Building indoor inspection group control system based on power grid friendliness

Abstract

The building indoor inspection group control system based on power grid friendliness 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 total energy consumption of the building at the next moment according to the obtained meteorological data and the historical operation data of the building, and then calculates the predicted operation parameters of the energy system and the air conditioning system according to the predicted total energy consumption of the building, so that the two systems operate according to the respective predicted operation parameters. And the indoor control module can receive an inspection parameter acquisition instruction sent by the calculation module. Each indoor inspection robot can collect actual inspection parameters of each indoor partition and upload the actual inspection parameters 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 and predict the error between the total energy consumption of the building and the actual total energy consumption of the building, and store the actual total energy consumption of the building and each actual operation parameter. The efficiency of building indoor inspection is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building indoor environment control, and in particular to a power grid-friendly building indoor inspection group control system. Background Art

[0002] The main usage scenario of existing indoor inspection robots in buildings is to travel along fixed routes within the entire area. They can receive user comfort data and communicate with air-conditioning equipment, and can control the operating parameters of air-conditioning equipment according to user comfort data.

[0003] Generally speaking, a patrol robot with human-machine interaction function and the ability to control the operation of air-conditioning equipment has many obvious shortcomings when conducting full-area patrol inspections of indoor areas of buildings. There is often a time delay in the feedback instructions of different indoor partitions, which affects the efficiency of the patrol inspection to a certain extent. The patrol robot with the above functions is only suitable for small buildings. When faced with the patrol task of large-scale buildings, it takes a very long time to complete the patrol inspection of all partitions, which is difficult to meet the needs of efficient patrol inspections. Summary of the invention

[0004] The object of the present invention is to provide a power grid-friendly building indoor inspection group control system, which can improve the efficiency of building indoor inspection.

[0005] The invention provides a power grid-friendly building indoor inspection group control system, comprising a calculation module, an indoor control module and several indoor inspection robots. The calculation module is configured to be able to: at time i (i is an integer greater than 0), obtain meteorological data at time i+1 from the Internet, and calculate the predicted total building energy consumption at time i+1 in combination with the historical building operation data stored therein, and then calculate the system predicted operation parameters of the energy system at time i+1 and the system predicted operation parameters of the air conditioning system according to the predicted total building energy consumption, and issue instructions to make the energy system and the air conditioning system operate according to their respective system predicted operation parameters at time i+1. The indoor control module is connected to the calculation module by signal. The calculation module is also configured to be able to issue an inspection parameter collection instruction at time i+1 to the indoor control module, and the inspection parameter collection instruction includes the predicted inspection parameters at time i+1 predicted according to the historical building operation data. Each indoor inspection robot corresponds to an indoor partition and is connected to the indoor control module by signal. The indoor control module is configured to be able to receive the inspection parameter collection instruction and issue instructions to each indoor inspection robot to collect the actual inspection parameters of each indoor partition. Each indoor inspection robot is configured to collect the actual inspection parameters of each indoor partition and upload them to the indoor control module. The indoor control module is also configured to summarize the actual inspection parameters of each partition 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; if not, 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 obtain the actual system operating parameters of the energy system and the air-conditioning system, calculate the actual total building energy consumption at that moment according to the actual inspection parameters at time i+1 and the actual system 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 system operating parameters of the energy system and the air-conditioning system are stored in the historical building operation data; if not, the error deviation point is determined by comparing the actual inspection parameters and the actual system operating parameters of the energy system and the air-conditioning system, and the actual total energy consumption and the error deviation value of the building are recalculated according to the data except the error deviation point, and the actual total energy consumption of the building and the data collected at time i+1 are stored in the historical building operation data.

[0006] By setting up multiple indoor inspection robots to inspect indoor areas in different areas, and controlling the indoor inspection robots for zoned inspections through an indoor control module, and then controlling the indoor control module through a computing module, the efficiency of indoor inspections in buildings can be improved, while the amount of calculations in the computing module can be reduced, thereby improving computing efficiency.

[0007] In another exemplary embodiment of the grid-friendly building indoor inspection group control system, an energy control module and several energy inspection robots are also included. The energy control module is connected to the computing module by signal and is configured to: receive instructions issued by the computing module, calculate the predicted equipment operation parameters of each energy device in the energy system at time i+1 and issue instructions. Each energy inspection robot corresponds to an energy side partition and is connected to the energy control module and at least one energy device by signal. Each energy inspection robot can receive instructions issued by the energy control module and control each energy device to operate according to its own predicted equipment operation parameters at time i+1, collect the actual equipment operation parameters of each energy device and upload them to the energy control module. The energy control module can also compare the actual equipment operation parameters of each energy device with the corresponding predicted equipment operation parameters, determine whether the error is within the set range, and if so, summarize the actual equipment operation parameters of each energy device as the actual system operation parameters of the energy system and upload them to the computing module; if not, check the inspection tasks of each energy inspection robot, upload the data collected by each energy inspection robot and report the abnormality to the computing module. This makes it easier to obtain the actual operating parameters of the energy system, realize zoning management of the energy system, and help improve the efficiency, timeliness and accuracy of inspections.

[0008] In another exemplary embodiment of the grid-friendly building indoor inspection group control system, an air conditioning control module and several air conditioning inspection robots are also included. The air conditioning control module is connected to the calculation module by signal and is configured to: receive instructions issued by the calculation module, calculate the predicted equipment operation parameters of each air conditioning device in the air conditioning system at time i+1 and issue instructions. Each air conditioning inspection robot corresponds to an air conditioning side partition and is connected to the air conditioning control module and at least one air conditioning device by signal. Each air conditioning inspection robot can receive instructions issued by the air conditioning control module, and control each air conditioning device to operate according to its own predicted equipment operation parameters at time i+1, collect the actual equipment operation parameters of each air conditioning device and upload them to the air conditioning control module. The air conditioning control module can also compare the actual equipment operation parameters of each air conditioning device with the corresponding predicted equipment operation parameters, and determine whether the error is within the set range. If so, the actual equipment operation parameters of each air conditioning device are summarized as the actual system operation parameters of the air conditioning system, and uploaded to the calculation module; if not, the inspection tasks of each air conditioning inspection robot are checked, the data collected by each air conditioning inspection robot is uploaded, and the abnormality is reported to the calculation module. This makes it easier to obtain the actual operating parameters of the air-conditioning system, realize zoning management of the air-conditioning system, and help improve the efficiency, timeliness and accuracy of inspections.

