Version control method and system for cloud platform and multiple robots

Abstract

The invention discloses a version control method and system for a cloud platform and multiple robots, and the method comprises the steps: triggering the autonomous classification of multiple pieces of interaction data according to the multiple pieces of interaction data inputted by each robot and a collection channel of the cloud platform, and forming a first data set of each version; a plurality of first data structures are defined based on the identification of the first data set of the respective versions, the plurality of first data structures being different from each other. Further, second data sets of all versions are output according to data processing of the multiple first data structures, and the second data sets of all the versions are located in the same data structure; according to the second data sets of all the versions and the cloud platform, corresponding logic processing is triggered to output corresponding execution logic, unified processing of the multiple first data structures is achieved, the second data sets of all the versions are located in the same data structure, the compatibility of the cloud platform to multiple robots is improved, and the user experience is improved. And the version control effect of the cloud platform on the multiple robots is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of a cloud platform and multiple robots, and in particular to a version control method and system for a cloud platform and multiple robots. Background Art

[0002] With the development of science and technology, cloud platforms interact dynamically with multiple robots, and version control is performed on multiple robots through a cloud platform. In the existing technology, traditional robot systems usually rely on local computing resources for data processing and decision-making. The version iteration progress of robots is inconsistent, which affects the version control effect of the cloud platform on multiple robots and reduces the compatibility of the cloud platform with multiple robots. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art. The present invention provides a version control method and system for a cloud platform and multiple robots, which dynamically interact with the cloud platform and multiple robots, and locate the collection channel of the cloud platform; based on the collection channel of the cloud platform, multiple interaction data input by each robot are collected; according to the multiple interaction data input by each robot and the collection channel of the cloud platform, autonomous classification of the multiple interaction data is triggered, and first data sets of each version are formed; based on the identification of the first data sets of each version, multiple first data structures are defined, and 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.

[0004] Furthermore, various versions of second data sets are output based on data processing of multiple first data structures, and the second data sets of various versions are in the same data structure; the second data sets of various versions and the cloud platform are associated, and corresponding logic processing is triggered based on the second data sets of various versions and the cloud platform to output corresponding execution logic, thereby realizing unified processing of multiple first data structures and introducing various versions of second data sets. At the same time, the second data sets of various versions are in the same data structure, and the output of execution logic is realized, so as to improve the compatibility of the cloud platform with multiple robots and ensure the version control effect of the cloud platform on multiple robots.

[0005] The embodiment of the present invention provides a version control method for a cloud platform and multiple robots, which is applied to a version control scenario of a cloud platform and multiple robots;

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

[0007] Dynamically interact with the cloud platform and multiple robots, and locate the collection channel of the cloud platform;

[0008] The cloud platform-based collection channel collects multiple interaction data input by each robot;

[0009] Triggering autonomous classification of the multiple interactive data according to the multiple interactive data input by each robot and the collection channel of the cloud platform, and forming the first data set of each version;

[0010] Defining a plurality of first data structures based on identification of the first data sets of the respective versions, wherein the plurality of first data structures are different from each other;

[0011] Outputting second data sets of various versions according to data processing of the plurality of first data structures, wherein the second data sets of various versions are in the same data structure;

[0012] The second data sets of various versions and the cloud platform are associated, and corresponding logic processing is triggered according to the second data sets of various versions and the cloud platform to output corresponding execution logic.

[0013] Optionally, dynamically interacting the cloud platform with the multiple robots and locating a collection channel of the cloud platform includes:

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

[0015] Match the types of interactions between the cloud platform and multiple robots based on the IP address of the cloud platform and the IP addresses of multiple robots;

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

[0017] Monitor the dynamic interaction between the cloud platform and multiple robots in real time, and define the interaction areas between the cloud platform and multiple robots;

[0018] Defining collection channels based on traversal of the interaction area between the cloud platform and multiple robots;

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

[0020] Optionally, the cloud platform-based collection channel collects multiple interaction data input by each robot, including:

[0021] The collection channel of the fixed cloud platform;

[0022] Collect the status of the collection channel of the cloud platform;

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

[0024] Define the corresponding collection mode according to the status of the collection channel, the status of multiple robots and the status of the cloud platform;

[0025] Based on this collection mode, the cloud platform triggers the directional collection of each robot;

[0026] Collect multiple interaction data input by each robot, and dynamically manage and control the multiple interaction data.

