Zero-code AI agent development and deployment data management system and method

Abstract

The invention relates to the field of agent development, in particular to a zero-code AI agent development and deployment data management system and method.The zero-code AI agent development and deployment data management system comprises a data flow testing module, a model connection module, a feedback processing module, a project migration module and a secondary development module; the model communication module is used for forward connection of a conversion interface and generation of a conversion queue, the feedback processing module is used for sharing input data streams among models, the project migration module is used for adjusting the shared data proportion of the models, and the secondary development module is used for developing new models according to the service providing proportion of the models. According to the method, more accurate output content can be generated through sharing data, the structure of the intelligent agent is continuously optimized, a good performance optimization curve is obtained in the service process of the intelligent agent, the data processing capacity is improved, and the operation efficiency of the intelligent agent system is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of intelligent agent development, and specifically to a zero-code AI intelligent agent development and deployment data management system and method. Background Art

[0002] Zero-code AI agent development refers to the technology of using existing AI development platforms and tools to quickly build usable AI agents through model recognition configuration and customized operations without writing complex codes. Agents deployed with zero code can perform tasks such as natural language conversation, image recognition, and predictive analysis.

[0003] Zero-code intelligent agents interact with users by processing data streams through intelligent models. Therefore, the development of zero-code intelligent agents requires matching and fitting big data models. However, due to the limitations of the functions of the development platform, the models are independent of each other, which limits developers' innovation and customization in the process of zero-code intelligent agent development. As a result, users can only choose one of the interaction models with the intelligent agent, and are unable to meet some complex or personalized needs.

[0004] In addition, after deploying the model, developers need to develop new models based on user feedback. However, most users do not understand how the model works and can only infer users' needs for model functions through their usage process. This requires developers to conduct additional investigations, which is not conducive to the secondary development of the intelligent model. Summary of the invention

[0005] The purpose of the present invention is to provide a zero-code AI agent development and deployment data management system and method to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a zero-code AI agent development and deployment data management system, including: a data flow testing module, a model connectivity module, a feedback processing module, a project migration module and a secondary development module;

[0007] The data flow test module is used to generate random vector data flows, perform data connectivity tests on each calling interface between development models, use the data flow corresponding to the highest data connectivity as the characteristic data flow, and test the delay of calling interfaces between models, and use the interface with the lowest delay as the conversion interface between models;

[0008] The model connectivity module is used to calculate the consistency of the feature data streams of each model, arrange all development models in descending order of consistency, connect the conversion interfaces between the models in a forward direction, form a model conversion queue, develop a supporting processing program based on the model conversion queue, and deploy the program on the service platform;

[0009] The feedback processing module is used to obtain the input data stream provided by the user, calculate the consistency of the input data stream with the feature data stream of each model, select the processing model on the model queue according to the consistency order, point the directional pointer to the model with the greatest consistency, make the sum of the communication delays of the conversion interface between models less than a preset threshold, share the input data stream in the processing model according to the consistency ratio, and output the reply data stream to the user on the service platform after model processing;

[0010] The project migration module is used to obtain the user's feedback data stream. When the consistency between the feedback data stream and the standard forward data stream is less than the consistency between the two model feature data streams, the shared data ratio of the subsequent model in the model conversion queue is increased by one unit, otherwise the shared data ratio is reduced by one unit, and the user's feedback data stream is re-acquired until the shared data ratio of the previous model drops to a threshold value, the conversion interface of the previous model is closed, and the directional pointer moves one position;

[0011] The secondary development module is used to output the position of the directional pointer and the data sharing ratio of each model in the model conversion queue during the service process after the user goes offline, and obtain the service weight of each model by multiplying the sharing ratio, sharing duration and time decay coefficient. After the development cycle is over, a new model is developed according to the service weight of the model, and the intelligent agent is deployed on the service platform again.

[0012] Further, the data flow test module includes: a connectivity test unit and a delay test unit;

[0013] The connectivity test unit is used to generate a random vector data stream, test connectivity between every two models, and store the data stream generated when the connectivity is the highest;

[0014] The delay testing unit is used to test the calling delay of each interface between models and mark the conversion interface with the lowest delay.

