Equipment observation result evaluation method and energy regulation and control method

Abstract

The invention provides an equipment observation result evaluation method and an energy regulation and control method. The equipment observation result evaluation method comprises the following steps: determining real-time parameters of any target monitoring equipment through a stream computing engine; determining update data of any target monitoring device through a pre-constructed digital model; determining synchronization delay and data deviation according to the real-time parameters and the updated data; and evaluating the observation result according to the synchronization delay and the data deviation. The energy regulation and control method comprises the following steps: determining electric equipment and power generation equipment in a target area; according to a stream calculation engine, determining a first power data stream of any electric equipment and a second power data stream of any power generation equipment; according to the first power data stream and the second power data stream, determining a net power consumption data stream of the target area in the target time period; and regulating and controlling the electric equipment and the power generation equipment according to the net electricity consumption data flow, the first power data flow and the second power data flow.

Description

Technical Field

[0001] This application relates to the field of energy regulation technology, and in particular to a method for evaluating equipment observation results and an energy regulation method. Background Technology

[0002] Driven by the dual waves of global energy cleanliness and digitalization, distributed energy resources, represented by distributed photovoltaics, decentralized wind power, electric vehicle charging stations, and smart homes, are being connected to the grid at an unprecedented speed and scale. This trend is profoundly reshaping the operating paradigm of the power system, forcing it to strategically transform from centralized, unidirectional control of "sources following loads" to distributed, bidirectional collaboration of "source-load interaction." However, the interaction between the power grid and massive customer-side resources faces severe technical barriers in practice. The vast number of customer-side devices, with their diverse brands and communication protocols, remain a low-transparency "black box" for dispatching operations: although advanced measurement systems collect massive amounts of user-side current, voltage, and power data, these data form serious "data silos" across systems and businesses, making them difficult to integrate and analyze.

[0003] This leads to the current problems of inability to assess equipment monitoring results and poor energy regulation capabilities in the power grid. Summary of the Invention

[0004] In view of this, the purpose of this application is to propose a method for evaluating equipment observation results and a method for energy regulation.

[0005] For the purposes described above, this application provides a method for evaluating equipment observation results, including: The real-time parameters of any target monitoring device are determined through the stream computing engine. The updated data of any of the target monitoring devices is determined by using a pre-built digital model; Based on the real-time parameters and the updated data, the synchronization delay and data deviation are determined; The observation results are evaluated based on the synchronization delay and the data deviation.

[0006] Optionally, determining the real-time parameters of any target monitoring device through a stream computing engine includes: Based on the stream computing engine, determine the real-time data stream of any of the target monitoring devices; The real-time parameters are determined based on the real-time data stream.

[0007] Optionally, determining the synchronization delay based on the real-time parameters and the updated data includes: Based on the real-time parameters and the updated data, the synchronization delay is determined using the following formula:

[0008] Wherein, tmodel_update is the updated data, specifically the timestamp of the completion of the update of the state data model attribute in the digital model, and tdevice_colltect is the real-time data, specifically the timestamp of the raw data collected by the physical device sensors of the target monitoring device.

[0009] Optionally, determining the data deviation based on the real-time parameters and the updated data includes: Based on the real-time parameters and the updated data, the data deviation is determined using the following formula:

[0010] Wherein, Vmodel is the updated data, specifically the parameter values ​​recorded in the digital model, and Vdevice is the real-time parameter, specifically the real-time data of the target monitoring device measured by the physical device.

[0011] Based on the same inventive concept, embodiments of this application also provide an energy regulation method, including: Identify the electrical equipment and power generation equipment in the target area; Based on the stream computing engine, determine the first power data stream of any of the electrical devices and the second power data stream of any of the power generation devices; Based on the first power data stream and the second power data stream, determine the net power consumption data stream of the target area during the target time period; The power-consuming equipment and the power-generating equipment are regulated based on the net power consumption data stream, the first power data stream, and the second power data stream.

