Data processing method, device, readable storage medium and computer program product

Abstract

Embodiments of the present application provide a data processing method, device, readable storage medium and computer program product. A control system is connected with a processing system through a gateway. A client is deployed in an artificial intelligence module of the control system based on a target communication protocol. The client is connected with a server. An operator can establish a connection with the server through registration. The operator is accessed to the artificial intelligence module. When the artificial intelligence module executes a data processing task, the operator can be quickly interacted with according to the target communication protocol, so as to complete the data processing task through the corresponding operator. Through a standardized interface, the operator is plug and play and distributed deployment. Multi-source heterogeneous data is quickly and conveniently accessed to the artificial intelligence model, the data island effect is avoided, and the complexity of integration of heterogeneous systems is reduced. The development process is relatively simple. The artificial intelligence model can quickly and accurately complete the task with the help of the operator, and the efficiency of data processing is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial intelligence, and in particular to a data processing method and device, a readable storage medium and a computer program product. BACKGROUND

[0002] In the industrial field, in order to realize device detection, maintenance and process optimization and other control functions, a processing system realized by Internet of Things technology emerges as the times require. The processing system can connect industrial devices, collect devices, and can provide computing tools to realize the above control functions. These computing tools can realize corresponding analysis capabilities based on specific algorithms or specific models, such as device control, data collection, signal processing, etc., also known as operators.

[0003] However, how to realize accurate and efficient data processing with the help of operators has become a technical problem to be solved at present. SUMMARY

[0004] The embodiments of the present application provide a data processing method, device, readable storage medium and computer program product, which can realize accurate and efficient data processing with the help of operators.

[0005] In a first aspect, the embodiments of the present application provide a data processing method applied to an artificial intelligence module in a control system, wherein the artificial intelligence module integrates a client configured according to a target communication protocol; the client establishes a connection with a server configured according to the target communication protocol, and the method comprises:

[0006] Calling the client to obtain registration information of at least one operator from the server; the registration information is obtained by registration according to the target communication protocol;

[0007] In response to a data processing task, at least one target operator meeting the task requirements is selected from the at least one operator according to the registration information of the at least one operator;

[0008] According to the target communication protocol, a corresponding task request is sent to the at least one target operator; the task request is used to trigger the target operator to execute a corresponding task operation and obtain an operation result;

[0009] According to the target communication protocol, the operation result returned by each of the at least one target operator is obtained;

[0010] The data processing task is executed in combination with the operation result of each of the at least one target operator.

[0011] In a second aspect, the embodiments of the present application provide a data processing method, applied to a server integrated in a gateway device or a processing system; the gateway device is connected with the processing system; the processing system accesses at least one operator; the server is connected with a client according to a target communication protocol; the client is an artificial intelligence module integrated in a control system and configured according to the target communication protocol; and the method comprises:

[0012] sending, to the artificial intelligence module, registration information of at least one operator registered according to the target communication protocol;

[0013] receiving, according to the target communication protocol, a task request corresponding to at least one target operator sent by the client, and sending the corresponding task request to the at least one target operator; the at least one target operator is an operator selected from the at least one operator by the artificial intelligence module according to the registration information of the at least one operator, in response to a data processing task; and the task request is used to trigger the target operator to perform a corresponding task operation and obtain an operation result;

[0014] receiving operation results respectively sent by the at least one target operator according to the target communication protocol, and sending the operation results to the artificial intelligence module, so that the artificial intelligence module executes the data processing task in combination with the operation results.

[0015] In a third aspect, the embodiments of the present application provide a data processing method, applied to a target operator accessed in a processing system; the processing system integrates a server or a gateway device connected with the processing system integrates a server; the server is connected with a client according to a target communication protocol; the client is an artificial intelligence module integrated in a control system and configured according to the target communication protocol; and the method comprises:

[0016] receiving a task request sent by the server; the task request is sent by the artificial intelligence module according to the target communication protocol, in response to a data processing task; and the target operator is an operator satisfying a task requirement of the data processing task;

[0017] performing a task operation corresponding to the task request to obtain an operation result;

[0018] sending the operation result to the server according to the target communication protocol, so that the server sends the operation result to the artificial intelligence module, and the artificial intelligence module executes the data processing task in combination with the operation result sent by the target operator.

[0019] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor, a communication interface; wherein the memory has stored executable codes, when the executable codes are executed by the processor, the processor executes the data processing method according to the first aspect.

[0020] In a fifth aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor, a communication interface; wherein the memory has stored executable codes, when the executable codes are executed by the processor, the processor executes the data processing method according to the second aspect.

[0021] In a sixth aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor, a communication interface; wherein the memory has stored executable codes, when the executable codes are executed by the processor, the processor executes the data processing method according to the third aspect.

[0022] In a seventh aspect, an embodiment of the present application provides a non-transitory machine readable storage medium, the non-transitory machine readable storage medium has stored executable codes, when the executable codes are executed by a processor of an electronic device, the processor can at least implement the data processing method according to the first aspect.

[0023] In an eighth aspect, an embodiment of the present application provides a non-transitory machine readable storage medium, the non-transitory machine readable storage medium has stored executable codes, when the executable codes are executed by a processor of an electronic device, the processor can at least implement the data processing method according to the second aspect.

[0024] In a ninth aspect, an embodiment of the present application provides a non-transitory machine readable storage medium, the non-transitory machine readable storage medium has stored executable codes, when the executable codes are executed by a processor of an electronic device, the processor can at least implement the data processing method according to the third aspect.

[0025] In a tenth aspect, an embodiment of the present application provides a computer program product, the computer program product comprises a computer program, when the computer program is executed by a processor, the computer program can implement the data processing method according to the first aspect.

[0026] In an eleventh aspect, an embodiment of the present application provides a computer program product, the computer program product comprises a computer program, when the computer program is executed by a processor, the computer program can implement the data processing method according to the second aspect.

[0027] In a twelfth aspect, an embodiment of the present application provides a computer program product, the computer program product comprising a computer program, the computer program being capable of implementing the data processing method according to the third aspect when executed by a processor.

[0028] In the data processing scheme provided by the embodiment of the present application, various operators in the processing system are connected to the artificial intelligence module in the control system through registration by the target communication protocol. When the artificial intelligence module performs a data processing task, the target communication protocol can be used to quickly interact with the operator to perform auxiliary processing through the corresponding operator, so as to complete the data processing task. Thus, various operators, including operators in a heterogeneous processing system, can be quickly and conveniently connected to the artificial intelligence model through the interface of the standardized target communication protocol, the development process is simple, the plug-and-play and distributed deployment of the operator are realized, and the multi-source heterogeneous data is quickly and conveniently connected to the artificial intelligence model, thereby avoiding the data island effect and reducing the complexity of the integration of the heterogeneous system. The artificial intelligence model can quickly and accurately complete the data processing task with the help of the operator, thereby improving the efficiency of data processing. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0030] Figure 1 An architecture diagram of a target communication protocol provided by an embodiment of the present application;

[0031] Figure 2 A system architecture diagram of a data processing system provided by an embodiment of the present application;

[0032] Figure 3 An interaction schematic diagram of a data processing method provided by an embodiment of the present application;

[0033] Figure 4 An interaction schematic diagram of another data processing method provided by an embodiment of the present application;

[0034] Figure 5 An interaction schematic diagram of another data processing method provided by an embodiment of the present application;

[0035] Figure 6 An interaction schematic diagram of another data processing method provided by an embodiment of the present application;

[0036] Figure 7 An interaction schematic diagram of another data processing method provided by an embodiment of the present application;

[0037] Figure 8 A schematic diagram of a data processing system according to an embodiment of the present application is provided.

