Building indoor inspection group control system based on grid-friendly

Abstract

The grid-friendly building indoor inspection group control system comprises a calculation module (10), an indoor control module (20) and a plurality of indoor inspection robots (22). The calculation module can calculate the predicted building total energy consumption at the next moment according to the obtained meteorological data and building historical operation data, and calculate the predicted operation parameters of the energy system and the air conditioning system according to the predicted building total energy consumption, so that the two systems operate according to the respective predicted operation parameters. The indoor control module can receive the inspection parameter acquisition instruction issued by the calculation module. Each indoor inspection robot can collect the actual inspection parameters of each indoor partition and upload them to the indoor control module. The indoor control module can judge the error between the actual inspection parameters and the predicted inspection parameters, and upload the actual inspection parameters to the calculation module. The calculation module can calculate the error between the predicted building total energy consumption and the actual building total energy consumption, and store the actual building total energy consumption and each actual operation parameter. It improves the efficiency of building indoor inspection.

Description

Technical Field

[0001] This invention relates to the field of building indoor environment control technology, and in particular to a grid-friendly building indoor inspection and control system. Background Technology

[0002] Existing building indoor inspection robots are mainly used to travel along fixed routes throughout the entire area. They can receive user comfort data and communicate with air conditioning equipment, and control the operating parameters of the air conditioning equipment based on the user comfort data.

[0003] Typically, an inspection robot with human-computer interaction capabilities and the ability to control air conditioning equipment has several significant drawbacks when conducting full-area inspections of a building's interior. Feedback commands from different indoor zones often experience time delays, which impacts inspection efficiency. Inspection robots with these features are only suitable for small buildings; when faced with the inspection of large buildings, completing the inspection of all zones takes a very long time, failing to meet the demands for efficient inspection. Summary of the Invention

[0004] The purpose of this invention is to provide a grid-friendly building indoor inspection group control system that can improve the efficiency of building indoor inspection.

[0005] This invention provides a grid-friendly building indoor inspection and control system, comprising a computing module, an indoor control module, and several indoor inspection robots. The computing module is configured to: at time i (where i is an integer greater than 0), acquire meteorological data for time i+1 from the internet, and calculate the predicted total building energy consumption for time i+1 by combining it with stored historical building operation data; then, based on the predicted total building energy consumption, calculate the predicted system operating parameters for the energy system and the air conditioning system for time i+1, and issue commands to ensure that the energy system and air conditioning system operate according to their respective predicted system operating parameters at time i+1. The indoor control module is signal-connected to the computing module. The computing module is also configured to send inspection parameter acquisition commands for time i+1 to the indoor control module, including predicted inspection parameters for time i+1 based on historical building operation data. Each indoor inspection robot corresponds to an indoor zone and is signal-connected to the indoor control module. The indoor control module is configured to receive inspection parameter acquisition commands and issue commands to each indoor inspection robot to collect the actual inspection parameters for each indoor zone. Each indoor inspection robot is configured to collect actual inspection parameters for each indoor zone and upload them to the indoor control module. The indoor control module is also configured to summarize the actual inspection parameters for each zone and determine whether the error between the actual inspection parameters at time i+1 and the predicted inspection parameters at that time is within a preset range. If yes, the collected actual inspection parameters are uploaded to the calculation module; otherwise, the error deviation point is calibrated, the data collected by each indoor inspection robot is uploaded, and the error is reported to the calculation module. The calculation module is also configured to acquire the actual operating parameters of the energy system and the air conditioning system, calculate the actual total building energy consumption at time i+1 based on the actual inspection parameters and the actual operating parameters of the energy system and the air conditioning system, and verify whether the error between the actual total building energy consumption and the predicted total building energy consumption is within a set range. If so, the actual total building energy consumption, the actual inspection parameters, and the actual operating parameters of the energy system and the air conditioning system are stored in the building's historical operating data. If not, the error deviation point is determined by comparing the actual inspection parameters and the actual operating parameters of the energy system and the air conditioning system, and the actual total building energy consumption and the error deviation value are recalculated based on the data excluding the error deviation point. The actual total building energy consumption and the data collected at time i+1 are then stored in the building's historical operating data.

[0006] By setting up multiple indoor inspection robots to inspect indoor areas in different zones, and controlling the indoor inspection robots in different zones through an indoor control module, and then controlling the indoor control module through a calculation module, the efficiency of building indoor inspection is improved, while the amount of calculation in the calculation module is reduced and the calculation efficiency is improved.

[0007] In another illustrative embodiment of a grid-friendly building indoor inspection and control system, an energy control module and several energy inspection robots are also included. The energy control module is signal-connected to the computing module and configured to: receive instructions from the computing module; calculate the predicted operating parameters of each energy device in the energy system at time i+1 and issue instructions accordingly. Each energy inspection robot corresponds to an energy-side partition and is signal-connected to the energy control module and at least one energy device. Each energy inspection robot can receive instructions from the energy control module and control each energy device to operate according to its respective predicted operating parameters at time i+1; collect the actual operating parameters of each energy device and upload them to the energy control module. The energy control module can also compare the actual operating parameters of each energy device with the corresponding predicted operating parameters, determine whether the error is within a set range, and if so, summarize the actual operating parameters of each energy device as the actual operating parameters of the energy system and upload them to the computing module; if not, verify the inspection tasks of each energy inspection robot, upload the data collected by each energy inspection robot, and report any anomalies to the computing module. This facilitates the acquisition of actual operating parameters of the energy system, enabling zoned management of the energy system and improving the efficiency, timeliness, and accuracy of inspections.

[0008] In another illustrative embodiment of a grid-friendly building indoor inspection and control system, an air conditioning control module and several air conditioning inspection robots are also included. The air conditioning control module is signal-connected to a computing module and configured to: receive instructions from the computing module, calculate the predicted operating parameters of each air conditioning unit in the air conditioning system at time i+1, and issue instructions accordingly. Each air conditioning inspection robot corresponds to an air conditioning side zone and is signal-connected to the air conditioning control module and at least one air conditioning unit. Each air conditioning inspection robot can receive instructions from the air conditioning control module and control each air conditioning unit to operate according to its respective predicted operating parameters at time i+1, collect the actual operating parameters of each air conditioning unit, and upload them to the air conditioning control module. The air conditioning control module can also compare the actual operating parameters of each air conditioning unit with the corresponding predicted operating parameters, determine whether the error is within a set range, and if so, summarize the actual operating parameters of each air conditioning unit as the actual operating parameters of the air conditioning system and upload them to the computing module; if not, verify the inspection tasks of each air conditioning inspection robot, upload the data collected by each air conditioning inspection robot, and report any anomalies to the computing module. This facilitates the acquisition of actual operating parameters of the air conditioning system, enabling zoned management of the air conditioning system and improving the efficiency, timeliness, and accuracy of inspections.

