Version control method and system for cloud platform and multiple robots

Abstract

The application discloses a cloud platform and a version control method and system for multiple robots. According to multiple interaction data input by each robot and a collection channel of the cloud platform, multiple interaction data are autonomously classified, and a first data set of each version is formed. Multiple first data structures are defined based on identification of the first data set of each version, and the multiple first data structures are different from each other. Further, a second data set of each version is output based on data processing of the multiple first data structures, and the second data set of each version is in the same data structure. According to the second data set of each version and the cloud platform, corresponding logical processing is triggered to output corresponding execution logic, unified processing of the multiple first data structures is realized, the second data set of each version is in the same data structure, the compatibility of the cloud platform for the multiple robots is improved, and the version control effect of the cloud platform for the multiple robots is ensured.

Description

Technical Field

[0001] This invention relates to the technical field of cloud platforms and multiple robots, and more particularly to a version control method and system for cloud platforms and multiple robots. Background Technology

[0002] With the development of technology, cloud platforms interact dynamically with multiple robots. By using a single cloud platform to manage the versions of multiple robots, traditional robot systems typically rely on local computing resources for data processing and decision-making. The inconsistent version iteration progress of the robots affects the effectiveness of the cloud platform in controlling the versions of multiple robots and reduces the compatibility of the cloud platform with multiple robots. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides a version control method and system for a cloud platform and multiple robots, which dynamically interacts with the cloud platform and multiple robots, and locates the cloud platform's acquisition channel; collects multiple interactive data input by each robot based on the cloud platform's acquisition channel; triggers the autonomous classification of multiple interactive data based on the multiple interactive data input by each robot and the cloud platform's acquisition channel, and forms a first data set for each version; defines multiple first data structures based on the identification of the first data sets for each version, and the multiple first data structures are different from each other, so as to facilitate subsequent management and control of the first data sets for each version.

[0004] Furthermore, based on the data processing of multiple first data structures, various versions of second data sets are output, with each version of the second data set residing in the same data structure. The various versions of the second data sets are associated with the cloud platform, and corresponding logical processing is triggered based on each version of the second data set and the cloud platform to output the corresponding execution logic. This achieves unified processing of multiple first data structures, introducing various versions of second data sets. Simultaneously, the various versions of the second data sets reside in the same data structure, enabling the output of execution logic. This improves the cloud platform's compatibility with multiple robots and ensures the cloud platform's version control effectiveness for multiple robots.

[0005] This invention provides a version control method for a cloud platform and multiple robots, applicable to version control scenarios involving cloud platforms and multiple robots;

[0006] The version control method for the cloud platform and multiple robots includes:

[0007] The cloud platform dynamically interacts with multiple robots and locates the data collection channels of the cloud platform.

[0008] The cloud platform-based acquisition channel collects multiple interactive data inputs from each robot;

[0009] Based on the multiple interactive data input by each robot and the collection channels of the cloud platform, the multiple interactive data are autonomously classified and a first data set for each version is formed.

[0010] Multiple first data structures are defined based on the identification of the first data sets of each version, and at this time, the multiple first data structures are different from each other;

[0011] The second data set of various versions is output based on the data processing of multiple first data structures, and the second data set of various versions is in the same data structure;

[0012] Associate the second data set of each version with the cloud platform, and trigger the corresponding logical processing according to the second data set of each version and the cloud platform to output the corresponding execution logic.

[0013] Optionally, the step of dynamically interacting the cloud platform with multiple robots and locating the cloud platform's data acquisition channels includes:

[0014] Collect the IP address of the cloud platform and the IP addresses of multiple robots;

[0015] The interaction types between the cloud platform and multiple robots are matched based on the IP address of the cloud platform and the IP addresses of multiple robots.

[0016] Based on this type of interaction, the cloud platform and multiple robots trigger dynamic interactions between the cloud platform and multiple robots;

[0017] Real-time monitoring of the dynamic interaction between the cloud platform and multiple robots, and definition of the interaction area between the cloud platform and multiple robots;

[0018] The acquisition channel is defined based on the traversal of the interaction area between the cloud platform and multiple robots;

[0019] Locate the data collection channel of the cloud platform.

[0020] Optionally, the cloud-based acquisition channel collects multiple interactive data input by each robot, including:

[0021] The data acquisition channel of the fixed-frame cloud platform;

[0022] The status of the data acquisition channels on the cloud platform;

[0023] The status of the associated data acquisition channels, the status of multiple robots, and the status of the cloud platform;

[0024] Define the corresponding acquisition mode based on the status of the acquisition channel, the status of multiple robots, and the status of the cloud platform;

[0025] This data collection mode triggers the cloud platform to collect data from each robot in a targeted manner;

[0026] Collect multiple interaction data input from each robot and dynamically manage these multiple interaction data.

