Park comprehensive energy intelligent monitoring method and related equipment

Abstract

The embodiment of the invention relates to the technical field of park energy, and discloses a park comprehensive energy intelligent monitoring method and related equipment.The method comprises the steps that energy related information input by a user on an interface is received, and an energy agent and energy equipment of the type corresponding to the energy agent are determined according to the information; acquiring acquisition data from the access node; inputting the collected data into a first target AI plug-in of the energy agent, and obtaining an energy analysis result output by the first target AI plug-in; inputting the energy related information and the energy analysis result into a second target AI plug-in of the energy agent, and obtaining a language analysis result output by the second target AI plug-in; and monitoring the energy equipment according to an energy analysis result, and outputting a language analysis result as a response of the energy related information to an interface. Through the above mode, the embodiment of the invention can comprehensively and intelligently analyze and monitor the park energy, the energy analysis accuracy is high, and the management and control of the complex park energy network are more efficient and intelligent.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of park energy, and particularly to a method and related devices for intelligent monitoring of integrated park energy. Background Art

[0002] Park energy is the sum of all energies in park places such as various industrial parks, commercial parks, and large communities, and belongs to a concentrated area of energy consumption. There are usually multiple energy systems in a park, such as electricity, heat, gas, etc., and different types of energy equipment are involved, such as generators, transformers, air conditioning systems, lighting equipment, etc. These energy systems and equipment are interrelated and interact with each other, jointly constituting a complex park energy network.

[0003] With the development of automation technology, some automation control systems have been introduced into parks, such as distributed control systems and supervisory control and data acquisition systems, which can collect the operation data of some energy equipment in real time and achieve a certain degree of remote monitoring and control. However, most of these systems only monitor and control the basic operation status of the equipment, lack in-depth analysis of energy data, and cannot comprehensively and intelligently analyze and monitor the complex park energy network. Summary of the Invention

[0004] In view of the above problems, the embodiments of the present invention provide a method and related devices for intelligent monitoring of integrated park energy to solve the problems existing in the prior art.

[0005] According to one aspect of the embodiments of the present invention, a method for intelligent monitoring of integrated park energy is provided. The method includes: Receiving energy-related information input by a user on an interface, and determining an energy intelligent agent and energy equipment of the corresponding type of the energy intelligent agent according to the energy-related information, where the energy equipment includes multiple different types, and one type of energy equipment corresponds to one energy intelligent agent; Obtaining acquisition data from an access node of the energy equipment of the corresponding type; Inputting the acquisition data into a first target AI plug-in of the energy intelligent agent to obtain an energy analysis result output by the first target AI plug-in; Inputting the energy-related information and the energy analysis result into a second target AI plug-in of the energy intelligent agent to obtain a language analysis result output by the second target AI plug-in; Monitoring the energy equipment according to the energy analysis result, and outputting the language analysis result as a response to the energy-related information to the interface.

[0006] In an optional manner, the method further includes: Create energy agents corresponding to various types of energy devices based on a multi-agent mode on a preset platform, and configure key information of the energy agents, where the key information includes at least a prompt word and a core description; Receiving energy-related information input by a user on an interface, and determining an energy agent and an energy device of the type corresponding to the energy agent according to the energy-related information, includes: Receiving energy-related information input by a user on an interface, and determining an energy agent and an energy device of the type corresponding to the energy agent according to the energy-related information and the key information of each energy agent.

[0007] In an alternative way, before inputting the collected data into a first target AI plug-in of the energy agent to obtain an energy analysis result output by the first target AI plug-in, further includes: Create a workflow for the working data of the energy device, and determine a first AI plug-in to be trained of the energy agent according to the workflow; Call a knowledge base pre-bound to the energy agent, and obtain metadata of the energy device from the knowledge base, where the metadata includes at least historical working data of the energy device; Extract feature data from the metadata, and input the feature data into the first AI plug-in to be trained for training, and obtain a first target AI plug-in of the energy agent after training.

[0008] In an alternative way, the metadata further includes description information of the energy device. Before inputting the energy-related information and the energy analysis result into a second target AI plug-in of the energy agent to obtain a language analysis result output by the second target AI plug-in, further includes: Determine a second AI plug-in to be trained of the energy agent, and input the description information and the historical working data into the second AI plug-in to be trained for training, and obtain a second target AI plug-in of the energy agent after training.

[0009] In an alternative way, inputting the collected data into a first target AI plug-in of the energy agent to obtain an energy analysis result output by the first target AI plug-in, includes: Obtain dynamic environment variables, where the dynamic environment variables are extracted from the energy-related information or obtained from a third-party API interface; Input the collected data and the dynamic environment variables into a first target AI plug-in of the energy agent to obtain an energy analysis result output by the first target AI plug-in.