[0015] Further, the model connectivity module includes: a consistent arrangement unit and an interface connection unit;

[0016] The consistent arrangement unit is used to calculate the consistency of the feature data streams between the models and arrange all the models in a consistent order;

[0017] The interface connection unit is used to unidirectionally connect all models through the conversion interface between models, form a model conversion queue, and set the initial pointer of the queue.

[0018] Further, the feedback processing module includes: an input and output unit, a model deployment unit and a proportional access unit;

[0019] The input and output unit is used to obtain the user's input content on the agent platform, convert it into an input data stream and upload it to the server, and convert the reply data stream fed back by the server into a natural language output;

[0020] The model deployment unit is used to deploy the model conversion queue in the server, receive the input data stream, and output the reply data stream;

[0021] The proportional access unit is used to select a model on the model conversion queue according to the consistency of the input data stream and the feature data stream, and receive the response data of the model in proportion.

[0022] Further, the project migration module includes: a pointer sliding unit, a model conversion unit and a proportional expansion unit;

[0023] The pointer sliding unit is used to judge the reply effect of the previous reply data according to the user's feedback data stream, and convert the reply model according to the reply effect;

[0024] The model conversion unit is used to move the directional pointer, adjust the data sharing ratio of the reply model, and turn the model on or off on the server;

[0025] The ratio expansion unit is used to calculate the adjustment amount of the sharing ratio so that the model selection method can be quickly matched with user needs.

[0026] Further, the secondary development module includes: a weight output unit and a redeployment unit;

[0027] The weight output unit is used to output the service weights of each model in the service process after the user goes offline from the agent platform;

[0028] The redeployment unit is used to provide a development direction for the new model during the development cycle and to redeploy the new model to the agent platform.

[0029] A zero-code AI agent development and deployment data management method includes the following steps:

[0030] Step S1. Select the interface with the lowest latency between development models as the conversion interface, generate a random data stream, and perform a data connectivity test on the conversion interface of the development model. The data stream generated when the connectivity is the highest is recorded as the characteristic data stream of the model;

[0031] Step S2 uses a data stream comparison method to obtain the consistency of each model's feature data stream, arranges all development models in descending order of consistency, connects the models as queues through conversion interfaces, and forms a model conversion queue that is deployed in the server;

[0032] Step S3. The user provides an input data stream to the agent, calculates the consistency of the input data stream with the feature data streams of each model, and uses the model with the highest consistency as the starting model. The input data is shared among the starting model and the subsequent models in the starting model queue according to the consistency ratio, and the output data is obtained after processing.

[0033] Step S4. The user provides a feedback data stream for the output data. Based on the consistency between the current feedback data stream and the historical feedback data stream, the sharing ratio of the input data of each model in the model conversion queue is adjusted. The process is repeated until the sharing ratio of the starting model is lower than the threshold, and the starting model is replaced in the server.

[0034] Step S5. After the user goes offline, the data sharing ratio, sharing duration and time decay coefficient of each model in the service process are accumulated to obtain the service weight of the model. After the development cycle is over, the ratio of the total service weight of the model in the development cycle is output, and the model is redeployed.

[0035] Further, step S1 includes:

[0036] Step S11. Perform a ping command test on all interfaces of the development model to obtain the feedback delay of the interface, and use the interface with the smallest delay in the model as the conversion interface of the model, which is used for signal transmission and data sharing between models;

[0037] Step S12. Connect every two models through a conversion interface, use natural language generation software to generate a random data stream suitable for the agent, the data volume of the random data stream is a fixed value, input the random data stream to the first model, and obtain output from the second model, calculate the ratio between the data volume of the output data and the data volume of the input data, and record it as the connectivity of the first model;

[0038] Step S13. Repeat generating random data streams a times, where a is the preset number of tests, and record the random data streams generated when the connectivity of each model is the highest during the test, and use the random data streams as the characteristic data streams of the model.

[0039] Further, step S2 includes:

[0040] Step S21. Calculate the consistency of each model feature data stream according to the data stream comparison method, the data stream comparison method is as follows:

[0041]

[0042] Among them, Q represents the consistency of the current model feature data stream, n represents the number of models, m represents the amount of data in the feature data stream, and C1 j and C1 j+1 represents the jth and j+1th data points in the current model feature data stream, Cij and Ci j+1 represent the j-th and (j + 1)-th data points in the feature data stream of the i-th model;

[0043] Step S22. Arrange all models in descending order of the consistency coefficient, and linearly connect the models in the arranged order through the conversion interfaces in the models to form a model conversion queue;

[0044] Step S23. Deploy the model conversion queue in the server, obtain the input content of the user on the front-end intelligent agent platform, convert it into an input data stream and upload it to the server, and after the server finishes processing the input data, output the output data stream fed back by the server through the intelligent agent platform.