[0009] In another exemplary implementation of the grid-friendly building indoor inspection group control system, the building indoor inspection group control system executes a flexible adjustment strategy, and 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 formula (1):

[0010]

[0011] Among them, t a is the flexible adjustment time, in hours; ΔT is the temperature difference between the wall temperature before the flexible adjustment strategy is executed and the target wall temperature, in °C; V is the wall volume, in m 3 ; ρ is the wall density, unit is kg / m 3 ;c p is the specific heat capacity of the wall, in J / (kg·K); P a is the cooling or heating power in W. The calculation method is simple and has a small amount of calculation, which is convenient for quickly obtaining the flexible adjustment time and is conducive to the implementation of the flexible adjustment strategy.

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

[0013]

[0014] in,

[0015] E i+1 is the predicted total building energy consumption at time i+1; The heat or cooling required to maintain the indoor temperature of the building at time i+1 under the basic design condition without the flexible adjustment strategy, which can be obtained by querying the historical operation data of the building; The heat or cooling capacity that the building flexibly adjusts when executing the flexible adjustment strategy at time i+1 can be obtained by querying the historical operation data of the building; C is the functional energy consumption of the building at time i+1, including lighting, elevators, domestic hot water, sockets and other functional energy consumption, which can be obtained by querying the historical operation data of the building; i+1 is the personnel utilization coefficient at time i+1, which is obtained according to the historical personnel load table and can be found in the historical operation data of the building; is the real-time system energy efficiency at time i+1, which is obtained based on the historical operation data of the building. This makes it easier to predict the total energy consumption of the building at any time, which helps save the computing resources of the computing module.

[0016] In another exemplary implementation of a grid-friendly building indoor inspection group control system, the system prediction operation parameters of the energy system include the predicted cooling (or heat) demand, which is obtained by a particle swarm algorithm based on the predicted total energy consumption of the building; the system prediction operation parameters of the air-conditioning system include the predicted output of the air-conditioning system, which is obtained by a particle swarm algorithm based on the predicted total energy consumption of the building; and / or the prediction inspection parameters of the indoor area include the predicted number of people in the room, the predicted overall user satisfaction, and the predicted number of people in the room with specific needs, which are obtained by a particle swarm algorithm based on the predicted total energy consumption of the building. This helps to quickly and accurately obtain the optimal solution.

[0017] In another exemplary implementation of a grid-friendly building indoor patrol group control system, the predicted operating parameters of each device in the energy system are obtained through a particle swarm 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 through a particle swarm algorithm based on the predicted operating parameters of the air-conditioning system; and / or the patrol paths and patrol tasks of each indoor patrol robot in the indoor area are obtained through a particle swarm algorithm based on the predicted patrol parameters, and the patrol tasks include collecting data including the real-time number of people in the room, user satisfaction, and the real-time number of people in the room with specific needs. This helps to quickly and accurately obtain the optimal solution.

[0018] In another exemplary implementation of the grid-friendly building indoor patrol group control system, the system prediction operation parameters of the air-conditioning system also include the predicted indoor temperature. The predicted indoor temperature at time i+1 is calculated by the formula shown in formula (3):

[0019]

[0020] in, is the predicted indoor temperature at time i+1, in °C; A is the building exterior wall area, in m 2 ;h in is the convection heat transfer coefficient of the inner surface of the building exterior wall, in w / m 2 k;T wall,in is the inner surface temperature of the building's exterior wall, set according to control requirements, in °C; T out is the outdoor temperature in °C; m1 is the mass of the building's exterior wall in kg; c p is the specific heat capacity of the wall, in K / kg℃; The heat or cooling required to maintain the indoor temperature of the building at time i+1 under the basic design condition without the implementation of the flexible adjustment strategy is obtained by querying the historical operation data of the building. This calculation method is simple and has a small amount of calculation, which is conducive to the implementation of the flexible adjustment strategy.

[0021] In another exemplary implementation of the grid-friendly building indoor patrol group control system, the system prediction operation parameters of the air-conditioning system also include the predicted wall temperature. The predicted wall temperature at time i+1 is calculated by the formula shown in formula (4):

[0022]

[0023] in,

[0024] is the predicted wall temperature at time i+1, in °C; k is the wall temperature distribution coefficient, which is obtained based on the test results of different wall material composition characteristics; m2 is the building wall mass, in kg; c p is the specific heat capacity of the wall, in K / kg℃; T in,set The wall surface temperature when the wall does not implement the flexible adjustment strategy is obtained by querying the historical operation data of the building; The heat or cooling capacity that the building flexibly adjusts when executing the flexible adjustment strategy at time i+1 is obtained by querying the historical operation data of the building. The calculation method is simple and has a small amount of calculation, which is conducive to the implementation of the flexible adjustment strategy.

