Public building energy consumption on-line monitoring and intelligent analysis system based on internet of things

Abstract

This invention relates to the field of energy management technology, specifically to an Internet of Things (IoT)-based online monitoring and intelligent analysis system for energy consumption in public buildings. The system includes a building heat loss module, a load monitoring module, an energy efficiency anomaly detection module, a temperature deviation extraction module, and a command issuance module. In this invention, theoretical heat loss rates are constructed by extracting indoor and outdoor environmental parameters and wall impedance properties. Simultaneously, the actual cooling load demand is calculated by capturing pipeline flow and temperature difference. The theoretical heat loss value is cross-referenced with the actual demand load, and the dynamic energy efficiency ratio is calculated by mapping the host power in real time. This breaks through fixed threshold limitations to accurately identify equipment operational deviations and generate energy consumption status indicators. A dynamic control target is established by integrating the outlet water reference temperature and directly translated into underlying variable frequency speed control commands, which are then issued to the hardware execution end. This constructs a closed-loop control link from macro-environmental perception to precise micro-hardware control, completely eliminating the lag in guidance from static reports.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, and in particular to an online monitoring and intelligent analysis system for energy consumption in public buildings based on the Internet of Things. Background Technology

[0002] The field of energy management technology covers the planning, coordination and control of the entire process of energy production, transmission, distribution and consumption. Its core tasks include the overall scheduling of the use of various energy media such as electricity, gas, water and heat, the collection of data from energy-consuming nodes by deploying underlying metering instruments to build a communication network, and the guidance of equipment start-up and shutdown and operation load adjustment in scenarios such as buildings and industrial parks by combining manual management and control strategies. The traditional public building energy consumption online monitoring and intelligent analysis system refers to a system that collects and statistically evaluates energy consumption data of air conditioning, lighting, power and other energy-consuming equipment on each floor of public buildings such as large commercial complexes, office buildings, and hospitals. It adopts manual periodic meter reading or deploys split-type electricity meters, water meters and heat meters distributed in series via RS485 bus. The raw physical quantities such as voltage, current, active power and pulse flow are transmitted to the industrial control computer in the local computer room through twisted-pair cables laid in conduits on site. The staff sets a fixed upper limit threshold for energy consumption in the stand-alone monitoring software, and uses linear regression formula or moving average formula to add, subtract and compare the electricity and water consumption of different floors. Based on the results of pre-written static rules, the system directly generates bar charts, pie charts and electronic spreadsheets containing numerical details on the LCD screen interface.

[0003] Existing technologies have long relied on fixed wired networks to transmit physical quantities, resulting in slow acquisition of underlying operational data and limited monitoring coverage. Setting fixed energy consumption thresholds and simple linear addition and subtraction comparisons in the processing stage are detached from the variable dynamic thermal environment of buildings and actual load demands. Judgments based on preset static rules are difficult to accurately capture abnormal operation of nonlinear equipment and deviations in overall energy efficiency. Directly generating reports results in outputs that only stop at the level of basic data display, lacking the ability to automatically and dynamically adjust and control the deep operational status of underlying core equipment, leading to high overall energy consumption. Summary of the Invention