[0009] In another illustrative implementation of a grid-friendly building indoor inspection group control system, the building indoor inspection group control system executes a flexible adjustment strategy. The calculation module calculates the flexible adjustment time before the flexible adjustment strategy is executed. The flexible adjustment time is calculated according to the formula shown in equation (1):

[0010]

[0011] Among them, t a The flexible adjustment time is expressed in hours; ΔT is the temperature difference between the wall surface temperature before the flexible adjustment strategy is implemented and the target wall surface temperature, expressed in °C; V is the wall volume, expressed in m³. 3 ρ represents the wall density, in kg / m³. 3 c p P represents the specific heat capacity of the wall, expressed in J / (kg·K); a The power supplied is for cooling or heating, measured in watts (W). This calculation method is simple, requires minimal computation, facilitates rapid determination of the flexible adjustment time, and is beneficial for implementing flexible adjustment strategies.

[0012] In another illustrative implementation of a grid-friendly building indoor inspection group control system, the predicted total building energy consumption at time i+1 is predicted using the formula shown in equation (2):

[0013]

[0014] in,

[0015] E i+1 Let i+1 be the predicted total building energy consumption. The amount of heat or cooling required to maintain the building's indoor temperature at time i+1 under the basic design conditions without implementing the flexible adjustment strategy can be found in the building's historical operation data. The amount of heat or cooling that the building flexibly adjusts when the flexible adjustment strategy is implemented at time i+1 can be found in the building's historical operation data. The energy consumption for internal building functions at time i+1, including lighting, elevators, domestic hot water, sockets, and other functional energy consumption, can be obtained from the building's historical operation data; C i+1 The personnel utilization coefficient at time i+1 is obtained from the historical personnel load table and can be found in the building's historical operation data. This represents the real-time system energy efficiency at time i+1, obtained from the building's historical operating data. This facilitates the prediction of the building's total energy consumption at any given time, thus saving computing resources in the calculation module.

[0016] In another illustrative implementation of a grid-friendly building indoor inspection and control system, the predicted operating parameters of the energy system include predicted cooling (or heating) demand, which is obtained using a particle swarm optimization (PSO) algorithm based on the predicted total building energy consumption; the predicted operating parameters of the air conditioning system include predicted air conditioning output, which is obtained using a PSO algorithm based on the predicted total building energy consumption; and / or the predicted inspection parameters for indoor areas include predicted number of people indoors, predicted overall user satisfaction, and predicted number of people with specific needs indoors, which are obtained using a PSO algorithm based on the predicted total building energy consumption. This facilitates the rapid and accurate acquisition of the optimal solution.

[0017] In another illustrative implementation of a grid-friendly building indoor inspection and control system, the predicted operating parameters of each device in the energy system are obtained using a particle swarm optimization (PSO) algorithm based on the system's predicted operating parameters; the predicted operating parameters of each device in the air conditioning system are obtained using the same PSO algorithm; and / or the inspection paths and tasks of each indoor inspection robot in the indoor area are obtained using the same PSO algorithm based on the predicted inspection parameters. The inspection tasks include collecting data on the real-time number of people in the room, user satisfaction, and the number of people with specific needs in the room. This facilitates the rapid and accurate acquisition of the optimal solution.

[0018] In another illustrative implementation of a grid-friendly building indoor inspection group control system, the system predictive operating parameters of the air conditioning system also include the predicted indoor temperature. The predicted indoor temperature at time i+1 is calculated using the formula shown in equation (3):

[0019]

[0020] in, Let A be the predicted indoor temperature at time i+1, in °C; and let A be the building's exterior wall area, in m². 2 h in The convective heat transfer coefficient of the inner surface of the building's exterior wall, expressed in W / m². 2 k;T wall,in Temperature of the inner surface of the building's exterior walls, set according to control requirements, in °C; T out Outdoor temperature, in °C; m1, mass of the building's exterior wall, in kg; c p This represents the specific heat capacity of the wall, expressed in K / kg℃. The heat or cooling required to maintain the building's indoor temperature at time i+1 under the basic design conditions without implementing a flexible adjustment strategy is obtained from the building's historical operating data. This calculation method is simple, computationally inexpensive, and beneficial for implementing a flexible adjustment strategy.

[0021] In another illustrative implementation of a grid-friendly building indoor inspection group control system, the system predictive operating parameters of the air conditioning system also include the predicted wall temperature. The predicted wall temperature at time i+1 is calculated using the formula shown in equation (4):

[0022]

[0023] in,

[0024] is the predicted wall temperature at time i+1, in °C; k is the wall temperature distribution coefficient, obtained from tests based on the composition characteristics of different wall materials; m2 is the mass of the building wall, in kg; c p T represents the specific heat capacity of the wall, expressed in K / kg℃. in,set The wall temperature when the flexible adjustment strategy is not implemented is obtained from historical building operation data. The amount of heat or cooling that the building flexibly adjusts at time i+1 when implementing the flexible adjustment strategy can be obtained from the building's historical operating data. This calculation method is simple, computationally inexpensive, and beneficial for implementing flexible adjustment strategies.

[0025] In another illustrative embodiment of a grid-friendly building indoor inspection group control system, the indoor control module is further configured to: monitor each indoor inspection robot, receive fault reports sent by the indoor inspection robots and determine the fault point, calculate incomplete work packages and establish a bidding mechanism, send the incomplete work packages to the indoor inspection robot with the shortest time to complete all task packages, and receive actual inspection parameters sent by the indoor inspection robots; the energy control module is further configured to: monitor each energy inspection robot, receive fault reports sent by the energy inspection robots and determine the fault point, calculate incomplete work packages and establish a bidding mechanism, send the incomplete work packages to the energy inspection robot with the shortest time to complete all task packages, and receive actual operating parameters of each energy device sent by the energy inspection robots; and / or the air conditioning control module is further configured to: receive fault reports sent by the air conditioning inspection robot and determine the fault point, calculate incomplete work packages and establish a bidding mechanism, send the incomplete work packages to the air conditioning inspection robot with the shortest time to complete all task packages, and receive actual operating parameters of each air conditioning device sent by the air conditioning inspection robot. By coordinating the indoor control module, energy control module, and air conditioning control module, inspection interruptions can be effectively avoided, resource utilization efficiency can be improved, fault response capabilities can be enhanced, and inspection collaboration can be optimized.

[0026] In another illustrative implementation of a grid-friendly building indoor inspection and control system, the indoor control module, energy control module, and air conditioning control module are configured to communicate with each other to exchange fault alarms, and to urgently compile currently collected data and upload it to the computing module upon receiving a fault alarm. This enhances the stability and autonomous correction capabilities of the grid-friendly building indoor control system. Attached Figure Description

[0027] The following figures are for illustrative purposes only and do not limit the scope of the invention.

[0028] Figure 1 This is a schematic plan view of one embodiment of a grid-friendly building indoor inspection group control system.

[0029] Figure 2 For illustrative purposes Figure 1 The diagram shows the workflow of a grid-friendly building indoor inspection group control system.

[0030] Figure 3 This is a schematic plan view of another illustrative implementation of a grid-friendly building indoor inspection and control system.

[0031] Figure 4 This is a flowchart illustrating the process of obtaining actual system operating parameters of an energy system.