[0038] Figure 9 A schematic diagram of an electronic device according to an embodiment of the present application is provided. DETAILED DESCRIPTION

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following will be combined with the accompanying drawings for the embodiments of the present application to make a clear and complete description of the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application. In addition, the sequence of steps in each of the following method embodiments is only an example, not a strict limitation.

[0040] It should be noted that, in the case that the embodiments of the present application involve user information, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the embodiments of the present application are all information and data authorized by the user or authorized by all parties, and the collection, use, and processing of related data need to comply with relevant laws, regulations, and standards of relevant countries and regions, and provide corresponding operation portal for user to choose authorization or refusal. In addition, the various models (including but not limited to large language models or other models) involved in the present application are in compliance with relevant laws and standard regulations.

[0041] First, the terms or concepts involved in the embodiments of the present application are explained:

[0042] Operator: refers to a computing tool that realizes corresponding analysis capability based on a specific algorithm or a specific model, which can also be referred to as an industrial operator in an industrial scene. The industrial operator is a core component of an industrial system.

[0043] Artificial intelligence: a technology aiming to realize automatic tasks and decision-making by simulating human intelligent behavior through a computer.

[0044] Artificial intelligence model: refers to a model trained based on deep learning and artificial intelligence technology, such as an artificial intelligence-based language model (Language Mode, LM), an image recognition model, or a multimodal model (Multimodal Model, MM), etc., which has great generalization ability and intelligent ability, thereby having various task processing capabilities, such as language understanding, generation, image recognition, etc.

[0045] Model Context Protocol (MCP): A standardized communication protocol for integrating language models of artificial intelligence with external data sources and tools, establishing a secure two-way connection between the model and the data source, ensuring the security of data transmission and the flexibility of operation.

[0046] Multi-source heterogeneous data: Data from different sources, formats, and structures in industrial scenarios, such as sensor data, device logs, video streams, etc., which need to be integrated and processed to support intelligent decision-making.

[0047] Data silo effect: The phenomenon of data being difficult to share and integrate due to scattered storage or lack of unified interface in industrial scenarios, hindering the landing of intelligent applications.

[0048] Local sensitive data: Data stored in industrial sites or local environments with high privacy and strict security requirements, such as device operating parameters, production process information, etc.

[0049] In the industrial field, it is often necessary to use operators to achieve accurate and efficient data processing. For example, in the intelligent manufacturing scenario, it is necessary to optimize the existing production process based on the data of the current multiple factories to improve production efficiency.

[0050] With the development of artificial intelligence technology, through artificial intelligence models, given tasks can be automatically completed, which can improve efficiency and decision-making ability and has been applied in many scenarios. If artificial intelligence technology can be applied in the industrial field, it will bring many conveniences to data processing in the industrial field.

[0051] The artificial intelligence model involved in the embodiments of the present application can be a language model (LM) or a multi-modal model (LM) based on artificial intelligence, and the embodiments of the present application do not limit the number of model parameters supported by the model, and the target is to meet the actual demand. If the model parameters are relatively more, the size of the model will be relatively larger, the performance of the model will be relatively better, and of course, more time and resources will be consumed in the inference or training process; if the model parameters are relatively less, the size of the model will be relatively smaller, and in the case that the performance meets the requirements, the model is more lightweight, and relatively less time and resources are consumed in the inference or training process. The artificial intelligence model can be a deep learning model used to process and generate natural language text or multi-modal data, which can be implemented based on a neural network architecture, and can be pre-trained on a large amount of data. In an optional implementation manner, the artificial intelligence model can include an encoder, a decoder, a self-attention layer, a feed-forward neural network, etc. The encoder is mainly used to convert input data (usually in sequence form) into vector representation, and this process can capture the semantic features of the input data. The decoder is responsible for converting the intermediate representation generated by the encoder into output data (usually in sequence form). The self-attention layer is a mechanism that allows the model to pay attention to other positions in the sequence to better encode the current position information. The feed-forward neural network can perform nonlinear transformation on the output of the self-attention layer to enhance the expression ability of the model. Each part works together to make the model based on them perform well in various complex processing tasks, such as natural language processing, computer vision, speech recognition, machine translation, text summarization, and intelligent question answering, etc.

[0052] The following introduces two implementation manners of applying the artificial intelligence model provided by the present application to the industrial field to perform complex data processing.

[0053] Embodiments of the present application provide a data processing method. Various operators in a processing system can be packaged as an application programming interface (API), for example, an API designed in the REST (Representational State Transfer) architecture style, to access an artificial intelligence model. Optionally, a HTTP service link constructed by a RAG (Retrieve Augment Generate) technology can be used to achieve loose coupling interaction between the artificial intelligence model and the processing system. However, the HTTP interface is not designed for the characteristics of artificial intelligence model communication, and cannot form a unified standard interface for accessing artificial intelligence models. Moreover, the operators in the processing system can be deployed on different platforms or frameworks, and usually need to be developed for compatibility.

[0054] Embodiments of the present application provide another data processing method. An artificial intelligence model can call an operator through a function call to complete a data processing task. Optionally, the operator can be packaged as a JSON Schema standard interface, and a function call mapping table can be established by fine-tuning the artificial intelligence model. However, only simple operator calls are usually supported, the artificial intelligence model can trigger sensitive operations by mistake, and the corresponding database also needs to be updated continuously.

[0055] In summary, the two data processing methods provided in the embodiments of the present application have their own shortcomings. In order to overcome these shortcomings, the embodiments of the present application propose a method of calling operators through an artificial intelligence model to achieve accurate and efficient data processing. Through a target communication protocol, various operators in the processing system are accessed to the artificial intelligence module in the control system through registration. When the artificial intelligence module performs a data processing task, the target communication protocol can quickly interact with the operator to assist in processing through the corresponding operator to complete the data processing task. Thus, various operators, including operators in a heterogeneous processing system, can quickly and conveniently access the artificial intelligence model through the interface of the standardized target communication protocol, the development process is simple, the artificial intelligence model can quickly and accurately complete the data processing task with the help of the operator, and the efficiency of data processing is improved.

[0056] The new data processing scheme provided by the embodiments of the present application is described below.

[0057] The target communication protocol mentioned in the embodiments of the present application can establish a communication channel between the artificial intelligence model and the operator, so that the artificial intelligence model and the operator can interact with each other, solving the problem that the artificial intelligence model cannot fully exert its potential due to data island restrictions. For example, the target communication protocol can be an MCP protocol. The architecture of the target communication protocol is described below taking the MCP protocol as an example.