[0027] Optionally, the autonomous classification of the plurality of interaction data is triggered according to the plurality of interaction data input by each robot and the collection channel of the cloud platform, and the first data set of each version is formed, including:

[0028] Freeze multiple interaction data input by each robot;

[0029] Associate multiple interactive data with the collection channels of the cloud platform, and trigger the management and control of the collection channels of the cloud platform;

[0030] Real-time monitoring and control of collection channels;

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

[0032] Based on the autonomous classification of the plurality of interaction data, first data sets of various versions are formed.

[0033] Optionally, the identification of the first data set of each version is based on defining a plurality of first data structures, in which case the plurality of first data structures are different from each other, including:

[0034] Freeze the first data set of each version;

[0035] Associating the first data set of each version with the corresponding autonomous recognition model;

[0036] triggering recognition of the first data set according to each version of the first data set and the corresponding autonomous recognition model;

[0037] A plurality of first data structures are defined according to the identification of the first data sets of the respective versions, and in this case, the plurality of first data structures are different from each other.

[0038] Optionally, the outputting of second data sets of various versions according to the data processing of the plurality of first data structures, wherein the second data sets of various versions are in the same data structure, includes:

[0039] collecting a plurality of first data structures;

[0040] Corresponding structure types are matched based on the plurality of first data structures, and a structure type combination is formed based on the plurality of structure types.

[0041] Optionally, the outputting of second data sets of various versions according to data processing of the plurality of first data structures, wherein the second data sets of various versions are in the same data structure, further comprises:

[0042] According to the combination of structure types and the cloud platform, corresponding data processing is triggered;

[0043] Controlling the combination of structure types based on the data processing, and optimizing the structures of the plurality of first data structures;

[0044] Outputting respective versions of second data sets by optimizing the structures of the plurality of first data structures;

[0045] The second data sets of various versions are traversed, and the second data sets of various versions are in the same data structure.

[0046] Optionally, associating the second data sets of each version and the cloud platform, and triggering corresponding logic processing according to the second data sets of each version and the cloud platform to output corresponding execution logic, includes:

[0047] Freeze the second data set of each version;

[0048] Associating the second data set of each version with the cloud platform;

[0049] A corresponding logical learning model based on the second data set of each version and the cloud platform matching;

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

[0051] Optionally, associating the second data sets of each version and the cloud platform, and triggering corresponding logic processing according to the second data sets of each version and the cloud platform to output corresponding execution logic, further includes:

[0052] Matching corresponding execution logic according to the second data set of each version and the logic learning model;

[0053] Based on the cloud platform, the execution logic is autonomously matched to the corresponding robot.

[0054] In addition, an embodiment of the present invention further provides a version control system for a cloud platform and multiple robots, wherein the version control system for the cloud platform and multiple robots includes:

[0055] Dynamic interaction module, used to dynamically interact with the cloud platform and multiple robots, and locate the collection channel of the cloud platform;

[0056] The interactive data module is used to collect multiple interactive data input by each robot based on the collection channel of the cloud platform;

[0057] A first data collection module, used to trigger autonomous classification of multiple interaction data according to multiple interaction data input by each robot and the collection channel of the cloud platform, and form first data sets of various versions;

[0058] A data structure module, used to define a plurality of first data structures based on the identification of the first data sets of each version, wherein the plurality of first data structures are different from each other;

[0059] A data processing module, configured to output second data sets of various versions according to data processing of the plurality of first data structures, wherein the second data sets of various versions are in the same data structure;

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

[0061] In an embodiment of the present invention, through the method in the embodiment of the present invention, a cloud platform and multiple robots are dynamically interacted, and the collection channel of the cloud platform is located; multiple interaction data input by each robot are collected based on the collection channel of the cloud platform; autonomous classification of the multiple interaction data is triggered according to the multiple interaction data input by each robot and the collection channel of the cloud platform, and each version of the first data set is formed; based on the identification of each version of the first data set, multiple first data structures are defined, and the multiple first data structures are different from each other, so as to facilitate subsequent management and control of the first data set of each version.