[0027] Optionally, the autonomous classification of multiple interactive data input by each robot and the cloud platform's acquisition channel is triggered to form a first data set for each version, including:

[0028] Freeze the multiple interactive data input by each robot;

[0029] It associates multiple interactive data with the cloud platform's collection channels and triggers the control of the cloud platform's collection channels;

[0030] Real-time monitoring and control of the data acquisition channels;

[0031] In the management and control of the data collection channels on the cloud platform, corresponding autonomous classification is triggered based on multiple interactive data.

[0032] Based on the autonomous classification of multiple interactive data, the first data set of each version is formed.

[0033] Optionally, multiple first data structures are defined based on the identification of each version of the first data set. In this case, the multiple first data structures are different from each other, including:

[0034] The first data set of each version is captured;

[0035] Associate the first dataset of each version with the corresponding autonomous recognition model;

[0036] The recognition of the first dataset is triggered based on the first dataset of each version and the corresponding autonomous recognition model;

[0037] Multiple first data structures are defined based on the identification of the first data set of each version, and these multiple first data structures are different from each other.

[0038] Optionally, the step of outputting various versions of the second data set based on data processing of multiple first data structures, wherein the various versions of the second data set are in the same data structure, includes:

[0039] Collect multiple first-level data structures;

[0040] Based on multiple first data structures, the corresponding structure types are matched, and a combination of structure types is formed based on multiple structure types.

[0041] Optionally, the step of outputting various versions of the second data set based on data processing of multiple first data structures, wherein the various versions of the second data set are in the same data structure, further includes:

[0042] Based on the combination of these structural types and the corresponding data processing triggered by the cloud platform;

[0043] Based on this data processing, the combination of this type of structure is controlled, and structural optimization is performed on multiple first data structures;

[0044] The second data set is output by optimizing the structure of multiple first data structures;

[0045] Iterate through the second data set of each version, where each version of the second data set is in the same data structure.

[0046] Optionally, the association of the second data set of each version and the cloud platform, and the triggering of corresponding logical processing based on the second data set of each version and the cloud platform to output the corresponding execution logic, includes:

[0047] The second dataset is frozen in time for each version;

[0048] Associate the second datasets of each version with the cloud platform;

[0049] Based on the second dataset of each version and the corresponding logical learning model matched with the cloud platform;

[0050] This logic learning model is trained based on previous execution logic.

[0051] Optionally, the step of associating the second data set of each version with the cloud platform, and triggering corresponding logical processing based on the second data set of each version with the cloud platform to output the corresponding execution logic, further includes:

[0052] Match the corresponding execution logic based on the second dataset of each version and the logical learning model;

[0053] The execution logic is automatically matched to the corresponding robot based on the cloud platform.

[0054] In addition, embodiments of the present invention also provide a version control system for a cloud platform and multiple robots, the version control system for the cloud platform and multiple robots including:

[0055] The dynamic interaction module is used to enable dynamic interaction between the cloud platform and multiple robots, and to locate the data acquisition channels of the cloud platform.

[0056] The interactive data module is used to collect multiple interactive data input by each robot through the cloud platform's acquisition channel;

[0057] The first data set module is used to trigger the autonomous classification of multiple interactive data input by each robot and the collection channel of the cloud platform, and form the first data set of each version.

[0058] The data structure module is used to define multiple first data structures based on the identification of the first data set of each version. At this time, the multiple first data structures are different from each other.

[0059] The data processing module is used to output various versions of the second data set based on the data processing of multiple first data structures, and the various versions of the second data set are in the same data structure;

[0060] The execution logic module is used to associate the second data set of each version with the cloud platform, and to trigger the corresponding logic processing according to the second data set of each version and the cloud platform, so as to output the corresponding execution logic.

[0061] In this embodiment of the invention, the method described herein enables dynamic interaction between a cloud platform and multiple robots, and locates the cloud platform's data acquisition channel. Multiple interactive data input by each robot are collected based on the cloud platform's data acquisition channel. The autonomous classification of these multiple interactive data is triggered based on the input data from each robot and the cloud platform's data acquisition channel, forming first data sets of various versions. Multiple first data structures are defined based on the identification of these first data sets, and these first data structures are distinct from each other to facilitate subsequent management and control of the first data sets of various versions.