[0010] In an alternative way, the method further includes: Provide equipment failure warning based on the monitored energy equipment and the predetermined warning mechanism; and / or, An energy-saving strategy is generated according to the collected data and the energy analysis result, the energy-saving strategy is sent to the energy device, and an execution result after the energy device adjusts parameters according to the energy-saving strategy is received.

[0011] In an optional manner, the collected data includes data of different modes, and after the collected data is acquired from the access node of the corresponding type of energy equipment, the method further includes: The collected data is input into an industrial protocol conversion layer, and the collected data is converted into data of the same communication protocol based on the industrial protocol conversion layer.

[0012] In an optional manner, before receiving energy-related information input by a user on an interface and determining an energy intelligent entity and an energy device of a type corresponding to the energy intelligent entity according to the energy-related information, the method further includes: Receiving original information input by the user on the interface, and calling the natural language AI plug-in predetermined by the energy intelligent agent to analyze whether the original information is energy-related information; If yes, the step of determining an energy intelligent entity and an energy device of a type corresponding to the energy intelligent entity according to the energy-related information is performed.

[0013] According to another aspect of an embodiment of the present invention, there is provided a park integrated energy intelligent monitoring device, comprising: A determination module, used to receive energy-related information input by a user on the interface, and determine an energy intelligent entity and an energy device of a type corresponding to the energy intelligent entity according to the energy-related information, wherein the energy device includes multiple different types, and one type of energy device corresponds to one energy intelligent entity; An acquisition module, used to acquire collected data from access nodes of energy equipment of the corresponding type; A first input module, used to input the collected data into a first target AI plug-in of the energy intelligent body, and obtain an energy analysis result output by the first target AI plug-in; A second input module, used to input the energy-related information and the energy analysis result into the second target AI plug-in of the energy intelligent body, and obtain the language analysis result output by the second target AI plug-in; A monitoring module is used to monitor the energy equipment according to the energy analysis result, and output the language analysis result as a response to the energy-related information to the interface.

[0014] According to another aspect of the embodiments of the present invention, there is provided a computer device, including: a processor, a memory, a communication interface, and a communication bus, and the processor, the memory, and the communication interface complete communication with each other through the communication bus; the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the method as described above.

[0015] According to another aspect of the embodiments of the present invention, there is provided a computer-readable storage medium, in which at least one executable instruction is stored, and when the executable instruction runs on a computer device, the computer device is caused to execute the method as described above.

[0016] In the embodiments of the present invention, a user inputs energy-related information in an interface, determines an energy agent and energy devices corresponding to the type of the energy agent according to the energy-related information, and after collecting data of the energy devices, inputs the data to a first target AI plug-in of the energy agent to analyze the collected data, and obtains an energy analysis result. The energy analysis result may be the operating efficiency of the energy device, the total carbon emission amount and intensity of the park, the future load demand of the park, the operating strategy of the energy device, etc. The energy device is monitored according to the energy analysis result. In this embodiment, multiple energy agents are integrated, and multiple AI plug-ins are integrated in the energy agent. The multiple energy agents can perform comprehensive, intelligent analysis and monitoring on different types of energy devices in the park. The energy analysis is highly accurate. Combining with a second target AI plug-in, a response corresponding to the energy-related information of the user can be obtained and output to the interface, making the control of the complex park energy network more efficient and intelligent.

[0017] The above description is only an overview of the technical solutions of the embodiments of the present invention. In order to be able to understand the technical means of the embodiments of the present invention more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of the embodiments of the present invention more obvious and understandable, the following specifically gives the specific implementation manners of the present invention. Description of the Drawings

[0018] The drawings are only used to illustrate the embodiments and are not considered to be a limitation of the present invention. And throughout the drawings, the same reference numerals are used to represent the same components. In the drawings: Figure 1 A flowchart showing the process of the comprehensive energy intelligent monitoring method for a park provided by the embodiments of the present invention is shown; Figure 2 A structural diagram showing the structure of the comprehensive energy intelligent monitoring device for a park provided by the embodiments of the present invention is shown; Figure 3 A structural diagram showing the structure of the computer device provided by the embodiments of the present invention is shown. Detailed Embodiments

[0019] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0020] Figure 1 The flowchart of the integrated energy intelligent monitoring method for a park provided by an embodiment of the present invention is shown. The method includes the following steps: Step 101, receive the energy-related information input by the user on the interface, and determine the energy intelligent agent and the energy equipment of the corresponding type of the energy intelligent agent according to the energy-related information. Among them, the energy equipment includes multiple different types, and one type of energy equipment corresponds to one energy intelligent agent; Among them, the energy-related information can be input into the interface in the form of a question, or it can not be in the form of a question. It can be a complete sentence or a combination of multiple words. The energy-related information includes at least one word related to energy or energy equipment. For example, information including words such as photovoltaic, solar energy, generator, or gas is energy-related information. There are multiple types of energy in the park, such as electricity, heat, and gas. Energy equipment includes types such as electrical equipment (such as generators, transformers, and switch cabinets), HVAC equipment (such as radiators, chillers, heat pumps, and ventilators), lighting equipment, and production equipment.