[0058] Referring to Figure 1 , Figure 1 is a schematic diagram of a target communication protocol provided by an embodiment of the present application. The MCP protocol divides the communication between the artificial intelligence model and the operator into three main parts: the client, the server and the operator. Among them, the client is connected with the operator through the server. Generally, the client can be set in the host program, and the host can refer to the application program of the artificial intelligence model. Figure 1 In the figure, three servers are exemplarily shown, which are a first server 102, a second server 103 and a third server 104. It can be understood that the number of servers can be more or less, Figure 1 In the figure, the number of servers is only an example, which does not constitute a limitation to the present application. Optionally, the server can be a lightweight service component, which can also be referred to as a lightweight server. The operator can be registered in the server through registration, so as to realize the access of the operator. Figure 1 In the figure, three operators are exemplarily shown, which are a first operator 105, a second operator 106 and a third operator 107. It can be understood that in actual application, the number of operators can be more or less, Figure 1 In the figure, the number of operators is only an example, which does not constitute a limitation to the present application. The server provides tools, resources and functions for the client. Among them, the operator can be a tool, can also be a resource, for example, a database resource, and can also be a processing function.

[0059] In a possible embodiment, the accessed operator can be compatible with multiple data types, for example, structured data, time series data, images, texts and the like. The needs of diversified data processing are met.

[0060] In actual use, the client is connected with the artificial intelligence model, and the artificial intelligence model obtains the information of the currently accessed operator from the server through the client. When the artificial intelligence model needs the operator to assist in completing the data processing task, according to the obtained information of the operator, the operator to be used and the task operation to be assisted by the operator are determined, and a task request is sent to the operator through the client and the server. After the operator executes the corresponding task operation, the operation result is obtained and sent to the server. The server sends the operation result to the artificial intelligence model through the client. Thus, the above completes the operation of the communication between the artificial intelligence model and the operator through the MCP protocol.

[0061] Based on the target communication protocol architecture of Figure 1 , the architecture of the data processing system to which the data processing method of the present application is applied will be introduced. Figure 2

[0062] Figure 2 ​A system architecture diagram of a data processing system provided in an embodiment of the present application is shown in Figure 2 The data processing system can include a control system device, a gateway device and a processing system device. The control system device is connected with the processing system device through the gateway device.

[0063] A control system runs in the control system device. The control system device can be a computer or a server, or a cluster composed of multiple computers and / or servers. Optionally, the control system can be an Internet of Things platform system, a Distributed Control System (DCS), etc. An artificial intelligence module is integrated in the control system device. The artificial intelligence module integrates a client configured according to a target communication protocol. The client can be the client in the embodiment shown in Figure 1 The artificial intelligence module can call an artificial intelligence model to complete a data processing task.

[0064] The gateway device is used to implement data conversion between the control system and the processing system.

[0065] The processing system device can be a computer or a server, or a cluster composed of multiple computers and / or servers. Figure 2 Three processing system devices are exemplarily shown in Figure 2 The number of processing system devices is only an example, which does not constitute a limitation to the present application. A processing system runs in the processing system device. One processing system can run in one or more processing system devices, and one processing system device can run one or more processing systems. The processing system can be a data acquisition and monitoring system, a production process management system, a data statistical analysis system, etc. The server can be the server in the embodiment shown in Figure 1 The server can be integrated in the gateway device or the processing system device. Each operator accesses the server through registration.

[0066] The operator can be deployed in the processing system or in the cloud device. For example, the operator involving sensitive data is deployed in the processing system to ensure data security. For example, the operator consuming a large amount of transmission resources is deployed in the processing system, for example, the operator requiring GPU resources can be deployed in the processing system, thereby saving a large amount of network transmission resources compared with deploying the operator in the cloud device. For example, the operator requiring complex computing power is deployed in the cloud device, which can enable the operator to execute the corresponding task more quickly, and in the case that the operator requiring complex computing power cannot be deployed in the processing system, the operator can be deployed in the cloud device to provide more capabilities for the artificial intelligence model.

[0067] Further, the operator deployed in the cloud device can communicate with the edge server deployed in the processing system through the Secure Socket Layer (SSL) protocol, thereby ensuring data security.

[0068] In an optional embodiment, the artificial intelligence model can be deployed in the control system device or in the cloud server. In this case, the execution process of the above data processing method can be: the artificial intelligence module in the control system receives a data processing task, wherein the data processing task can be input by a user, can be set in the control system to be completed at a preset time, or can be obtained in other forms. The artificial intelligence module calls the client to obtain the registration information of at least one operator registered according to the target communication protocol from the server. The artificial intelligence module selects at least one target operator satisfying the task requirement from the at least one operator according to the registration information of the at least one operator in response to the data processing task, sends a corresponding task request to the at least one target operator through the client and the server according to the target communication protocol. After the target operator receives the task request, the target operator executes a corresponding task operation to obtain an operation result, and sends the operation result to the artificial intelligence module through the server and the client according to the target communication protocol. The artificial intelligence module combines the respective operation results of the at least one target operator to execute the data processing task. After executing the data processing task, the artificial intelligence module outputs the task result of the data processing task.

[0069] The data processing system and the data processing method provided by the embodiments of the present application can be applied in various industrial scenarios. The following exemplary introduces the industrial scenarios that can be applied. In the intelligent manufacturing scenario, the production process can be optimized, the equipment utilization rate and product quality can be improved by integrating the artificial intelligence model and the operator. Predictive maintenance based on real-time data analysis is realized, and equipment failure and downtime are reduced. In the energy management scenario in the fields of electric power, oil, natural gas and the like, the artificial intelligence model can call the operator to analyze multi-source heterogeneous data and optimize energy scheduling and consumption. The running state of the key equipment is monitored to discover potential risks in time and take measures. In the logistics and supply chain scenario, the warehouse automation, path optimization and inventory management are upgraded by combining the operator and the artificial intelligence model. Precise demand prediction and resource allocation suggestions are provided to improve the efficiency of the supply chain. In the quality detection scenario, the image recognition model is combined with the operator to realize automatic quality detection of products, improve the detection accuracy and efficiency, and support defect identification and classification in complex scenarios to reduce the cost of manual intervention.

[0070] The execution process of the data processing method provided by the embodiments of the present application is described in detail below with reference to the accompanying drawings. Optionally, the data processing method can be applied in the data processing system shown in the above Figure 2 The control system in the data processing method can be the control system in the data processing system shown in the above Figure 2 The artificial intelligence module in the data processing method can be the artificial intelligence module in the data processing system shown in the above Figure 2 The client in the data processing method can be the client in the data processing system shown in the above Figure 2 The processing system in the data processing method can be the processing system in the data processing system shown in the above Figure 2 The server in the data processing method can be the server in the data processing system shown in the above Figure 2 The operator and the target operator in the data processing method can be the operator in the data processing system shown in the above Figure 3 The operator in the data processing method can be the operator in the data processing system shown in the above.

[0071] Figure 3 The interaction schematic diagram of the data processing method provided by the embodiments of the present application is shown in the above Figure 3 The method comprises the following steps:

[0072] 301. The artificial intelligence module calls the client to obtain the registration information of at least one operator from the server, and the registration information is obtained by registration according to the target communication protocol.

[0073] 302. The artificial intelligence module selects at least one target operator that meets the task requirements of the data processing task from at least one operator according to the registration information of at least one operator in response to the data processing task.