[0032] Figure 5 This is a flowchart illustrating the process of obtaining actual system operating parameters of an air conditioning system.

[0033] Figure 6 This is a flowchart illustrating the process of the particle swarm optimization algorithm.

[0034] Label Explanation

[0035] 100 Building Indoor Inspection and Control System Based on Grid-Friendly Architecture

[0036] 10 Calculation Module

[0037] 20 Indoor Control Module

[0038] 22 Indoor Inspection Robots

[0039] 30 Energy Control Module

[0040] 32 Energy Inspection Robots

[0041] 40 Air Conditioning Control Module

[0042] 42. Air conditioning inspection robot. Detailed Implementation

[0043] To provide a clearer understanding of the technical features, objectives, and effects of the invention, specific embodiments of the invention are now described with reference to the accompanying drawings, in which the same reference numerals denote the same parts.

[0044] In this document, “illustrative” means “serving as an example, illustration or description”, and any illustration or implementation described herein as “illustrative” should not be construed as a more preferred or advantageous technical solution.

[0045] To keep the drawings simple, each drawing only schematically shows the parts related to the present invention, and they do not represent the actual structure of the product.

[0046] Figure 1 This is a schematic diagram of the floor plan of a grid-friendly building indoor inspection and control system. See also the illustrative implementation details. Figure 1 The grid-friendly building indoor inspection group control system 100 includes a computing module 10, an indoor control module 20, and two indoor inspection robots 22. However, it is not limited to this, and the number of indoor inspection robots 22 can be set to other numbers according to the actual application scenario.

[0047] The calculation module 10 is configured to: at time i, obtain meteorological data for time i+1 from the Internet, and calculate the predicted total building energy consumption for time i+1 by combining it with the stored historical building operation data; then, based on the predicted total building energy consumption, calculate the predicted system operating parameters of the energy system and the air conditioning system for time i+1, and issue instructions to make the energy system and the air conditioning system operate according to their respective predicted system operating parameters at time i+1. Here, i is an integer greater than 0, for example, 1, 2...24. If a 24-hour day is divided into 48 time periods, i can also be 1, 2...48.

[0048] The indoor control module 20 is signal-connected to the calculation module 10. The calculation module 10 is also configured to send an inspection parameter acquisition command at time i+1 to the indoor control module 20. This command includes predicted inspection parameters at time i+1, based on historical building operation data. In an illustrative embodiment, the predicted inspection parameters include predicted real-time indoor occupancy, predicted overall user satisfaction, and predicted occupancy of individuals with specific needs.

[0049] Each indoor inspection robot 22 corresponds to an indoor zone and is signal-connected to the indoor control module 20. The indoor control module 20 is configured to receive inspection parameter acquisition commands and issue commands to each indoor inspection robot 22 to collect the actual inspection parameters of each indoor zone. Each indoor inspection robot 22 is configured to collect the actual inspection parameters of each indoor zone and upload them to the indoor control module 20. The indoor control module 20 is also configured to summarize the actual inspection parameters of each zone and determine whether the error between the actual inspection parameters at time i+1 and the predicted inspection parameters at that time is within a preset range. In the illustrative embodiment, the summarized actual inspection parameters include the real-time number of people in the room, overall user satisfaction, and the number of people with specific needs in the room.

[0050] If so, the collected actual inspection parameters will be uploaded to the calculation module 10.

[0051] If not, the error deviation point is calibrated, and the data collected by each indoor inspection robot 22 is uploaded, along with the error report to the calculation module 10. In the illustrative embodiment, the data collected by each indoor inspection robot 22 when there is no feedback on comfort issues includes the real-time number of people in the room, overall user satisfaction, number of people with specific needs in the room, and indoor temperature and humidity. When there is feedback, it is also necessary to collect the feedback issue and the temperature at the time of feedback to help the system determine whether there are people with special needs. For example, when the room temperature is 25℃, and most people in the room do not report any comfort issues, but a few people report that the temperature is too high, when the data is fed back to the system, the system determines that the person who made the feedback has special needs and marks that there is one person with special needs in that area.

[0052] The calculation module 10 is also configured to acquire the actual operating parameters of the energy system and the air conditioning system, calculate the actual total building energy consumption at time i+1 based on the actual inspection parameters and the actual operating parameters of the energy system and the air conditioning system, and verify whether the error between the actual total building energy consumption and the predicted total building energy consumption is within the set range.

[0053] If so, the actual total building energy consumption, actual inspection parameters, and the actual operating parameters of the energy system and air conditioning system respectively will be stored in the building's historical operating data.

[0054] If not, the error deviation point is determined by comparing the actual inspection parameters with the actual operating parameters of the energy system and the air conditioning system, and the actual total energy consumption and error deviation value of the building are recalculated based on the data other than the error deviation point. The actual total energy consumption of the building and the data collected at time i+1 are then stored in the building's historical operating data.

[0055] By setting up multiple indoor inspection robots to inspect indoor areas in different zones, and controlling the indoor inspection robots in different zones through an indoor control module, and then controlling the indoor control module through a calculation module, the efficiency of building indoor inspection is improved, while the amount of calculation in the calculation module is reduced and the calculation efficiency is improved.

[0056] In practical applications, due to time-of-use (TOU) pricing, building owners typically employ flexible adjustment strategies to reduce electricity costs. These strategies leverage the differences in electricity prices at different times of day to rationally manage building temperature control. For example, in summer, when grid electricity prices are relatively low (usually in the early morning), pre-cooling is implemented in advance; similarly, in winter, pre-heating is carried out during periods of lower grid electricity prices. Thus, when grid electricity prices rise to high levels (generally during peak working hours), it is only necessary to maintain the temperature achieved after pre-cooling or pre-heating. This significantly reduces electricity consumption during peak hours, thereby effectively lowering building electricity costs.

[0057] Figure 2 For illustrative purposes Figure 1 The diagram shows a workflow of a grid-friendly building indoor inspection and control system. See also the illustrative implementation details. Figure 2 The flexible adjustment strategy implemented by the building indoor inspection group control system includes steps S10 to S73.

[0058] S10: The calculation module calculates the flexible adjustment time. The flexible adjustment time is calculated, for example, according to the formula shown in equation (1):

[0059]

[0060] in,

[0061] t a For flexible time adjustment, the unit is hours.

[0062] ΔT represents the temperature difference between the wall surface temperature before the flexible adjustment strategy is implemented and the target wall surface temperature, expressed in °C. The wall surface temperature before the flexible adjustment strategy is implemented is the measured value, and the target wall surface temperature is the input value.

[0063] V represents the volume of the wall, in meters (m). 3 ,

[0064] ρ is the density of the wall, in kg / m³. 3 ,

[0065] c p This refers to the specific heat capacity of the wall, expressed in J / (kg·K).

[0066] Pa The power output for cooling or heating is measured in watts (W).