[0062] Furthermore, based on the data processing of multiple first data structures, various versions of second data sets are output, with each version of the second data set residing in the same data structure. The various versions of the second data sets are associated with the cloud platform, and corresponding logical processing is triggered based on each version of the second data set and the cloud platform to output the corresponding execution logic. This achieves unified processing of multiple first data structures, introducing various versions of second data sets. Simultaneously, the various versions of the second data sets reside in the same data structure, enabling the output of execution logic. This improves the cloud platform's compatibility with multiple robots and ensures the cloud platform's version control effectiveness for multiple robots. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a flowchart illustrating the version control method for a cloud platform and multiple robots in an embodiment of the present invention.

[0065] Figure 2 This is a flowchart illustrating step S11 of the version control method for cloud platform and multiple robots in an embodiment of the present invention.

[0066] Figure 3 This is a flowchart illustrating step S12 of the version control method for cloud platform and multiple robots in an embodiment of the present invention.

[0067] Figure 4 This is a flowchart illustrating S13 of the version control method for cloud platform and multiple robots in an embodiment of the present invention.

[0068] Figure 5 This is a flowchart illustrating S14 of the version control method for cloud platform and multiple robots in an embodiment of the present invention.

[0069] Figure 6 This is a flowchart illustrating S15 of the version control method for cloud platform and multiple robots in an embodiment of the present invention.

[0070] Figure 7 This is a flowchart illustrating step S16 of the version control method for cloud platform and multiple robots in an embodiment of the present invention.

[0071] Figure 8 This is a schematic diagram of the structure of the cloud platform and the version control system for multiple robots in an embodiment of the present invention;

[0072] Figure 9 This is a hardware diagram of an electronic device according to an exemplary embodiment. Detailed Implementation

[0073] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0074] Please see Figures 1 to 9 A version control method for a cloud platform and multiple robots, applied to a version control scenario involving a cloud platform and multiple robots; the version control method for a cloud platform and multiple robots includes:

[0075] Step S11: Dynamically interact between the cloud platform and multiple robots, and locate the data acquisition channel of the cloud platform;

[0076] Step S12: Collect multiple interactive data input by each robot using the cloud platform's acquisition channel;

[0077] Step S13: Collect multiple interactive data input by each robot using the cloud platform's acquisition channel;

[0078] Step S14: Define multiple first data structures based on the identification of the first data sets of each version. At this time, the multiple first data structures are different from each other.

[0079] Step S15: Output the second data set of each version based on the data processing of multiple first data structures, with each version of the second data set located in the same data structure;

[0080] Step S16: Associate the second data set of each version with the cloud platform, and trigger the corresponding logic processing according to the second data set of each version and the cloud platform to output the corresponding execution logic.

[0081] In this embodiment of the invention, the method described herein enables dynamic interaction between a cloud platform and multiple robots, and locates the cloud platform's data acquisition channel. Multiple interactive data input by each robot are collected based on the cloud platform's data acquisition channel. The autonomous classification of these multiple interactive data is triggered based on the input data from each robot and the cloud platform's data acquisition channel, forming first data sets of various versions. Multiple first data structures are defined based on the identification of these first data sets, and these first data structures are distinct from each other to facilitate subsequent management and control of the first data sets of various versions.

[0082] Furthermore, based on the data processing of multiple first data structures, various versions of second data sets are output, with each version of the second data set residing in the same data structure. The various versions of the second data sets are associated with the cloud platform, and corresponding logical processing is triggered based on each version of the second data set and the cloud platform to output the corresponding execution logic. This achieves unified processing of multiple first data structures, introducing various versions of second data sets. Simultaneously, the various versions of the second data sets reside in the same data structure, enabling the output of execution logic. This improves the cloud platform's compatibility with multiple robots and ensures the cloud platform's version control effectiveness for multiple robots.

[0083] refer to Figure 2 In step S11, the cloud platform interacts dynamically with multiple robots, and the cloud platform's data acquisition channel is located.

[0084] In the specific implementation of this invention, the specific steps can be as follows:

[0085] S111: Collect the IP address of the cloud platform and the IP addresses of multiple robots;

[0086] S112: Match the interaction types between the cloud platform and multiple robots based on the IP address of the cloud platform and the IP addresses of multiple robots;

[0087] S113: Based on this interaction type, the cloud platform and multiple robots trigger dynamic interaction between the cloud platform and multiple robots;

[0088] S114: Real-time monitoring of the dynamic interaction between the cloud platform and multiple robots, and definition of the interaction area between the cloud platform and multiple robots;

[0089] S115: Define the acquisition channel based on the traversal of the interaction area between the cloud platform and multiple robots;

[0090] S116: Location cloud platform data acquisition channel.