[0012] Optionally, determining the net power consumption data stream of the target area during the target time period based on the first power data stream and the second power data stream includes: Based on the first power data stream, determine the total power load of the electrical equipment; Based on the second power data stream, determine the sum of the power generation power of the power generation equipment; The net electricity consumption data stream is determined based on the sum of the total electricity load and the power generation.

[0013] Optionally, the step of regulating the power-consuming equipment and the power-generating equipment based on the net power consumption data stream, the first power data stream, and the second power data stream includes: Based on the net power consumption data stream, the first power data stream, and the second power data stream, determine the energy consumption curve of the target area during the target time period; Based on the energy consumption curve, the regulation prediction of any electrical device in the target area is performed using a pre-built digital model to determine the predicted power load of any electrical device. Based on the predicted electricity load, determine the total predicted electricity load for the target area; Based on the predicted total electricity load, the power-consuming equipment and the power-generating equipment are regulated.

[0014] Optionally, the method further includes: The power value under the control state is determined through the stream computing engine; The actual total load reduction is determined based on the predicted power value and the power value under the control state. The energy regulation effect is determined based on the actual total load reduction.

[0015] Based on the same inventive concept, embodiments of this application also provide a device for evaluating equipment observation results, comprising: The first determination module is configured to determine the real-time parameters of any target monitoring device through a stream computing engine; The second determining module is configured to determine the updated data of any of the target monitoring devices using a pre-built digital model; The calculation module is configured to determine the synchronization delay and data deviation based on the real-time parameters and the updated data; The evaluation module is configured to evaluate the observation results based on the synchronization delay and the data deviation.

[0016] Based on the same inventive concept, this application also provides an energy regulation device, including: The equipment identification module is configured to identify electrical equipment and power generation equipment in a target area; The data stream module is configured to determine a first power data stream of any of the electrical devices and a second power data stream of any of the power generating devices based on the stream computing engine. The power consumption calculation module is configured to determine the net power consumption data stream of the target area in the target time period based on the first power data stream and the second power data stream; The energy control module is configured to control the electrical equipment and the power generation equipment based on the net power consumption data stream, the first power data stream, and the second power data stream.

[0017] As can be seen from the above, the equipment observation result evaluation method and energy regulation method provided in this application continuously calculate key performance indicators (such as efficiency and load rate) based on real-time data streams and deeply integrate equipment mechanism models for predictive diagnosis, thereby laying a precise data foundation for predictive maintenance and individual energy efficiency optimization. Furthermore, by performing dynamic window aggregation calculations on logically related equipment groups, the overall operational status of a region, production line, or business unit can be perceived in real time. It can also quantitatively track and evaluate the execution progress, overall efficiency, and business contribution of complex collaborative control tasks such as "demand response" and "load scheduling," realizing the transformation from isolated data points to system-level business value. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the device observation result evaluation method according to an embodiment of this application; Figure 2 This is a schematic diagram of the digital model layer and visualization reference layer architecture in an embodiment of this application; Figure 3 This is a schematic diagram of the core process of state synchronization and visualization in an embodiment of this application; Figure 4 This is a flowchart illustrating the energy regulation method according to an embodiment of this application; Figure 5 This is a schematic diagram of the collaborative control task observation process according to an embodiment of this application; Figure 6 This is a schematic diagram of the device for evaluating the observation results according to an embodiment of this application; Figure 7 This is a schematic diagram of an energy regulation device according to an embodiment of this application; Figure 8 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0021] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0022] To facilitate understanding of the technical solutions disclosed herein, some technical terms involved in this disclosure will be introduced below.

[0023] Real-time digital model (hereinafter referred to as digital model): A "dynamic twin" of a physical device (such as an electricity meter or a charging station) in the digital world. It uses sensor data to change synchronously with the physical device and can simulate, predict, and analyze its real-world state. Essentially, it uses digital means to construct an identical, real-time, interactive, and intelligently controllable digital mirror of things in the physical world in virtual space. It is the bridge connecting the physical and digital worlds and the cornerstone for achieving intelligent management and analysis.