[0004] To address the technical problems of existing technologies, such as setting fixed energy consumption thresholds and using simple linear addition and subtraction comparisons that are detached from the dynamic thermal environment and actual load demands of buildings, failing to accurately capture nonlinear equipment malfunctions and overall energy efficiency deviations based on preset static rules, and directly generating reports that only display basic data without providing automatic dynamic adjustment and control guidance for the underlying core equipment's operational status, leading to high overall energy consumption, this invention provides an Internet of Things-based online monitoring and intelligent analysis system for public building energy consumption. The technical solution is as follows: On the one hand, it provides an Internet of Things-based online monitoring and intelligent analysis system for energy consumption in public buildings, which includes: The building heat loss module obtains the indoor and outdoor temperatures and wall thermal resistance of public buildings, calculates the weighted cumulative result of outdoor temperature increment and extracts the difference between it and indoor temperature, and establishes the theoretical heat loss rate. The load monitoring module collects the instantaneous flow rate of chilled water and the supply and return water temperatures of the public building pipe network. It performs a differential extraction operation on the supply and return water temperatures to obtain temperature difference data. It then combines the temperature difference data with the instantaneous flow rate of chilled water to perform a proportional calculation to obtain the building's cooling load demand. The energy efficiency anomaly determination module calls the theoretical heat loss rate and the building cooling load demand, monitors the power of the central air conditioning unit of the public building, performs a difference extraction operation on the building cooling load demand and the theoretical heat loss rate to obtain cooling load deviation data, calculates the ratio of the power of the central air conditioning unit to the building cooling load demand to extract the real-time energy efficiency ratio, and compares it with the preset standard energy efficiency ratio critical value and tolerance limit value to generate an excess energy consumption status indicator. The temperature offset extraction module, based on the excess energy consumption status identifier, obtains the return water temperature of the pipeline network and the preset target return water temperature, extracts the temperature difference to convert control deviation data, and obtains the equipment control offset. The instruction sending module extracts the reference temperature of the air conditioner's outlet water and merges it with the execution value of the device control offset to establish the target temperature of the outlet water. The target temperature of the outlet water is then converted into a level pulse and sent to the compressor to obtain the variable frequency speed control instruction.

[0005] As a further aspect of the present invention, the theoretical heat loss rate includes conduction loss rate, convection loss rate, and radiation loss rate; the building cooling load demand includes building envelope cooling load, personnel heat dissipation cooling load, and equipment heat dissipation cooling load; the excess energy consumption status indicator includes a yellow warning code for slight over-limit, a red warning code for severe energy consumption, and a black code for equipment failure; the equipment control offset includes the host power adjustment difference, the water pump speed correction value, and the fan start / stop threshold compensation; and the variable frequency speed control command includes the operating frequency given parameter, the acceleration time preset value, and the torque limit characteristic quantity.

[0006] As a further aspect of the present invention, the building heat dissipation module includes: The temperature weighted accumulation submodule obtains the indoor and outdoor temperatures and wall thermal resistance of public buildings. It extracts the meteorological temperature values ​​corresponding to each time node for the outdoor temperature, collects the time-incrementing sequence values, performs single-item multiplication operations on the meteorological temperature values ​​and the time-incrementing sequence values ​​in the order of time nodes, collects the output values ​​of the multiplication operations and performs an addition operation to generate the outdoor temperature weighted accumulation value. The temperature difference extraction submodule calls the outdoor temperature increment weighted cumulative value and the indoor temperature, determines the relationship between the two values. If the outdoor temperature increment weighted cumulative value is greater than the indoor temperature, the indoor temperature is subtracted from the outdoor temperature increment weighted cumulative value and the one-way calculated difference is output. If it is not greater, the outdoor temperature increment weighted cumulative value is subtracted from the indoor temperature and the reverse calculated difference is output to obtain the indoor and outdoor temperature difference. The heat loss rate calculation submodule, based on the indoor-outdoor temperature difference and the wall thermal resistance, inputs the wall thermal resistance into the reciprocal operation term and outputs the reciprocal conversion value, collects the external surface area data of the public building, performs continuous multiplication operation on the indoor-outdoor temperature difference and the reciprocal conversion value with the external surface area data of the public building, extracts the continuous multiplication operation term output result and performs synchronous conversion and adjustment processing of dimensional state quantities to establish the theoretical heat loss rate.