[0025] In another schematic implementation of a grid-friendly building indoor inspection group control system, the indoor control module is also configured to: monitor each indoor inspection robot, receive fault reports sent by the indoor inspection robot and determine the fault point, calculate unfinished work packages and establish a bidding mechanism, send the unfinished work packages to the indoor inspection robot that completes all task packages in the shortest time, and receive actual inspection parameters sent by the indoor inspection robot; the energy control module is also configured to: monitor each energy inspection robot, receive fault reports sent by the energy inspection robot and determine the fault point, calculate unfinished work packages and establish a bidding mechanism, send the unfinished work packages to the energy inspection robot that completes all task packages in the shortest time, and receive the actual equipment operating parameters of each energy device sent by the energy inspection robot; and / or the air conditioning control module is also configured to: receive fault reports sent by the air conditioning inspection robot 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 that completes all task packages in the shortest time, and receive the actual equipment operating parameters of each air conditioning device sent by the air conditioning inspection robot. Through the coordinated processing of the indoor control module, energy control module and air-conditioning control module, it is possible to effectively avoid inspection interruptions, improve resource utilization efficiency, enhance fault response capabilities and optimize inspection collaboration.

[0026] In another exemplary embodiment of the grid-friendly building indoor inspection group control system, the indoor control module, the energy control module and the air conditioning control module are configured to communicate with each other to exchange fault alarms, and to urgently count the currently collected data and upload it to the calculation module after receiving the fault alarm. This helps to increase the stability and autonomous correction capability of the grid-friendly building indoor group control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The following drawings are only used to schematically illustrate and explain the present invention, and do not limit the scope of the present invention.

[0028] Figure 1 The diagram is a planar structural diagram of an exemplary implementation of a power grid-friendly building indoor patrol group control system.

[0029] Figure 2 For illustration Figure 1 The workflow diagram of the grid-friendly building indoor patrol group control system is shown.

[0030] Figure 3 The diagram is a planar structural diagram of another exemplary implementation of a power grid-friendly building indoor patrol group control system.

[0031] Figure 4 A flowchart for illustrating the process of obtaining actual system operating parameters of an energy system.

[0032] Figure 5 A flowchart for explaining how to obtain actual system operating parameters of an air-conditioning system.

[0033] Figure 6 The flowchart for illustrating the particle swarm algorithm.

[0034] Description of symbols

[0035] 100 Grid-friendly building indoor inspection group control system

[0036] 10 Computing Module

[0037] 20 Indoor control module

[0038] 22 Indoor inspection robot

[0039] 30 Energy Control Module

[0040] 32 Energy Inspection Robot

[0041] 40 Air conditioning control module

[0042] 42 Air conditioning inspection robot. DETAILED DESCRIPTION

[0043] In order to have a clearer understanding of the technical features, purposes and effects of the invention, the specific embodiments of the invention are now described with reference to the accompanying drawings, in which the same reference numerals represent the same parts.

[0044] In this document, “exemplary” means “serving as an example, instance or illustration”, and any diagram or implementation described in this document as “exemplary” should not be interpreted as a more preferred or more advantageous technical solution.

[0045] In order to simplify the drawings, each figure only schematically shows the parts related to the present invention, which do not represent the actual structure of the product.

[0046] Figure 1 FIG. 1 is a schematic diagram of the planar structure of a power grid-friendly building indoor inspection group control system. 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 thereto. The number of indoor inspection robots 22 can be set to other numbers according to actual application scenarios.

[0047] The calculation module 10 is configured to: at time i, obtain the meteorological data at time i+1 from the Internet, and calculate the predicted total building energy consumption at time i+1 in combination with the historical building operation data stored therein, and then calculate the system predicted operation parameters of the energy system and the system predicted operation parameters of the air conditioning system at time i+1 according to the predicted total building energy consumption, and issue instructions to make the energy system and the air conditioning system operate according to their respective system predicted operation parameters at time i+1. Wherein, i is an integer greater than 0, i is, for example, 1, 2...24, and if 24 hours a day is divided into 48 time periods, i can also be 1, 2...48.

[0048] The indoor control module 20 is connected to the calculation module 10 by signal. The calculation module 10 is also configured to send an inspection parameter collection instruction at time i+1 to the indoor control module 20, and the inspection parameter collection instruction includes the predicted inspection parameters at time i+1 predicted based on the historical operation data of the building. In an exemplary embodiment, the predicted inspection parameters include the predicted number of people in the room in real time, the predicted overall user satisfaction, and the predicted number of people in the room with specific needs.

[0049] Each indoor inspection robot 22 corresponds to an indoor partition and is connected to the indoor control module 20 by signal. The indoor control module 20 is configured to receive inspection parameter collection instructions and issue instructions to each indoor inspection robot 22 to collect the actual inspection parameters of each indoor partition. Each indoor inspection robot 22 is configured to collect the actual inspection parameters of each indoor partition 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 partition 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 an exemplary embodiment, the summarized actual inspection parameters include the real-time number of people in the room, the overall user satisfaction, and the number of people with specific needs in the room.

[0050] If yes, the actual inspection parameters collected are uploaded to the calculation module 10.

[0051] If not, the error deviation point is calibrated, the data collected by each indoor inspection robot 22 is uploaded and the error is reported to the calculation module 10. In an illustrative embodiment, the data collected by each indoor inspection robot 22 without feedback on comfort issues include the real-time number of people in the room, overall user satisfaction, the number of people with specific needs in the room, and the indoor temperature and humidity. In the case of feedback, it is also necessary to collect the feedback issues 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°C, when most of the people in the room have no feedback on comfort issues, some individuals report that the temperature is too high. After the data is fed back to the system, the system determines that the feedback person has special needs and calibrates that there is one person with special needs in the partition.

[0052] The calculation module 10 is further configured to obtain the actual system operation parameters of the energy system and the air conditioning system, calculate the actual total building energy consumption at the time i+1 according to the actual inspection parameters and the actual system operation parameters of the energy system and the air conditioning system, and check whether the error between the actual total building energy consumption and the predicted total building energy consumption is within a set range.

[0053] If yes, the actual total building energy consumption, actual inspection parameters, and actual system operation parameters of the energy system and the air conditioning system are stored in the building historical operation data.