[0067] S20: The calculation module calculates and predicts the building's total energy consumption. After calculating the flexibility adjustment time, the calculation module determines the starting time for implementing the flexibility adjustment strategy. For example, if the target wall temperature needs to reach 25°C from 8:00 AM, and the flexibility adjustment time is 5 hours, then the starting time for implementing the flexibility adjustment strategy is 3:00 AM. During the implementation of the flexibility adjustment strategy, at time i, the calculation module obtains meteorological data and electricity price data for the next time, i.e., time i+1, from the internet, and also obtains the data for time i+1 from its stored historical building operation data. C i+1 and

[0068] in, The amount of heat or cooling required to maintain the building's indoor temperature at time i+1 under the basic design conditions without a flexible adjustment strategy can be obtained from the building's historical operation data. The amount of heat or cooling that the building flexibly adjusts at time i+1 under the flexible adjustment condition can be found in the building's historical operation data. The energy consumption for internal building functions at time i+1, including lighting, elevators, domestic hot water, sockets, and other functional energy consumption, can be obtained from the building's historical operation data; C i+1 The personnel utilization coefficient at time i+1 is obtained from the historical personnel load table. For an example of the historical personnel load table, please refer to Table 1, which can be found in the building's historical operation data. The real-time system energy efficiency at time i+1 is obtained based on the building's historical operating data.

[0069] It should be noted that, and C i+1 These four data points are derived from the same historical day. This historical date must have similar or identical weather data to the current date, including real-time temperature, wind speed, and sunshine information. Furthermore, the selected historical date should be in the same week as the current date, for example, both being Wednesdays, to ensure that the data for C is similar or identical. and C i+1 Once determined, the calculation module can immediately obtain a match. The value.

[0070] Table 1 Historical Personnel Load Table

[0071]

[0072]

[0073] The calculation module calculates the predicted total building energy consumption at time i+1 based on the data obtained from the building's historical operation data. The predicted total building energy consumption at time i+1 is calculated, for example, using the formula shown in equation (2):

[0074]

[0075] Among them, E i+1 This represents the predicted total building energy consumption at time i+1. By querying historical building operation data, the predicted total building energy consumption at any given time can be calculated, which helps save computational resources in the calculation module and facilitates rapid result acquisition.

[0076] It is important to note that many buildings now have photovoltaic power generation capabilities. Therefore, when predicting the electricity cost at time i+1, the predicted photovoltaic power generation at that time should be subtracted from the predicted total building energy consumption at that time. The predicted photovoltaic power generation at that time can also be obtained by querying the building's historical operating data. The time at that time and the historical time in the building's historical operating data must have similar or identical weather data, including real-time temperature, wind speed, sunshine, and other information.

[0077] S30: The calculation module generates predicted operating parameters for the energy system and air conditioning system, as well as predicted inspection parameters for indoor areas, and issues instructions. Based on the predicted total building energy consumption at time i+1, the calculation module generates predicted operating parameters for the energy system and air conditioning system at time i+1, and issues instructions to the energy control module and air conditioning control module respectively, so that the energy system and air conditioning system operate according to their respective predicted operating parameters at time i+1. At the same time, it issues an inspection parameter collection instruction for time i+1 to the indoor control module.

[0078] Predictive operating parameters of an energy system, such as the predicted cooling (or heating) demand at time i+1 (i.e., the predicted output), can be obtained using the particle swarm optimization algorithm.

[0079] The predicted operating parameters of the air conditioning system include, for example, the predicted output power, predicted indoor temperature, and predicted wall temperature at time i+1. The predicted output power of the air conditioning system at time i+1 can be obtained using the particle swarm optimization algorithm.

[0080] The predicted indoor temperature at time i+1 can be calculated, for example, using the formula shown in equation (3):

[0081]

[0082] in,

[0083] The predicted indoor temperature at time i+1, in °C.

[0084] A represents the area of ​​the building's exterior walls, in square meters (m²).2 ,

[0085] h in The convective heat transfer coefficient of the inner surface of the building's exterior wall, expressed in W / m². 2 k,

[0086] T wall,in This refers to the temperature of the inner surface of the building's exterior walls, set according to control requirements, and expressed in degrees Celsius (°C).

[0087] T out Outdoor temperature, in °C.

[0088] m1 represents the mass of the building's exterior walls, expressed in kg.

[0089] c p This represents the specific heat capacity of the wall, expressed in K / kg℃.

[0090] The predicted wall temperature at time i+1 can be calculated, for example, using the formula shown in equation (4):

[0091]

[0092] in,

[0093] Let i+1 be the predicted wall temperature, in °C.

[0094] k is the temperature distribution coefficient of the wall, which is obtained by testing the composition characteristics of different wall materials.

[0095] m2 represents the mass of the building walls, expressed in kg.

[0096] c p This refers to the specific heat capacity of the wall, expressed in K / kg℃.

[0097] T in,set The wall temperature is the temperature when the flexible adjustment strategy is not implemented, obtained from historical building operation data.

[0098] The amount of heat or cooling that the building flexibly adjusts at time i+1 under flexible adjustment conditions can be found in the building's historical operation data.

[0099] The inspection parameter collection instructions include predicted inspection parameters for time i+1 based on the building's historical operation data. These predicted inspection parameters include, for example, the predicted number of people in the room in real time, the predicted overall user satisfaction, and the predicted number of people with specific needs in the room. They can be obtained by querying historical Wednesday data from the building's historical operation data based on the current date, for example, if the current date is Wednesday.

[0100] S40: The energy system operates according to the predicted operating parameters calculated by the calculation module. The calculation module obtains the actual operating parameters of the energy system at time i+1. These actual operating parameters include, for example, the actual cooling (or heating) output of the energy system, i.e., the actual power output, and the aggregated actual operating parameters of each energy device. The calculation module can obtain the actual operating parameters of the energy system at time i+1 manually or through the control module.

[0101] S50: The air conditioning system operates according to the predicted operating parameters calculated by the calculation module. The calculation module obtains the actual operating parameters of the air conditioning system at time i+1. These actual operating parameters include, for example, the actual cooling (or heating) supply (actual output) of the air conditioning system, the actual operating parameters of each device in the air conditioning system, the actual wall temperature, and the actual indoor temperature and humidity. The calculation module can obtain the actual operating parameters of the air conditioning system at time i+1 manually or through the control module.

[0102] S60: The indoor control module generates instructions to collect actual inspection parameters for each indoor zone and issues these instructions to each indoor inspection robot. The indoor control module is signal-connected to the computing module, and each indoor inspection robot corresponds to an indoor zone and is signal-connected to the indoor control module. After receiving the instructions from the computing module, the indoor control module generates and issues instructions for the optimal path and optimal task package allocation for each indoor inspection robot at time i+1, based on principles such as minimizing execution time.

[0103] S61: After receiving the instruction, each indoor inspection robot collects the actual inspection parameters of each indoor zone and uploads them to the indoor control module. Each indoor inspection robot is equipped with sensors and a human-machine interface, which can obtain the actual inspection parameters at that moment in a timely and accurate manner. The actual inspection parameters include the number of people in the room in real time, user satisfaction, the number of people with specific needs in the room, and indoor temperature and humidity. If there is feedback, it also includes the problem reported and the temperature at the time of feedback.