[0062] Furthermore, various versions of second data sets are output based on data processing of multiple first data structures, and the second data sets of various versions are in the same data structure; the second data sets of various versions and the cloud platform are associated, and corresponding logic processing is triggered based on the second data sets of various versions and the cloud platform to output corresponding execution logic, thereby realizing unified processing of multiple first data structures and introducing various versions of second data sets. At the same time, the second data sets of various versions are in the same data structure, and the output of execution logic is realized, so as to improve the compatibility of the cloud platform with multiple robots and ensure the version control effect of the cloud platform on multiple robots. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying creative work.

[0064] Figure 1 is a flow chart of a version control method for a cloud platform and multiple robots in an embodiment of the present invention;

[0065] Figure 2 is a flow chart of S11 in the version control method of the cloud platform and multiple robots in an embodiment of the present invention;

[0066] Figure 3 is a flow chart of S12 in the version control method of the cloud platform and multiple robots in an embodiment of the present invention;

[0067] Figure 4 is a flow chart of S13 in the version control method of the cloud platform and multiple robots in an embodiment of the present invention;

[0068] Figure 5 is a flow chart of S14 in the version control method of the cloud platform and multiple robots in an embodiment of the present invention;

[0069] Figure 6 is a flow chart of S15 in the version control method of the cloud platform and multiple robots in an embodiment of the present invention;

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

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

[0072] Fig. 9 The figure is a hardware diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0073] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0074] See also Figures 1 to 9 A version control method for a cloud platform and multiple robots is applied to a version control scenario for a cloud platform and multiple robots; the version control method for a cloud platform and multiple robots includes:

[0075] Step S11: dynamically interacting the cloud platform with multiple robots and locating the collection channel of the cloud platform;

[0076] Step S12: collecting multiple interaction data input by each robot based on the collection channel of the cloud platform;

[0077] Step S13: collecting multiple interaction data input by each robot based on the collection channel of the cloud platform;

[0078] Step S14: defining a plurality of first data structures based on the identification of the first data sets of each version, wherein the plurality of first data structures are different from each other;

[0079] Step S15: outputting second data sets of various versions according to data processing of the plurality of first data structures, wherein the second data sets of various versions are in the same data structure;

[0080] Step S16: Associating the second data sets of each version and the cloud platform, and triggering corresponding logic processing according to the second data sets of each version and the cloud platform to output corresponding execution logic.

[0081] In an embodiment of the present invention, through the method in the embodiment of the present invention, a cloud platform and multiple robots are dynamically interacted, and the collection channel of the cloud platform is located; multiple interaction data input by each robot are collected based on the collection channel of the cloud platform; autonomous classification of the multiple interaction data is triggered according to the multiple interaction data input by each robot and the collection channel of the cloud platform, and each version of the first data set is formed; based on the identification of each version of the first data set, multiple first data structures are defined, and the multiple first data structures are different from each other, so as to facilitate subsequent management and control of the first data set of each version.

[0082] Furthermore, various versions of second data sets are output based on data processing of multiple first data structures, and the second data sets of various versions are in the same data structure; the second data sets of various versions and the cloud platform are associated, and corresponding logic processing is triggered based on the second data sets of various versions and the cloud platform to output corresponding execution logic, thereby realizing unified processing of multiple first data structures and introducing various versions of second data sets. At the same time, the second data sets of various versions are in the same data structure, and the output of execution logic is realized, so as to improve the compatibility of the cloud platform with multiple robots and ensure the version control effect of the cloud platform on multiple robots.