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

[0055] By setting up multiple indoor inspection robots to inspect indoor areas in different areas, and controlling the indoor inspection robots for zoned inspections through an indoor control module, and then controlling the indoor control module through a computing module, the efficiency of indoor inspections in buildings can be improved, while the amount of calculations in the computing module can be reduced, thereby improving computing efficiency.

[0056] In actual applications, due to the existence of time-of-use electricity prices, building owners usually adopt flexible adjustment strategies to reduce electricity costs. The so-called flexible adjustment strategy is to make full use of the difference in electricity prices in different time periods to reasonably arrange the temperature adjustment operation of the building. For example, in summer, when the city electricity price is at a relatively low time (usually in the early hours of the morning), the building is pre-cooled in advance; and in winter, the building is preheated in advance during the period when the city electricity price is also low. Therefore, when the city electricity price climbs to a high price period (generally during the peak working hours of electricity consumption), it is only necessary to maintain the temperature state reached after pre-cooling or preheating. In this way, the power consumption during peak electricity consumption periods can be significantly reduced, thereby effectively reducing the electricity cost of the building.

[0057] Figure 2 For illustration Figure 1 The work flow chart of the grid-friendly building indoor inspection group control system is shown in FIG. Figure 2 The flexible adjustment strategy executed 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 formula (1):

[0059]

[0060] in,

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

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

[0063] V is the wall volume, in m 3 ,

[0064] ρ is the wall density, in kg / m 3 ,

[0065] c p is the specific heat capacity of the wall, in J / (kg·K),

[0066] Pa It is the cooling or heating power, in W.

[0067] S20: The calculation module calculates and predicts the total energy consumption of the building. After calculating the flexible adjustment time, the calculation module determines the starting time point for executing the flexible adjustment strategy. For example, the target wall temperature needs to reach 25°C at 8 a.m., and the flexible adjustment time is 5 hours. Then the starting time point for executing the flexible adjustment strategy is 3 a.m. In the process of executing the flexible adjustment strategy, at time i, the calculation module obtains the meteorological data and electricity price data of the next time, that is, time i+1, from the Internet, and obtains the data of time i+1 from the historical operation data of the building stored in itself. C i+1 and

[0068] in, The heat or cooling required to maintain the indoor temperature of the building under the basic design condition without flexible adjustment strategy at time i+1 can be obtained by querying the historical operation data of the building; It is the heat or cooling capacity that the building flexibly adjusts under the flexible adjustment condition at time i+1, which can be obtained by querying the historical operation data of the building; C is the functional energy used in the building at time i+1, including lighting, elevators, domestic hot water, sockets and other functional energy, which can be obtained by querying the historical operation data of the building; i+1 is the personnel utilization coefficient at time i+1, which is obtained according to the historical personnel load table. The example of the historical personnel load table is shown in Table 1, and is obtained by querying the historical operation data of the building; is the real-time system energy efficiency at time i+1, obtained based on the historical operation data of the building.

[0069] It should be noted that and C i+1 These four data come from the same day in history. The historical date and the current date must have similar or identical weather data, including real-time temperature, wind speed, sunshine and other information. At the same time, the selected historical date should be the same week as the current date, such as Wednesday, to ensure that the data of C is similar or identical. and C i+1 After confirmation, the calculation module can immediately match The numerical value of .

[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 historical building operation data. The predicted total building energy consumption at time i+1 is calculated, for example, by the formula shown in formula (2):

[0074]

[0075] Among them, E i+1 is the predicted total building energy consumption at time i+1. Thus, by querying the historical operation data of the building to calculate the predicted total building energy consumption at any time, it is beneficial to save the computing resources of the computing module and facilitate quick results.

[0076] It should be noted that currently a large number of buildings have photovoltaic power generation capabilities. Therefore, when predicting the electricity cost at time i+1, it is necessary to subtract the predicted photovoltaic power generation at that moment from the predicted total building energy consumption at that moment. The predicted photovoltaic power generation at that moment can also be obtained through the historical operation data of the building. This moment and the historical moments in the historical operation data of the building must have similar or identical weather data, including real-time temperature, wind speed, sunshine and other information.

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

[0078] The system prediction operation parameters of the energy system, such as the predicted cooling (or heating) demand at time i+1, that is, the predicted output, can be obtained through the particle swarm algorithm.

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

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

[0081]

[0082] in,

[0083] is the predicted indoor temperature at time i+1, in °C,

[0084] A is the building exterior wall area, unit is m2 ,

[0085] h in is the convection heat transfer coefficient of the inner surface of the building exterior wall, in w / m 2 k,

[0086] T wall,in The inner surface temperature of the building's exterior wall is set according to control requirements, in degrees Celsius.

[0087] T out is the outdoor temperature in °C,

[0088] m1 is the mass of the building's exterior wall, in kg.

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

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

[0091]

[0092] in,

[0093] is the predicted wall temperature at time i+1, in °C,

[0094] k is the wall temperature distribution coefficient, which is obtained based on the test results of different wall material composition characteristics.

[0095] m2 is the mass of the building wall, in kg.

[0096] c p is the specific heat capacity of the wall, in K / kg℃,

[0097] T in,set is the wall temperature when the flexible adjustment strategy is not implemented, which is obtained by querying the historical operation data of the building.

[0098] It is the heat or cooling amount that the building flexibly adjusts under the flexible adjustment condition at time i+1, which can be obtained by querying the historical operation data of the building.

[0099] The inspection parameter collection instruction includes the predicted inspection parameters at time i+1 predicted based on the historical operation data of the building. These predicted inspection parameters include, for example, the predicted real-time number of people in the room, the predicted overall user satisfaction, and the predicted number of people in the room with specific needs. It can be based on the current date. For example, if the current date is Wednesday, the data on historical Wednesdays can be queried from the historical operation data of the building to obtain the data.