[0091] In the embodiments of this application, the IP address of the cloud platform and the IP addresses of multiple robots are collected; the interaction types between the cloud platform and the multiple robots are matched based on the IP address of the cloud platform and the IP address of the multiple robots. This approach takes into account the overall consideration of the IP address of the cloud platform and the IP address of the multiple robots, and achieves multi-dimensional control over the matching of the IP address of the cloud platform and the IP address of the multiple robots, thus ensuring the accuracy of the matching of the interaction types between the cloud platform and the multiple robots.

[0092] Furthermore, based on this interaction type, the cloud platform triggers dynamic interactions between the cloud platform and multiple robots; the dynamic interactions between the cloud platform and multiple robots are monitored in real time, and the interaction area between the cloud platform and multiple robots is defined. The interaction types between the cloud platform and multiple robots are introduced, which realizes further control over the interaction types between the cloud platform and multiple robots and ensures the accuracy of the interaction types between the cloud platform and multiple robots.

[0093] Therefore, the acquisition channels are defined based on the traversal of the interaction areas between the cloud platform and multiple robots; the acquisition channels of the cloud platform are located, and further control is exercised over the acquisition channels of the cloud platform to facilitate the processing of the acquisition channels of the cloud platform.

[0094] refer to Figure 3 In step S12, multiple interactive data input by each robot are collected through the acquisition channel of the cloud platform.

[0095] In the specific implementation of this invention, the specific steps can be as follows:

[0096] S121: Data acquisition channel of the fixed-frame cloud platform;

[0097] S122: Status of the data acquisition channel on the cloud platform;

[0098] S123: Status of associated acquisition channels, status of multiple robots, and status of the cloud platform;

[0099] S124: Define the corresponding acquisition mode based on the status of the acquisition channel, the status of multiple robots, and the status of the cloud platform;

[0100] S125: Based on this acquisition mode, the cloud platform is triggered to collect data directionally from each robot;

[0101] S126: Collect multiple interactive data input by each robot and dynamically manage these multiple interactive data.

[0102] In the embodiments of this application, the acquisition channel of the fixed cloud platform is captured; at the same time, the state of the acquisition channel of the acquisition cloud platform is captured; the state of the acquisition channel, the state of multiple robots and the state of the cloud platform are associated, and the state of the acquisition channel, the state of multiple robots and the state of the cloud platform are introduced, so as to realize the control of the state of the acquisition channel, the state of multiple robots and the state of the cloud platform in multiple dimensions, and ensure the dynamic interaction of the state of the acquisition channel, the state of multiple robots and the state of the cloud platform.

[0103] Therefore, a corresponding acquisition mode is defined based on the status of the acquisition channel, the status of multiple robots, and the status of the cloud platform. Based on this acquisition mode, the cloud platform is triggered to acquire data from each robot in a targeted manner, thereby enabling the cloud platform to acquire multiple interactive data input by each robot and to dynamically manage these multiple interactive data.

[0104] refer to Figure 4 In step S13, the autonomous classification of multiple interactive data is triggered based on the multiple interactive data input by each robot and the collection channel of the cloud platform, and a first data set of each version is formed.

[0105] In the specific implementation of this invention, the specific steps can be as follows:

[0106] S131: Freeze multiple interactive data input by each robot;

[0107] S132: Associate multiple interactive data and cloud platform collection channels, and trigger the control of cloud platform collection channels;

[0108] S133: Real-time monitoring and control of the data acquisition channel;

[0109] S134: In the management and control of the data collection channels on the cloud platform, corresponding autonomous classification is triggered based on multiple interactive data.

[0110] S135: Based on the autonomous classification of multiple interactive data, the first data set of each version is formed.

[0111] In the embodiments of this application, multiple interactive data input by each robot are captured; the multiple interactive data are associated with the cloud platform's acquisition channel, and the control of the cloud platform's acquisition channel is triggered so as to enable real-time control of the cloud platform's acquisition channel.

[0112] Therefore, real-time monitoring and control of the data acquisition channel are required. In the control of the data acquisition channel on the cloud platform, corresponding autonomous classification is triggered based on multiple interactive data. Based on the autonomous classification of multiple interactive data, the first data set of each version is formed. The first data set of each version is introduced, and multiple data for each version are integrated to form the first data set of each version, thereby enabling overall control of the first data set of each version.