[0021] In this embodiment, the energy intelligent agent is pre-configured. The energy intelligent agent relates to the field of artificial intelligence, can sense environmental changes through sensors or data input, make decisions based on the learned knowledge and algorithms, and affect the environment or achieve goals by executing actions. It is an entity (software, hardware, or system) with autonomy, adaptability, and interaction capabilities. In one embodiment, the method further includes: Create energy intelligent agents corresponding to multiple types of energy equipment respectively based on the multi-agent mode on a preset platform, and configure the key information of the energy intelligent agent. The key information includes at least a prompt word and a core description.

[0022] Among them, the preset platform can be a button platform or other suitable intelligent integration platform. Taking the button platform as an example, energy intelligent bodies corresponding to various types of energy equipment are created based on the multi-agent model, including photovoltaic intelligent bodies, HVAC intelligent bodies, energy storage intelligent bodies and lighting intelligent bodies. The role of the energy intelligent body can be defined as a comprehensive energy assistant for the park, and the core descriptions such as the equipment information, function information, management information and energy consumption information of the corresponding energy equipment are input for each energy intelligent body, and the prompt words of the energy intelligent body are configured to clarify the boundaries of each energy intelligent body. The prompt words are, for example, photovoltaic, energy storage, HVAC and lighting. This embodiment configures energy intelligent bodies corresponding to various types of energy equipment, so that the corresponding types of energy equipment can be specially managed intelligently through multiple energy intelligent bodies. Through the collaboration of multiple energy intelligent bodies, functions such as energy linkage detection, prediction and equipment maintenance can be realized.

[0023] Further, receiving energy-related information input by a user on the interface, and determining an energy intelligent entity and an energy device of a type corresponding to the energy intelligent entity according to the energy-related information, including: The energy-related information input by the user on the interface is received, and the energy intelligent body and the energy equipment of the type corresponding to the energy intelligent body are determined according to the energy-related information and the key information of each energy intelligent body.

[0024] When the user inputs energy-related information, each word segment in the energy-related information can be matched with the key information of each energy intelligent body. The energy intelligent body corresponding to the successfully matched key information is the energy intelligent body determined by this embodiment. The match can be completely identical or associated. For example, if the energy-related information input is "the photovoltaic power generation of a certain park tomorrow", the energy-related information input includes "photovoltaic", which is the same as the prompt word "photovoltaic" of the photovoltaic intelligent body; for another example, if the energy-related information input is "the estimated solar power generation of a certain park tomorrow", the energy-related information input includes "solar energy", which is associated with the prompt word "photovoltaic" of the photovoltaic intelligent body, and the energy intelligent body corresponding to the energy-related information is determined to be the photovoltaic intelligent body. By matching the energy-related information with the key information of each energy intelligent body, this embodiment can accurately locate the corresponding energy intelligent body according to the energy-related information, thereby enabling the energy intelligent body.

[0025] In one embodiment, before receiving energy-related information input by a user on an interface and determining an energy intelligent entity and an energy device of a type corresponding to the energy intelligent entity according to the energy-related information, the method further includes: Receiving original information input by the user on the interface, and calling the natural language AI plug-in predetermined by the energy intelligent agent to analyze whether the original information is energy-related information; If so, perform the step of determining the energy agent and the energy equipment corresponding to the type of the energy agent according to the energy-related information.

[0026] Since the original information input by the user has strong randomness, in this embodiment, the pre-determined natural language AI plug-in of the energy agent is called to analyze the original information and correct the original information, so as to be able to accurately judge whether it is energy-related information. Among them, the natural language AI plug-in can be Doubao or other suitable natural language AI plug-ins. This embodiment can accurately identify the original information input by the user. After the energy-related information is input, the energy agent is enabled and corresponding responses are made. If it is not energy-related information, no processing is performed.

[0027] Step 102, obtain the acquisition data from the access node of the energy equipment of the corresponding type; In this embodiment, each type of energy equipment has a corresponding access node, and the acquisition data of the energy equipment collected through the access node is input and used by the corresponding energy agent. Before using the AI plug-in, it is necessary to load the acquisition data of various energy equipment in the park. There are many types of energy equipment in the park, and different data acquisition methods are adopted for different types of energy equipment. For example, for power equipment, its operation data such as voltage, current, and power can be collected in real time by installing smart meters, sensors, etc.; for HVAC equipment, data such as temperature, humidity, and flow can be collected; for lighting equipment, its switch state, brightness adjustment, etc. can be collected.