[0104] S62: The indoor control module summarizes the actual inspection parameters of each zone and determines whether the error between the actual inspection parameters at time i+1 and the predicted inspection parameters at that time is within the preset range. The summarized actual inspection parameters include the number of people in the room in real time, the overall user satisfaction, the number of people with specific needs in the room, and the indoor temperature and humidity. If there is feedback, it also includes the feedback problem and the temperature at the time of feedback.

[0105] If so, proceed to S63: The indoor control module uploads the collected actual inspection parameters to the calculation module, awaiting the next task.

[0106] If not, proceed to S64: Indoor control module calibrates error deviation point, uploads data collected by each indoor inspection robot and reports the error to the calculation module, and waits for the next task.

[0107] S70: The calculation module calculates the actual total building energy consumption at time i+1 based on the actual inspection parameters at time i+1 and the actual operating parameters of the energy system and the air conditioning system.

[0108] S71: The calculation module verifies whether the error between the actual total building energy consumption and the predicted total building energy consumption is within the set range.

[0109] If so, proceed to S72: Store the actual total building energy consumption, actual inspection parameters, and the actual operating parameters of the energy system and air conditioning system respectively into the building's historical operating data, then return to S20.

[0110] If not, proceed to S73: By comparing the actual inspection parameters with the actual operating parameters of the energy system and the air conditioning system, determine the error deviation point, and recalculate the actual total energy consumption of the building and the error deviation value based on the data other than the error deviation point. Store the actual total energy consumption of the building and the data collected at time i+1 into the building's historical operating data, and return to S20.

[0111] Figure 3 This is a schematic plan view of another illustrative implementation of a grid-friendly building indoor inspection and control system. See also the illustrative implementation details. Figure 3 To obtain the actual operating parameters of the energy system and inspect each zone of the energy system, the building indoor inspection and control system 100 also includes an energy control module 30 and two energy inspection robots 32. However, it is not limited to these; the number of energy inspection robots 32 can be set to other numbers according to the actual application scenario. The energy control module 30 is signal-connected to the calculation module 10 and is configured to: receive instructions from the calculation module 10, calculate the predicted operating parameters of each energy device in the energy system at time i+1, and issue instructions.

[0112] The actual operating parameters of an energy system include the actual output of the energy system (i.e., the actual cooling or heating provided) and the actual operating parameters of each energy device. Actual or predicted operating parameters of each energy device in the energy system include, for example, the outlet and return temperatures of chilled water (or hot water), the operating current and voltage of the equipment, the flow rate of chilled water (or hot water), the inlet and outlet pressures of the water pumps, the system flow rate, and the current and voltage of the water pump motors.

[0113] Each energy inspection robot 32 corresponds to an energy-side zone and is signal-connected to the energy control module 30 and at least one energy device. Each energy inspection robot 32 can receive instructions from the energy control module 30 and control each energy device to operate according to its predicted operating parameters at time i+1. It also collects the actual operating parameters of each energy device and uploads them to the energy control module 30. The energy control module 30 can also compare the actual operating parameters of each energy device with the corresponding predicted operating parameters to determine whether the error is within a set range.

[0114] If so, the actual operating parameters of each energy device are summarized as the actual operating parameters of the energy system and uploaded to the calculation module 10.

[0115] If not, the inspection tasks of each energy inspection robot 32 are checked, the data collected by each energy inspection robot 32 is uploaded, and an anomaly is reported to the calculation module 10. The data collected by each energy inspection robot 32 includes the actual operating parameters of each energy device. This enables zoned management of the energy system, which helps improve the efficiency, timeliness, and accuracy of inspections.

[0116] Figure 4 This is a flowchart illustrating the process of obtaining actual system operating parameters of an energy system. See also the illustrative implementation. Figure 4 Step S40 includes S41 to S45.

[0117] S41: The energy control module generates predicted operating parameters for each energy device at time i+1 and issues instructions.

[0118] The energy control module is connected to the computing module to receive instructions from the computing module, calculate the predicted operating parameters of each energy device in the energy system at that moment, and issue instructions to each energy inspection robot.

[0119] S42: Each energy inspection robot receives and executes instructions and controls the operation of each energy device at time i+1, collects the actual operating parameters of the device at time i+1 and uploads them to the energy control module.

[0120] Each energy inspection robot corresponds to an energy-side zone and is signal-connected to the energy control module and at least one energy device. At time i+1, each energy inspection robot receives instructions from the energy control module and controls each energy device to operate according to its predicted operating parameters, collects the actual operating parameters of each energy device, and uploads them to the energy control module.

[0121] S43: The energy control module compares the actual operating parameters of each energy device with the corresponding predicted operating parameters to determine whether the error is within the set range.

[0122] If so, proceed to S44: summarize the actual operating parameters of each energy device into the actual operating parameters of the energy system and upload them to the calculation module, awaiting the next task.

[0123] If not, proceed to S45: Verify the inspection tasks of each energy inspection robot, upload the data collected by each energy inspection robot and report any abnormalities to the calculation module, and wait for the next task.

[0124] exist Figure 3 In the illustrative embodiment shown, to obtain the actual operating parameters of the air conditioning system and inspect each zone of the air conditioning system, the building indoor inspection group control system 100 also includes an air conditioning control module 40 and two air conditioning inspection robots 42. However, it is not limited to this; the number of air conditioning inspection robots 42 can be set to other numbers according to the actual application scenario. The air conditioning control module 40 is signal-connected to the calculation module 10 and configured to: receive instructions issued by the calculation module 10, calculate the predicted operating parameters of each air conditioning device in the air conditioning system at time i+1, and issue instructions.

[0125] The actual operating parameters of an air conditioning system include the actual output of the system (i.e., the actual cooling or heating supply), the actual operating parameters of each piece of equipment, wall temperature, and indoor temperature and humidity. Actual or predicted operating parameters of each air conditioning unit in the system include, for example, outlet and return air temperatures, refrigerant pressure and flow rate, fan speed, and fan operating status.

[0126] Each air conditioning inspection robot 42 corresponds to an air conditioning side zone and is signal-connected to the air conditioning control module 40 and at least one air conditioning device. It can receive instructions from the air conditioning control module 40 and, at time i+1, control each air conditioning device to operate according to its predicted operating parameters. It collects the actual operating parameters, actual wall temperature, and actual indoor temperature and humidity of each air conditioning device and uploads them to the air conditioning control module 40. The air conditioning control module 40 can also compare the actual operating parameters of each air conditioning device with the corresponding predicted operating parameters, predicted wall temperature, and actual wall temperature, as well as the predicted indoor temperature and actual indoor temperature, to determine whether the error is within the set range.

[0127] If so, then the actual operating parameters of each air conditioning unit, the actual wall temperature, and the actual indoor temperature and humidity are summarized as the actual operating parameters of the air conditioning system, and uploaded to the calculation module 10.