[0113] refer to Figure 5 S14: Based on the identification of the first data set of each version, multiple first data structures are defined. At this time, the multiple first data structures are different from each other.

[0114] In the specific implementation of this invention, the specific steps can be as follows:

[0115] S141: The first data set for each version is frozen;

[0116] S142: Associate the first dataset of each version with the corresponding autonomous recognition model;

[0117] S143: Trigger the recognition of the first data set based on the first data set of each version and the corresponding autonomous recognition model;

[0118] S144: Define multiple first data structures based on the identification of the first data set of each version, wherein the multiple first data structures are different from each other.

[0119] In the embodiments of this application, the cloud platform dynamically interacts with multiple robots and locates the cloud platform's acquisition channel; multiple interactive data input by each robot are collected based on the cloud platform's acquisition channel; the multiple interactive data input by each robot and the cloud platform's acquisition channel are used to trigger the autonomous classification of the multiple interactive data and form a first data set of each version; multiple first data structures are defined based on the identification of the first data sets of each version, and the multiple first data structures are different from each other to facilitate subsequent management and control of the first data sets of each version.

[0120] At this point, the first data set of each version is fixed; the first data set of each version and the corresponding autonomous recognition model are associated; the recognition of the first data set is triggered according to the first data set of each version and the corresponding autonomous recognition model. This takes into account the overall consideration of the first data set of each version and the corresponding autonomous recognition model, realizes multi-dimensional control of the first data set of each version and the corresponding autonomous recognition model, and ensures the recognition of the first data set.

[0121] Therefore, multiple first data structures are defined based on the identification of the first data sets of each version, and multiple first data structures are introduced to achieve overall control of multiple first data structures. At this time, the multiple first data structures are different from each other, so as to facilitate subsequent management and control of the first data sets of each version.

[0122] refer to Figure 6 S15: Output the second data set of each version based on the data processing of multiple first data structures, and the second data set of each version is in the same data structure;

[0123] In the specific implementation of this invention, the specific steps can be as follows:

[0124] S151: Collect multiple first data structures;

[0125] S152: Match the corresponding structure types based on multiple first data structures, and form a combination of structure types based on multiple structure types;

[0126] S153: Based on the combination of the structure types and the cloud platform, trigger the corresponding data processing;

[0127] S154: Based on this data processing, manage the combination of this type of structure and optimize the structure of multiple first data structures;

[0128] S155: Output the second data set for each version by optimizing the structure of multiple first data structures;

[0129] S156: Traverse the second data set of each version, where the second data set of each version is in the same data structure.

[0130] In the embodiments of this application, multiple first data structures are collected, and corresponding structure types are matched based on the multiple first data structures, and a structure type combination is formed based on the multiple structure types; according to the structure type combination and the cloud platform, corresponding data processing is triggered. The structure type combination and the cloud platform are introduced to realize the dynamic interaction between the structure type combination and the cloud platform, thereby realizing the corresponding data processing.

[0131] Therefore, based on this data processing, the combination of the structure types is controlled, and the structure of multiple first data structures is optimized; the second data sets of each version are output along the structure optimization of multiple first data structures; the second data sets of each version are traversed, and the second data sets of each version are in the same data structure, so as to optimize the structure of multiple first data structures, thereby unifying the structure of multiple first data structures and ensuring the uniformity of the second data sets of each version.

[0132] refer to Figure 7 S16: Associate the second data set of each version with the cloud platform, and trigger the corresponding logic processing according to the second data set of each version with the cloud platform to output the corresponding execution logic;

[0133] In the specific implementation of this invention, the specific steps can be as follows:

[0134] S161: The second data set that captures each version;

[0135] S162: Associate the second datasets of each version with the cloud platform;

[0136] S163: Based on the second dataset of each version and the cloud platform, match the corresponding logical learning model;

[0137] S164: This logic learning model is trained based on previous execution logic;

[0138] S165: Match the corresponding execution logic based on the second dataset of each version and the logical learning model;

[0139] S166: Based on the cloud platform, the execution logic will be automatically matched to the corresponding robot.

[0140] In the specific implementation of this invention, various versions of second data sets are output based on the data processing of multiple first data structures, and the various versions of second data sets are located in the same data structure. The various versions of second data sets are associated with the cloud platform, and corresponding logical processing is triggered based on the various versions of second data sets and the cloud platform to output the corresponding execution logic. This achieves unified processing of multiple first data structures, introduces various versions of second data sets, and at the same time, the various versions of second data sets are located in the same data structure, realizing the output of execution logic. This improves the compatibility of the cloud platform with multiple robots and ensures the version control effect of the cloud platform for multiple robots.