[0007] As a further aspect of the present invention, the load monitoring module includes: The data separation and extraction submodule collects the instantaneous flow rate and supply and return water temperature of chilled water in the public building pipe network. It performs local node caching operation on the instantaneous flow rate of chilled water in the public building pipe network, extracts the supply side temperature reading value within the supply and return water temperature, reads the return side temperature reading value within the supply and return water temperature, compares the arrangement sequence of the supply side temperature reading value and the return side temperature reading value, sets the reading value at the top of the sequence as the minuend, and sets the reading value at the bottom of the sequence as the subtrahend, to obtain the supply and return water temperature separation value. The temperature difference calculation submodule calls the supply and return water temperature separation value, reads the minuend and subtrahend, subtracts the subtrahend from the minuend to perform a difference extraction operation, records the difference value output by the difference extraction operation, determines the positive and negative range characteristics of the difference value, removes the difference value in the negative range, retains the difference value in the positive range, and performs a data format synchronization conversion operation on the retained difference value in the positive range to obtain the supply and return water temperature difference data. The load ratio calculation submodule, based on the supply and return water temperature difference data and the instantaneous flow rate of chilled water buffered at the local node, uses the supply and return water temperature difference data as the base term for multiplication and the instantaneous flow rate of chilled water buffered at the local node as the multiplier term. It performs a proportional product operation on the base term and the multiplier term, extracts the output value of the proportional product operation, collects the water specific heat capacity constant parameter, and multiplies the output value of the proportional product operation by the water specific heat capacity constant parameter to generate the building cooling load demand.

[0008] As a further aspect of the present invention, the energy efficiency anomaly determination module includes: The difference parameter extraction submodule calls the theoretical heat loss rate and the building cooling load demand, monitors the power of the central air conditioning unit of the public building, and stores the monitored power in the local cache node. It performs a difference extraction operation on the building cooling load demand and the theoretical heat loss rate, takes the building cooling load demand as the minuend, sets the theoretical heat loss rate as the subtrahend, performs numerical subtraction, extracts the difference parameter from the numerical subtraction operation, and obtains the cooling load deviation data. The power demand calculation submodule reads the electrical power in the local cache node based on the cooling load deviation data, calls the building cooling load demand, adds the building cooling load demand to the cooling load deviation data as the denominator, sets the electrical power in the local cache node as the numerator, performs a division operation on the numerator and denominator, extracts the division operation output quotient value, and obtains the real-time energy efficiency ratio. The cross-boundary coding determination submodule collects a preset standard energy efficiency ratio threshold value and a tolerance limit value for the real-time energy efficiency ratio. It compares and determines the real-time energy efficiency ratio with the preset standard energy efficiency ratio threshold value. If the real-time energy efficiency ratio is greater than the preset standard energy efficiency ratio threshold value, it calls the tolerance limit value and performs a second comparison between the real-time energy efficiency ratio and the tolerance limit value. It extracts the cross-boundary trigger signal from the second comparison and converts the cross-boundary trigger signal into a status coding sequence to establish an excess energy consumption status identifier.

[0009] As a further aspect of the present invention, the real-time energy efficiency ratio is set as the data to be compared, and the tolerance limit value is set as the benchmark data; if the data to be compared is greater than the benchmark data, a first-level cross-limit instruction is generated, and the first-level cross-limit instruction is configured as a cross-limit trigger signal. If the data to be compared is equal to or less than the benchmark data, a secondary cross-boundary instruction is generated and configured as a cross-boundary trigger signal.

[0010] As a further aspect of the present invention, the temperature offset extraction module includes: The difference extraction and conversion submodule, for the excess energy consumption status identifier, obtains the pipe network return water temperature and the preset target return water temperature, sets the pipe network return water temperature as the minuend, sets the preset target return water temperature as the subtrahend, performs a subtraction calculation operation by subtracting the subtrahend from the subtrahend, extracts the difference parameter output by the subtraction calculation, collects the temperature conversion proportional coefficient constant, performs a multiplication operation operation between the difference parameter and the temperature conversion proportional coefficient constant, and obtains the control deviation data; The adjustment parameter generation submodule collects preset adjustment extreme value parameters, compares the control deviation data with the preset adjustment extreme value parameters, and if the control deviation data is greater than the preset adjustment extreme value parameters, the preset adjustment extreme value parameters are extracted as boundary output items; if they are not greater, the control deviation data are used as in-situ output items. The boundary output items and in-situ output items are summarized, and a data frame format conversion and adjustment operation is performed to obtain the device control offset.