[0083] refer to Figure 2 ,In step S11, the cloud platform and the multiple robots are dynamically interacted, and the collection channel of the cloud platform is located;

[0084] In the specific implementation process of the present invention, the specific steps may be:

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

[0086] S112: Matching interaction types between the cloud platform and the multiple robots according to the IP address of the cloud platform and the IP addresses of the multiple robots;

[0087] S113: triggering dynamic interaction between the cloud platform and the multiple robots based on the interaction type, the cloud platform and the multiple robots;

[0088] S114: monitor the dynamic interaction between the cloud platform and the multiple robots in real time, and define the interaction area between the cloud platform and the multiple robots;

[0089] S115: defining a collection channel according to the traversal of the interaction area between the cloud platform and the plurality of robots;

[0090] S116: The collection channel of the positioning cloud platform.

[0091] In an embodiment of the present application, the IP address of the cloud platform and the IP addresses of multiple robots are collected; the types of interaction between the cloud platform and the multiple robots are matched according to the IP address of the cloud platform and the IP addresses of the multiple robots, which is compatible with the overall consideration of the IP address of the cloud platform and the IP addresses of the multiple robots, and realizes multi-dimensional control of the matching of the IP address of the cloud platform and the IP addresses of the multiple robots, thereby ensuring the matching accuracy of the types of interaction between the cloud platform and the multiple robots.

[0092] Furthermore, based on the interaction type, the cloud platform and the multiple robots trigger dynamic interaction between the cloud platform and the multiple robots; the dynamic interaction between the cloud platform and the multiple robots is monitored in real time, and the interaction areas between the cloud platform and the multiple robots are defined, the interaction types between the cloud platform and the multiple robots are introduced, and the further control of the interaction types between the cloud platform and the multiple robots is achieved, thereby ensuring the accuracy of the interaction types between the cloud platform and the multiple robots.

[0093] Therefore, a collection channel is defined according to the traversal of the interaction area between the cloud platform and multiple robots; the collection channel of the cloud platform is located, and the collection channel of the cloud platform is further controlled so as to facilitate processing of the collection channel of the cloud platform.

[0094] refer to Figure 3 , in step S12, a plurality of interaction data input by each robot is collected based on a collection channel of the cloud platform;

[0095] In the specific implementation process of the present invention, the specific steps may be:

[0096] S121: Collection channel of the Dingge cloud platform;

[0097] S122: collecting the status of the collection channel of the cloud platform;

[0098] S123: Associating the status of the acquisition channel, the status of multiple robots, and the status of the cloud platform;

[0099] S124: defining a corresponding collection mode according to the state of the collection channel, the states of the multiple robots, and the state of the cloud platform;

[0100] S125: triggering the cloud platform to perform directional collection of each robot based on the collection mode;

[0101] S126: Collect multiple interaction data input by each robot, and dynamically manage the multiple interaction data.

[0102] In an embodiment of the present application, the collection channel of the cloud platform is frozen; at the same time, the state of the collection channel of the cloud platform is collected; the state of the collection channel, the state of multiple robots and the state of the cloud platform are associated, and the state of the collection channel, the state of multiple robots and the state of the cloud platform are introduced, so that the state of the collection channel, the state of multiple robots and the state of the cloud platform can be controlled in multiple dimensions, and the dynamic interaction of the state of the collection channel, the state of multiple robots and the state of the cloud platform is ensured.

[0103] Therefore, the corresponding collection mode is defined according to the status of the collection channel, the status of multiple robots and the status of the cloud platform; based on the collection mode, the cloud platform is triggered to carry out directional collection of each robot, thereby realizing the directional collection of each robot by the cloud platform, so as to collect multiple interaction data input by each robot and dynamically manage and control the multiple interaction data.

[0104] refer to Figure 4 In step S13, the multiple interactive data are autonomously classified according to the multiple interactive data input by each robot and the collection channel of the cloud platform, and the first data sets of each version are formed;

[0105] In the specific implementation process of the present invention, the specific steps may be:

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

[0107] S132: Associating multiple interactive data with the collection channel of the cloud platform, and triggering the management and control of the collection channel of the cloud platform;

[0108] S133: Real-time monitoring and control of collection channels;

[0109] S134: In the control of the collection channel of the cloud platform, trigger corresponding autonomous classification based on multiple interaction data;

[0110] S135: Autonomously classify the plurality of interactive data and form first data sets of various versions.