[0100] S40: The energy system operates according to the system predicted operating parameters calculated by the calculation module, and the calculation module obtains the system actual operating parameters of the energy system at time i+1. The system actual operating parameters of the energy system include, for example, the actual cooling (or heat) output of the energy system, i.e., the actual output, and the summarized actual operating parameters of each energy device. The calculation module obtains the system actual operating parameters of the energy system at time i+1 by manual acquisition or by the control module.

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

[0102] S60: The indoor control module generates instructions for collecting actual inspection parameters of each indoor partition, and issues instructions to each indoor inspection robot. The indoor control module is connected to the calculation module by signal, and each indoor inspection robot corresponds to an indoor partition and is connected to the indoor control module by signal. After receiving the instructions issued by the calculation 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. The optimal path and optimal task package allocation are, for example, based on the principle of shortest execution time.

[0103] S61: After receiving the instruction, each indoor inspection robot collects the actual inspection parameters of each indoor partition and uploads them to the indoor control module. Each indoor inspection robot is configured with a sensor and a human-computer interaction interface, and can timely and accurately obtain the actual inspection parameters at that moment. The actual inspection parameters include the real-time number of people in the room, user satisfaction, the number of people with specific needs in the room, and the indoor temperature and humidity. In the case of feedback, it also includes the feedback problem and the temperature at the time of feedback.

[0104] S62: The indoor control module summarizes the actual inspection parameters of each partition 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 real-time number of people in the room, 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 yes, then enter S63: the indoor control module uploads the collected actual inspection parameters to the calculation module and waits for the next task.

[0106] If not, then enter S64: the indoor control module calibrates the error deviation point, uploads the 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 the time i+1 according to the actual inspection parameters at the time i+1 and the actual system operation parameters of the energy system and the air-conditioning system.

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

[0109] If yes, then go to S72: store the actual total building energy consumption, actual inspection parameters, and actual operating parameters of the energy system and the air conditioning system in the building historical operating data, and return to S20.

[0110] If not, go to S73: determine the error deviation point by comparing the actual inspection parameters and the actual operation parameters of the energy system and the air-conditioning system, and recalculate the actual total energy consumption of the building and the error deviation value based on the data except the error deviation point, store the actual total energy consumption of the building and the data collected at time i+1 in the historical operation data of the building, and return to S20.

[0111] Figure 3 FIG. 1 is a planar structural diagram of another exemplary implementation of a power grid-friendly building indoor patrol group control system. Figure 3 In order to obtain the actual operating parameters of the energy system and inspect each partition of the energy system, the building indoor inspection group control system 100 also includes an energy control module 30 and two energy inspection robots 32. However, it is not limited thereto, and 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 connected to the calculation module 10 by signal and is configured to: receive instructions issued by 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 the 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. The actual or predicted operating parameters of each energy device in the energy system include, for example, the chilled water (or hot water) outlet and return water temperatures, the operating current and voltage of the equipment, the cold water (or hot water) flow rate, the pump inlet and outlet pressures, the system flow rate, the current and voltage of the pump motor, etc.

[0113] Each energy inspection robot 32 corresponds to an energy side partition 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 own predicted device operating parameters at time i+1, collect the actual device operating parameters of each energy device and upload them to the energy control module 30. The energy control module 30 can also compare the actual device operating parameters of each energy device with the corresponding predicted device operating parameters to determine whether the error is within the 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 the abnormality 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. In this way, the energy system can be managed in different areas, which is conducive to improving the efficiency, timeliness and accuracy of the inspection.

[0116] Figure 4 FIG. 1 is a flowchart for explaining the process of obtaining the actual system operating parameters of the energy system. 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 by signal so as to receive the instruction issued by the computing module, calculate the predicted operation 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 operation 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 partition 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 issued by the energy control module and controls each energy device to operate according to its own 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 of the device to determine whether the error is within the set range.

[0122] If yes, then enter S44: summarize the actual operating parameters of each energy device as the actual operating parameters of the energy system and upload them to the calculation module, waiting for the next task.

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

[0124] exist Figure 3 In the illustrated exemplary embodiment, in order to obtain the actual operating parameters of the air-conditioning system and inspect each partition of the air-conditioning system, the building indoor inspection group control system 100 further includes an air-conditioning control module 40 and two air-conditioning inspection robots 42. However, it is not limited thereto, and 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 connected to the computing module 10 by signal and is configured to: receive instructions issued by the computing module 10, calculate the predicted operating parameters of each air-conditioning device in the air-conditioning system at the time i+1, and issue instructions.

[0125] The actual operating parameters of the air conditioning system include the actual output of the air conditioning system (i.e., the actual cooling or heat supply), the actual operating parameters of each device, the wall temperature, and the indoor temperature and humidity. The actual or predicted operating parameters of each air conditioning device in the air conditioning system include, for example, the outlet and return air temperature, refrigerant pressure and flow, fan speed, fan operating status, etc.

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

[0127] If yes, the actual operating parameters of each air-conditioning device, 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 abnormality is reported to the calculation module 10. In this way, the air-conditioning system can be managed by zones, which is conducive to improving the efficiency, timeliness and accuracy of the inspection. At the same time, the operation error is checked by collecting the actual wall temperature and the actual indoor temperature and comparing them with the predicted wall temperature and the predicted indoor temperature, which effectively improves the accuracy of the control.

[0129] In other exemplary embodiments, each air conditioning inspection robot 42 may not collect the actual wall temperature and the actual indoor temperature and humidity. The air conditioning control module 40 only needs to compare the actual operating parameters of each device. At this time, the actual operating parameters of the air conditioning system include the actual output of the air conditioning system (i.e., the actual cooling or heat supply) and the actual operating parameters of each device. This can also achieve zoning management of the air conditioning system, which is conducive to reducing the inspection workload and improving the inspection efficiency.