[0128] If not, the inspection tasks of each air conditioning inspection robot 42 are checked, the data collected by each air conditioning inspection robot 42 is uploaded, and the anomaly is reported to the calculation module 10. This enables zoned management of the air conditioning system, which helps improve the efficiency, timeliness, and accuracy of inspections. At the same time, by collecting the actual wall temperature and the actual indoor temperature and comparing them with the predicted wall temperature and the predicted indoor temperature, the operating error is checked, which effectively improves the accuracy of control.

[0129] In other illustrative embodiments, the air conditioning inspection robots 42 may not collect actual wall temperatures and actual indoor temperature and humidity. The air conditioning control module 40 only needs to compare the actual operating parameters of each device. In this case, the actual operating parameters of the air conditioning system include the actual output of the air conditioning system (i.e., the actual cooling or heating supply) and the actual operating parameters of each device. This also enables zoned management of the air conditioning system, which helps reduce the workload of inspections and improves inspection efficiency.

[0130] Figure 5 This is a flowchart illustrating the process of obtaining actual system operating parameters of an air conditioning system. See the illustrative embodiment below. Figure 5 Step S50 includes S51 to S55.

[0131] S51: The air conditioning control module calculates and generates predicted operating parameters for each air conditioning device at time i+1 and issues instructions.

[0132] The air conditioning control module is connected to the computing module to receive instructions from the computing module, calculate the predicted operating parameters of each air conditioning device in the air conditioning system at time i+1, and issue instructions to each air conditioning inspection robot.

[0133] S52: Each air conditioning inspection robot receives and executes instructions, controls the operation of each air conditioning device at time i+1, collects the actual operating parameters of the device at time i+1, the actual wall temperature and the actual indoor temperature, and uploads them to the air conditioning control module.

[0134] Each air conditioning inspection robot corresponds to an air conditioning side zone and is signal-connected to the air conditioning control module and at least one air conditioning device. At time i+1, after receiving the instruction from the air conditioning control module, each air conditioning inspection robot controls each air conditioning device to operate according to its own predicted operating parameters, collects the actual operating parameters of each air conditioning device, the actual wall temperature, and the actual indoor temperature and humidity, and uploads them to the air conditioning control module.

[0135] S53: The air conditioning control module compares the actual operating parameters of each air conditioning unit with the corresponding predicted operating parameters, predicted wall temperature, and actual wall temperature, as well as the predicted indoor temperature and actual indoor temperature, to determine whether the error is within the set range.

[0136] If so, proceed to S54: Summarize the actual operating parameters of each air conditioning unit, the actual wall temperature, and the actual indoor temperature and humidity as the actual operating parameters of the air conditioning system and upload them to the calculation module, awaiting the next task.

[0137] If not, proceed to S55: Verify the inspection tasks of each air conditioner inspection robot, upload the data collected by each air conditioner inspection robot and report any abnormalities to the calculation module, and wait for the next task.

[0138] It should be noted that the instructions issued by the calculation module simultaneously reach the energy control module, the air conditioning control module, and the indoor control module; that is, steps S40, S50, and S60 are performed concurrently. Although the calculation module calculates the predicted operating parameters of the energy system and the air conditioning system, as well as the predicted inspection parameters of the indoor areas, and these three systems initially operate according to these parameters, if the overall user satisfaction in the indoor areas changes during operation, the indoor inspection robot can promptly feed back the change to the indoor control module. The indoor control module, after verification, uploads the actual inspection parameters and errors to the calculation module. The calculation module can then regenerate the actual total building energy consumption and issue instructions to the energy system and the air conditioning system to adjust their predicted operating parameters, ensuring they operate according to the new predicted operating parameters.

[0139] The calculation module can calculate Q at any given time based on the actual operating parameters of the energy equipment, the actual operating parameters of the air conditioning equipment, and the actual inspection parameters uploaded by the energy control module, the air conditioning control module, and the indoor control module, respectively. base Q flex and Q inner The data is recorded and stored in the building's historical operational data. Additionally, the energy control module, air conditioning control module, and indoor control module all have data cleaning functions, capable of cleaning the data uploaded by their respective inspection robots, and then uploading the aggregated data to the calculation module. This helps reduce the computational load on the calculation module, improves computational efficiency, and saves computational resources.

[0140] The calculation module calculates the predicted operating parameters for the energy system, air conditioning system, and indoor areas. Then, the energy control module, air conditioning control module, and indoor control module refine the predicted operating parameters and predicted inspection parameters into data that can be directly collected by each inspection robot. Through data verification, the actual operating parameters of the energy system, air conditioning system, and indoor areas are stored in the building's historical operating data for future reference. This enables zoned management of the energy system, air conditioning system, and indoor areas, which helps improve the efficiency, timeliness, and accuracy of inspections.

[0141] Figure 6 This is a flowchart illustrating the particle swarm optimization algorithm. See also... Figure 6In this illustrative implementation, the predicted operating parameters of the energy system include predicted cooling (or heating) demand, which is obtained using a particle swarm optimization (PSO) algorithm based on the predicted total building energy consumption. The predicted operating parameters of the air conditioning system include the predicted output of the air conditioning system, which is obtained using a PSO algorithm based on the predicted total building energy consumption. The predicted inspection parameters for indoor areas include datasets of predicted number of people indoors, predicted overall user satisfaction, and predicted number of people with specific needs indoors, which are obtained using a PSO algorithm based on the predicted total building energy consumption. The calculation process of the PSO algorithm includes steps S1 to S5.

[0142] S1: Based on the predicted total building energy consumption at time i+1, initialize the particle swarm and set the algorithm parameters. Initializing the particle swarm includes setting the number of particles, initial positions, and velocities. The number of particles can be set empirically, for example, 400. Algorithm parameters, such as inertia weight, cognitive coefficient, and social coefficient, can also be set empirically.

[0143] S2: Set the number of iterations k max Number of iterations k max You can set it based on experience, for example, 10000.

[0144] S3: Iteratively calculate the predicted operating parameters of the energy system, the predicted operating parameters of the air conditioning system, and the predicted inspection parameters to determine the individual optimal position of each particle and the global optimal position of the particle swarm.

[0145] S4: When the number of iterations k is less than k max At that time, whether the function converges, that is, whether the individual optimal position of each particle and the global optimal position of the particle swarm have been determined.

[0146] If so, proceed to S5: Output the predicted operating parameters of the energy system, the predicted operating parameters of the air conditioning system, and the predicted inspection parameters of the optimal solution.

[0147] If not, return to S3 and recalculate.

[0148] In other illustrative embodiments, one or two of the predicted cooling (or heating) demand of the energy system, the predicted output of the air conditioning system, and the indoor area dataset can be obtained using a particle swarm optimization algorithm based on the predicted total building energy consumption, while the others can be solved using other methods well known to those skilled in the art. Alternatively, all three can be solved using other methods well known to those skilled in the art.