[0111] In an embodiment of the present application, multiple interaction data input by each robot are frozen; the multiple interaction data and the collection channel of the cloud platform are associated, and the management and control of the collection channel of the cloud platform are triggered to facilitate real-time control of the collection channel of the cloud platform.

[0112] Therefore, the management and control of the collection channel is monitored in real time; in the management and control of the collection channel of the cloud platform, the corresponding autonomous classification is triggered based on multiple interaction data; according to the autonomous classification of multiple interaction data, the first data set of each version is formed, and the first data set of each version is introduced, and data integration is performed on multiple data of each version to form the first data set of each version, so as to perform overall management and control of the first data set of each version.

[0113] refer to Figure 5 , S14: defining a plurality of first data structures based on the identification of the first data sets of each version, wherein the plurality of first data structures are different from each other;

[0114] In the specific implementation process of the present invention, the specific steps may be:

[0115] S141: freeze the first data set of each version;

[0116] S142: Associating the first data sets of various versions with the corresponding autonomous recognition models;

[0117] S143: triggering recognition of the first data set according to each version of the first data set and the corresponding autonomous recognition model;

[0118] S144: defining a plurality of first data structures according to the identification of the first data sets of each version, wherein the plurality of first data structures are different from each other.

[0119] In an embodiment of the present application, a cloud platform dynamically interacts with multiple robots, and a collection channel of the cloud platform is located; multiple interaction data input by each robot is collected based on the collection channel of the cloud platform; autonomous classification of the multiple interaction data is triggered according to the multiple interaction data input by each robot and the collection channel of the cloud platform, and each version of the first data set is formed; based on the identification of each version of the first data set, multiple first data structures are defined, and the multiple first data structures are different from each other, so as to facilitate subsequent management and control of each version of the first data set.

[0120] At this time, the first data set of each version is frozen; 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, which is compatible with the overall consideration of the first data set of each version and the corresponding autonomous recognition model, realizes the 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 each version of the first data set, and multiple first data structures are introduced to control the multiple first data structures as a whole. 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: outputting second data sets of various versions according to data processing of the plurality of first data structures, wherein the second data sets of various versions are in the same data structure;

[0123] In the specific implementation process of the present invention, the specific steps may be:

[0124] S151: Collect multiple first data structures;

[0125] S152: matching corresponding structure types based on the plurality of first data structures, and forming a structure type combination based on the plurality of structure types;

[0126] S153: triggering corresponding data processing according to the combination of structure types and the cloud platform;

[0127] S154: Controlling the combination of structure types based on the data processing, and optimizing the structures of the plurality of first data structures;

[0128] S155: outputting second data sets of various versions along the structural optimization of the plurality of first data structures;

[0129] S156: traverse the second data sets of each version, where the second data sets of each version are in the same data structure.

[0130] In an embodiment of the present application, multiple first data structures are collected, and at the same time, 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; corresponding data processing is triggered according to the structure type combination and the cloud platform, and the structure type combination and the cloud platform are introduced to realize dynamic interaction between the structure type combination and the cloud platform, thereby realizing corresponding data processing.

[0131] Therefore, based on the data processing, the structure type combination is controlled, and the structures of multiple first data structures are optimized; along the structural optimization of multiple first data structures, the second data sets of various versions are output; the second data sets of various versions are traversed, and the second data sets of various versions are in the same data structure, so as to facilitate structural optimization of multiple first data structures, thereby structurally unifying the multiple first data structures and ensuring the uniformity of the second data sets of various versions.

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

[0133] In the specific implementation process of the present invention, the specific steps may be:

[0134] S161: Freeze the second data set of each version;

[0135] S162: Associating the second data sets of each version with the cloud platform;

[0136] S163: Matching corresponding logic learning models based on the second data sets of each version and the cloud platform;

[0137] S164: The logic learning model is formed based on previous execution logic training;

[0138] S165: Matching corresponding execution logic according to the second data set of each version and the logic learning model;

[0139] S166: Autonomously match the execution logic to the corresponding robot based on the cloud platform.