[0045] Further, step S3 includes:

[0046] Step S31. The user interacts with the intelligent agent, and the front-end of the intelligent agent converts the user's interactive input into a data stream format and sends it to the server as the user's input data stream, calculates the consistency between the input data stream and the feature data streams of each model, and the model with the highest consistency is used as the starting model;

[0047] Step S32. Set a pointer to the starting model in the queue, and select the c models after the starting model in the queue as the shared models, where c is the maximum value that satisfies the inequality T1 + T2 + … + Tc < TR, where T1, T2, … Tc respectively represent the communication delays between the 1st to c-th models after the starting model and the subsequent adjacent models, and TR is the preset delay threshold;

[0048] Step S33. Calculate the input data sharing ratio yc of each shared model, where yc = Qc / ΣQ, where Qc represents the consistency between the feature data stream of the shared model and the input data stream, and ΣQ represents the sum of the consistencies between the feature data streams of all models and the input data stream;

[0049] Step S34. Split the input data according to the ratio and send it to the shared models for processing respectively, and use the Cluster data integration tool to summarize the output data processed by each model and send it to the front-end intelligent agent as the feedback data.

[0050] Further, step S4 includes:

[0051] Step S41. The front-end intelligent agent obtains the feedback data of the user and adjusts the data sharing ratio according to the following method:

[0052] S41-1. If there is no historical feedback data, go to S41-2; if there is historical feedback data, go to S41-3;

[0053] S41-2. Calculate the consistency between the feedback data stream and the standard forward data stream. If it is less than the consistency between the starting model and the subsequent model in the queue, the data sharing ratio of the starting model is reduced by a preset unit, and the reduced ratio is distributed among the remaining shared models. Otherwise, the data sharing ratio of the starting model is increased by a preset unit, and the increased ratio is provided by the remaining shared models.

[0054] S41-3. Calculate the consistency between the current feedback data stream and the previous feedback data stream. If it is less than the consistency between the starting model and the subsequent model in the queue, reduce the data sharing ratio of the starting model by u units, where u = (QB-QN) / QE, where QB represents the consistency between the current feedback data stream and the previous feedback data stream, QN represents the mean of the consistency between historical feedback data streams, and QE represents the consistency between the starting model and the subsequent model in the queue. Otherwise, increase the data sharing ratio of the starting model by u units;

[0055] Step S42. Execute step S41 once each time the user's feedback data stream is obtained. If the data sharing ratio of the starting model after execution is lower than the preset threshold, the model with the highest data sharing ratio among the remaining shared models is marked as the new starting model.

[0056] Further, step S5 includes:

[0057] Step S51. After the interaction between the user and the agent is completed, the service weight of each model is calculated:

[0058]

[0059] Among them, Rc represents the service weight of the model, k represents the number of user interactions, yv represents the data sharing ratio of the model at the vth interaction, Tv represents the duration of the user's vth interaction, and h represents the preset time decay coefficient;

[0060] Step S52. After the development cycle is completed, the sum of the service weights of each model during the development cycle is calculated, a new model is developed according to the proportion of the calculation results, and it is redeployed in the intelligent server.

[0061] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0062] 1. The present invention can use random data streams to test the data connectivity of various models, take the data stream corresponding to the highest data connectivity as the characteristic data stream, arrange the models according to the vector consistency of the characteristic data stream of each model, connect the conversion interface forward, and make the isolated models interconnected. By sharing data to generate more accurate output content, the ability to process data can be improved, and the accuracy, robustness and operating efficiency of the intelligent system can be enhanced.

[0063] 2. The present invention can analyze the consistency of the input data stream and the feature data stream based on the data stream provided by the user to the intelligent agent, share the input data stream between models through the conversion interface according to the consistency ratio, adjust the type and call ratio of the model according to the user's preference, improve the user experience, and continuously optimize the output content of the intelligent agent, so as to obtain a good performance optimization curve in the intelligent agent service process.