[0028] Since different types of energy equipment use the same or different industrial communication protocols, for example, the industrial communication protocols include Modbus, OPC UA, etc., the modalities of the acquisition data transmitted using different industrial communication protocols are different. In one embodiment, in order to be able to be compatible with the acquisition data of various different types of energy equipment in the park, after obtaining the acquisition data from the access node of the energy equipment of the corresponding type, it further includes: Input the acquisition data into the industrial protocol conversion layer, and convert the acquisition data into data of the same communication protocol based on the industrial protocol conversion layer.

[0029] Among them, the industrial protocol conversion layer refers to a functional module in an industrial automation system that is used to implement data conversion between different communication protocols. It can convert the data of one communication protocol into the data of another communication protocol, thereby ensuring seamless connection and data transmission of the collected data between different types of energy devices. This embodiment includes access nodes corresponding to multiple types of energy devices. Through the industrial protocol conversion layer, various different modalities of collected data can be uniformly converted into data of the same industrial communication protocol. Under this condition, heterogeneous energy devices can be integrated, and the collected data can be uniformly processed without replacing or modifying the energy devices. It has strong compatibility and can greatly improve the multi-modal data processing ability.

[0030] Furthermore, the collected data is original and messy and needs to be preprocessed. The preprocessing steps include data cleaning to remove outliers and noise data, and data normalization to unify data in different ranges into a suitable interval for subsequent use.

[0031] Step 103: Input the collected data into the first target AI plug-in of the energy intelligent agent to obtain the energy analysis result output by the first target AI plug-in; In this embodiment, the first target AI plug-in is a trained AI plug-in, which includes one model or integrates multiple models. The first target AI plug-in can include models such as an energy efficiency analysis model, a carbon emission tracking and accounting model, an energy demand prediction model, a comprehensive optimization and scheduling model, and / or a scenario-based energy management model. Among them, the energy efficiency analysis model can analyze the operating efficiency of equipment based on the collected data (such as unit energy consumption and energy intensity); the carbon emission tracking and accounting model can associate the collected data with carbon emission factors to calculate the total carbon emission and intensity of a region or park; the energy demand prediction model can predict the future load demand of the park through algorithms such as multiple linear regression and decision trees; the comprehensive optimization and scheduling model can dynamically adjust the equipment operation strategy (such as peak-shaving power consumption and air-conditioning temperature control) by combining the equipment energy efficiency evaluation result and the load prediction, and the scenario-based energy management model can standardize the energy consumption index through multi-dimensional data (such as production scale and equipment type) to achieve horizontal comparison of energy efficiency between different parks or enterprises. The energy analysis result can be the operating efficiency of energy equipment, the total carbon emission and intensity of the park, the future load demand of the park (such as peak, valley, and flat periods, energy efficiency curve), the operating strategy of energy equipment (such as peak-shaving power consumption and air-conditioning temperature control), etc.

[0032] In the above-mentioned model, a deep learning network structure with strong processing capabilities for time series data and spatial data can be used, such as recurrent neural network (RNN) and its variants (e.g., long short-term memory network LSTM, gated recurrent unit GRU) and convolutional neural network (CNN), etc., to effectively mine the potential laws in the operation data of the park energy equipment. For example, for the energy consumption prediction of power equipment, due to its obvious time series characteristics, LSTM can be used. LSTM can handle long-term dependency problems and effectively capture the trend of energy consumption of power equipment over time through memory units and gating mechanisms. For spatial data such as equipment layout and energy distribution relationships in different areas of the park, CNN can be used for feature extraction and analysis. This embodiment uses AI plug-ins to efficiently and accurately analyze data of various types of energy equipment in the complex energy network of the park. Preferably, the first target AI plug-in can be deepseek, and the deep learning network training in deepseek is efficient and accurate, with superior performance.

[0033] Preferably, inputting the collected data into the first target AI plug-in of the energy intelligent body, and obtaining the energy analysis result output by the first target AI plug-in, comprises: Acquire dynamic environment variables, where the dynamic environment variables are extracted from the energy-related information or acquired from a third-party API interface; The collected data and the dynamic environment variables are input into the first target AI plug-in of the energy intelligent body to obtain the energy analysis result output by the first target AI plug-in.

[0034] Among them, dynamic environmental variables are dynamic variables that affect energy equipment, including weather data, geographic location, altitude, facility scale, production order quantity, production schedule, etc. Dynamic environmental variables can be extracted from energy-related information. For example, the user enters the production schedule while entering energy-related information. Dynamic environmental variables can also be obtained from a third-party API interface, such as obtaining meteorological data from a meteorological data interface. This embodiment analyzes the collected data and dynamic environmental variables through the first target AI plug-in, which can combine dynamic environmental variables to make the energy analysis results more accurate.