[0141] At this point, the second datasets for each version are defined; the second datasets for each version are correlated with the cloud platform; and corresponding logical learning models are matched based on the second datasets for each version and the cloud platform. This comprehensive approach, considering all versions of the second datasets and the cloud platform, achieves multi-dimensional control over them, ensuring the accuracy of the logical learning model. This logical learning model is trained based on previous execution logic.

[0142] Therefore, the corresponding execution logic is matched according to the second dataset of each version and the logical learning model; the execution logic is autonomously matched to the corresponding robot based on the cloud platform. At the same time, the second dataset of each version is in the same data structure, realizing the output of the execution logic, so as to improve the compatibility of the cloud platform with multiple robots and ensure the version control effect of the cloud platform for multiple robots.

[0143] In its specific implementation, this invention employs Spring custom annotations and AOP proxy aspects to provide an intelligent version switching and selection technology for a multi-version parallel execution environment.

[0144] Define annotations:

[0145] The @VersionMethod annotation is used to mark a method in a class to indicate the version number of that method.

[0146] Initialize annotation methods:

[0147] After the container starts, all annotated methods are loaded into two global maps at once through annotation scanning: VERSION_METHOD_MAP: of type Map <String,Map<String,Method> The outer key is the fully qualified class name of the interface plus the method name, the inner key is the version number, and the value is the corresponding method object.

[0148] VERSION_MAP: of type Map <String,Set <string>>, where the key is the fully qualified class name of the interface plus the method name, and the value is a list of version numbers.

[0149] Automatic selection:

[0150] When calling a service via API, the AOP proxy retrieves the machine version number of the current request before method execution. It then obtains a list of all version numbers for the current API method from the VERSION_MAP. If no version number list exists, version selection is skipped, and the default implementation method is executed directly. If a version number list exists, the proxy filters the largest version number less than or equal to the current version from the list (a Set collection), retrieves the corresponding method from the VERSION_METHOD_MAP, and executes the implementation method for that version.

[0151] This invention allows multiple versions of the same service to coexist in the system and can automatically select the appropriate version to respond to client requests. This design enables seamless coexistence of old and new versions, reduces service interruptions during version upgrades, and improves system flexibility and scalability.

[0152] By using Spring custom annotations and AOP proxy aspects, this invention achieves non-intrusive enhancements to existing code. Developers do not need to modify existing business logic code; they only need to add a few annotations to achieve parallel execution of multiple versions. This not only simplifies the development process but also reduces maintenance costs.

[0153] Furthermore, this invention supports dynamic version management at runtime, automatically selecting the appropriate version for execution based on configuration or policies. This dynamic management mechanism enables the system to quickly adapt to different needs and environmental changes, improving the system's responsiveness and robustness.

[0154] Through intelligent version switching technology, the system can automatically select the optimal version for execution based on performance indicators such as current load and resource utilization. This not only improves the overall performance of the system but also ensures stability and reliability in high-concurrency scenarios.

[0155] Because this invention provides an automated version management and switching mechanism, it reduces the workload of manual configuration and testing, thereby lowering development and deployment costs. Enterprises can launch new features and services faster, improving their market competitiveness.

[0156] Through intelligent version switching, the system can provide the best service experience for different client versions. Regardless of whether the client is using an older or newer version, it can obtain a reasonable response result that matches its version, thereby improving user satisfaction and loyalty.

[0157] In summary, this invention, by combining Spring custom annotations and AOP proxy aspect technology, achieves intelligent version switching and selection in a multi-version parallel execution environment. This not only improves the system's flexibility, scalability, and performance but also simplifies development and maintenance, and enhances system security and user experience. This technical solution provides enterprises with a more efficient and reliable multi-version management solution and has broad application prospects.

[0158] In this embodiment of the invention, the method described herein enables dynamic interaction between a cloud platform and multiple robots, and locates the cloud platform's data acquisition channel. Multiple interactive data input by each robot are collected based on the cloud platform's data acquisition channel. The autonomous classification of these multiple interactive data is triggered based on the input data from each robot and the cloud platform's data acquisition channel, forming first data sets of various versions. Multiple first data structures are defined based on the identification of these first data sets, and these first data structures are distinct from each other to facilitate subsequent management and control of the first data sets of various versions.