[0140] During the specific implementation of the present invention, second data sets of various versions are output according to data processing of multiple first data structures, and the second data sets of various versions are in the same data structure; the second data sets of various versions and the cloud platform are associated, and corresponding logical processing is triggered according to the second data sets of various versions and the cloud platform to output corresponding execution logic, thereby realizing unified processing of multiple first data structures and introducing second data sets of various versions. At the same time, the second data sets of various versions are in the same data structure, and the output of execution logic is realized, so as to improve the compatibility of the cloud platform with multiple robots and ensure the version control effect of the cloud platform on multiple robots.

[0141] At this time, freeze the second data set of each version; associate the second data set of each version with the cloud platform; match the corresponding logic learning model based on the second data set of each version and the cloud platform, and be compatible with the overall consideration of the second data set of each version and the cloud platform, so as to achieve multi-dimensional control of the second data set of each version and the cloud platform, and ensure the accuracy of the logic learning model. The logic learning model is based on the previous execution logic training.

[0142] Therefore, the corresponding execution logic is matched according to the second data set 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 data sets of each version are in the same data structure, and the output of the execution logic is realized, so as to improve the compatibility of the cloud platform with multiple robots and ensure the version control effect of the cloud platform on multiple robots.

[0143] As for the specific implementation method, the present invention adopts Spring custom annotations and AOP proxy aspects, an intelligent version switching and selection technology in a multi-version parallel execution environment.

[0144] Definition annotation:

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

[0146] Initialize the annotation method:

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

[0148] VERSION_MAP: Type is Map <String,Set <string>>, the key is the full class name + method name of the interface, and the value is a list of version numbers.

[0149] Automatic selection:

[0150] When the interface calls the service, the AOP proxy obtains the machine version number of the current request before the method is executed. Then it obtains the list of all version numbers of the current interface method from VERSION_MAP. If there is no version number list, it skips the version selection and directly executes the default implementation method. If there is a version number list, it filters the maximum version number less than or equal to the current version number from the version number list (Set collection), obtains the corresponding version method (Method) in VERSION_METHOD_MAP to execute the implementation method.

[0151] At this time, the present invention allows multiple versions of the same service to exist in the system at the same time, and can automatically select the appropriate version to respond according to the client's request. This design allows the new and old versions to coexist seamlessly, reduces service interruptions during version upgrades, and improves the flexibility and scalability of the system.

[0152] By using Spring custom annotations and AOP proxy aspect technology, the present invention realizes non-invasive enhancement of existing code. Developers do not need to modify the existing business logic code, but only need to add a small amount of annotations to achieve multi-version parallel execution. This not only simplifies the development process, but also reduces maintenance costs.

[0153] In addition, the present invention supports dynamic management of versions at runtime, and can automatically select the appropriate version for execution according to configuration or strategy. 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] Since the present invention provides an automated version management and switching mechanism, the workload of manual configuration and testing is reduced, thereby reducing development and deployment costs. Enterprises can launch new functions and services more quickly and improve market competitiveness.

[0156] Through intelligent version switching, the system can provide the best service experience for clients of different versions. Regardless of whether the client is using the old version or the new version, it can obtain a reasonable response result that matches its version, which improves user satisfaction and loyalty.

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

[0158] In an embodiment of the present invention, through the method in the embodiment of the present invention, a cloud platform and multiple robots are dynamically interacted, and the collection channel of the cloud platform is located; multiple interaction data input by each robot are collected based on the collection channel of the cloud platform; autonomous classification of the multiple interaction data is triggered according to the multiple interaction data input by each robot and the collection channel of the cloud platform, and each version of the first data set is formed; based on the identification of each version of the first data set, multiple first data structures are defined, and the multiple first data structures are different from each other, so as to facilitate subsequent management and control of the first data set of each version.