[0035] In one embodiment, the method further includes: generating an energy-saving strategy based on the collected data and the energy analysis results, sending the energy-saving strategy to the energy device, and receiving the execution result of the energy device after adjusting parameters according to the energy-saving strategy.

[0036] In this embodiment, the energy-saving strategy includes adjusting the optimal operating parameters of energy equipment, optimizing the start-stop time of equipment, energy distribution schemes, etc. The energy-saving strategy is sent to each energy equipment in the park to achieve precise control of the energy equipment. This embodiment also has a feedback mechanism that can monitor the execution results of the energy-saving strategy in real time and feedback the execution results to the energy intelligent agent for further optimization of the energy-saving strategy. This embodiment has the ability of intelligent decision-making for the dynamic changes of complex energy systems and strong adaptability. When facing the real-time fluctuations of energy demand, changes in equipment performance, and the influence of external factors, it can make optimal decisions, enabling the effective coordination of the energy equipment in the park and achieving precise distribution and efficient utilization of energy.

[0037] In one embodiment, before inputting the collected data into the first target AI plug-in of the energy intelligent agent and obtaining the energy analysis result output by the first target AI plug-in, it further includes: Create a workflow for the working data of the energy equipment, and determine the first AI plug-in to be trained of the energy intelligent agent according to the workflow; Call the knowledge base pre-bound by the energy intelligent agent, and obtain the metadata of the energy equipment from the knowledge base. The metadata at least includes the historical working data of the energy equipment; Extract feature data from the metadata, input the feature data into the first AI plug-in to be trained for training, and obtain the first target AI plug-in of the energy intelligent agent after training.

[0038] Among them, the workflow of the working data of the energy equipment is a process of converting raw data into executable insights through a systematic process. The workflow of this embodiment supports creation in a visual manner. For example, for heating, ventilation, and air conditioning equipment, a workflow for intelligent control of heating, ventilation, and air conditioning equipment can be created. The workflow includes data collection and preprocessing, data storage, data analysis and modeling, data application and feedback, etc. Determine the first AI plug-in to be trained of the energy intelligent agent according to the workflow.

[0039] The knowledge base is a collection of data middle platforms of each platform, including the metadata of all energy devices in the park. The metadata includes the historical working data of the energy devices. Before training the first AI plugin to be trained, extract the feature data from the metadata as samples. The feature data is representative data, such as the operation mode features of energy devices, the energy consumption change trend and other features. Input the feature data into the first AI plugin to be trained for training. During training, divide the samples into a training set, a validation set and a test set. The training set is used for parameter learning and optimization of the model. Among them, optimization algorithms such as Stochastic Gradient Descent (SGD) and its variants (such as Adagrad, Adadelta or Adam) are used to update the parameters of the model to minimize the loss function. The loss function is selected according to the specific task. For example, for the energy consumption prediction task, the Mean Squared Error (MSE) loss function can be used. The validation set is used to evaluate the performance of the model during training to prevent overfitting of the model, and the test set is used to finally evaluate the generalization ability of the model. After training, the first target AI plugin of the energy intelligent agent is obtained.

[0040] Step 104, input the energy-related information and the energy analysis result into the second target AI plugin of the energy intelligent agent to obtain the language analysis result output by the second target AI plugin; In this embodiment, the second target AI plugin is a trained AI plugin, which includes a natural language model or integrates multiple natural language models. The natural language model has good knowledge reasoning and semantic understanding capabilities. Preferably, the second target AI plugin can be Doubao, which has strong knowledge reasoning and semantic understanding capabilities and excellent performance.

[0041] In this embodiment, by analyzing the energy-related information and the energy analysis result through the second target AI plugin, the language analysis result output after integrated analysis can be obtained. When the energy-related information is a problem related to park energy, the second target AI plugin can analyze the energy-related information and the energy analysis result through the natural language model and give the corresponding language analysis result as the answer to the problem. When the energy-related information is not a problem, the second target AI plugin can still analyze through the natural language model and give the corresponding language analysis result as the answer.

[0042] In one embodiment, the metadata further includes the description information of the energy device. Before inputting the energy-related information and the energy analysis result into the second target AI plugin of the energy intelligent agent to obtain the language analysis result output by the second target AI plugin, it further includes: Determine the second AI plugin to be trained of the energy intelligent agent, input the description information and the historical working data into the second AI plugin to be trained for training, and obtain the second target AI plugin of the energy intelligent agent after training.

[0043] Among them, the description information of the energy equipment includes the sum of the core description and non-core description of the energy equipment. For example, the core description includes equipment information, function information, management information, energy consumption information, etc., and the non-core description includes equipment procurement, maintenance and troubleshooting, etc., and the park energy equipment manual, operation and maintenance historical data and other documents can be further imported into the knowledge base as part of the non-core description. This embodiment enables the AI plug-in to learn comprehensive knowledge of energy equipment by inputting the description information and historical work data of the knowledge base into the second AI plug-in to be trained for training, which helps to improve the accuracy of the model.