[0074] 303. The artificial intelligence module sends a corresponding task request to at least one target operator according to the target communication protocol; the task request is used to trigger the target operator to perform a corresponding task operation to obtain an operation result.

[0075] 304. The target operator performs a task operation corresponding to the task request and obtains an operation result.

[0076] 305. The target operator sends the operation result to the artificial intelligence module according to the target communication protocol.

[0077] 306. The artificial intelligence module combines the respective operation results of the at least one target operator to perform a data processing task.

[0078] In this embodiment, if an operator is needed to assist the artificial intelligence model to complete the corresponding function, the operator can be registered based on the target communication protocol. Generally, based on the target communication protocol, the server can register the operator according to the registration information of the operator, and the registration information of the operator is information formed based on the content and format of the registration information specified in the target communication protocol. Optionally, the server can form an operator list based on the registration information of the registered operator to more clearly know the currently registered operator, that is, the operator that has been accessed.

[0079] The registration information of the operator refers to the description information of the operator that needs to be provided by the operator when registering according to the target communication protocol. For example, according to the requirements of the target communication protocol, the registration information can include but is not limited to at least one of function description information, input format, output format, and supported operation type. For example, for a material layer thickness running state detection operator, its function description information can be: the operator can query various state detection index information, such as: "material layer thickness state amplitude index, furnace pressure state amplitude index, CO (carbon monoxide) content state amplitude index".

[0080] A possible implementation manner of the registration of the operator can be that the server receives the input registration information of the operator to complete the registration of the operator. Generally, the user can input the registration information of the corresponding operator in the server, and the server registers the operator based on the registration information. Another possible implementation manner of the registration of the operator can be that the to-be-accessed operator sends a registration request to the server, and the registration request contains the registration information of the to-be-accessed operator, and the registration request is used to instruct the server to complete the registration of the to-be-accessed operator based on the registration information. The operator is usually integrated in a processing system device, and the registration request can be sent to the server through the processing system device. After receiving the registration request, the server completes the registration of the operator according to the registration information.

[0081] Optionally, the operator needs to be authorized by a user with corresponding permissions to complete the registration. Thus, misoperation is avoided, and the security of the access operator is ensured.

[0082] In actual application, the artificial intelligence module can acquire a data processing task and call the artificial intelligence model to complete the data processing task. The artificial intelligence module can acquire the data processing task in the following ways: receiving the data processing task in the human-computer interaction interface of the artificial intelligence module; receiving the data processing task from a device connected to the control system, or setting a data processing task completed at a fixed time. The artificial intelligence module can integrate the human-computer interaction interface, which can be used to receive a data processing task input by a user and complete the data processing task by the artificial intelligence module, and display the task result of the data processing task after the artificial intelligence module completes the data processing task.

[0083] After the artificial intelligence module acquires the data processing task, the artificial intelligence model is called to process the data processing task. The process of the data processing task is described below. The process of the artificial intelligence module calling the artificial intelligence model is simply referred to as the process of the artificial intelligence module.

[0084] The artificial intelligence module selects at least one target operator that meets the task requirements of the data processing task from at least one operator according to the registration information of the at least one operator in response to the data processing task. The task requirements refer to the requirement information indicated by the data processing task, such as the data range required to execute the data processing task, the format of the task result, the content requirement of the task result, or other information related to the data processing task.

[0085] Optionally, the registration information of the at least one operator can be acquired from the server by calling the client after the artificial intelligence module receives the data processing task; or the server can send the registration information of the operator to the client after the operator is registered each time, and the client can save the registration information of the operator. The artificial intelligence module can directly call the client to acquire the registration information of the operator when needed.

[0086] The selected at least one target operator can be one target operator or multiple target operators. Figure 4An exemplary target operator is shown in the figure, which does not constitute a limitation on the number of target operators. In addition, the artificial intelligence module may not be able to determine all target operators at one time when processing the data processing task, and the target operators can also be determined at different times. If all target operators are determined at one time, the corresponding task request is sent to the target operator. If the target operators are determined at different times, the corresponding task request can be sent to the target operator after the target operator is determined. Regardless of how the target operator is determined, the target operator can be interacted with in the manner described in the embodiment. The process of interaction between the artificial intelligence module and the operator is described in detail below.

[0087] Then, the artificial intelligence module sends a task request to at least one target operator according to the target communication protocol, which is used to trigger the target operator to execute the corresponding task operation and obtain the operation result. According to the target communication protocol, the artificial intelligence module needs to interact with the operator, which needs to call the client and then interact through the server.

[0088] In an optional embodiment, the task request can include a task parameter. The task parameter is used to indicate the task operation executed by the target operator and the operation result that needs to be obtained.

[0089] It can be understood that different target operators can have different functions, and the artificial intelligence module may need one target operator or multiple target operators to assist in obtaining the corresponding operation result during the process of processing the data processing task. When multiple target operators are needed to assist in obtaining the corresponding operation result, the corresponding task request needs to be sent to multiple target operators respectively. For example, two target operators are determined, which are target operator A and target operator B, target operator A is needed to assist in completing task operation C, and target operator B is needed to assist in completing task operation D, then the task request for indicating the completion of task operation C is sent to target operator A, and the task request for indicating the completion of task operation D is sent to target operator B.

[0090] In an optional embodiment, if the artificial intelligence module determines that multiple target operators are needed to assist in obtaining the corresponding operation result, and the multiple target operators execute the task request in a sequence, the priority information can be carried in the sent task request. The priority information is used to indicate the order of the target operator executing the task request.

[0091] The target operator, upon receiving the task request, completes a task operation corresponding to the task request according to an indication of the task request, and obtains an operation result. The task operation can be an operation that the target operator can perform, for example, the task operation can be an operation of obtaining sampling data, predicting a data trend, or other operations. The operation result is a result that can be obtained by performing the task operation, that is, information that the task request indicates needs to be returned to the artificial intelligence module by the target operator.

[0092] The target operator sends the operation result to the artificial intelligence module according to a target communication protocol. According to the target communication protocol, the operator needs to send data through the server and the client when sending data to the artificial intelligence module. Here, the target operator sends the operation result to the server, the server sends the operation result to the client, and the artificial intelligence module receives the operation result.

[0093] The artificial intelligence module combines the received operation result to perform a data processing task and obtains a data processing result.

[0094] In an optional embodiment, after the artificial intelligence module obtains the data processing result, the artificial intelligence module can output the data processing result.