[0064] 3. After the user goes offline, the present invention outputs the connection weight of the model according to the current model access ratio. In each development cycle, the model with the highest connection weight is developed as a new model, eliminating the process of user research, speeding up the development progress of the intelligent body, enhancing the humanized interaction of the intelligent body, and ensuring the high competitiveness and efficiency of the development process. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0066] Figure 1 It is a structural diagram of a zero-code AI agent development and deployment data management system of the present invention;

[0067] Figure 2 It is a schematic diagram of the steps of a zero-code AI agent development and deployment data management method of the present invention. DETAILED DESCRIPTION

[0068] 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.

[0069] See also Figure 1 , the present invention provides a technical solution: a zero-code AI agent development and deployment data management system, including: a data flow testing module, a model connectivity module, a feedback processing module, a project migration module and a secondary development module;

[0070] The data flow test module is used to generate random vector data flows, perform data connectivity tests on each calling interface between development models, use the data flow corresponding to the highest data connectivity as the characteristic data flow, and test the delay of calling interfaces between models, and use the interface with the lowest delay as the conversion interface between models;

[0071] The data flow test module includes: a connectivity test unit and a delay test unit;

[0072] The connectivity test unit is used to generate a random vector data stream, test connectivity between every two models, and store the data stream generated when the connectivity is the highest;

[0073] The delay testing unit is used to test the calling delay of each interface between models and mark the conversion interface with the lowest delay.

[0074] The model connectivity module is used to calculate the consistency of the feature data streams of each model, arrange all development models in descending order of consistency, connect the conversion interfaces between the models in a forward direction, form a model conversion queue, develop a supporting processing program based on the model conversion queue, and deploy the program on the service platform;

[0075] The model connectivity module includes: a consistent arrangement unit and an interface connection unit;

[0076] The consistent arrangement unit is used to calculate the consistency of the feature data streams between the models and arrange all the models in a consistent order;

[0077] The interface connection unit is used to unidirectionally connect all models through the conversion interface between models, form a model conversion queue, and set the initial pointer of the queue.

[0078] The feedback processing module is used to obtain the input data stream provided by the user, calculate the consistency of the input data stream with the feature data stream of each model, select the processing model on the model queue according to the consistency order, point the directional pointer to the model with the greatest consistency, make the sum of the communication delays of the conversion interface between models less than a preset threshold, share the input data stream in the processing model according to the consistency ratio, and output the reply data stream to the user on the service platform after model processing;

[0079] The feedback processing module includes: an input and output unit, a model deployment unit and a proportional access unit;

[0080] The input and output unit is used to obtain the user's input content on the agent platform, convert it into an input data stream and upload it to the server, and convert the reply data stream fed back by the server into a natural language output;

[0081] The model deployment unit is used to deploy the model conversion queue in the server, receive the input data stream, and output the reply data stream;

[0082] The proportional access unit is used to select a model on the model conversion queue according to the consistency of the input data stream and the feature data stream, and receive the response data of the model in proportion.

[0083] The project migration module is used to obtain the user's feedback data stream. When the consistency between the feedback data stream and the standard forward data stream is less than the consistency between the two model feature data streams, the shared data ratio of the subsequent model in the model conversion queue is increased by one unit, otherwise the shared data ratio is reduced by one unit, and the user's feedback data stream is re-acquired until the shared data ratio of the previous model drops to a threshold value, the conversion interface of the previous model is closed, and the directional pointer moves one position;

[0084] The project migration module includes: a pointer sliding unit, a model conversion unit and a proportional expansion unit;

[0085] The pointer sliding unit is used to judge the reply effect of the previous reply data according to the user's feedback data stream, and convert the reply model according to the reply effect;

[0086] The model conversion unit is used to move the directional pointer, adjust the data sharing ratio of the reply model, and turn the model on or off on the server;

[0087] The ratio expansion unit is used to calculate the adjustment amount of the sharing ratio so that the model selection method can be quickly matched with user needs.

[0088] The secondary development module is used to output the position of the directional pointer and the data sharing ratio of each model in the model conversion queue during the service process after the user goes offline, and obtain the service weight of each model by multiplying the sharing ratio, sharing duration and time decay coefficient. After the development cycle is over, a new model is developed according to the service weight of the model, and the intelligent agent is deployed on the service platform again.