[0011] As a further embodiment of the present invention, a preset blank matrix is ​​established, and the boundary output items and the original output items are written into the preset blank matrix to extract the full-load data matrix. Collect the preset frame header code sequence, preset terminal address, and preset frame tail code sequence; set the full-load data matrix as the core segment of the effective payload; Set the preset frame header code sequence as the start bit, set the preset terminal address as the address bit, and set the preset frame tail code sequence as the stop bit.

[0012] As a further aspect of the present invention, the instruction issuing module includes: The temperature parameter synthesis submodule calls the device control offset, extracts the air conditioning unit's outlet water reference temperature, sets the outlet water reference temperature as the addend, sets the device control offset as the augend, performs a combined addition operation on the addend and augend, extracts the sum of the addition operation, collects the preset operating temperature limit parameters, compares the sum with the limit parameters, discards sums that exceed the limit, retains sums within the range and performs a format synchronization operation, and establishes the outlet water target temperature. The speed control signal conversion submodule collects the state transition mapping table for the target outlet water temperature, uses the target outlet water temperature as an index item to perform internal table matching extraction, obtains the corresponding pulse duty cycle parameter, constructs a level alternating waveform sequence based on the pulse duty cycle parameter, converts the level alternating waveform sequence into hardware level pulses, reads the compressor communication protocol specification parameters, performs frame data encapsulation and packaging operation on the hardware level pulses, and obtains the frequency conversion speed control command.

[0013] As a further aspect of the present invention, a preset period constant is obtained, and the pulse duty cycle parameter is multiplied by the preset period constant to extract the high-level duration parameter. Subtract the high-level duration parameter from the preset period constant to obtain the low-level duration parameter by subtraction; perform cyclic splicing according to the order of the high-level duration parameter and the low-level duration parameter to generate an alternating level waveform sequence.