[0159] Furthermore, based on the data processing of multiple first data structures, various versions of second data sets are output, with each version of the second data set residing in the same data structure. The various versions of the second data sets are associated with the cloud platform, and corresponding logical processing is triggered based on each version of the second data set and the cloud platform to output the corresponding execution logic. This achieves unified processing of multiple first data structures, introducing various versions of second data sets. Simultaneously, the various versions of the second data sets reside in the same data structure, enabling the output of execution logic. This improves the cloud platform's compatibility with multiple robots and ensures the cloud platform's version control effectiveness for multiple robots.

[0160] Please see Figure 8 , Figure 8 This is a schematic diagram illustrating the structural composition of the cloud platform and the version control system for multiple robots in an embodiment of the present invention.

[0161] like Figure 8 As shown, a version control system for a cloud platform and multiple robots includes:

[0162] The dynamic interaction module 21 is used to enable dynamic interaction between the cloud platform and multiple robots, and to locate the data acquisition channel of the cloud platform.

[0163] Interactive data module 22 is used to collect multiple interactive data input by each robot based on the acquisition channel of the cloud platform;

[0164] The first data set module 23 is used to trigger the autonomous classification of multiple interactive data based on the multiple interactive data input by each robot and the collection channel of the cloud platform, and to form the first data set of each version.

[0165] Data structure module 24 is used to define multiple first data structures based on the identification of the first data set of each version, wherein the multiple first data structures are different from each other;

[0166] Data processing module 25 is used to output various versions of second data sets based on the data processing of multiple first data structures, and the various versions of second data sets are in the same data structure;

[0167] The execution logic module 26 is used to associate the second data set of each version with the cloud platform, and to trigger the corresponding logic processing according to the second data set of each version with the cloud platform, so as to output the corresponding execution logic.

[0168] Please see Figure 9 See below for reference. Figure 9 To describe an electronic device 40 according to this embodiment of the present invention. Figure 9 The electronic device 40 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0169] like Figure 9 As shown, the electronic device 40 is manifested in the form of a general-purpose computing device. The components of the electronic device 40 may include, but are not limited to: at least one processing unit 41, at least one storage unit 42, and a bus 43 connecting different system components (including storage unit 42 and processing unit 41).

[0170] The storage unit stores program code, which can be executed by the processing unit 41 to perform the steps described in the "Embodiment Methods" section of this specification according to various exemplary embodiments of the present invention.

[0171] Storage unit 42 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 421 and / or cache memory 422, and may further include a read-only memory (ROM) 423.

[0172] Storage unit 42 may also include a program / utility 424 having a set (at least one) of program modules 425, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0173] Bus 43 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0174] Electronic device 40 can also communicate with one or more external devices (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 40, and / or with any device that enables electronic device 40 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed through input / output (I / O) interface 44. Furthermore, electronic device 40 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) through network adapter 45. Figure 9 As shown, network adapter 45 communicates with other modules of electronic device 40 via bus 43. It should be understood that, although... Figure 9 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 40, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup planning systems.

[0175] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0176] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. This program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc. Furthermore, it stores computer program instructions, which, when executed by a computer, cause the computer to perform the methods described above.

[0177] Furthermore, the cloud platform and version control method and system for multiple robots provided in the embodiments of the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.< / string>

Claims

1. A version control method for a cloud platform and multiple robots, characterized in that, Applications include version control scenarios for cloud platforms and multiple robots; The version control method for the cloud platform and multiple robots includes: The cloud platform dynamically interacts with multiple robots and locates the data collection channels of the cloud platform. The cloud platform-based acquisition channel collects multiple interactive data inputs from each robot; Based on the multiple interactive data input by each robot and the collection channels of the cloud platform, the multiple interactive data are autonomously classified and a first data set for each version is formed. Multiple first data structures are defined based on the identification of the first data sets of each version, and at this time, the multiple first data structures are different from each other; The second data set of various versions is output based on the data processing of multiple first data structures, and the second data set of various versions is in the same data structure; Associate the second data set of each version with the cloud platform, and trigger the corresponding logical processing according to the second data set of each version and the cloud platform to output the corresponding execution logic.

2. The version control method for a cloud platform and multiple robots according to claim 1, characterized in that, The process of dynamically interacting between the cloud platform and multiple robots, and locating the cloud platform's data acquisition channels, includes: Collect the IP address of the cloud platform and the IP addresses of multiple robots; The interaction types between the cloud platform and multiple robots are matched based on the IP address of the cloud platform and the IP addresses of multiple robots. Based on this type of interaction, the cloud platform and multiple robots trigger dynamic interactions between the cloud platform and multiple robots; Real-time monitoring of the dynamic interaction between the cloud platform and multiple robots, and definition of the interaction area between the cloud platform and multiple robots; The acquisition channel is defined based on the traversal of the interaction area between the cloud platform and multiple robots; Locate the data collection channel of the cloud platform.

