device communication in large-scale lighting systems, reduce communication errors, and reduce communication costs.

[0057] As an optional embodiment, the classification module groups all lighting communication data according to the similarity between device parameters to obtain a specific method of multiple device data sets, including: For any two lighting communication data, calculating a first similarity between device parameters of the two lighting communication data; Calculating a second similarity between the data contents of the two lighting communication data; Based on a dynamic programming algorithm and preset objective functions and constraints, all lighting communication data are grouped according to a first similarity and a second similarity to obtain a plurality of device data sets.

[0058] It can be seen that through the above-mentioned optional embodiments, it is possible to reasonably group multiple device data sets with higher mutual similarity based on the similarity algorithm and the dynamic programming algorithm, so as to reduce errors in the subsequent identification of transmission parameters, thereby helping to improve the accuracy and efficiency of device communication in large-scale lighting systems, reduce communication errors, and reduce communication costs.

[0059] As an optional embodiment, the objective function includes minimizing the number of all device data sets and maximizing the amount of lighting communication data in any device data set; The restriction conditions include that the similarity parameter between any two lighting communication data that are not in the same device data set is less than the average of the similarity parameters of the two with all other lighting communication data in their respective device data sets, and the first similarity corresponding to any two lighting communication data in each device data set is greater than a first threshold and the second similarity is greater than a second threshold; optionally, the first threshold is greater than the second threshold; the similarity parameter is the weighted sum average of the first similarity and the second similarity; wherein the weight of the first similarity is greater than the weight of the second similarity.

[0060] It can be seen that through the above-mentioned optional embodiments, the contents of the objective function and the restriction conditions are clarified, so that the present invention can reasonably group based on the similarity algorithm and the dynamic programming algorithm to obtain multiple device data sets with higher mutual similarity, so as to reduce errors in the subsequent identification of transmission parameters, thereby assisting in improving the accuracy and efficiency of device communication in large-scale lighting systems, reducing communication errors, and reducing communication costs.

[0061] As an optional embodiment, the transmission parameters include the transmission protocol type, the transmission purpose, the transmission data encryption and decryption information and the transmission target device.

[0062] It can be seen that through the above optional embodiments, the content and type of transmission parameters are clarified, thereby helping to improve the accuracy and efficiency of device communication in large-scale lighting systems, reduce communication errors, and reduce communication costs.

[0063] As an optional embodiment, the identification module identifies the specific manner of the transmission parameters corresponding to each device data set based on the neural network algorithm, including: Count the device parameter statistical set corresponding to each device data set; optionally, the device parameter statistical set includes all device parameters whose statistical values ​​are ranked in the first preset number of places among the device parameters corresponding to all lighting communication data in the device data set; the statistical value is the weighted sum average of the data volume of all lighting communication data corresponding to the device parameter and the number of occurrences of the device parameter; Each device data set and the corresponding device parameter statistical set are input into the trained neural network model to obtain the transmission parameters corresponding to each device data set; the neural network model is trained by a training data set including multiple training device data sets and corresponding device parameter statistical set annotations and transmission parameter annotations.

[0064] It can be seen that through the above-mentioned optional embodiments, it is possible to obtain a statistical set of device parameters corresponding to different data sets based on the statistical calculation of the device parameters, and then combine the data set and the device parameter set to identify more accurate transmission parameters through a trained neural network, thereby helping to improve the accuracy and efficiency of device communication in large-scale lighting systems, reduce communication errors, and reduce communication costs.

[0065] Embodiment 3 See also Figure 3 , Figure 3 It is another protocol data processing system for lighting system control disclosed in an embodiment of the present invention. Figure 3 The described protocol data processing system for lighting system control is applied in a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the protocol data processing system for lighting system control may include: A memory 301 storing executable program codes; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the protocol data processing method for lighting system control described in the first embodiment.

[0066] Embodiment 4 An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the protocol data processing method for lighting system control described in the first embodiment.

[0067] Embodiment 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the protocol data processing method for lighting system control described in the first embodiment.

[0068] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0070] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0071] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0072] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0075] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0076] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0077] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0078] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0079] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media including storage devices.