[0044] Furthermore, before training the first AI plug-in to be trained and the second AI plug-in to be trained, this embodiment performs targeted parameter optimization and configuration on each AI plug-in so that it can adapt to the data characteristics and task requirements in the field of park energy services. Then, using the integrated framework, these optimized plug-ins are organically combined to form an AI plug-in integrated training environment. Through this integrated training method, the advantages of each AI plug-in are fully utilized to improve the training efficiency and performance of the model.

[0045] Step 105: monitor the energy equipment according to the energy analysis result, and output the language analysis result as a response to the energy-related information to the interface.

[0046] In this embodiment, the energy equipment is monitored according to the energy analysis results, and abnormal conditions or faulty equipment can be discovered in time, such as abnormal operating efficiency, abnormal total carbon emissions and intensity, abnormal load demand, etc., so as to coordinate the relevant energy equipment to eliminate the abnormal conditions. In addition, this embodiment can also output the language analysis results to the interface as a response or reply to energy-related information.

[0047] In one embodiment, the method further includes: providing an equipment failure warning based on the monitored energy equipment and a predetermined warning mechanism.

[0048] In this embodiment, the energy equipment is continuously monitored to issue an early warning before the energy equipment fails, thereby facilitating the staff to coordinate the relevant energy equipment and avoid equipment failure.

[0049] In other embodiments, the energy-related information input by the user each time and the corresponding language analysis results may be formed into question-answer pairs, which are added to the knowledge base to expand the content in the knowledge base.

[0050] In the embodiments of the present invention, the user inputs energy-related information on the interface, determines the energy agent and the energy equipment corresponding to the type of the energy agent according to the energy-related information. After collecting the data of the energy equipment, the data is input into the first target AI plug-in of the energy agent to analyze the collected data, and an energy analysis result is obtained. The energy analysis result can be the operating efficiency of the energy equipment, the total carbon emission and intensity of the park, the future load demand of the park, the operating strategy of the energy equipment, etc. The energy equipment is monitored according to the energy analysis result. In this embodiment, multiple energy agents are integrated, and multiple AI plug-ins are integrated in the energy agent. The multiple energy agents can perform comprehensive, intelligent analysis and monitoring of different types of energy equipment in the park, and the energy analysis accuracy is high. Combining with the second target AI plug-in, a response corresponding to the user's energy-related information can be obtained and output to the interface, making the control of the complex park energy network more efficient and intelligent.

[0051] Figure 2 FIG. shows a schematic structural diagram of a comprehensive energy intelligent monitoring device 200 in an embodiment of the present invention. As Figure 2 shown, the device 200 includes: A determination module 201, configured to receive energy-related information input by the user on the interface, and determine an energy agent and the energy equipment corresponding to the type of the energy agent according to the energy-related information, where the energy equipment includes multiple different types, and one type of energy equipment corresponds to one energy agent; An acquisition module 202, configured to acquire acquisition data from an access node of the corresponding type of energy equipment; A first input module 203, configured to input the acquisition data into a first target AI plug-in of the energy agent to obtain an energy analysis result output by the first target AI plug-in; A second input module 204, configured to input the energy-related information and the energy analysis result into a second target AI plug-in of the energy agent to obtain a language analysis result output by the second target AI plug-in; A monitoring module 205, configured to monitor the energy equipment according to the energy analysis result, and output the language analysis result as a response to the energy-related information to the interface.

[0052] Among them, the embodiment of the comprehensive energy intelligent monitoring device 200 in the park is basically the same as the embodiment of the above-mentioned comprehensive energy intelligent monitoring method in the park, and reference can be made to the above-mentioned embodiment.

[0053] Figure 3 FIG. shows a schematic structural diagram of a computer device according to an embodiment of the present invention. The specific implementation of the computer device is not limited in the specific embodiments of the present invention.

[0054] As Figure 3As shown in the figure, the computer device may include: a processor 302, a communications interface 304, a memory 306, and a communication bus 308.

[0055] Among them: The processor 302, the communications interface 304, and the memory 306 communicate with each other through the communication bus 308. The communications interface 304 is used to communicate with network elements of other computer devices such as clients or other servers. The processor 302 is used to execute the program 310, and specifically can execute the relevant steps in the above embodiments for the computer device.

[0056] Specifically, the program 310 may include program code, and the program code includes computer-executable instructions.

[0057] The processor 302 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention. One or more processors included in the computer device may be of the same type of processor, such as one or more CPUs; or may be of different types of processors, such as one or more CPUs and one or more ASICs.

[0058] The memory 306 is used to store the program 310. The memory 306 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk memory.