[0159] Furthermore, various versions of second data sets are output based on data processing of multiple first data structures, and the second data sets of various versions are in the same data structure; the second data sets of various versions and the cloud platform are associated, and corresponding logic processing is triggered based on the second data sets of various versions and the cloud platform to output corresponding execution logic, thereby realizing unified processing of multiple first data structures and introducing various versions of second data sets. At the same time, the second data sets of various versions are in the same data structure, and the output of execution logic is realized, so as to improve the compatibility of the cloud platform with multiple robots and ensure the version control effect of the cloud platform on multiple robots.

[0160] See also Figure 8 , Figure 8 It is a schematic diagram of the structural composition of the version control system of the cloud platform and multiple robots in an embodiment of the present invention.

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

[0162] The dynamic interaction module 21 is used to dynamically interact the cloud platform with multiple robots and locate the collection channel of the cloud platform;

[0163] An interactive data module 22, used for collecting a plurality of interactive data input by each robot based on a collection channel of a cloud platform;

[0164] The first data collection module 23 is used to trigger the autonomous classification of the multiple interaction data according to the multiple interaction data input by each robot and the collection channel of the cloud platform, and form the first data collection of each version;

[0165] A data structure module 24, configured to define a plurality of first data structures based on the identification of the first data sets of various versions, wherein the plurality of first data structures are different from each other;

[0166] A data processing module 25, configured to output second data sets of various versions according to data processing of the plurality of first data structures, wherein the second data sets of various versions are in the same data structure;

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

[0168] See also Fig. 9 , refer to the following Fig. 9 The electronic device 40 according to this embodiment of the present invention will be described. Fig. 9 The electronic device 40 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0169] like Fig. 9 As shown, the electronic device 40 is in the form of a general 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 the storage unit 42 and the processing unit 41).

[0170] The storage unit stores program codes, which can be executed by the processing unit 41, so that the processing unit 41 executes the steps according to various exemplary embodiments of the present invention described in the above “Embodiment Method” section of this specification.

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

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

[0173] Bus 43 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0174] The electronic device 40 may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device 40, and / or any device that enables the electronic device 40 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 44. Furthermore, the electronic device 40 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 45. Fig. 9 As shown, the network adapter 45 communicates with other modules of the electronic device 40 via the bus 43. It should be understood that although Fig. 9 Not shown, other hardware and / or software modules may be used in conjunction with the 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] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0176] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium can include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc. In addition, it stores computer program instructions, and when the computer program instructions are executed by a computer, the computer executes the above method.

[0177] In addition, the above describes in detail the version control method and system of the cloud platform and multiple robots provided in the embodiments of the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the present invention.< / string>

Claims

1. A version control method for a cloud platform and multiple robots, characterized in that: Applicable to version control scenarios of cloud platforms and multiple robots; The version control method of the cloud platform and multiple robots includes: Dynamically interact with the cloud platform and multiple robots, and locate the collection channel of the cloud platform; The cloud platform-based collection channel collects multiple interaction data input by each robot; Triggering autonomous classification of the multiple interactive data according to the multiple interactive data input by each robot and the collection channel of the cloud platform, and forming the first data set of each version; Defining a plurality of first data structures based on identification of the first data sets of the respective versions, wherein the plurality of first data structures are different from each other; Outputting second data sets of various versions according to data processing of the plurality of first data structures, wherein the second data sets of various versions are in the same data structure; The second data sets of various versions and the cloud platform are associated, and corresponding logic processing is triggered according to the second data sets of various versions and the cloud platform to output corresponding execution logic.

2. The version control method for a cloud platform and multiple robots according to claim 1, characterized in that: The cloud platform and the multiple robots are dynamically interacted and the collection channel of the cloud platform is located, including: Collect the IP address of the cloud platform and the IP addresses of multiple robots; Match the types of interactions between the cloud platform and multiple robots based on the IP address of the cloud platform and the IP addresses of multiple robots; Based on the interaction type, the cloud platform and the multiple robots trigger dynamic interaction between the cloud platform and the multiple robots; Monitor the dynamic interaction between the cloud platform and multiple robots in real time, and define the interaction areas between the cloud platform and multiple robots; Defining collection channels based on traversal of the interaction area between the cloud platform and multiple robots; Locate the 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 platform-based collection channel collects multiple interactive data input by each robot, including: The collection channel of the fixed cloud platform; Collect the status of the collection channel of the cloud platform; The status of the associated acquisition channel, the status of multiple robots, and the status of the cloud platform; Define the corresponding collection mode according to the status of the collection channel, the status of multiple robots and the status of the cloud platform; Based on this collection mode, the cloud platform triggers the directional collection of each robot; Collect multiple interaction data input by each robot, and dynamically manage and control the multiple interaction data.