[0024] Data silos, also known as information silos, refer to the state within an organization or system where data is isolated from different departments, business units, or systems, hindering its smooth flow, sharing, and integration. Breaking down data silos requires building bridges in technology, management, and standards to allow data to flow freely and securely across departments and systems, thereby maximizing its value. In the power sector, breaking down data silos within the power grid and between the power grid and users / equipment suppliers is a crucial prerequisite for achieving precise control and value extraction of customer-side resources.

[0025] Flexible resource pool: This refers to aggregating a large number of dispersed, adjustable electrical loads (such as air conditioners, charging piles, and water heaters) or distributed power sources (such as photovoltaics and energy storage) through advanced control and communication technologies to form a large-scale, unified, and controllable virtual whole. The resources in this "pool" can flexibly and dynamically adjust their power consumption or generation capacity according to the needs of the power grid.

[0026] To make the technical solutions of this disclosure clearer and easier to understand, the equipment observation result evaluation method and energy regulation method provided in the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings.

[0027] As described in the background section, driven by the dual waves of global energy cleanliness and digitalization, distributed energy resources, represented by distributed photovoltaics, decentralized wind power, electric vehicle charging piles, and smart homes, are being connected to the grid at an unprecedented speed and scale. This trend is profoundly reshaping the operating paradigm of the power system, forcing it to strategically transform from centralized unidirectional control of "sources following loads" to distributed bidirectional collaboration of "source-load interaction." However, the interaction between the power grid and massive customer-side resources faces severe technical barriers in practice. The large number of customer-side devices, with heterogeneous brands and different communication protocols, remain a low-transparency "black box" for dispatching operations: although advanced measurement systems collect massive amounts of user-side current, voltage, and power data, these data form serious "data silos" across systems and businesses, making them difficult to integrate and analyze. Therefore, this leads to the current problems of inability to assess equipment observation results and poor energy regulation capabilities within the power grid.

[0028] Specifically, existing technical solutions suffer from fundamental limitations at the cognitive level. The core problem lies in the lack of a high-fidelity digital model that accurately reflects the true operating status of equipment. Current systems primarily rely on discrete data points uploaded by equipment for monitoring. This data is often limited to basic electrical parameters and switch states, failing to form a complete understanding of the overall equipment operation. Due to the lack of a digital model integrating equipment physical characteristics, operating history, and environmental factors, the system cannot accurately assess the real-time health status of the equipment, nor can it predict its performance degradation trends. This cognitive deficiency directly leads to two serious consequences: firstly, the system cannot identify early warning signals before a fault occurs, making preventative maintenance difficult; secondly, because the actual adjustment capability of each piece of equipment under current operating conditions cannot be precisely quantified, the system can only rely on rough estimates when formulating control strategies, which creates hidden dangers for subsequent precise control.

[0029] Furthermore, at the control execution level, existing technologies lack in-depth observation capabilities based on digital models, resulting in a pronounced open-loop characteristic in the control process and significant safety and efficiency risks. Most existing systems' observation mechanisms remain at the command transmission level, only confirming whether control commands were successfully issued, but failing to track and analyze subtle changes in the internal state of the equipment after command execution. This observation blind spot prevents the system from assessing the impact of control commands on the equipment in real time, and from accurately judging deviations between the control effect and expectations. Specifically, the system cannot perceive potential safety hazards such as equipment overload and abnormal efficiency decline during the control process; simultaneously, due to the lack of quantitative evaluation of the user experience, it is difficult to ensure that the control strategy meets grid demands while maintaining a good user experience. This open-loop control mode not only affects control accuracy but may also lead to equipment failure due to the inability to promptly identify abnormal states, ultimately undermining users' enthusiasm for demand response and hindering the sustainable realization of the value of distributed resource aggregation.