[0014] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: By extracting indoor and outdoor environmental parameters and wall impedance properties, a theoretical heat loss rate is constructed, and the actual cooling load demand is calculated by simultaneously capturing the pipe network flow and temperature difference. The theoretical heat loss value and the actual demand load are cross-compared and mapped to the host power in real time to calculate the dynamic energy efficiency ratio. This breaks through the fixed threshold limit to accurately identify the equipment operation deviation and generate energy consumption status indicators. Based on the deviation indicators, the pipe network return water temperature difference is further captured and converted into equipment control deviation data. The outlet water reference temperature is integrated to establish a dynamic control target and directly translated into the underlying variable frequency speed regulation control command and issued to the hardware execution end. This constructs a closed-loop control link from macro-environmental perception to micro-hardware precise control, completely eliminating the lag of static report guidance. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the system provided by the present invention; Figure 2 This is a schematic diagram of the system framework of the present invention; Figure 3 This is a flowchart of the building heat dissipation module in this invention; Figure 4 This is a flowchart of the load monitoring module in this invention; Figure 5 This is a flowchart of the energy efficiency anomaly determination module in this invention; Figure 6 This is a flowchart of the temperature offset extraction module in this invention; Figure 7 This is a flowchart of the instruction issuance module in this invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] This invention provides an Internet of Things (IoT)-based online monitoring and intelligent analysis system for energy consumption in public buildings, such as... Figure 1-2 The diagram shown illustrates an IoT-based online energy consumption monitoring and intelligent analysis system for public buildings. The system includes: The building heat loss module obtains the indoor and outdoor temperatures and wall thermal resistance of public buildings, calculates the weighted cumulative result of outdoor temperature and extracts the difference between it and indoor temperature, combines the difference with the wall thermal resistance to perform ratio conversion, and establishes the theoretical heat loss rate. The load monitoring module collects the instantaneous flow rate of chilled water and the supply and return water temperatures of the public building pipe network. It performs a differential extraction operation on the supply and return water temperatures to obtain temperature difference data. It then combines the temperature difference data with the instantaneous flow rate of chilled water to perform a proportional calculation to obtain the building's cooling load demand. The energy efficiency anomaly judgment module calls the theoretical heat loss rate and the building cooling load demand, monitors the power of the central air conditioning unit of the public building, performs a difference extraction operation on the building cooling load demand and the theoretical heat loss rate to obtain cooling load deviation data, calculates the ratio of the power of the central air conditioning unit to the building cooling load demand to extract the real-time energy efficiency ratio, obtains the preset standard energy efficiency ratio critical value and the tolerance limit value, and generates an excess energy consumption status mark when it is determined that the real-time energy efficiency ratio does not exceed the preset standard energy efficiency ratio critical value and the cooling load deviation data exceeds the tolerance limit value. The temperature deviation extraction module, based on the excess energy consumption status indicator, obtains the return water temperature of the pipeline network and the preset target return water temperature, extracts the temperature difference and converts it into control deviation data, and combines the control deviation data with the building cooling load demand to perform proportional conversion to obtain the equipment control deviation. The instruction sending module extracts the reference temperature of the air conditioner's outlet water and merges it with the execution value of the equipment control offset to establish the target temperature of the outlet water. The target temperature of the outlet water is then converted into a level pulse and sent to the compressor to obtain the variable frequency speed control instruction. Theoretical heat loss rate includes conduction loss rate, convection loss rate, and radiation loss rate; building cooling load demand includes building envelope cooling load, personnel cooling load, and equipment cooling load; excess energy consumption status indicators include yellow warning code for slight over-limit, red warning code for severe energy consumption, and black code for equipment failure; equipment control offset includes host power adjustment difference, water pump speed correction value, and fan start / stop threshold compensation; variable frequency speed control commands include operating frequency given parameter, acceleration time preset value, and torque limit characteristic quantity.

[0019] Specifically, such as Figure 2 , 3 As shown, the building heat dissipation module includes: The temperature weighted accumulation submodule obtains the indoor and outdoor temperatures and wall thermal resistance of public buildings. It extracts the meteorological temperature values ​​corresponding to each time node for the outdoor temperature, collects the time-incrementing sequence values, performs single-item multiplication operations on the meteorological temperature values ​​and the time-incrementing sequence values ​​in the order of time nodes, collects the output values ​​of the multiplication operations and performs an addition operation to generate the outdoor temperature weighted accumulation value. Analog signals are collected using a high-precision temperature sensor array deployed at designated measuring points on the exterior and interior of public buildings. These signals are then converted from analog to digital to generate an initial indoor and outdoor temperature array, and the wall thermal impedance properties are retrieved from the as-built database. For meteorological data, meteorological temperature values ​​at specific time points are extracted via a long-term connection to the meteorological bureau gateway. A timer interrupt is used to set the sampling period, generating an arithmetic progression sequence of values ​​for the corresponding period, with an initial baseline of zero and a tolerance of one. After cleaning the data using a sliding window mean filter, the meteorological temperature values ​​are multiplied by the corresponding time progression sequence values ​​to obtain the weighted temperature product parameter for each node. The parameters for each node are then fed into an accumulation register for summation, generating an incremental weighted cumulative value for the outdoor temperature. The filtered meteorological temperature value is set to 30 degrees Celsius, the current sampling period's time progression sequence value is 10, and the wall thermal impedance property is 0.5 square Kelvin per watt. Multiplying 30 degrees Celsius by the sequence value 10 yields a weighted temperature product parameter of 300 for each node. Assuming that there are 10 sensor nodes of the same specification arranged on the facade, their product parameters are pushed into the accumulator register and added together to obtain the weighted accumulator value of the outdoor temperature at that moment as 3000 degrees Celsius.