[0095] For example, in a certain manufacturing enterprise, a user wants to complete the task of optimizing the predictive maintenance process of the equipment of the production line through an artificial intelligence model. The operator in the existing equipment monitoring system can be registered in a registered manner on the server. The operator in the equipment monitoring system can obtain real-time monitoring data of the production line equipment provided by an industrial data acquisition and control terminal and realize functions such as vibration signal analysis. The operator in the equipment monitoring system is referred to as an industrial operator hereinafter. The user inputs “please optimize the predictive maintenance process of the equipment of the production line” in the human-computer interaction interface of the artificial intelligence module. The artificial intelligence module obtains the list of currently registered industrial operators through the MCP protocol, selects industrial operator E from the list of industrial operators, and sends a task request to industrial operator E through the MCP protocol, the task request indicating that industrial operator E provides an optimized scheme for the predictive maintenance process of the production line equipment. Industrial operator E receives the task request, performs a fault prediction operation through the obtained real-time monitoring data of the production line equipment, obtains optimized control parameters, and feeds back the optimized control parameters to the artificial intelligence module through the MCP protocol. The artificial intelligence module receives the optimized control parameters and continues to execute the task according to the optimized control parameters. Subsequently, the artificial intelligence module needs to assist industrial operators to complete corresponding operations in the process of executing the task, therefore, industrial operators F and G are selected from the list of industrial operators, and task requests are sent to industrial operators F and G through the MCP protocol, respectively, the task requests indicating that industrial operators F and G perform equipment predictive maintenance, respectively. The above process enables the industrial operators to respond quickly.

[0096] In summary, through the scheme provided by the embodiments of the present application, in the industrial field, the control system is connected with the processing system through the gateway. The client is deployed in the artificial intelligence module of the control system based on the target communication protocol, and the server is deployed in the processing system or the gateway. The operator can establish a connection with the server through registration, so as to access the artificial intelligence module. The artificial intelligence module can call the client to obtain the registration information of at least one operator currently accessed from the server. In the process of executing the data processing task, if the operator is needed to assist in completing the data processing task, at least one target operator is selected from the at least one operator according to the obtained registration information of the at least one operator, and a task request for indicating a corresponding task operation is sent to the target operator through the client and the server according to the target communication protocol. After the target operator performs the corresponding task operation, an operation result is obtained, and the operation result is sent to the server according to the target communication protocol. The server sends the operation result to the client. The artificial intelligence module combines the respective operation results of the at least one target operator to execute the data processing task. Thus, various operators, including operators in a heterogeneous processing system, can quickly and conveniently access an artificial intelligence model through an interface of a standardized target communication protocol, and the operators can be plugged and played and distributedly deployed through the standardized interface, so as to effectively integrate multi-source heterogeneous data, quickly and conveniently access the multi-source heterogeneous data to the artificial intelligence model, avoid data island effect, and reduce the complexity of integration of heterogeneous systems. The cross-platform integration of the operators reduces the system transformation cost. When the artificial intelligence module executes the data processing task, the task can be decomposed into multiple subtasks, the target communication protocol can be used to quickly interact with the operators, and the subtasks can be completed with the assistance of the operators, so as to improve the task execution efficiency, simplify the development process, and enable the artificial intelligence model to quickly and accurately complete the data processing task, thereby improving the data processing efficiency and improving the execution capability for complex data processing tasks.

[0097] In an optional embodiment, when the artificial intelligence module issues a task request to the target operator, the task request can be converted into an input format that can be received by the target operator, and sent to the target operator through the server.

[0098] Referring to Figure 4 , Figure 4 An interaction schematic diagram of another data processing method provided by the embodiments of the present application is shown in FIG. 6. As shown in FIG. 6, the method includes the following steps: Figure 5

[0099] 401、The artificial intelligence module calls the client to obtain the registration information of at least one operator from the server. The registration information is obtained by registration according to the target communication protocol.

[0100] ​402、The artificial intelligence module selects at least one target operator meeting the task requirements from the at least one operator according to the registration information of the at least one operator in response to the data processing task.

[0101] 403、The artificial intelligence module converts the task request corresponding to each of the at least one target operator into a format converted task request according to the registration information of the at least one target operator in the target communication protocol, and the input format is contained in the registration information.

[0102] 404、The artificial intelligence module calls the client to send the corresponding format converted task request to the server; the format converted task request is used to trigger the target operator to perform the corresponding task operation and obtain the operation result.

[0103] 405、The server sends the corresponding format converted task request to the at least one target operator.

[0104] 406、The target operator performs the task operation corresponding to the received format converted task request and obtains the operation result.

[0105] 407、The target operator sends the operation result to the artificial intelligence module according to the target communication protocol.

[0106] 408、The artificial intelligence module combines the operation results of the at least one target operator to execute the data processing task.

[0107] It should be noted that the execution processes of steps 401, 402, 406 and 407 are similar to those of the above embodiments, which will not be described here.

[0108] In this embodiment, the registration information of the operator can contain the input format of the operator, which refers to the format of the input task request that the operator can recognize and execute.

[0109] After the artificial intelligence module determines the at least one target operator, it converts the task request into the input format of the at least one target operator according to the input format contained in the registration information of the at least one target operator in the target communication protocol, and obtains the format converted task request. The artificial intelligence module calls the client to send the format converted task request corresponding to the at least one target operator to the server. The server sends the format converted task request to the corresponding target operator respectively.

[0110] By converting the task request into the input format that the operator can recognize and execute based on the target communication protocol when the artificial intelligence module sends the task request to the operator, the operator can accurately and quickly execute the task assigned by the artificial intelligence module, so as to quickly realize the interaction between the artificial intelligence module and the operator through the target communication protocol.

[0111] In an optional embodiment, the artificial intelligence module can convert the received operation result into context information that can be received by the artificial intelligence model, and send it to the artificial intelligence model for processing.

[0112] Please refer to Figure 5 , Figure 5 The interaction schematic diagram of another data processing method provided by the embodiment of the application is shown in FIG. 5. Figure 6 The method includes the following steps:

[0113] 501. The artificial intelligence module calls the client to obtain the registration information of at least one operator from the server. The registration information is obtained by registration according to the target communication protocol.

[0114] 502. The artificial intelligence module selects at least one target operator that meets the task requirements from the at least one operator according to the registration information of the at least one operator in response to the data processing task.

[0115] 503. The artificial intelligence module calls the client to send a task request to the server according to the target communication protocol. The task request is used to trigger the target operator to perform the corresponding task operation and obtain the operation result.

[0116] 504. The server sends the corresponding task request to the at least one target operator.

[0117] 505. The target operator performs the task operation corresponding to the task request and obtains the operation result.

[0118] 506. The target operator sends the operation result to the artificial intelligence module through the server.

[0119] 507. The artificial intelligence module converts the operation result into model context information.

[0120] 508. The artificial intelligence module executes the data processing task in combination with the respective model context information of the at least one target operator.

[0121] It should be noted that the execution process of steps 501-506 is similar to the above-mentioned embodiment, which will not be described here.

[0122] In this embodiment, the target operator sends the operation result to the server after obtaining the operation result. The server sends the operation result to the artificial intelligence module through the client.

[0123] The artificial intelligence module converts the operation result into model context information that can be understood by the artificial intelligence model, and sends the model context information to the artificial intelligence model. The artificial intelligence module obtains the model context information by calling the client to execute the data processing task.

[0124] Based on the target communication protocol, after the operator sends the operation result to the artificial intelligence module, the artificial intelligence module performs format conversion on the operation result, converts it into model context information that the artificial intelligence model can understand, and sends it to the artificial intelligence module to execute the data processing task, so as to quickly realize the interaction between the operator and the artificial intelligence module through the target communication protocol.

[0125] In an industrial scenario, when a given data processing task is solved by an artificial intelligence model, the artificial intelligence model cannot well understand various given data processing tasks, so that the obtained result is not satisfactory. Therefore, when the artificial intelligence model executes the data processing task, the task operation process corresponding to the data processing task can be obtained, and the data processing task is executed according to the task operation process.