[0030] In view of this, embodiments of this application provide a method for evaluating equipment observation results and an energy regulation method. By continuously calculating key performance indicators (such as efficiency and load rate) based on real-time data streams and deeply integrating equipment mechanism models for predictive diagnosis, a precise data foundation is laid for predictive maintenance and individual energy efficiency optimization. Furthermore, by performing dynamic window aggregation calculations on logically related equipment groups, the overall operational status of a region, production line, or business unit can be perceived in real time. The execution progress, overall efficiency, and business contribution of complex collaborative control tasks such as "demand response" and "load scheduling" can be quantitatively tracked and evaluated in real time, realizing the progression from isolated data points to system-level business value.

[0031] like Figure 1 As shown, the evaluation method for the device observation results includes: Step S102: Determine the real-time parameters of any target monitoring device through the stream computing engine; Step S104: Determine the updated data of any of the target monitoring devices using a pre-built digital model; Step S106: Determine the synchronization delay and data deviation based on the real-time parameters and the updated data; Step S108: Evaluate the observation results based on the synchronization delay and the data deviation.

[0032] Optionally, determining the real-time parameters of any target monitoring device through a stream computing engine includes: Based on the stream computing engine, determine the real-time data stream of any of the target monitoring devices; The real-time parameters are determined based on the real-time data stream.

[0033] Optionally, determining the synchronization delay based on the real-time parameters and the updated data includes: Based on the real-time parameters and the updated data, the synchronization delay is determined using the following formula: , Wherein, tmodel_update is the updated data, specifically the timestamp of the completion of the update of the state data model attribute in the digital model, and tdevice_colltect is the real-time data, specifically the timestamp of the raw data collected by the physical device sensors of the target monitoring device.

[0034] For example, when monitoring the current of an air conditioner compressor, if = 1699980000000ms (November 14, 10:00:00.000) and = 1699980000035ms, then = 35ms, which meets the target of ≤50ms and the synchronization timeliness is qualified; if = 120ms, it is necessary to check the processing queue of the stream computing engine (such as whether there is data backlog) or network transmission delay (such as whether the bandwidth utilization of the IoT gateway exceeds 80%) to ensure the real-time performance of status synchronization.

[0035] Among them, the stream processing engine is a computing system specifically designed to process continuous, unbounded, real-time data streams. Unlike traditional batch processing, the stream processing engine can process, analyze, and respond to data immediately as it is generated, making it suitable for scenarios with high timeliness requirements.

[0036] Optionally, determining the data deviation based on the real-time parameters and the updated data includes: Based on the real-time parameters and the updated data, the data deviation is determined using the following formula: , Wherein, Vmodel is the updated data, specifically the parameter values ​​recorded in the digital model, and Vdevice is the real-time parameter, specifically the real-time data of the target monitoring device measured by the physical device.

[0037] For example, if the physical measured speed of a wind turbine is 1500 r / min and the updated speed in the digital model is 1525 r / min, then it meets the accuracy requirement of ≤2%; if it is 5.2%, the sensor needs to be calibrated (e.g., whether the installation position of the speed sensor is offset) or the encoding error in the data transmission needs to be checked (e.g., whether there is a loss of binary data bits) to ensure that the parameters of the digital model are consistent with the actual state of the physical equipment.

[0038] In some implementations, synchronization delay is specifically reflected in state synchronization. State synchronization is driven by a stream computing engine. When the engine receives real-time data (such as key parameters like current, temperature, and switch status) transmitted from the device, it needs to quantify the accuracy through a dual dimension of "synchronization delay" and "data deviation" to avoid observation errors caused by synchronization lag or data distortion. Synchronization delay refers to the time difference between the physical device generating data and the digital model updating the corresponding attributes, directly determining the "near real-time" effect of state synchronization. Data deviation refers to the difference rate between the updated parameter values ​​in the digital model and the actual values ​​of the physical device, reflecting the accuracy of data synchronization.