[0020] The temperature difference extraction submodule calls the outdoor temperature increment weighted cumulative value and the indoor temperature, determines the relationship between the two values. If the outdoor temperature increment weighted cumulative value is greater than the indoor temperature, the indoor temperature is subtracted from the outdoor temperature increment weighted cumulative value and the one-way calculated difference is output. If they are not greater, the outdoor temperature increment weighted cumulative value is subtracted from the indoor temperature and the reverse calculated difference is output to obtain the indoor and outdoor temperature difference. Through IoT access, the system retrieves the incrementally weighted cumulative outdoor temperature value generated by the previous stage and reads the filtered average indoor temperature. Both are loaded into the main and auxiliary data registers respectively, and a hardware digital comparator is called to determine their magnitudes. If the outdoor cumulative value is greater than the indoor temperature, a forward path for the subtractor is configured to subtract the indoor temperature from the outdoor cumulative value, outputting a one-way calculated difference. If they are not greater, a cross-flip mechanism is triggered, subtracting the outdoor cumulative value from the indoor temperature, outputting a reverse calculated difference, thus obtaining the indoor-outdoor temperature difference. The one-way and reverse calculated differences represent the driving potential energy difference for heat energy to penetrate from the outside to the inside or dissipate from the inside to the outside, respectively. In the engineering calculation scenario, the acquired incrementally weighted cumulative outdoor temperature is set to 3000 degrees Celsius, while the average indoor temperature is 2980 degrees Celsius. The data is fed into a digital comparator, which determines that 3000 is greater than 2980. The forward subtractor logic then begins, using 3000 as the minuend and 2980 as the subtrahend. The underlying subtraction yields a one-way difference of 20 degrees Celsius. This data is then used as the indoor-outdoor temperature difference data acquired at this stage and sent to the cache pool.

[0021] The heat loss rate calculation submodule is based on the indoor and outdoor temperature difference and the wall thermal resistance. The wall thermal resistance is input into the reciprocal operation term and the reciprocal conversion value is output. The external surface area data of the public building is collected. The indoor and outdoor temperature difference and the reciprocal conversion value are continuously multiplied with the external surface area data of the public building. The continuous multiplication operation term is extracted and the output result is processed and the dimensional state quantity synchronous conversion adjustment is performed to establish the theoretical heat loss rate. The system receives the indoor-outdoor temperature difference from the preceding buffer pool and reads the previously initialized wall thermal impedance data. It then activates a floating-point coprocessor to divide the natural constant by the wall thermal impedance, obtaining the inverse conversion value equivalent to the heat transfer coefficient. Simultaneously, it retrieves the three-dimensional coordinate point set of the target building from the Building Information Modeling (BIM) database and accumulates the external surface area data of the public building, including the exterior walls, roof, and curtain wall. A hardware multiplier-accumulator pipeline is constructed. In the first stage, the indoor-outdoor temperature difference is multiplied by the inverse conversion value to obtain the power loss per unit area; in the second stage, it is multiplied again by the building's external surface area data. After dimensional alignment and removal of intermediate units, the data is uniformly converted to standard watt units, ultimately establishing the theoretical heat loss rate. Based on the preceding data, the indoor-outdoor temperature difference is 20 degrees Celsius, the wall thermal impedance data is 0.5 square Kelvin per watt, and the external surface area data is 10,000 square meters. First, a division operation (1 divided by 0.5) is performed, yielding the inverse conversion value of 2. Subsequently, the difference of 20, the reciprocal conversion value of 2, and the area of ​​10000 are fed into the pipeline for continuous multiplication, resulting in an output of 400000. After dimensional synchronization conversion, the theoretical heat loss rate is established as 400000 watts.