[0130] Figure 5 FIG. 1 is a flowchart for explaining the process of obtaining the actual system operating parameters of the air conditioning system. 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 calculation module by signal to receive the instructions issued by the calculation 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 the instruction, controls the operation of each air-conditioning device at time i+1, collects the actual operating parameters, actual wall temperature and actual indoor temperature of the device at time i+1, and uploads them to the air-conditioning control module.

[0134] Each air-conditioning inspection robot corresponds to an air-conditioning side partition and is signal-connected to the air-conditioning control module and at least one air-conditioning device. At time i+1, after receiving the instructions 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 device with the corresponding predicted operating parameters of the device, the predicted wall temperature and the actual wall temperature, and the predicted indoor temperature and the actual indoor temperature, and determines whether the error is within the set range.

[0136] If yes, then enter S54: summarize the actual operating parameters of each air-conditioning device, 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, waiting for the next task.

[0137] If not, enter S55: check the inspection tasks of each air-conditioning inspection robot, upload the data collected by each air-conditioning inspection robot and report the abnormality to the calculation module, and wait for the next task.

[0138] It should be noted that the instructions issued by the calculation module reach the energy control module, the air conditioning control module and the indoor control module at the same time, that is, step S40, step S50 and step S60 are performed at the same time. Although the calculation module calculates the system prediction operation parameters of the energy system and the air conditioning system and the prediction inspection parameters of the indoor area, the three systems are also operated according to these parameters at the beginning. However, during the operation, if the overall user satisfaction of the indoor area changes, the indoor inspection robot can timely feedback the change to the indoor control module. The indoor control module uploads the actual inspection parameters and errors to the calculation module after verification. The calculation module can regenerate the actual total energy consumption of the building and issue instructions to the energy system and the air conditioning system to adjust their system prediction operation parameters so that they can operate according to the new system prediction operation parameters.

[0139] The calculation module can calculate the Q at any time according to 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. base , Q flex and Q inner The value is stored in the historical operation data of the building. In addition, the energy control module, air conditioning control module and indoor control module also have the function of data cleaning, which can clean the data uploaded by the corresponding inspection robots and upload them to the calculation module after aggregation. This helps to reduce the calculation amount of the calculation module, improve calculation efficiency and save calculation resources.

[0140] The predicted operating parameters of the energy system, air-conditioning system and indoor area are calculated through the calculation module, and the system predicted operating parameters and predicted inspection parameters are refined into data that can be directly collected by each inspection robot through the energy control module, air-conditioning control module and indoor control module respectively. Through data verification, the actual operating parameters of the energy system, air-conditioning system and indoor area are stored in the historical operating data of the building for future query. In this way, the energy system, air-conditioning system and indoor area can be zoned and managed, which is conducive to improving the efficiency, timeliness and accuracy of inspections.

[0141] Figure 6 This is a flowchart for explaining the workflow of the particle swarm algorithm. Figure 6In an exemplary embodiment, the system prediction operation parameters of the energy system include predicted cooling (or heat) demand, which is obtained by a particle swarm algorithm based on the predicted total energy consumption of the building. The system prediction operation parameters of the air-conditioning system include predicted output of the air-conditioning system, which is obtained by a particle swarm algorithm based on the predicted total energy consumption of the building. The predicted inspection parameters of the indoor area include a data set of predicted number of people in the room, predicted overall user satisfaction, and predicted number of people with specific needs in the room, which is obtained by a particle swarm algorithm based on the predicted total energy consumption of the building. The calculation process of the particle swarm algorithm includes steps S1 to S5.

[0142] S1: Initialize the particle swarm and set the algorithm parameters according to the predicted total building energy consumption at time i+1. Initializing the particle swarm includes setting the number of particle swarms, initialization position and speed. The number of particle swarms can be set according to empirical values, such as 400. Algorithm parameters such as inertia weight, cognitive coefficient, social coefficient, etc., their values ​​can also be set according to empirical values.

[0143] S2: Set the number of iterations k max The number of iterations k max It can be set according to empirical values, such as 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 group.

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

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

[0147] If not, return to S3 and iterate the calculation again.

[0148] In other exemplary 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 data set of the indoor area can be obtained by a particle swarm algorithm based on the predicted total energy consumption of the building, and the rest can be solved by other methods well known to those skilled in the art. Alternatively, all three of the above can be solved by other methods well known to those skilled in the art.

[0149] The predicted operation parameters of each device in the energy system are obtained by the particle swarm algorithm based on the predicted operation parameters of the energy system, the predicted operation parameters of each device in the air-conditioning system are obtained by the particle swarm algorithm based on the predicted operation parameters of the air-conditioning system, and the inspection paths and inspection tasks of each inspection robot in the indoor area are obtained by the particle swarm algorithm based on the predicted inspection parameters. The calculation process of these three groups of particle swarm algorithms is the same as the principle and process of steps S1 to S5, and will not be repeated here.

[0150] In the exemplary embodiment, the indoor control module 20 is further configured to: monitor each indoor inspection robot 22, receive the fault report sent by the indoor inspection robot 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 that completes all task packages in the shortest time, and receive the actual inspection parameters sent by the indoor inspection robot 22. The bidding mechanism is that when an indoor inspection robot 22 fails, its unfinished work packages are allocated to the remaining indoor inspection robots 22 that are operating normally, calculate the total time for the remaining indoor inspection robots 22 to complete all their task packages, and select the winning indoor inspection robot 22 based on the principle of the shortest total time.