[0039] like Figure 2 As shown, this application also provides a digital model layer and a visualization reference layer architecture. The dynamic data model of the digital model layer, as a "digital mapping of physical devices", is the core carrier of microscopic observation. The application-layer visualization and management platform enables "intuitive information presentation and practical application," allowing managers to clearly understand the details of equipment operation.

[0040] like Figure 3 As shown, this application also provides a core flowchart for state synchronization and visualization.

[0041] Furthermore, task observation, as the core of the real-time observation system at the macro level, focuses on the overall monitoring of a group of devices or specific business objectives. It is no longer limited to the micro-state of individual devices, but rather provides decision support for park-level and area-level energy management and task scheduling by tracking the overall behavior of the system and evaluating its collaborative effects. Its core functions are divided into two main modules: aggregated observation and collaborative control task observation. The former (aggregated observation) summarizes and analyzes the operating data of multiple devices, while the latter (collaborative control task observation) conducts full-cycle monitoring and effect evaluation for specific control tasks. Together, they form a complete closed loop for macro-level device management, ensuring precise control at every stage from data aggregation to task execution.

[0042] Therefore, as Figure 4 As shown, this application also provides an energy regulation method, comprising: Step S402: Determine the electrical equipment and power generation equipment in the target area; Step S404: Determine the first power data stream of any of the electrical devices and the second power data stream of any of the power generation devices according to the stream computing engine; Step S406: Determine the net power consumption data stream of the target area in the target time period based on the first power data stream and the second power data stream; Step S408: Adjust the power-consuming equipment and the power-generating equipment according to the net power consumption data stream, the first power data stream and the second power data stream.

[0043] Optionally, determining the net power consumption data stream of the target area during the target time period based on the first power data stream and the second power data stream includes: Based on the first power data stream, determine the total power load of the electrical equipment; Based on the second power data stream, determine the sum of the power generation power of the power generation equipment; The net electricity consumption data stream is determined based on the sum of the total electricity load and the power generation.

[0044] Optionally, the step of regulating the power-consuming equipment and the power-generating equipment based on the net power consumption data stream, the first power data stream, and the second power data stream includes: Based on the net power consumption data stream, the first power data stream, and the second power data stream, determine the energy consumption curve of the target area during the target time period; Based on the energy consumption curve, the regulation prediction of any electrical device in the target area is performed using a pre-built digital model to determine the predicted power load of any electrical device. Based on the predicted electricity load, determine the total predicted electricity load for the target area; Based on the predicted total electricity load, the power-consuming equipment and the power-generating equipment are regulated.

[0045] Optionally, the method further includes: The power value under the control state is determined through the stream computing engine; The actual total load reduction is determined based on the predicted power value and the power value under the control state. The energy regulation effect is determined based on the actual total load reduction.

[0046] In some embodiments, aggregated observation aims at "overall energy consumption perception." Typical application scenarios include monitoring the total real-time load and net electricity consumption of commercial parks, covering electrical equipment such as air conditioners, lighting, and charging piles, as well as power generation equipment such as photovoltaics. In terms of technical implementation, the stream processing engine plays a core role in data processing. First, it subscribes to the power data streams of all equipment within the park to ensure real-time acquisition of the operating power data of each device. Then, it groups the data according to the "park ID" (i.e., KeyBy operation) and defines sliding or rolling windows (commonly calculated every minute) to set the statistical range of the time dimension for data aggregation. Within each window period, the system sums and aggregates the power data of all equipment in the park, deriving key indicators through preset calculation logic: total load is the sum of the power of all electrical equipment, and net electricity consumption is calculated by subtracting the sum of photovoltaic power generation from the total load. Finally, these real-time calculation results are synchronously output to the total load curve and net electricity consumption dashboard, allowing managers to intuitively view the overall energy consumption trend and real-time electricity gap of the park, providing data support for park energy dispatch and peak-shifting electricity planning.