[0149] The predicted operating parameters of each device in the energy system are obtained using a particle swarm optimization (PSO) algorithm based on the system's predicted operating parameters. Similarly, the predicted operating parameters of each device in the air conditioning system are obtained using the same PSO algorithm. The inspection paths and tasks of each inspection robot in the indoor area are also obtained using the PSO algorithm based on the predicted inspection parameters. The calculation process for these three sets of PSO algorithms is the same as and similar to steps S1 to S5, and will not be repeated here.

[0150] In the illustrative embodiment, the indoor control module 20 is further configured to: monitor each indoor inspection robot 22, receive fault reports sent by the indoor inspection robots 22 and determine the fault points, calculate unfinished work packages and establish a bidding mechanism, send the unfinished work packages to the indoor inspection robot 22 with the shortest time to complete all task packages, and receive actual inspection parameters sent by the indoor inspection robots 22. The bidding mechanism works as follows: when an indoor inspection robot 22 malfunctions, its unfinished work packages are allocated to the remaining normally operating indoor inspection robots 22; the total time for the remaining indoor inspection robots 22 to complete all their respective task packages is calculated; and the winning indoor inspection robot 22 is selected based on the shortest total time.

[0151] The energy control module 30 is also configured to: monitor each energy inspection robot 32, receive fault reports sent by the energy inspection robots 32 and determine the fault point, calculate incomplete work packages and establish a bidding mechanism, send the incomplete work packages to the energy inspection robot 32 with the shortest time to complete all task packages, and receive the actual operating parameters of each energy device sent by the energy inspection robots 32. Its bidding mechanism is the same as the bidding mechanism established by the indoor control module 20.

[0152] The air conditioning control module 40 is also configured to: receive fault reports sent by the air conditioning inspection robot 42 and determine the fault point; calculate unfinished work packages and establish a bidding mechanism; send the unfinished work packages to the air conditioning inspection robot 42 with the shortest time to complete all task packages; and receive the actual operating parameters of each air conditioning device sent by the air conditioning inspection robot 42. Its bidding mechanism is the same as the bidding mechanism established by the indoor control module 20.

[0153] In other illustrative embodiments, one or both of the indoor control module 20, energy control module 30 and air conditioning control module 40 may not have the function of establishing a bidding mechanism, and the problem can be solved by manually assigning unfinished tasks.

[0154] By coordinating the indoor control module, energy control module, and air conditioning control module, inspection interruptions can be effectively avoided, resource utilization efficiency can be improved, fault response capabilities can be enhanced, and inspection collaboration can be optimized.

[0155] In the illustrative implementation, the indoor control module, energy control module, and air conditioning control module are configured to communicate with each other to exchange fault alarms, and to urgently compile currently collected data and upload it to the computing module upon receiving a fault alarm. The computing module can quickly adjust its strategy upon receiving the urgently uploaded data. Under normal circumstances, the indoor control module, energy control module, and air conditioning control module typically upload data to the computing module only at the next specified time (e.g., every hour). Therefore, the mutual communication among these three modules enhances the stability and autonomous correction capabilities of the grid-friendly building indoor group control system.

[0156] It should be understood that although this specification is described according to various embodiments, not every embodiment contains only one independent technical solution. This way of describing the specification is only for clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

[0157] The detailed descriptions listed above are merely specific descriptions of feasible embodiments of the present invention and are not intended to limit the scope of protection of the present invention. All equivalent implementation schemes or modifications made without departing from the spirit of the present invention, such as combinations, divisions or repetitions of features, should be included within the scope of protection of the present invention.

Claims

1. A building indoor inspection and control system based on grid-friendly technology, characterized in that: include: A computing module (10) is configured to: at time i, obtain meteorological data at time i+1 from the Internet, and calculate the predicted total energy consumption of the building at time i+1 in combination with the historical building operation data stored therein; then calculate the system predicted operating parameters of the energy system and the system predicted operating parameters of the air conditioning system at time i+1 based on the predicted total energy consumption of the building; and issue an instruction to make the energy system and the air conditioning system operate according to their respective system predicted operating parameters at time i+1, where i is an integer greater than 0; An indoor control module (20) is signal-connected to the computing module (10), the computing module (10) being further configured to send an inspection parameter acquisition command at time i+1 to the indoor control module (20), the inspection parameter acquisition command including predicted inspection parameters at time i+1 based on historical building operation data; and Several indoor inspection robots (22) are provided, each corresponding to an indoor zone and connected to the indoor control module (20). The indoor control module (20) is configured to receive the inspection parameter acquisition command and send commands to each of the indoor inspection robots (22) to acquire the actual inspection parameters of each indoor zone. Each of the indoor inspection robots (22) is configured to acquire the actual inspection parameters of each indoor zone and upload them to the indoor control module. The indoor control module is also configured to summarize the actual inspection parameters of each zone and determine whether the error between the actual inspection parameters at time i+1 and the predicted inspection parameters at that time is within a preset range. If so, the collected actual inspection parameters are uploaded to the calculation module (10). If not, then the error deviation point is calibrated, the data collected by each of the indoor inspection robots (22) is uploaded, and the error is reported to the calculation module (10). The calculation module (10) is also configured to acquire the actual operating parameters of the energy system and the air conditioning system, calculate the actual total building energy consumption at time i+1 based on the actual inspection parameters and the actual operating parameters of the energy system and the air conditioning system, and verify whether the error between the actual total building energy consumption and the predicted total building energy consumption is within a set range. If so, the actual total building energy consumption, the actual inspection parameters, and the actual operating parameters of the energy system and the air conditioning system are stored in the building's historical operating data. If not, the error deviation point is determined by comparing the actual inspection parameters with the actual operating parameters of the energy system and the air conditioning system, and the actual total building energy consumption and error deviation value are recalculated based on the data other than the error deviation point. The actual total building energy consumption and the data collected at time i+1 are stored in the building historical operating data. The building indoor inspection group control system implements a flexible adjustment strategy. The calculation module (10) calculates the flexible adjustment time before the flexible adjustment strategy is implemented. The flexible adjustment time is calculated according to the formula shown in equation (1): Equation (1) in, For flexible time adjustment, the unit is hours. The temperature difference between the wall surface temperature before the implementation of the flexible adjustment strategy and the target wall surface temperature, expressed in °C. This refers to the wall volume, in m³. This refers to the wall density, expressed in kg / m³. This refers to the specific heat capacity of the wall, expressed in J / (kg·K). The power output for cooling or heating is measured in watts (W).

2. The building indoor inspection and control system based on a power grid-friendly architecture as described in claim 1, characterized in that, Also includes: An energy control module (30), signal-connected to the computing module (10) and configured to: receive instructions from the computing module (10), calculate the predicted operating parameters of each energy device in the energy system at time i+1 and issue instructions, and Several energy inspection robots (32) are provided, each corresponding to an energy-side partition and signal-connected to the energy control module (30) and at least one energy device. Each energy inspection robot (32) can receive instructions from the energy control module (30) and control each energy device to operate according to its predicted operating parameters at time i+1. The robot collects the actual operating parameters of each energy device and uploads them to the energy control module (30). The energy control module (30) can also compare the actual operating parameters of each energy device with the corresponding predicted operating parameters to determine whether the error is within a set range. If so, the actual operating parameters of each of the energy devices are summarized as the actual operating parameters of the energy system and uploaded to the calculation module (10). If not, the inspection tasks of each of the energy inspection robots (32) are checked, the data collected by each of the energy inspection robots (32) are uploaded, and the abnormality is reported to the computing module (10).