[0022] Specifically, such as Figure 2 , 4 As shown, the load monitoring module includes:

[0023] The data separation and extraction submodule collects the instantaneous flow rate and supply and return water temperature of chilled water in the public building pipe network. It performs local node caching operation on the instantaneous flow rate of chilled water in the public building pipe network, extracts the supply side temperature reading value within the supply and return water temperature, reads the return side temperature reading value within the supply and return water temperature, compares the arrangement sequence of the supply side temperature reading value and the return side temperature reading value, sets the reading value at the top of the sequence as the minuend, and sets the reading value at the bottom of the sequence as the subtrahend, to obtain the supply and return water temperature separation value.

[0024] Instantaneous chilled water flow rate and supply / return water temperature reports are acquired using an ultrasonic flow sensor and a thermistor probe array. For instantaneous flow rate, a first-in-first-out queue is established and stored in the local high-speed sector to prevent data loss. For temperature reports, the parsing engine extracts the temperature readings from the supply and return water sides. To effectively eliminate channel hazards caused by misaligned wiring during construction, the system does not rely on preset channel labels but instead uses a bubble sort comparator to compare the temperatures on both sides. Because the return temperature scale is usually higher than the supply temperature scale in standard cooling mode, the sorting is forced to place the higher value at the top as the minuend and the lower value at the bottom as the subtrahend. This recalibrated variable combination is directly latched to obtain the supply and return water temperature separation values. The acquired instantaneous flow rate of the pipe network is set to 100 kg / s, and the two temperature readings obtained by the pipe network probe are 7 degrees Celsius and 12 degrees Celsius, respectively. The flow rate data is directly stored in the high-speed sector. 7 and 12 are sent to the comparator array. After determining that 12 is greater than 7, 12 is automatically locked as the top-side minuend and 7 as the bottom-side subtrahend. Finally, a data packet containing the supply and return water temperature separation values ​​of minuend 12 and minuend 7 was obtained.

[0025] The temperature difference calculation submodule calls the supply and return water temperature separation values, reads the minuend and subtrahend, subtracts the subtrahend from the minuend to perform the difference extraction operation, records the difference value output by the difference extraction operation, determines the positive and negative range characteristics of the difference value, removes the difference value in the negative range, retains the difference value in the positive range, performs a data format synchronous conversion operation on the retained difference value in the positive range, and obtains the supply and return water temperature difference data. The system accesses the bus via IoT to read the pre-locked supply and return water temperature separation values. The control unit then sends the minuend and subtrahend from these values ​​to the arithmetic logic unit to perform a difference extraction operation, recording the output difference value. To prevent sensor zero-point drift or polarization reversal caused by frequency converter electromagnetic interference, a bipolar window comparator is used to strictly determine the positive and negative ranges of the difference. If the difference is less than zero, the system issues an alarm and discards the negative dirty data; if it is greater than or equal to zero, the difference value within the positive range is retained. For the retained difference value, the effective bit width is truncated, a format synchronous conversion is performed, and a dedicated identifier is added to obtain the supply and return water temperature difference data. Continuing with the previous parameters, the minuend is set to 12 degrees Celsius, and the subtrahend to 7 degrees Celsius. The arithmetic unit subtracts 7 from 12, capturing a difference value of 5 degrees Celsius. After being sent to the isolation comparator circuit, since 5 is greater than 0, the system determines that it is in the positive range and retains it. After performing data format conversion on the value 5 and filling the protocol header, the final valid supply and return water temperature difference data is 5 degrees Celsius.