[0047] The collaborative control task observation revolves around the full-cycle management of specific control tasks. Taking the demand response task of "reducing the air conditioning load of a region by 100kW during the evening peak period" as an example, its process covers three key stages: pre-task simulation, in-task observation, and effect presentation. The pre-task simulation stage relies on a digital model (which can be selected in offline or low-latency online mode). First, a batch of air conditioning equipment that meets the control conditions is selected from the region. The model simulates the operation of uniformly raising the set temperature of these air conditioners by 2°C. Then, combining historical operating data and equipment mechanism models, the expected power reduction value of each air conditioner under the control command is calculated, and the total predicted load reduction is obtained (e.g., the simulation results show that 105kW can be reduced). This prediction result not only verifies the feasibility of the task execution but also provides a reference for achieving the actual control target, helping managers to judge in advance whether the task can meet the demand.

[0048] In some embodiments, such as Figure 5As shown, this application provides observations during the control task. Observation during the task is crucial to ensuring the effective implementation of control commands, and is divided into two dimensions: object observation and task observation. Object observation focuses on individual participating devices, monitoring the current power, set temperature, and operating status of each air conditioner in real time. This confirms whether control commands (such as raising the set temperature) are correctly executed and checks for equipment anomalies (such as power not decreasing, temperature runaway, etc.), preventing single device failures from affecting the overall task effect. Task observation, centered on "overall target achievement," uses a flow engine to aggregate the power reduction values ​​of all participating air conditioners in real time. It calculates the overall control effect according to the logic of "actual total load reduction = Σ(pre-task baseline power - current real-time power)," and compares the actual total load reduction with the target load reduction of 100kW in real time. At the presentation level, the system intuitively displays the task completion progress (i.e., (actual total load reduction / 100kW)*100%) through a progress bar. At the same time, the effect evaluation panel displays the reduction curve, the gap with the target, and the execution success rate of the participating equipment in real time, allowing managers to clearly grasp the progress of the task. If it is found that the actual reduction does not meet expectations, the control strategy can be adjusted in time (such as increasing the number of participating equipment) to ensure that the task objectives are ultimately achieved.

[0049] As can be seen from the above, the equipment observation result evaluation method and energy regulation method provided in this application continuously calculate key performance indicators (such as efficiency and load rate) based on real-time data streams and deeply integrate equipment mechanism models for predictive diagnosis, thereby laying a precise data foundation for predictive maintenance and individual energy efficiency optimization. Furthermore, by performing dynamic window aggregation calculations on logically related equipment groups, the overall operational status of a region, production line, or business unit can be perceived in real time. It can also quantitatively track and evaluate the execution progress, overall efficiency, and business contribution of complex collaborative control tasks such as "demand response" and "load scheduling," realizing the transformation from isolated data points to system-level business value.

[0050] This real-time observation system, based on data streams, fundamentally transcends traditional equipment monitoring paradigms, creatively integrating a dual perspective of in-depth micro-level individual observation and macro-level system-wide collaborative control. At the micro level, the system not only synchronizes the status of equipment with digital models but also continuously calculates key performance indicators (such as efficiency and load rate) based on real-time data streams and deeply integrates equipment mechanism models for predictive diagnostics, thus laying a precise data foundation for predictive maintenance and individual energy efficiency optimization. At the macro level, the system dynamically aggregates logically related equipment groups through window calculations, enabling real-time perception of the overall operational status of regions, production lines, or business units. It can also quantitatively track and evaluate the execution progress, overall efficiency, and business contribution of complex collaborative control tasks such as "demand response" and "load scheduling," achieving progress from isolated data points to system-level business value.