[0089] The secondary development module includes: a weight output unit and a redeployment unit;

[0090] The weight output unit is used to output the service weights of each model in the service process after the user goes offline from the agent platform;

[0091] The redeployment unit is used to provide a development direction for the new model during the development cycle and to redeploy the new model to the agent platform.

[0092] like Figure 2 As shown, a zero-code AI agent development and deployment data management method includes the following steps:

[0093] Step S1. Select the interface with the lowest latency between development models as the conversion interface, generate a random data stream, and perform a data connectivity test on the conversion interface of the development model. The data stream generated when the connectivity is the highest is recorded as the characteristic data stream of the model;

[0094] Step S1 includes:

[0095] Step S11. Perform a ping command test on all interfaces of the development model to obtain the feedback delay of the interface, and use the interface with the smallest delay in the model as the conversion interface of the model, which is used for signal transmission and data sharing between models;

[0096] Step S12. Connect every two models through a conversion interface, use natural language generation software to generate a random data stream suitable for the agent, the data volume of the random data stream is a fixed value, input the random data stream to the first model, and obtain output from the second model, calculate the ratio between the data volume of the output data and the data volume of the input data, and record it as the connectivity of the first model;

[0097] Step S13. Repeat generating random data streams a times, where a is the preset number of tests, and record the random data streams generated when the connectivity of each model is the highest during the test, and use the random data streams as the characteristic data streams of the model.

[0098] Step S2 uses a data stream comparison method to obtain the consistency of each model's feature data stream, arranges all development models in descending order of consistency, connects the models as queues through conversion interfaces, and forms a model conversion queue that is deployed in the server;

[0099] Step S2 includes:

[0100] Step S21. Calculate the consistency of each model feature data stream according to the data stream comparison method, the data stream comparison method is as follows:

[0101]

[0102] Among them, Q represents the consistency of the current model feature data stream, n represents the number of models, m represents the amount of data in the feature data stream, and C1 j and C1 j+1 represents the jth and j+1th data points in the current model feature data stream, Ci j Ci j+1 Represents the jth and j+1th data points in the feature data stream of the i-th model;

[0103] Step S22. Arrange all models in descending order according to the consistency coefficient, and linearly connect the models through the conversion interface in the model according to the arrangement order to form a model conversion queue;

[0104] Step S23. Deploy the model conversion queue in the server, obtain the user's input content on the front-end intelligent platform, convert it into an input data stream and upload it to the server, and after the server has processed the input data, output the output data stream fed back by the server through the intelligent platform.

[0105] Step S3. The user provides an input data stream to the agent, calculates the consistency between the input data stream and each model feature data stream, selects the model with the highest consistency as the starting model, and shares the input data among the starting model and the subsequent models in the starting model queue according to the consistency ratio, and processes it to obtain the output data;

[0106] Step S3 includes:

[0107] Step S31. The user interacts with the agent, and the front-end of the agent converts the user's interaction input into a data stream format and sends it to the server as the user's input data stream, calculates the consistency between the input data stream and each model feature data stream, and selects the model with the highest consistency as the starting model;

[0108] Step S32. Set a pointer to the starting model in the queue, select the c subsequent models of the starting model in the queue as the shared models, where c is the maximum value that satisfies the inequality T1 + T2 + … + Tc < TR, where T1, T2, … Tc respectively represent the communication delays between the 1st to cth models after the starting model and the subsequent adjacent models, and TR is the preset delay threshold;

[0109] Step S33. Calculate the input data sharing ratio yc of each shared model, where yc = Qc / ΣQ, where Qc represents the consistency between the shared model feature data stream and the input data stream, and ΣQ represents the sum of the consistencies between all model feature data streams and the input data stream;

[0110] Step S34. Split the input data according to the ratio, send it to the shared models for processing respectively, and use the Cluster data integration tool to summarize the output data processed by each model and send it to the front-end agent as the feedback data.