[0151] The energy control module 30 is also configured to: monitor each energy inspection robot 32, receive the fault report sent by the energy inspection robot 32 and determine the fault point, calculate the unfinished work packages and establish a bidding mechanism, send the unfinished work packages to the energy inspection robot 32 that completes all task packages in the shortest time, and receive the actual operation parameters of each energy device sent by the energy inspection robot 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 the fault report 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 that completes all task packages in the shortest time, 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 exemplary embodiments, one or both of the indoor control module 20, the energy control module 30 and the air conditioning control module 40 may not have the function of establishing a bidding mechanism, and the problem may be solved by manually allocating unfinished tasks.

[0154] Through the coordinated processing of the indoor control module, energy control module and air-conditioning control module, it is possible to effectively avoid inspection interruptions, improve resource utilization efficiency, enhance fault response capabilities and optimize inspection collaboration.

[0155] In an illustrative embodiment, the indoor control module, the energy control module and the air conditioning control module are configured to be able to communicate with each other to exchange fault alarms, and to urgently count the currently collected data and upload it to the calculation module after receiving the fault alarm. After receiving the urgently uploaded data, the calculation module can quickly make policy adjustments. In the absence of a fault, the indoor control module, the energy control module and the air conditioning control module will often upload data to the calculation module at the next moment (for example, set to one moment every hour). Therefore, the mutual communication between the indoor control module, the energy control module and the air conditioning control module is conducive to increasing the stability and autonomous correction capability of the indoor group control system of the grid-friendly building.

[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 narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

[0157] The series of detailed descriptions listed above are only specific descriptions of feasible embodiments of the present invention. They are not intended to limit the scope of protection of the present invention. Any equivalent implementation scheme or changes that do not deviate from the technical spirit of the present invention, such as combination, division or repetition of features, should be included in the scope of protection of the present invention.

Claims

1. Based on the grid-friendly building indoor inspection group control system, it is characterized by: include: A calculation module (10) is configured to: at time i (i is an integer greater than 0), obtain meteorological data at time i+1 from the Internet, and calculate the predicted total building energy consumption at time i+1 in combination with the historical building operation data stored therein, and then calculate the system predicted operation parameters of the energy system and the system predicted operation parameters of the air-conditioning system at time i+1 based on the predicted total building energy consumption, and issue instructions to enable the energy system and the air-conditioning system to operate according to their respective system predicted operation parameters at time i+1; an indoor control module (20), whose signal is connected to the calculation module (10), and the calculation module (10) is further configured to be able to send an inspection parameter collection instruction at time i+1 to the indoor control module (20), the inspection parameter collection instruction including a predicted inspection parameter at time i+1 predicted based on historical building operation data; as well as A plurality of indoor inspection robots (22), each of which corresponds to an indoor partition and is signal-connected to the indoor control module (20), the indoor control module (20) being configured to receive the inspection parameter collection instruction and to issue instructions to each of the indoor inspection robots (22) to collect the actual inspection parameters of each indoor partition, each of which is configured to collect the actual inspection parameters of each indoor partition and upload them to the indoor control module, the indoor control module being further configured to summarize the actual inspection parameters of each partition 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 actual inspection parameters collected are uploaded to the calculation module (10). If not, the error deviation point is calibrated, the data collected by each indoor inspection robot (22) is uploaded and the error is reported to the calculation module (10), The calculation module (10) is further configured to obtain the actual system operating parameters of the energy system and the air conditioning system, calculate the actual total building energy consumption at the time i+1 according to the actual inspection parameters and the actual system operating parameters of the energy system and the air conditioning system, and check whether the error between the actual total building energy consumption and the predicted total building energy consumption is within a set range. If yes, the actual total building energy consumption, the actual inspection parameters, and the actual system operation parameters of the energy system and the air conditioning system are stored in the building historical operation data. If not, the error deviation point is determined by comparing the actual inspection parameters with the actual system operation parameters of the energy system and the air-conditioning system, and the actual total energy consumption of the building and the error deviation value are recalculated based on the data other than the error deviation point, and the actual total energy consumption of the building and the data collected at time i+1 are stored in the historical operation data of the building.

2. The grid-friendly building indoor inspection group control system according to claim 1, characterized in that: Also includes: an energy control module (30), whose signal is connected to the calculation module (10) and is configured to: receive instructions issued by the calculation module (10), calculate the predicted operating parameters of each energy device in the energy system at time i+1 and issue instructions, and A plurality of energy inspection robots (32), each of which corresponds to an energy side partition and is signal-connected to the energy control module (30) and at least one of the energy devices, each of which is capable of receiving instructions issued by the energy control module (30) and controlling each of the energy devices to operate according to its own predicted device operating parameters at time i+1, collecting the actual device operating parameters of each of the energy devices and uploading them to the energy control module (30), and the energy control module (30) is also capable of comparing the actual device operating parameters of each of the energy devices with the corresponding predicted device operating parameters to determine whether the error is within a set range. If yes, then the actual operating parameters of each of the energy devices are aggregated into 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 calculation module (10).

3. The grid-friendly building indoor inspection group control system according to claim 1 is characterized in that: Also includes: an air conditioning control module (40), whose signal is connected to the calculation module (10) and is 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, and A plurality of air conditioning inspection robots (42), each of the air conditioning inspection robots (42) corresponds to an air conditioning side partition and is signal-connected to the air conditioning control module and at least one of the air conditioning devices, each of the air conditioning inspection robots (42) is capable of receiving instructions issued by the air conditioning control module (40), and at time i+1, controls each of the air conditioning devices to operate according to its own predicted device operating parameters, collects actual device operating parameters of each of the air conditioning devices and uploads them to the air conditioning control module (40), and the air conditioning control module (40) is also capable of comparing the actual device operating parameters of each of the air conditioning devices with the corresponding predicted device operating parameters to determine whether the error is within a 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 of the air-conditioning inspection robots (42) are checked, the data collected by each of the air-conditioning inspection robots (42) are uploaded, and the abnormality is reported to the calculation module (10).