[0051] This application represents a leap from passive monitoring to proactive closed-loop intelligent operation. Employing a high-throughput, low-latency stream-batch integrated technology architecture, it ensures the ultimate real-time performance from data access to the generation of insights. By seamlessly integrating "pre-task digital simulation prediction" with "real-time observation and control during the task," a complete autonomous closed loop of "perception-decision-execution-evaluation-optimization" is formed. Specifically, before issuing control commands, the system can perform strategy simulation in digital space to predict effects and mitigate risks; during task execution, it aggregates micro-execution data in real time to generate macro-effects and dynamically compares them with predicted or target values, thus forming quantifiable execution progress bars and performance dashboards. This transforms the system from a mere cold observer of the state into an intelligent control entity capable of dynamically adjusting based on real-time feedback and continuously approaching the optimal goal.

[0052] Ultimately, this invention systematically addresses the core industry pain points of traditional monitoring—high data latency, isolated analytical perspectives, and the disconnect between decision-making and control—by organically unifying high-performance computing, multi-level observation models, and closed-loop control logic. This technical solution efficiently transforms raw equipment operation data into multi-level, actionable business insights, ranging from micro-diagnosis to macro-decision-making, achieving a fundamental improvement in capabilities from simple "monitoring status" to driving "system autonomous optimization."

[0053] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0054] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0055] Based on the same inventive concept, corresponding to any of the above embodiments, this application also provides a device for evaluating equipment observation results.

[0056] refer to Figure 6 The device for evaluating the observation results of the equipment includes: The first determining module 602 is configured to determine the real-time parameters of any target monitoring device through a stream computing engine; The second determining module 604 is configured to determine the updated data of any of the target monitoring devices through a pre-built digital model; The calculation module 606 is configured to determine the synchronization delay and data deviation based on the real-time parameters and the updated data; Evaluation module 608 is configured to evaluate the observation results based on the synchronization delay and the data deviation.

[0057] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing this application, the functions of each module can be implemented in one or more software and / or hardware.

[0058] The apparatus of the above embodiments is used to implement the corresponding device observation result evaluation method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0059] Based on the same inventive concept, corresponding to any of the above embodiments, this application also provides an energy regulation device.

[0060] refer to Figure 7 The energy regulation device includes: The device determination module 702 is configured to determine the electrical equipment and power generation equipment in the target area; The data stream module 704 is configured to determine a first power data stream of any of the electrical devices and a second power data stream of any of the power generating devices based on the stream computing engine. The power consumption calculation module 706 is configured to determine the net power consumption data stream of the target area in the target time period based on the first power data stream and the second power data stream; The energy control module 708 is configured to control the electrical equipment and the power generation equipment based on the net power consumption data stream, the first power data stream and the second power data stream.

[0061] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing this application, the functions of each module can be implemented in one or more software and / or hardware.

[0062] The apparatus of the above embodiments is used to implement the corresponding energy regulation method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0063] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the device observation result evaluation method and / or energy regulation method described in any of the above embodiments.

[0064] Figure 8 This embodiment illustrates a more specific hardware structure of an electronic device. The device may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0065] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0066] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0067] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0068] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0069] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0070] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0071] The electronic devices described above are used to implement the corresponding device observation result evaluation method and / or energy regulation method in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0072] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the device observation result evaluation method and / or energy regulation method as described in any of the above embodiments.

[0073] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change 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), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0074] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the device observation result evaluation method and / or energy regulation method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0075] Based on the same inventive concept, corresponding to the device observation result evaluation method and / or energy regulation method described in any of the above embodiments, this disclosure also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer to cause the computer and / or the processor to perform the device observation result evaluation method and / or energy regulation method. Corresponding to the execution entity for each step in each embodiment of the device observation result evaluation method and / or energy regulation method, the processor executing the corresponding step can belong to the corresponding execution entity.

[0076] The computer program product of the above embodiments is used to enable the computer and / or the processor to execute the device observation result evaluation method and / or energy regulation method as described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0077] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0078] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0079] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0080] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A method for evaluating equipment observation results, characterized in that, include: The real-time parameters of any target monitoring device are determined through the stream computing engine. The updated data of any of the target monitoring devices is determined by using a pre-built digital model; Based on the real-time parameters and the updated data, the synchronization delay and data deviation are determined; The observation results are evaluated based on the synchronization delay and the data deviation.