[0111] Step S4. The user provides a feedback data stream for the output data, adjusts the sharing ratio of the input data of each model in the model conversion queue according to the consistency between the current feedback data stream and the historical feedback data stream, and repeats the execution until the sharing ratio of the starting model is lower than the threshold, and then replaces the starting model in the server;

[0112] Step S4 includes:

[0113] Step S41. The front-end agent obtains the feedback data of the user and adjusts the data sharing ratio according to the following method:

[0114] S41-1. If there is no historical feedback data, go to S41-2; if there is historical feedback data, go to S41-3;

[0115] S41-2. Calculate the consistency between the feedback data stream and the standard forward data stream. If it is less than the consistency between the starting model and the subsequent model in the queue, the data sharing ratio of the starting model is reduced by a preset unit, and the reduced ratio is distributed among the remaining shared models. Otherwise, the data sharing ratio of the starting model is increased by a preset unit, and the increased ratio is provided by the remaining shared models.

[0116] S41-3. Calculate the consistency between the current feedback data stream and the previous feedback data stream. If it is less than the consistency between the starting model and the subsequent model in the queue, reduce the data sharing ratio of the starting model by u units, where u = (QB-QN) / QE, where QB represents the consistency between the current feedback data stream and the previous feedback data stream, QN represents the mean of the consistency between historical feedback data streams, and QE represents the consistency between the starting model and the subsequent model in the queue. Otherwise, increase the data sharing ratio of the starting model by u units;

[0117] Step S42. Execute step S41 once each time the user's feedback data stream is obtained. If the data sharing ratio of the starting model after execution is lower than the preset threshold, the model with the highest data sharing ratio among the remaining shared models is marked as the new starting model.

[0118] Step S5. After the user goes offline, the data sharing ratio, sharing duration and time decay coefficient of each model in the service process are accumulated to obtain the service weight of the model. After the development cycle is over, the ratio of the total service weight of the model in the development cycle is output, and the model is redeployed.

[0119] Step S5 includes:

[0120] Step S51. After the interaction between the user and the agent is completed, the service weight of each model is calculated:

[0121]

[0122] Among them, Rc represents the service weight of the model, k represents the number of user interactions, yv represents the data sharing ratio of the model at the vth interaction, Tv represents the duration of the user's vth interaction, and h represents the preset time decay coefficient;

[0123] Step S52. After the development cycle is completed, the sum of the service weights of each model during the development cycle is calculated, a new model is developed according to the proportion of the calculation results, and it is redeployed in the intelligent server.

[0124] Embodiment: There are 3 models in the intelligent agent server, and the feature data streams are [2,4,1,3,5], [1,4,2,2,1] and [5,3,3,1,4], and the consistency is 0.18, 0.06 and 0.23 respectively. Then they are arranged in the order of model 3, model 1 and model 2 to form a model queue, with model 3 as the starting model; during the user's interaction with the intelligent agent, the sharing ratios obtained according to the provided input data streams are 0.5, 0.3 and 0.2. After the user feedback, the consistency of the feedback data stream decreases, and the sharing ratios are reallocated to 0.4, 0.36 and 0.24.

[0125] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0126] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A zero-code AI agent development and deployment data management method, characterized in that: The method comprises the following steps: Step S1. Select the interface with the lowest latency between development models as the conversion interface, generate a random data stream, and perform a data connectivity test on the conversion interface of the development model. The data stream generated when the connectivity is the highest is recorded as the characteristic data stream of the model; Step S2. Use the data stream comparison method to obtain the consistency of the feature data streams of each model, arrange all development models in descending order of consistency, connect the models as queues through the conversion interface, and form a model conversion queue deployed in the server; Step S3. The user provides an input data stream to the agent, calculates the consistency of the input data stream with the feature data streams of each model, and uses the model with the highest consistency as the starting model. The input data is shared among the starting model and the subsequent models in the starting model queue according to the consistency ratio, and the output data is obtained after processing. Step S4. The user provides a feedback data stream for the output data. Based on the consistency between the current feedback data stream and the historical feedback data stream, the sharing ratio of the input data of each model in the model conversion queue is adjusted. The process is repeated until the sharing ratio of the starting model is lower than the threshold, and the starting model is replaced in the server. Step S5. After the user goes offline, the data sharing ratio, sharing duration and time decay coefficient of each model in the service process are accumulated to obtain the service weight of the model. After the development cycle is over, the ratio of the total service weight of the model in the development cycle is output, and the model is redeployed.