3. The building indoor inspection and control system based on a power grid-friendly architecture as described in claim 2, characterized in that, Also includes: An air conditioning control module (40), which is signal-connected to the computing module (10) and configured to: receive instructions from the computing module (10), calculate the predicted operating parameters of each air conditioning device in the air conditioning system at time i+1 and issue instructions, and Several air conditioning inspection robots (42) are provided. Each air conditioning inspection robot (42) corresponds to an air conditioning side zone and is signal-connected to the air conditioning control module (40) and at least one air conditioning device. Each air conditioning inspection robot (42) can receive instructions issued by the air conditioning control module (40) and control each air conditioning device to operate according to its own predicted operating parameters at time i+1. The robot collects the actual operating parameters of each air conditioning device and uploads them to the air conditioning control module (40). The air conditioning control module (40) can also compare the actual operating parameters of each air conditioning device with the corresponding predicted operating parameters to determine whether the error is within the set range. If so, the actual operating parameters of each of the air conditioning devices are summarized as the actual operating parameters of the air conditioning system and uploaded to the calculation module (10). If not, the inspection tasks of each air conditioner inspection robot (42) are checked, the data collected by each air conditioner inspection robot (42) is uploaded, and the abnormality is reported to the calculation module (10).

4. The building indoor inspection and control system based on a power grid-friendly architecture, as described in any one of claims 1 to 3, is characterized in that... The predicted total building energy consumption at time i+1 is predicted using the formula shown in equation (2): Equation (2) in, The predicted total building energy consumption at time i+1; The amount of heat or cooling required to maintain the building's indoor temperature at time i+1 under the basic design conditions without implementing the flexible adjustment strategy can be obtained from the building's historical operating data. The amount of heat or cooling that the building flexibly adjusts when the flexible adjustment strategy is executed at time i+1 can be obtained from the building's historical operating data. The energy consumption for internal building functions at time i+1, including lighting, elevators, domestic hot water, sockets, and other functional energy consumption, is obtained by querying the building's historical operation data; The personnel utilization coefficient at time i+1 is obtained from the historical personnel load table and can be retrieved from the building's historical operation data. The real-time system energy efficiency at time i+1 is obtained based on the building's historical operating data.

5. The building indoor inspection and control system based on a power grid-friendly architecture as described in claim 4, characterized in that, The predicted operating parameters of the energy system include predicted cooling / heating demand, which is obtained based on the predicted total building energy consumption using a particle swarm optimization algorithm. The predicted operating parameters of the air conditioning system include the predicted output of the air conditioning system, which is obtained based on the predicted total building energy consumption using a particle swarm optimization algorithm; and / or The predicted inspection parameters for the indoor area include the predicted number of people in the room, the predicted overall user satisfaction, and the predicted number of people in need of being in the room. The predicted number of people in the room, the predicted overall user satisfaction, and the predicted number of people in need of being in the room are obtained based on the predicted total building energy consumption using a particle swarm optimization algorithm.

6. The building indoor inspection and control system based on a power grid-friendly architecture as described in claim 5, characterized in that, The predicted operating parameters of each device in the energy system are obtained using a particle swarm optimization algorithm based on the predicted operating parameters of the energy system. The predicted operating parameters of each device in the air conditioning system are obtained using a particle swarm optimization algorithm based on the system's predicted operating parameters; and / or The inspection paths and tasks of each indoor inspection robot in the indoor area are obtained through particle swarm optimization based on the predicted inspection parameters. The inspection task is to collect data including the number of people in the room in real time, user satisfaction, and the number of people in the room in real time who need to be present.

7. The building indoor inspection and control system based on a power grid-friendly architecture as described in claim 4, characterized in that, The system predicted operating parameters of the air conditioning system also include the predicted indoor temperature, which is calculated at time i+1 using the formula shown in equation (3): Equation (3) in, The predicted indoor temperature at time i+1, in °C. This refers to the exterior wall area of ​​the building, in square meters (m²). 2 , The convective heat transfer coefficient of the inner surface of the building's exterior wall, expressed in W / m². 2 k, This refers to the temperature of the inner surface of the building's exterior walls, set according to control requirements, and expressed in degrees Celsius (°C). Outdoor temperature, in °C. The mass of the building's exterior walls is expressed in kg. This refers to the specific heat capacity of the wall, expressed in K / kg℃. The amount of heat or cooling required to maintain the building's indoor temperature at time i+1 under the basic design conditions without implementing a flexible adjustment strategy can be obtained from the building's historical operating data.

8. The building indoor inspection and control system based on a power grid-friendly architecture as described in claim 4, characterized in that, The system predicted operating parameters of the air conditioning system also include the predicted wall temperature, which is calculated at time i+1 using the formula shown in equation (4): Equation (4) in, The predicted wall temperature at time i+1 is given in °C. The temperature distribution coefficient of the wall is obtained by testing based on the compositional characteristics of different wall materials. The mass of the building walls is expressed in kg. This refers to the specific heat capacity of the wall, expressed in K / kg℃. The wall temperature when the flexible adjustment strategy is not implemented is obtained from the building's historical operating data. The amount of heat or cooling that the building flexibly adjusts when implementing the flexible adjustment strategy at time i+1 can be found in the building's historical operation data.

9. The building indoor inspection and control system based on a power grid-friendly architecture as described in claim 3, characterized in that, The indoor control module (20) is also configured to: monitor each of the indoor inspection robots (22), receive fault reports sent by the indoor inspection robots (22) and determine the fault point, calculate the unfinished work packages and establish a bidding mechanism, send the unfinished work packages to the indoor inspection robot (22) with the shortest time to complete all task packages, and receive the actual inspection parameters sent by the indoor inspection robots (22). The energy control module (30) is also configured to: monitor each of the energy inspection robots (32), receive fault reports sent by the energy inspection robots (32) and determine the fault point, calculate unfinished work packages and establish a bidding mechanism, send the unfinished work packages to the energy inspection robot (32) with the shortest time to complete all task packages, and receive the actual operating parameters of each of the energy devices sent by the energy inspection robots (32); and / or The air conditioning control module (40) is also configured to: receive fault reports sent by the air conditioning inspection robot (42) and determine the fault point, calculate the unfinished work packages and establish a bidding mechanism, send the unfinished work packages to the air conditioning inspection robot (42) with the shortest time to complete all task packages, and receive the actual operating parameters of each air conditioning device sent by the air conditioning inspection robot (42).

10. The building indoor inspection and control system based on a power grid-friendly architecture as described in claim 3, characterized in that, The indoor control module (20), the energy control module (30) and the air conditioning control module (40) are configured to communicate with each other to exchange fault alarms, and to urgently collect the currently collected data and upload it to the calculation module (10) after receiving a fault alarm.

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