2. The method according to claim 1, characterized in that, The process of determining the real-time parameters of any target monitoring device through a stream computing engine includes: Based on the stream computing engine, determine the real-time data stream of any of the target monitoring devices; The real-time parameters are determined based on the real-time data stream.

3. The method according to claim 1, characterized in that, The step of determining the synchronization delay based on the real-time parameters and the updated data includes: Based on the real-time parameters and the updated data, the synchronization delay is determined using the following formula: ， Among them, t model_update The updated data specifically refers to the timestamp t, indicating the completion of the update of the state data model attributes in the digital model. device_colltect The real-time data specifically refers to the timestamp of the raw data collected by the physical sensors of the target monitoring device.

4. The method according to claim 1, characterized in that, The step of determining the data deviation based on the real-time parameters and the updated data includes: Based on the real-time parameters and the updated data, the data deviation is determined using the following formula: ， Among them, V model The updated data specifically refers to the parameter values ​​recorded in the digital model, V. device The real-time parameter specifically refers to the real-time data of the target monitoring device measured by physical equipment.

5. An energy regulation method, characterized in that, include: Identify the electrical equipment and power generation equipment in the target area; Based on the stream computing engine, determine the first power data stream of any of the electrical devices and the second power data stream of any of the power generation devices; Based on the stream computing engine, determine the first power data stream of any of the electrical devices and the second power data stream of any of the power generation devices; Based on the stream computing engine, a first power data stream of any of the electrical devices and a second power data stream of any of the power generation devices are determined.

6. The method according to claim 5, characterized in that, The step of determining the net power consumption data stream of the target area in the target time period based on the first power data stream and the second power data stream includes: Based on the first power data stream, determine the total power load of the electrical equipment; Based on the second power data stream, determine the sum of the power generation power of the power generation equipment; The net electricity consumption data stream is determined based on the sum of the total electricity load and the power generation.

7. The method according to claim 5, characterized in that, The step of regulating the power-consuming equipment and the power-generating equipment based on the net power consumption data stream, the first power data stream, and the second power data stream includes: Based on the net power consumption data stream, the first power data stream, and the second power data stream, determine the energy consumption curve of the target area during the target time period; Based on the energy consumption curve, the regulation prediction of any electrical device in the target area is performed using a pre-built digital model to determine the predicted power load of any electrical device. Based on the predicted electricity load, determine the total predicted electricity load for the target area; Based on the predicted total electricity load, the power-consuming equipment and the power-generating equipment are regulated.

8. The method according to claim 5, characterized in that, The method further includes: The power value under the control state is determined through the stream computing engine; The actual total load reduction is determined based on the predicted power value and the power value under the control state. The energy regulation effect is determined based on the actual total load reduction.

9. A device for evaluating equipment observation results, characterized in that, include: The first determination module is configured to determine the real-time parameters of any target monitoring device through a stream computing engine; The second determining module is configured to determine the updated data of any of the target monitoring devices using a pre-built digital model; The calculation module is configured to determine the synchronization delay and data deviation based on the real-time parameters and the updated data; The evaluation module is configured to evaluate the observation results based on the synchronization delay and the data deviation.

10. An energy regulation device, characterized in that, include: The equipment identification module is configured to identify electrical equipment and power generation equipment in a target area; The data stream module is configured to determine a first power data stream of any of the electrical devices and a second power data stream of any of the power generating devices based on the stream computing engine. The power consumption calculation module is configured to determine the net power consumption data stream of the target area in the target time period based on the first power data stream and the second power data stream; The energy control module is configured to control the electrical equipment and the power generation equipment based on the net power consumption data stream, the first power data stream, and the second power data stream.

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