2. A zero-code AI agent development and deployment data management method according to claim 1, characterized in that: Step S1 includes: Step S11. Perform a ping command test on all interfaces of the development model to obtain the feedback delay of the interface, and use the interface with the smallest delay in the model as the conversion interface of the model, which is used for signal transmission and data sharing between models; Step S12. Connect every two models through a conversion interface, use natural language generation software to generate a random data stream suitable for the agent, the data volume of the random data stream is a fixed value, input the random data stream to the first model, and obtain output from the second model, calculate the ratio between the data volume of the output data and the data volume of the input data, and record it as the connectivity of the first model; Step S13. Repeat generating random data streams a times, where a is the preset number of tests, and record the random data streams generated when the connectivity of each model is the highest during the test, and use the random data streams as the characteristic data streams of the model.

3. A zero-code AI agent development and deployment data management method according to claim 2, characterized in that: Step S2 includes: Step S21. Calculate the consistency of each model feature data stream according to the data stream comparison method, the data stream comparison method is as follows: Among them, Q represents the consistency of the current model feature data stream, n represents the number of models, m represents the amount of data in the feature data stream, and C1 j and C1 j+1 represents the jth and j+1th data points in the current model feature data stream, Ci j Ci j+1 Represents the jth and j+1th data points in the feature data stream of the i-th model; Step S22. Arrange all models in descending order according to the consistency coefficient, and linearly connect the models through the conversion interface in the model according to the arrangement order to form a model conversion queue; Step S23. Deploy the model conversion queue in the server, obtain the user's input content on the front-end intelligent platform, convert it into an input data stream and upload it to the server, and after the server has processed the input data, output the output data stream fed back by the server through the intelligent platform.

4. A zero-code AI agent development and deployment data management method according to claim 3, characterized in that: Step S3 includes: Step S31. The user interacts with the agent. The front end of the agent converts the user's interaction input into a data stream format and sends it to the server as the user's input data stream. Calculate the consistency between the input data stream and each model feature data stream, and the model with the highest consistency is used as the starting model. Step S32. Set a pointer to the starting model in the queue, and select the c models after the starting model in the queue as the shared models, where c is the maximum value that satisfies the inequality T1 + T2 + … + Tc < TR, where T1, T2, …, Tc represent the communication delays between the 1st to the cth models after the starting model and their subsequent adjacent models respectively, and TR is the preset delay threshold. Step S33. Calculate the input data sharing ratio yc of each shared model, where yc = Qc / ΣQ, Qc represents the consistency between the shared model feature data stream and the input data stream, and ΣQ represents the sum of the consistencies between all model feature data streams and the input data stream. Step S34. Split the input data according to the ratio and send it into the shared models for processing respectively. Use the Cluster data integration tool to summarize the output data after the processing of each model and send it to the front-end agent as the feedback data.

5. A zero-code AI agent development and deployment data management method according to claim 4, characterized in that: Step S4 includes: Step S41. The front-end agent obtains the user's feedback data and adjusts the data sharing ratio according to the following method: S41-1. If there is no historical feedback data, go to S41-2; if there is historical feedback data, go to S41-3. S41-2. Calculate the consistency between the feedback data stream and the standard positive data stream. If it is less than the consistency between the starting model and the subsequent model in the queue, reduce the data sharing ratio of the starting model by a preset unit, and distribute the reduced ratio among the remaining shared models; otherwise, increase the data sharing ratio of the starting model by a preset unit, and the increased ratio is provided by the remaining shared models. S41-3. Calculate the consistency between the current feedback data stream and the previous feedback data stream. If it is less than the consistency between the starting model and the subsequent model in the queue, reduce the data sharing ratio of the starting model by u units, where u = (QB - QN) / QE, QB represents the consistency between the current feedback data stream and the previous feedback data stream, QN represents the average value of the consistencies between historical feedback data streams, and QE represents the consistency between the starting model and the subsequent model in the queue; otherwise, increase the data sharing ratio of the starting model by u units. Step S42. Execute Step S41 every time the user's feedback data stream is obtained. If the data sharing ratio of the starting model is lower than the preset threshold after execution, mark the model with the highest data sharing ratio among the remaining shared models as the new starting model. Step S5 includes: Step S51. After the interaction between the user and the agent ends, calculate the service weights of each model: where Rc represents the service weight of the model, k represents the number of user interactions, yv represents the data sharing ratio of the model during the vth interaction, Tv represents the duration of the vth user interaction, and h represents the preset time decay coefficient. Step S52. After the development cycle is completed, the sum of the service weights of each model during the development cycle is calculated, a new model is developed according to the proportion of the calculation results, and it is redeployed in the intelligent server.