4. The version control method for a cloud platform and multiple robots according to claim 3, characterized in that: The autonomous classification of the plurality of interaction data is triggered according to the plurality of interaction data input by each robot and the collection channel of the cloud platform, and the first data set of each version is formed, including: Freeze multiple interaction data input by each robot; Associate multiple interactive data with the collection channels of the cloud platform, and trigger the management and control of the collection channels of the cloud platform; Real-time monitoring and control of collection channels; In the management and control of the cloud platform's collection channels, corresponding autonomous classification is triggered based on multiple interactive data; Based on the autonomous classification of the plurality of interaction data, first data sets of various versions are formed.

5. The version control method for a cloud platform and multiple robots according to claim 4, characterized in that: The multiple first data structures are defined based on the identification of the first data sets of each version. In this case, the multiple first data structures are different from each other and include: Freeze the first data set of each version; Associating the first data set of each version with the corresponding autonomous recognition model; triggering recognition of the first data set according to each version of the first data set and the corresponding autonomous recognition model; A plurality of first data structures are defined according to the identification of the first data sets of the respective versions, and in this case, the plurality of 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 outputting of the second data sets of various versions according to the data processing of the plurality of first data structures, wherein the second data sets of various versions are in the same data structure, comprises: collecting a plurality of first data structures; Corresponding structure types are matched based on the plurality of first data structures, and a structure type combination is formed based on the plurality of structure types.

7. The version control method for a cloud platform and multiple robots according to claim 6, characterized in that: The method of outputting second data sets of various versions according to data processing of the plurality of first data structures, wherein the second data sets of various versions are in the same data structure, further comprises: According to the combination of structure types and the cloud platform, corresponding data processing is triggered; Controlling the combination of structure types based on the data processing, and optimizing the structures of the plurality of first data structures; Outputting respective versions of second data sets by optimizing the structures of the plurality of first data structures; The second data sets of various versions are traversed, and the second data sets of various versions are 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 associating the second data sets of each version and the cloud platform, and triggering corresponding logic processing according to the second data sets of each version and the cloud platform to output corresponding execution logic, includes: Freeze the second data set of each version; Associating the second data set of each version with the cloud platform; A corresponding logical learning model based on the second data set of each version and the cloud platform matching; The 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 associating the second data sets of each version and the cloud platform, and triggering corresponding logic processing according to the second data sets of each version and the cloud platform to output corresponding execution logic, further includes: Matching corresponding execution logic according to the second data set of each version and the logic learning model; Based on the cloud platform, the execution logic is autonomously matched to the corresponding robot.

10. A version control system for a cloud platform and multiple robots, characterized in that: The version control system of the cloud platform and multiple robots is applied to the version control method of the cloud platform and multiple robots as claimed in any one of claims 1 to 9, and the version control system of the cloud platform and multiple robots includes: Dynamic interaction module, used to dynamically interact with the cloud platform and multiple robots, and locate the collection channel of the cloud platform; The interactive data module is used to collect multiple interactive data input by each robot based on the collection channel of the cloud platform; A first data collection module, used to trigger autonomous classification of multiple interaction data according to multiple interaction data input by each robot and the collection channel of the cloud platform, and form first data sets of various versions; A data structure module, used to define a plurality of first data structures based on the identification of the first data sets of each version, wherein the plurality of first data structures are different from each other; A data processing module, configured to output second data sets of various versions according to data processing of the plurality of first data structures, wherein the second data sets of various versions are in the same data structure; The execution logic module is used to associate the second data sets of each version with the cloud platform, and trigger corresponding logic processing according to the second data sets of each version and the cloud platform to output corresponding execution logic.

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