3. The version control method for a cloud platform and multiple robots according to claim 1, characterized in that, The cloud-based acquisition channel collects multiple interactive data input from each robot, including: The data acquisition channel of the fixed-frame cloud platform; The status of the data acquisition channels on the cloud platform; The status of the associated data acquisition channels, the status of multiple robots, and the status of the cloud platform; Define the corresponding acquisition mode based on the status of the acquisition channel, the status of multiple robots, and the status of the cloud platform; This data collection mode triggers the cloud platform to collect data from each robot in a targeted manner; Collect multiple interaction data input from each robot and dynamically manage these multiple interaction data.

4. The version control method for a cloud platform and multiple robots according to claim 3, characterized in that, The process involves triggering the autonomous classification of multiple interactive data input from each robot and the cloud platform's data collection channels, forming a first data set for each version, including: Freeze the multiple interactive data input by each robot; It associates multiple interactive data with the cloud platform's collection channels and triggers the control of the cloud platform's collection channels; Real-time monitoring and control of the data acquisition channels; In the management and control of the data collection channels on the cloud platform, corresponding autonomous classification is triggered based on multiple interactive data. Based on the autonomous classification of multiple interactive data, the first data set of each version is formed.

5. The version control method for a cloud platform and multiple robots according to claim 4, characterized in that, The first data structure is defined based on the identification of the first data set of each version. These first data structures are distinct from each other and include: The first data set of each version is captured; Associate the first dataset of each version with the corresponding autonomous recognition model; The recognition of the first dataset is triggered based on the first dataset of each version and the corresponding autonomous recognition model; Multiple first data structures are defined based on the identification of the first data set of each version, and these multiple first data structures are different from each other.

6. The version control method for a cloud platform and multiple robots according to claim 5, characterized in that, The process of processing data based on multiple first data structures to output various versions of second data sets, wherein each version of the second data set resides in the same data structure, includes: Collect multiple first-level data structures; Based on multiple first data structures, the corresponding structure types are matched, and a combination of structure types is formed based on multiple structure types.

7. The version control method for a cloud platform and multiple robots according to claim 6, characterized in that, The step of outputting various versions of the second data set based on data processing of multiple first data structures, wherein the various versions of the second data set are in the same data structure, further includes: Based on the combination of these structural types and the corresponding data processing triggered by the cloud platform; Based on this data processing, the combination of this type of structure is controlled, and structural optimization is performed on multiple first data structures; The second data set is output by optimizing the structure of multiple first data structures; Iterate through the second data set of each version, where each version of the second data set is in the same data structure.

8. The version control method for a cloud platform and multiple robots according to claim 7, characterized in that, The second data set of each version is associated with the cloud platform, and corresponding logical processing is triggered based on the second data set of each version and the cloud platform to output the corresponding execution logic, including: The second dataset is frozen in time for each version; Associate the second datasets of each version with the cloud platform; Based on the second dataset of each version and the corresponding logical learning model matched with the cloud platform; This logic learning model is trained based on previous execution logic.

9. The version control method for a cloud platform and multiple robots according to claim 8, characterized in that, The process of associating the second data set of each version with the cloud platform, and triggering corresponding logical processing based on the second data set of each version with the cloud platform to output the corresponding execution logic, also includes: Match the corresponding execution logic based on the second dataset of each version and the logical learning model; The execution logic is automatically matched to the corresponding robot based on the cloud platform.

10. A version control system for a cloud platform and multiple robots, characterized in that, The version control system for the cloud platform and multiple robots is applied to the version control method for the cloud platform and multiple robots as described in any one of claims 1-9, wherein the version control system for the cloud platform and multiple robots includes: The dynamic interaction module is used to enable dynamic interaction between the cloud platform and multiple robots, and to locate the data acquisition channels of the cloud platform. The interactive data module is used to collect multiple interactive data input by each robot through the cloud platform's acquisition channel; The first data set module is used to trigger the autonomous classification of multiple interactive data input by each robot and the collection channel of the cloud platform, and form the first data set of each version. The data structure module is used to define multiple first data structures based on the identification of the first data set of each version. At this time, the multiple first data structures are different from each other. The data processing module is used to output various versions of the second data set based on the data processing of multiple first data structures, and the various versions of the second data set are in the same data structure; The execution logic module is used to associate the second data set of each version with the cloud platform, and to trigger the corresponding logic processing according to the second data set of each version and the cloud platform, so as to output the corresponding execution logic.

Images