6. A zero-code AI agent development and deployment data management system, characterized in that: The system includes the following modules: Data flow testing module, model connectivity module, feedback processing module, project migration module and secondary development module; The data flow test module is used to generate random vector data flows, perform data connectivity tests on each calling interface between development models, use the data flow corresponding to the highest data connectivity as the characteristic data flow, and test the delay of calling interfaces between models, and use the interface with the lowest delay as the conversion interface between models; The model connectivity module is used to calculate the consistency of the feature data streams of each model, arrange all development models in descending order of consistency, connect the conversion interfaces between the models in a forward direction, form a model conversion queue, develop a supporting processing program based on the model conversion queue, and deploy the program on the service platform; The feedback processing module is used to obtain the input data stream provided by the user, calculate the consistency of the input data stream with the feature data stream of each model, select the processing model on the model queue according to the consistency order, point the directional pointer to the model with the greatest consistency, make the sum of the communication delays of the conversion interface between models less than a preset threshold, share the input data stream in the processing model according to the consistency ratio, and output the reply data stream to the user on the service platform after model processing; The project migration module is used to obtain the user's feedback data stream. When the consistency between the feedback data stream and the standard forward data stream is less than the consistency between the two model feature data streams, the shared data ratio of the subsequent model in the model conversion queue is increased by one unit, otherwise the shared data ratio is reduced by one unit, and the user's feedback data stream is re-acquired until the shared data ratio of the previous model drops to a threshold value, the conversion interface of the previous model is closed, and the directional pointer moves one position; The secondary development module is used to output the position of the directional pointer and the data sharing ratio of each model in the model conversion queue during the service process after the user goes offline, and obtain the service weight of each model by multiplying the sharing ratio, sharing duration and time decay coefficient. After the development cycle is over, a new model is developed according to the service weight of the model, and the intelligent agent is deployed on the service platform again.

7. A zero-code AI agent development and deployment data management system according to claim 6, characterized in that: The data flow test module includes: a connectivity test unit and a delay test unit; The connectivity test unit is used to generate a random vector data stream, test connectivity between every two models, and store the data stream generated when the connectivity is the highest; The delay test unit is used to test the call delay of each interface between models and mark the conversion interface with the lowest delay; The model connectivity module includes: a consistent arrangement unit and an interface connection unit; The consistent arrangement unit is used to calculate the consistency of the feature data streams between the models and arrange all the models in a consistent order; The interface connection unit is used to unidirectionally connect all models through the conversion interface between models, form a model conversion queue, and set the initial pointer of the queue.

8. A zero-code AI agent development and deployment data management system according to claim 7, characterized in that: The feedback processing module includes: an input and output unit, a model deployment unit and a proportional access unit; The input and output unit is used to obtain the user's input content on the agent platform, convert it into an input data stream and upload it to the server, and convert the reply data stream fed back by the server into a natural language output; The model deployment unit is used to deploy the model conversion queue in the server, receive the input data stream, and output the reply data stream; The proportional access unit is used to select a model on the model conversion queue according to the consistency of the input data stream and the feature data stream, and receive the response data of the model in proportion.

9. A zero-code AI agent development and deployment data management system according to claim 8, characterized in that: The project migration module includes: a pointer sliding unit, a model conversion unit and a proportional expansion unit; The pointer sliding unit is used to judge the reply effect of the previous reply data according to the user's feedback data stream, and convert the reply model according to the reply effect; The model conversion unit is used to move the directional pointer, adjust the data sharing ratio of the reply model, and turn the model on or off on the server; The ratio expansion unit is used to calculate the adjustment amount of the sharing ratio so that the model selection method can be quickly matched with user needs.

10. A zero-code AI agent development and deployment data management system according to claim 9, characterized in that: The secondary development module includes: a weight output unit and a redeployment unit; The weight output unit is used to output the service weights of each model in the service process after the user goes offline from the agent platform; The redeployment unit is used to provide a development direction for the new model during the development cycle and to redeploy the new model to the agent platform.

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