[0059] The program 310 can specifically be called by the processor 302 to enable the computer device to perform the following operations: Receive energy-related information input by the user on the interface, and determine an energy agent and an energy device of the corresponding type of the energy agent according to the energy-related information. Among them, the energy device includes multiple different types, and one type of energy device corresponds to one energy agent; Obtain acquisition data from the access node of the energy device of the corresponding type; Input the acquisition data into the first target AI plug-in of the energy agent, and obtain the energy analysis result output by the first target AI plug-in; Input the energy-related information and the energy analysis result into the second target AI plug-in of the energy agent, and obtain the language analysis result output by the second target AI plug-in; Monitor the energy device according to the energy analysis result, and output the language analysis result as a response to the energy-related information to the interface.

[0060] In an alternative manner, the method further includes: Create energy agents corresponding to various types of energy devices based on a multi-agent mode on a preset platform, and configure key information of the energy agents, where the key information at least includes prompt words and core descriptions; The receiving of the energy-related information input by the user on the interface and determining the energy agent and the energy device of the type corresponding to the energy agent includes: Receive the energy-related information input by the user on the interface, and determine the energy agent and the energy device of the type corresponding to the energy agent according to the energy-related information and the key information of each energy agent.

[0061] In an alternative manner, before inputting the collected data into the first target AI plug-in of the energy agent to obtain the energy analysis result output by the first target AI plug-in, it further includes: Create a workflow of the working data of the energy device, and determine the first AI plug-in to be trained of the energy agent according to the workflow; Call the knowledge base pre-bound to the energy agent, and obtain the metadata of the energy device from the knowledge base, where the metadata at least includes the historical working data of the energy device; Extract feature data from the metadata, input the feature data into the first AI plug-in to be trained for training, and obtain the first target AI plug-in of the energy agent after training.

[0062] In an alternative manner, the metadata further includes the description information of the energy device. Before inputting the energy-related information and the energy analysis result into the second target AI plug-in of the energy agent to obtain the language analysis result output by the second target AI plug-in, it further includes: Determine the second AI plug-in to be trained of the energy agent, input the description information and the historical working data into the second AI plug-in to be trained for training, and obtain the second target AI plug-in of the energy agent after training.

[0063] In an alternative manner, the inputting of the collected data into the first target AI plug-in of the energy agent to obtain the energy analysis result output by the first target AI plug-in includes: Obtain dynamic environment variables, where the dynamic environment variables are extracted from the energy-related information or obtained from a third-party API interface; Input the collected data and the dynamic environment variables into the first target AI plug-in of the energy intelligent agent, and obtain the energy analysis result output by the first target AI plug-in.

[0064] In an optional manner, the method further includes: Perform device failure warning based on the monitored energy devices and a predetermined warning mechanism; and / or, Generate an energy-saving strategy according to the collected data and the energy analysis result, send the energy-saving strategy to the energy device, and receive the execution result after the energy device adjusts parameters according to the energy-saving strategy.

[0065] In an optional manner, the collected data includes data of different modalities. After obtaining the collected data from the access node of the corresponding type of energy device, it further includes: Input the collected data into the industrial protocol conversion layer, and convert the collected data into data of the same communication protocol based on the industrial protocol conversion layer.

[0066] In an optional manner, before receiving the energy-related information input by the user on the interface and determining the energy intelligent agent and the energy device of the corresponding type of the energy intelligent agent according to the energy-related information, it further includes: Receive the original information input by the user on the interface, and call the predetermined natural language AI plug-in of the energy intelligent agent to analyze whether the original information is energy-related information; If so, perform the step of determining the energy intelligent agent and the energy device of the corresponding type of the energy intelligent agent according to the energy-related information.

[0067] An embodiment of the present invention provides a computer-readable storage medium, and the storage medium stores at least one executable instruction. When the executable instruction runs on a computer device, the computer device executes any of the above method embodiments.

[0068] An embodiment of the present invention provides a computer program, and the computer program can be called by a processor to make a computer device execute any of the above method embodiments.

[0069] An embodiment of the present invention provides a computer program product. The computer program product includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions run on a computer, the computer executes any of the above method embodiments.

[0070] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. A variety of general-purpose systems can also be used in conjunction with the teachings provided herein. The structure required to construct such systems will be apparent from the above description. In addition, embodiments of the present invention are not directed to any particular programming language. It should be understood that the teachings of the present invention described herein can be implemented in a variety of programming languages, and the description of a particular language above is for the purpose of disclosing the best mode of the present invention.

[0071] In the specification provided herein, a number of specific details are set forth. However, it will be understood that embodiments of the present invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure an understanding of the present specification.

[0072] Similarly, it should be understood that, in order to streamline the present invention and assist in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present invention, the various features of the embodiments of the present invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, the disclosed method should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim.