4. The grid-friendly building indoor inspection group control system according to any one of claims 1 to 3, characterized in that: The building indoor inspection group control system executes a flexible adjustment strategy, and the calculation module (10) calculates a flexible adjustment time before the flexible adjustment strategy is executed. The flexible adjustment time is calculated according to the formula shown in formula (1): in, t a is the flexible adjustment time, in hours. ΔT is the temperature difference between the wall temperature before the flexible adjustment strategy is executed and the target wall temperature, in °C. V is the wall volume, in m 3 , ρ is the wall density, in kg / m 3 , c p is the specific heat capacity of the wall, in J / (kg·K), P a It is the cooling or heating power, in W.

5. The grid-friendly building indoor inspection group control system according to any one of claims 1 to 3, characterized in that: The predicted total building energy consumption at time i+1 is predicted by the formula shown in formula (2): in, E i+1 is the predicted total building energy consumption at time i+1; The amount of heat or cooling required to maintain the indoor temperature of the building under the basic design condition at time i+1 without implementing the flexible adjustment strategy, which is obtained by querying the historical operation data of the building; The heat or cooling capacity flexibly adjusted by the building when the flexible adjustment strategy is executed at time i+1, which is obtained by querying the historical operation data of the building; The energy used by the internal functions of the building at time i+1, including lighting, elevators, domestic hot water, sockets and other functional energy, is obtained by querying the historical operation data of the building; C i+1 is the personnel utilization coefficient at time i+1, obtained according to the historical personnel load table and queried in the historical operation data of the building; is the real-time system energy efficiency at time i+1, which is obtained based on the historical operation data of the building.

6. The grid-friendly building indoor inspection group control system according to claim 5, characterized in that: The system prediction operation parameters of the energy system include predicted cooling (or heating) demand, and the predicted cooling (or heating) demand is obtained by particle swarm algorithm based on the predicted total building energy consumption; The system prediction operation parameters of the air-conditioning system include the predicted output of the air-conditioning system, and the predicted output of the air-conditioning system is obtained by a particle swarm algorithm based on the predicted total energy consumption of the building; and / or The predicted inspection parameters of the indoor area include the predicted number of people in the room, the predicted overall user satisfaction and the predicted number of people with specific needs in the room, and the predicted number of people in the room, the predicted overall user satisfaction and the predicted number of people with specific needs in the room are obtained based on the predicted total energy consumption of the building through a particle swarm algorithm.

7. The grid-friendly building indoor inspection group control system according to claim 6, characterized in that: The predicted operating parameters of each device in the energy system are obtained by using a particle swarm algorithm based on the predicted operating parameters of the energy system; The predicted equipment operating parameters of each equipment in the air-conditioning system are obtained by using a particle swarm algorithm based on the predicted system operating parameters of the air-conditioning system; and / or The inspection path and inspection task of each indoor inspection robot in the indoor area are obtained through a particle swarm algorithm based on the predicted inspection parameters, and the inspection task is to collect data including the real-time number of people in the room, user satisfaction and the real-time number of people with specific needs in the room.

8. The grid-friendly building indoor inspection group control system according to claim 5, characterized in that: The system prediction operation parameters of the air conditioning system also include the predicted indoor temperature. The predicted indoor temperature at time i+1 is calculated by the formula shown in formula (3): in, is the predicted indoor temperature at time i+1, in °C, A is the building exterior wall area, unit is m 2 , h in is the convection heat transfer coefficient of the inner surface of the building exterior wall, in w / m 2 k, T wall,in The inner surface temperature of the building's exterior wall is set according to control requirements, in degrees Celsius. T out is the outdoor temperature in °C, m1 is the mass of the building's exterior wall, in kg. c p is the specific heat capacity of the wall, in K / kg℃, The heat or cooling required to maintain the indoor temperature of the building under the basic design condition without implementing the flexible adjustment strategy at time i+1 is obtained by querying the historical operation data of the building.

9. The grid-friendly building indoor inspection group control system according to claim 5, characterized in that: The system prediction operation parameters of the air conditioning system also include the predicted wall temperature. The predicted wall temperature at time i+1 is calculated by the formula shown in formula (4): in, is the predicted wall temperature at time i+1, in °C, k is the wall temperature distribution coefficient, which is obtained based on the test results of different wall material composition characteristics. m2 is the mass of the building wall, in kg. c p is the specific heat capacity of the wall, in K / kg℃, T in,set is the wall surface temperature when the wall does not implement the flexible adjustment strategy, which is obtained by querying the historical operation data of the building. The heat or cooling capacity that the building flexibly adjusts when executing the flexible adjustment strategy at time i+1 can be obtained by querying the historical operation data of the building.

10. The grid-friendly building indoor inspection group control system according to any one of claims 1 to 3, characterized in that: The indoor control module (20) is further configured to: monitor each of the indoor inspection robots (22), receive a fault report sent by the indoor inspection robot (22) and determine the fault point, calculate unfinished work packages and establish a bidding mechanism, send the unfinished work packages to the indoor inspection robot (22) that completes all task packages in the shortest time, and receive actual inspection parameters sent by the indoor inspection robot (22); The energy control module (30) is further configured to: monitor each of the energy inspection robots (32), receive a fault report sent by the energy inspection robot (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) that completes all task packages in the shortest time, and receive the actual operating parameters of each of the energy devices sent by the energy inspection robot (32); and / or The air conditioning control module (40) is also configured to be able to: receive a fault report 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) that completes all task packages in the shortest time, and receive actual equipment operating parameters of each of the air conditioning equipment sent by the air conditioning inspection robot (42).

11. The grid-friendly building indoor inspection group control system according to any one of claims 1 to 3, characterized in that: The indoor control module (20), the energy control module (30) and the air conditioning control module (40) are configured to be able to communicate with each other to exchange fault alarms, and to urgently count currently collected data and upload them to the calculation module (10) after receiving a fault alarm.

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