[0126] In one possible implementation manner of obtaining the task operation process, the task operation process can be obtained at the same time when the data processing task is obtained. The following will be described in detail with reference to the embodiment shown in Figure 6 .

[0127] Please refer to Figure 6 , Figure 6 the interaction schematic diagram of another data processing method provided by the embodiment of the present application. As shown in Figure 7 , the method comprises the following steps:

[0128] 601, the artificial intelligence module calls the client to obtain the registration information of at least one operator from the server; the registration information is obtained by registration according to the target communication protocol.

[0129] 602, the artificial intelligence module obtains a data processing task and obtains operation flow information corresponding to the data processing task.

[0130] 603, the artificial intelligence module selects at least one target operator meeting the task requirement from at least one operator according to the operation flow information in response to the data processing task.

[0131] 604, the artificial intelligence module calls the client to send a task request to the server according to the target communication protocol; the task request is used to trigger the target operator to execute corresponding task operation and obtain operation result.

[0132] 605, the server sends the corresponding task request to at least one target operator.

[0133] 606, the target operator executes the task operation corresponding to the task request to obtain the operation result.

[0134] 607, the target operator sends the operation result to the artificial intelligence module according to the target communication protocol.

[0135] 608、The artificial intelligence module combines the operation results of the at least one target operator to perform the data processing task.

[0136] It should be noted that the execution processes of steps 601 and 604-608 are similar to those of the above embodiments, and will not be described here.

[0137] In this embodiment, the operation flow information is used to indicate the flow of performing the data processing task. For example, the operation flow information can be the content of the operation steps listed in the order of execution. The artificial intelligence module obtains the data processing task and obtains the operation flow information corresponding to the data processing task. For example, the user inputs the data processing task in the human-computer interaction interface and simultaneously inputs the operation flow information corresponding to the data processing task, and the artificial intelligence module executes the data processing task according to the operation flow information. For another example, other devices send the data processing task to the artificial intelligence module while sending the operation flow information corresponding to the data processing task.

[0138] The artificial intelligence module executes the data processing task according to the flow indicated by the operation flow information.

[0139] The method provided in this embodiment can better enable the artificial intelligence model to understand the data processing task in the industrial field by simultaneously issuing the corresponding operation flow information to the artificial intelligence model when issuing the data processing task to the artificial intelligence model, so as to save the computing power of the artificial intelligence model for processing the data processing task, improve the efficiency of data processing, and make the obtained data processing result more suitable for the industrial scene, thereby improving the accuracy and usability of the data processing result.

[0140] In another possible implementation manner of obtaining the task operation flow, the task operation flow can also be obtained through one or more operators, and the operator is connected to the artificial intelligence module. The following will be described in detail with reference to the Figure 7 embodiment shown in the accompanying drawings.

[0141] Please refer to Figure 7 , Figure 7 The interaction schematic diagram of another data processing method provided in this embodiment is shown in the accompanying drawings. Figure 8 The method includes the following steps:

[0142] 701、The artificial intelligence module calls the client to obtain the registration information of at least one operator from the server; the registration information is obtained by registration according to a target communication protocol.

[0143] 702、The artificial intelligence module selects at least one target operator from the at least one operator according to the registration information of the at least one operator in response to the data processing task; the target operator is used to provide the operation flow information of the data processing task.

[0144] 703. The artificial intelligence module invokes the client according to the target communication protocol, and sends a task request to the server, the task request being used to trigger the target operator to return operation flow information of the data processing task.

[0145] 704. The server sends a corresponding task request to at least one target operator.

[0146] 705. The target operator executes a task operation corresponding to the task request, and obtains an operation result; the operation result being used to indicate the operation flow information of the data processing task.

[0147] 706. The device where the target operator is located sends the operation result to the artificial intelligence module according to the target communication protocol.

[0148] 707. The artificial intelligence module executes the data processing task in combination with respective operation results of the at least one target operator.

[0149] In the embodiment, in the industrial field, the control system is connected with the processing system through the gateway. The client is deployed in the artificial intelligence module of the control system based on the target communication protocol, and the server is deployed in the processing system or the gateway. The operator can establish a connection with the server through registration, so as to access the artificial intelligence module. The operator can include an operator for providing operation flow information.

[0150] In the process that the artificial intelligence module invokes the artificial intelligence model to process the data processing task, if the artificial intelligence model needs to obtain operation flow information corresponding to the data processing task, the client can be invoked, and registration information of at least one operator currently accessed from the server can be obtained. The artificial intelligence model selects at least one target operator for providing operation flow information from the at least one operator according to the obtained registration information of the at least one operator, and sends a task request for indicating the operation flow information of the data processing task to the selected target operator through the client and the server according to the target communication protocol.

[0151] The target operator obtains an operation result for indicating the operation flow information of the data processing task, and sends the operation result to the server according to the target communication protocol. For example, the target operator can be a vector database, the operation flow information is stored as a vector in the vector database, and the target operator obtains corresponding operation flow information from the vector database according to the data processing task as the operation result.

[0152] The server sends the operation result to the client. The artificial intelligence module executes the data processing task in combination with respective operation results, i.e., operation flow information, of the at least one target operator.

[0153] In an optional embodiment, the number of operation results returned by the at least one target operator can be one or more, that is, the operation flow information can be one or more. For example, the number of target operators can be one, and the target operator returns one or more operation flow information for the task request; the number of target operators can be multiple, and each target operator returns one or more operation flow information for the respective received task request. Then the artificial intelligence model can combine one or more operation flow information to execute the data processing task. For example, if the artificial intelligence model receives multiple operation flow information, it can select a target operation flow information from the multiple operation flow information, and execute the data processing task according to the target operation flow information.

[0154] In this embodiment, the artificial intelligence module obtains the operation flow information corresponding to the data processing task from the operator during processing the data processing task, and the artificial intelligence model executes the data processing task by referring to the operation flow information, so that the artificial intelligence model can better understand the data processing task in the industrial field, save the computing power of the artificial intelligence model for processing the data processing task, improve the efficiency of data processing, and make the obtained data processing result more suitable for the industrial scene, improve the accuracy and usability of the data processing result.

[0155] In an optional embodiment, the artificial intelligence module determines the function that needs to be assisted by the operator to implement at present, and multiple operators can implement the function, then the artificial intelligence module can determine at least one operator as a target operator from the multiple operators that have the ability to implement the function, that is, determine to assist to implement the function through the target operator.

[0156] In an optional embodiment, the number of artificial intelligence models that can be called by the artificial intelligence module can be one or more. If the number of artificial intelligence models is one, the artificial intelligence model can be directly called to execute the data processing task. If the number of artificial intelligence models is multiple, after obtaining the data processing task, an artificial intelligence model can be selected from the multiple artificial intelligence models to execute the data processing task.