[0073] Those skilled in the art will appreciate that the modules in the computer devices in the embodiments can be adaptively changed and disposed in one or more computer devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and can be divided into multiple sub-modules or sub-units or sub-components. Except that at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all the features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all the processes or units of any method or computer device so disclosed. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) can be replaced by an alternative feature that provides the same, equivalent, or similar purpose.

[0074] It should be noted that the above embodiments are illustrative of the present invention and not restrictive thereof, and alternative embodiments can be designed by those skilled in the art without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in the claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention can be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In the unit claims listing several devices, several of these devices can be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not denote any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. An intelligent monitoring method for integrated energy in a park, characterized in that, The method includes: Receiving energy-related information input by a user on an interface, and determining an energy agent and energy devices of the corresponding type of the energy agent according to the energy-related information, wherein the energy devices include multiple different types, and one type of energy device corresponds to one energy agent; Obtaining acquisition data from an access node of the energy devices of the corresponding type; Inputting the acquisition data into a first target AI plug-in of the energy agent to obtain an energy analysis result output by the first target AI plug-in; Inputting the energy-related information and the energy analysis result into a second target AI plug-in of the energy agent to obtain a language analysis result output by the second target AI plug-in; Monitoring the energy devices according to the energy analysis result, and outputting the language analysis result as a response to the energy-related information to the interface.

2. The method according to claim 1, characterized in that, The method further includes: Creating energy agents corresponding to multiple types of energy devices respectively based on a multi-agent mode on a preset platform, and configuring key information of the energy agents, where the key information at least includes prompt words and core descriptions; The step of receiving energy-related information input by a user on an interface and determining an energy agent and energy devices of the corresponding type of the energy agent according to the energy-related information includes: Receiving energy-related information input by a user on an interface, and determining an energy agent and energy devices of the corresponding type of the energy agent according to the energy-related information and the key information of each energy agent.

3. The method according to claim 1, characterized in that, Before inputting the acquisition data into a first target AI plug-in of the energy agent to obtain an energy analysis result output by the first target AI plug-in, it further includes: Creating a workflow of the working data of the energy devices, and determining a first AI plug-in to be trained of the energy agent according to the workflow; Invoking a knowledge base pre-bound to the energy agent, and obtaining metadata of the energy devices from the knowledge base, where the metadata at least includes historical working data of the energy devices; Extracting feature data from the metadata, inputting the feature data into the first AI plug-in to be trained for training, and obtaining a first target AI plug-in of the energy agent after training.

4. The method according to claim 3, characterized in that The metadata further includes description information of the energy devices. Before inputting the energy-related information and the energy analysis result into a second target AI plug-in of the energy agent to obtain a language analysis result output by the second target AI plug-in, it further includes: Determining a second AI plug-in to be trained of the energy agent, inputting the description information and the historical working data into the second AI plug-in to be trained for training, and obtaining a second target AI plug-in of the energy agent after training.

5. The method according to claim 1, wherein The step of inputting the acquisition data into a first target AI plug-in of the energy agent to obtain an energy analysis result output by the first target AI plug-in includes: Obtaining dynamic environment variables, where the dynamic environment variables are extracted from the energy-related information or obtained from a third-party API interface; Input the collected data and the dynamic environment variables into the first target AI plug-in of the energy intelligent agent to obtain the energy analysis result output by the first target AI plug-in.

6. The method according to claim 1, wherein The method further includes: Performing equipment failure early warning based on the monitored energy equipment and a predetermined early warning mechanism; and / or Generating an energy-saving strategy according to the collected data and the energy analysis result, sending the energy-saving strategy to the energy equipment, and receiving the execution result after the energy equipment adjusts parameters according to the energy-saving strategy.

7. The method according to claim 1, characterized in that The collected data includes data of different modalities. After obtaining the collected data from the access node of the corresponding type of energy equipment, it further includes: Inputting the collected data into the industrial protocol conversion layer, and converting the collected data into data of the same communication protocol based on the industrial protocol conversion layer.

8. The method according to claim 1, wherein Before receiving the energy-related information input by the user on the interface and determining the energy intelligent agent and the energy equipment of the corresponding type of the energy intelligent agent according to the energy-related information, it further includes: Receiving the original information input by the user on the interface, and calling the predetermined natural language AI plug-in of the energy intelligent agent to analyze whether the original information is energy-related information; If so, execute the step of determining the energy intelligent agent and the energy equipment of the corresponding type of the energy intelligent agent according to the energy-related information.

9. A computer device, characterized in that, It includes: A processor, a memory, a communication interface, and a communication bus. The processor, the memory, and the communication interface complete mutual communication through the communication bus; The memory is used to store at least one executable instruction, and the executable instruction causes the processor to execute the method according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, At least one executable instruction is stored in the storage medium. When the executable instruction runs on the computer device, the computer device is caused to execute the method according to any one of claims 1-8.

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