[0080] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0081] Finally, it should be noted that the protocol data processing method and system for lighting system control disclosed in the embodiment of the present invention are only preferred embodiments of the present invention, which are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A protocol data processing method for lighting system control, characterized in that: The method comprises: Acquire multiple lighting communication data sent by multiple intelligent lighting devices to be processed in the target lighting system; Based on the classifier model, identifying and classifying the device parameters corresponding to each lighting communication data to obtain multiple device data sets; Based on a neural network algorithm, identifying transmission parameters corresponding to each of the device data sets; Each lighting communication data is forwarded and processed according to the device parameters and the transmission parameters.

2. The protocol data processing method for lighting system control according to claim 1, characterized in that: The lighting communication data includes data type and data content; the data type is switch control data, parameter adjustment data, notification data or connection establishment data.

3. The protocol data processing method for lighting system control according to claim 1, characterized in that: The device parameters include device type, device location, device computing capability, device interface transmission speed and device status.

4. The protocol data processing method for lighting system control according to claim 1, characterized in that: Based on the classifier model, the device parameters corresponding to each lighting communication data are identified and classified to obtain multiple device data sets, including: Input each lighting communication data into a trained classifier model to obtain a device parameter corresponding to each lighting communication data; the classifier model is trained by a training data set including a plurality of training lighting communication data and corresponding device parameter annotations; All the lighting communication data are grouped according to the similarities between the device parameters to obtain multiple device data sets.

5. The protocol data processing method for lighting system control according to claim 4, characterized in that: The step of grouping all the lighting communication data according to the similarity between the device parameters to obtain a plurality of device data sets includes: For any two pieces of the lighting communication data, calculating a first similarity between the device parameters of the two pieces of the lighting communication data; Calculating a second similarity between the data contents of the two lighting communication data; Based on a dynamic programming algorithm and preset objective functions and restriction conditions, all the lighting communication data are grouped according to the first similarity and the second similarity to obtain a plurality of device data sets.

6. The protocol data processing method for lighting system control according to claim 5, characterized in that: The objective function includes minimizing the number of all the device data sets and maximizing the amount of the lighting communication data in any one of the device data sets; The restriction conditions include that the similarity parameter between any two lighting communication data that are not in the same device data set is less than the average of the similarity parameters of the two with all other lighting communication data in their respective device data sets, and the first similarity corresponding to any two lighting communication data in each device data set is greater than a first threshold and the second similarity is greater than a second threshold; the first threshold is greater than the second threshold; the similarity parameter is the weighted sum average of the first similarity and the second similarity; wherein the weight of the first similarity is greater than the weight of the second similarity.

7. The protocol data processing method for lighting system control according to claim 1, characterized in that: The transmission parameters include the transmission protocol type, transmission purpose, transmission data encryption and decryption information and transmission target device.

8. The protocol data processing method for lighting system control according to claim 1, characterized in that: The identifying, based on a neural network algorithm, a transmission parameter corresponding to each of the device data sets comprises: Counting a device parameter statistical set corresponding to each device data set; the device parameter statistical set includes all the device parameters whose statistical values ​​rank in the first preset number of places among the device parameters corresponding to all the lighting communication data in the device data set; the statistical value is a weighted sum average of the data volume of all the lighting communication data corresponding to the device parameter and the number of occurrences of the device parameter; Each of the device data sets and the corresponding device parameter statistical set are input into a trained neural network model to obtain the transmission parameters corresponding to each of the device data sets; the neural network model is trained by a training data set including multiple training device data sets and corresponding device parameter statistical set annotations and transmission parameter annotations.

9. A protocol data processing system for lighting system control, characterized in that: The system comprises: An acquisition module, used for acquiring a plurality of lighting communication data sent by a plurality of intelligent lighting devices to be processed in a target lighting system; A classification module, used for identifying and classifying device parameters corresponding to each lighting communication data based on a classifier model to obtain multiple device data sets; An identification module, used for identifying the transmission parameters corresponding to each of the device data sets based on a neural network algorithm; A processing module is used to forward and process each lighting communication data according to the device parameters and the transmission parameters.

10. A protocol data processing system for lighting system control, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the protocol data processing method for lighting system control as described in any one of claims 1-8.

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