[0157] In one possible implementation of selecting an artificial intelligence model, the identifier of the target artificial intelligence model corresponding to the data processing task can be acquired when the data processing task is acquired. The identifier of the artificial intelligence model is used to indicate that the data processing task is executed by the target artificial intelligence model corresponding to the identifier. For example, when the user inputs the data processing task in the human-computer interaction interface, the target artificial intelligence model is selected from the artificial intelligence models that can be called, so that the current input data processing task is executed by the target artificial intelligence model. For another example, the default artificial intelligence model is set in the artificial intelligence module, and when the user inputs the data processing task in the human-computer interaction interface, the artificial intelligence model is not selected, and the artificial intelligence module calls the default artificial intelligence model to execute the data processing task.

[0158] In another possible implementation of selecting an artificial intelligence model, after the artificial intelligence module acquires the data processing task, the target artificial intelligence model corresponding to the data processing task can be determined from the multiple artificial intelligence models based on the data processing task, so that the data processing task is executed by the target artificial intelligence model.

[0159] Further, since the artificial intelligence model usually needs large computing power support, the local control system, such as the edge device in the control system, has limited computing resources, and it is difficult to deploy a complex artificial intelligence model. The locally deployed artificial intelligence model can be difficult to support complex data processing tasks. The edge device is widely distributed in the industrial system, and deploying the artificial intelligence model in the edge device requires a large amount of manpower and material resources for upgrading and maintenance. In addition, if the artificial intelligence model deployed in the cloud device is used, the stability of the network needs to be relied on when using the artificial intelligence model, and some local sensitive data can be involved in the industrial scene. In the transmission process of sending to the cloud device and the process of processing by the cloud device, data leakage is easy to occur, which brings data security risks. For long-term use of the artificial intelligence model deployed in the cloud device, the cost will also increase.

[0160] Due to the above factors, the artificial intelligence model can be deployed in the cloud device and / or the control system. For example, when the data processing task involves sensitive data, the artificial intelligence model deployed in the control system is called to ensure the security of the local sensitive data. For example, when a complex data processing task is involved, the artificial intelligence model deployed in the cloud device is called to use the high capability of the artificial intelligence model deployed in the cloud device to complete the complex data processing task.

[0161] By selecting different artificial intelligence models, dynamic switching can be performed for the artificial intelligence models to adapt to the execution of different data processing tasks. In the case where a person artificial intelligence model is not applicable, there can be multiple other choices, which reduces the dependence on a certain artificial intelligence model and reduces the risk of technical lock-in.

[0162] In an optional embodiment, in actual application, by accessing various operators through the target communication protocol, the professional knowledge in the industrial field, such as process flow, equipment characteristics, and industry standards, can be embedded into the artificial intelligence model to enhance the understanding and reasoning ability of the model for specific industrial scenarios. The artificial intelligence module can learn from the running data of the operator by executing the data processing task and dynamically optimize its decision-making strategy to form a knowledge updating system, enhance the robustness of the system, and improve the accuracy.

[0163] In an optional embodiment, a log can be stored in the control system, which records the interaction information between the artificial intelligence module and the operator during the completion of the data processing task. Thus, the whole process of task execution can be traced back to facilitate problem troubleshooting and performance optimization.

[0164] The following will be described in combination with Figure 8 The principle of a data processing system provided by the embodiments of the present application is exemplarily introduced below taking the target communication protocol MCP protocol as an example.

[0165] Figure 8 The principle diagram of a data processing system provided by the embodiments of the present application is as shown in FIG. 1. Figure 9As shown, the data processing system provided by the embodiment includes a control system 801, an artificial intelligence module 802, an MCP server 803, a first artificial intelligence model 804, a second artificial intelligence model 805, a first operator resource 806, and a second operator resource 807. The control system 801 corresponds to the control system in the above embodiment. The artificial intelligence module 802 can be integrated in the control system 801, and the operator resource 806 can be accessed through HTTP or other protocols in the control system 801. The artificial intelligence module 802 corresponds to the artificial intelligence module in the above embodiment. The MCP client is integrated in the artificial intelligence module 802, and the MCP client corresponds to the client in the above embodiment. The MCP server 803 is integrated into the artificial intelligence module 802. The MCP server 803 corresponds to the server in the above embodiment. The artificial intelligence module 802 can call the first artificial intelligence model 804 deployed in the public cloud or the second artificial intelligence model 805 deployed in the industrial site environment, thereby freely realizing model switching. The MCP server 803 can connect the second operator resource 807 deployed in the public cloud through the SSL protocol and the post office protocol (POP), thereby ensuring the security of data communication. The first operator resource 806 deployed in the industrial site environment can also be connected. The first operator resource 806 and the second operator resource 807 can each include an operator constituted by an industrial internet model and an operator constituted by a data set, etc. Thus, through the above deployment, the service opening reduces the threshold of operator integration, realizes the integration of operators and artificial intelligence models through the MCP protocol, and the artificial intelligence module can call the operator to quickly perform data processing.

[0166] The MCP protocol has built-in data encryption and access control mechanisms to ensure the security of industrial data during transmission and processing, and meets the compliance requirements of the industrial field.

[0167] Based on the above data processing system, the integration process of industrial operators and artificial intelligence models according to the MCP protocol can include the following processes:

[0168] Step 1, system initialization.

[0169] The system initialization includes operator registration and model configuration. The operator registration refers to that each operator registers with the server of the MCP protocol when starting, and provides its function description information (such as input and output format, supported operation type, etc.). The model configuration refers to that the artificial intelligence model obtains the list of available operators through the MCP protocol, and selects appropriate operators according to the data processing task.

[0170] Step 2, distribution and execution of data processing tasks.

[0171] When the artificial intelligence model processes a data processing task, it sends a task request to the operator through the MCP protocol, and the task request contains task parameters and priority information. After receiving the task request, the operator performs calculation according to its internal logic to obtain an operation result.

[0172] Step 3, result feedback and optimization

[0173] After the operator completes the task, the operation result is returned to the artificial intelligence model through the MCP protocol.

[0174] Figure 9 A structural schematic diagram of an electronic device provided by an embodiment of the present application is shown in FIG. 1. Figure 9 As shown in the figure, the electronic device includes a memory 21 and a processor 22.

[0175] The memory 21 is used to store computer programs and can be configured to store various other data to support operations on the electronic device. Examples of these data include instructions for any application or method operating on the electronic device, data structures, contact data, phonebook data, messages, pictures, videos, etc.

[0176] The processor 22 is coupled to the memory 21 and is used to execute the computer programs in the memory 21 to implement the data processing method provided by the foregoing embodiments.

[0177] Further, as shown in FIG. 1, the electronic device further includes a communication component 23, a display 24, a power supply component 25, an audio component 26, and other components. Figure 9 Only part of the components are shown in the figure, and it does not mean that the electronic device only includes the components shown in the figure. The electronic device of the present embodiment can be implemented as a terminal device such as a desktop computer, a notebook computer, a smart phone, or an IOT device, or as a server device such as a conventional server, a cloud server, or a server array. Figure 9 ​

[0178] ​​The above-described memory can be implemented by any type of volatile or nonvolatile memory devices or a combination thereof, such as static random-access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0179] The above-described communication component is configured to facilitate communication between the device in which the communication component is located and other devices in a wired or wireless manner. The device in which the communication component is located can access a wireless network based on a communication standard, such as a 2G, 3G, 4G / LTE, 5G, or the like mobile communication network, or a combination thereof. In an example embodiment, the communication component receives a broadcast signal or broadcast-related information from an external broadcast managing system via a broadcast channel.

[0180] The above-described display includes a screen, which can include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense a touch, a slide, and a gesture on the touch panel. The touch sensor can not only sense a boundary of a touching or a sliding action, but also detect a duration and a pressure related to a touch or a slide operation.

[0181] The above-described power component provides power to various components of the device in which the power component is located. The power component can include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to the device in which the power component is located.

[0182] The above-described audio component can be configured to output and / or input audio signals. For example, the audio component includes a microphone (MIC) configured to receive external audio signals when the device in which the audio component is located is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals can be further stored in the memory or transmitted via the communication component. In some embodiments, the audio component also includes a speaker for outputting audio signals.

[0183] Accordingly, the embodiments of the present application also provide a computer readable storage medium storing a computer program, which, when executed by a processor, causes the processor to enable each step in the above-mentioned method embodiments. The computer readable storage medium includes volatile or non-volatile or a combination thereof, and can be removable or non-removable. Examples of the computer readable storage medium include, but are not limited to, phase-change random access memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital video disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium.

[0184] Accordingly, the embodiments of the present application also provide a computer program product, which includes a computer program or instructions, when executed by a processor, causes the processor to enable each step in the above-mentioned method embodiments. It should be understood that each of the above-mentioned method processes or a combination of multiple processes can be implemented by the computer program or instructions. In addition, these computer programs or instructions can be applied to the processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing devices, so that the processor of the general-purpose computer, the special-purpose computer, the embedded processor or other programmable data processing devices can be implemented as a device for implementing the corresponding functions in the above-mentioned method embodiments.

[0185] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the above-mentioned embodiments of the present application have been described in detail, those skilled in the art should understand that: it can still modify the technical solutions recorded in the above-mentioned embodiments, or make equivalent replacement for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A data processing method, characterized by, An artificial intelligence module applied to a control system, wherein a client configured according to a target communication protocol is integrated in the artificial intelligence module; the client establishes a connection with a server configured according to the target communication protocol, and the target communication protocol is used to establish a communication channel between an artificial intelligence model and an operator; The method comprises: calling the client to obtain registration information of at least one operator from the server; the registration information is obtained by registration according to the target communication protocol; in response to a data processing task, calling the artificial intelligence model to enable the artificial intelligence model to perform the following steps: selecting at least one target operator that meets the task requirements of the data processing task from the at least one operator according to the registration information of the at least one operator; sending a corresponding task request to the at least one target operator according to the target communication protocol; the task request is used to trigger the target operator to perform a corresponding task operation and obtain an operation result; obtaining the operation result returned by each of the at least one target operator according to the target communication protocol; combining the operation result of each of the at least one target operator to execute the data processing task.

2. The method of claim 1, wherein, The method further comprises: format-converting the task request corresponding to each of the at least one target operator according to the registration information of the at least one target operator to obtain a format-converted task request; the registration information includes an input format; calling the client to send the format-converted task request to the server to enable the server to send the format-converted task request to the at least one target operator.

3. The method according to claim 1 or 2, characterized in that, The method further comprises: converting the operation result returned by each of the at least one target operator into model context information.

4. The method according to claim 1 or 2, characterized in that, The method further comprises: obtaining the data processing task; determining a target artificial intelligence model corresponding to the data processing task from a plurality of artificial intelligence models; the plurality of artificial intelligence models are respectively deployed in a cloud device and / or a control system; The method further comprises: in response to the data processing task, selecting at least one target operator that meets the task requirements from the at least one operator according to the registration information of the at least one operator through the target artificial intelligence model.

5. The method according to claim 1 or 2, characterized in that, The method further comprises: obtaining the data processing task and obtaining operation flow information corresponding to the data processing task; The method further comprises: in response to the data processing task, selecting at least one target operator that meets the task requirements from the at least one operator according to the registration information of the at least one operator according to the operation flow information. The method further comprises: in response to the data processing task, selecting at least one target operator that meets the task requirements from the at least one operator according to the registration information of the at least one operator according to the operation flow information.

6. The method of claim 1 or 2, wherein, The at least one target operator is configured to provide operation flow information of the data processing task; and the operation result comprises the operation flow information of the data processing task.

7. A data processing method, characterized by, The method is applied to a server integrated in a gateway device or a processing system; the gateway device is connected to the processing system; the processing system accesses at least one operator; and the server is connected to a client according to a target communication protocol. The client is an artificial intelligence module integrated in a control system and configured according to the target communication protocol. The target communication protocol is configured to establish a communication channel between an artificial intelligence model and an operator; and the method comprises the following steps: Sending, to the artificial intelligence module, registration information of at least one operator registered according to the target communication protocol; According to the target communication protocol, receiving a task request corresponding to at least one target operator sent by the client, and sending the corresponding task request to the at least one target operator; the at least one target operator is an operator selected by the artificial intelligence model from the at least one operator according to the registration information of the at least one operator in response to a data processing task, so that the artificial intelligence model performs the following steps: selecting an operator satisfying a task requirement of the data processing task from the at least one operator according to the registration information of the at least one operator; the task request is configured to trigger the target operator to perform a corresponding task operation and obtain an operation result; Receiving operation results respectively sent by the at least one target operator according to the target communication protocol, and sending the operation results to the artificial intelligence module, so that the artificial intelligence module combines the operation results to perform the data processing task.

8. The method of claim 7, wherein, The method further comprises the following steps: Receiving a registration request; the registration request comprises registration information of a target operator; According to the registration information, completing registration of the target operator.

9. A data processing method, characterized by, The method is applied to a target operator accessed in a processing system; a server is integrated in the processing system or a gateway device connected to the processing system; and the server is connected to a client according to a target communication protocol. The client is an artificial intelligence module integrated in a control system and configured according to the target communication protocol. The target communication protocol is configured to establish a communication channel between an artificial intelligence model and an operator; and the method comprises the following steps: Receiving a task request sent by the server; the task request is sent by the artificial intelligence model according to the target communication protocol in response to a data processing task; and the target operator is an operator satisfying a task requirement of the data processing task; Performing a task operation corresponding to the task request to obtain an operation result; According to the target communication protocol, sending the operation result to the server, so that the server sends the operation result to the artificial intelligence module, and the artificial intelligence module combines the operation result sent by the target operator to perform the data processing task.

10. The method of claim 9, wherein, The method further comprises the following steps: Sending a registration request to the server; the registration request comprises registration information of a target operator, so that the server completes registration of the target operator based on the registration information.

11. An electronic device, comprising: The method further comprises the following steps: a memory, a processor, a communication interface; wherein the memory has stored thereon executable code that, when executed by the processor, causes the processor to perform the method of any one of claims 1 to 10.

12. A non-transitory machine-readable storage medium, comprising: The non-transitory machine-readable storage medium has stored thereon executable code that, when executed by a processor of an electronic device, causes the processor to perform the method of any one of claims 1 to 10.

13. A computer program product, characterised in that, comprising: a computer program that, when executed by a processor of an electronic device, causes the processor to perform the method of any one of claims 1 to 10.

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