[0026] The load ratio calculation submodule, based on the supply and return water temperature difference data and the instantaneous flow rate of chilled water buffered by the local node, uses the supply and return water temperature difference data as the base term for the multiplication operation and the instantaneous flow rate of chilled water buffered by the local node as the multiplier term. It performs a proportional product operation on the base term and the multiplier term, extracts the output value of the proportional product operation, collects the water specific heat capacity constant parameter, and multiplies the output value of the proportional product operation by the water specific heat capacity constant parameter to generate the building cooling load demand. The system reads the supply and return water temperature difference data after cleaning via a high-speed bus and retrieves the instantaneous chilled water flow rate from the local cache. A hardware multiplier is activated to perform a proportional product operation on the temperature difference data and flow rate data, generating an integral value representing the instantaneous thermal potential of the pipe network and storing it in a buffer. Subsequently, the water specific heat capacity constant parameter (set to 4187 joules per kilogram of degree Celsius) is read from the solidified parameter table. A secondary hardware multiplier is then activated, multiplying the aforementioned integral value by the water specific heat capacity constant, converting the relative reference value into a standard physical quantity, and generating the final building cooling load demand. Data from the preceding module is retrieved; the supply and return water temperature difference is 5 degrees Celsius, and the cached instantaneous chilled water flow rate is 100 kilograms per second. The temperature difference of 5 and the flow rate of 100 are fed into the first-stage multiplier to perform a proportional product operation, resulting in an output value of 500. Next, the specific heat capacity of water is retrieved as a constant of 4187. The second-stage multiplier is then activated to multiply 500 by 4187. The final calculated building cooling load demand is 2,093,500 watts.

[0027] Specifically, such as Figure 2 , 5 As shown, the energy efficiency anomaly detection module includes: The difference parameter extraction submodule calls the theoretical heat loss rate and the building cooling load demand, monitors the power of the central air conditioning unit of the public building, and stores the monitored power in the local cache node. It performs a difference extraction operation on the building cooling load demand and the theoretical heat loss rate, takes the building cooling load demand as the minuend, sets the theoretical heat loss rate as the subtrahend, performs numerical subtraction, extracts the difference parameter from the numerical subtraction operation, and obtains the cooling load deviation data. The system synchronously retrieves the theoretical heat loss rate and the building's cooling load demand, and monitors the instantaneous power consumption of the air conditioning unit via the inverter's power communication interface, storing the data in a cache for later use. The system activates a logic gate array to perform a difference extraction operation on the cooling load demand and the heat loss rate. Following a strict instruction strategy, the dynamically injected building cooling load demand is locked as the minuend, and the statically lost theoretical heat loss rate is set as the subtrahend. A subtraction operator is then activated to perform the subtraction, extracting the output difference parameter to obtain cooling load deviation data. This data directly reveals the actual wasted cooling capacity caused by pipe network leakage, insulation failure, or equipment internal losses. The theoretical heat loss rate is known to be 400,000 watts, the building cooling load demand is 2,093,500 watts, and the monitored and stored power consumption is 500,000 watts. The cooling load demand of 2,093,500 is allocated to the minuend register, and the heat loss rate of 400,000 is allocated to the subtrahend register. The subtraction operation (2,093,500-400,000) is performed, and the output difference parameter is extracted as 1,693,500 watts. This is the cooling load deviation data obtained in this cycle.

[0028] The power demand calculation submodule reads the electrical power in the local cache node based on the cooling load deviation data, calls the building cooling load demand, adds the cooling load deviation data to the building cooling load demand as the denominator, sets the electrical power in the local cache node as the numerator, performs a division operation on the numerator and denominator, extracts the division output quotient value, and obtains the real-time energy efficiency ratio. The system reads the power consumption from the cache and retrieves the building's cooling load demand. To calculate the efficiency ratio, a hybrid arithmetic unit is constructed. In the summation calculation, the building's cooling load demand is added to the cooling load deviation data, and the actual cooling energy value at the unit's outlet is reconstructed and used as the denominator of the divider. In the quotient calculation, the power consumption, representing the actual power consumption, is set as the numerator. The floating-point divider is activated to perform the division operation, and the result is truncated to four significant decimal places using a bitmasking mechanism to obtain the real-time energy efficiency ratio. This ratio accurately quantifies the dynamic efficiency of the host's power conversion. The system extracts the cooling load deviation data of 1,693,500 watts, the power consumption of 500,000 watts, and the building's cooling load demand of 2,093,500 watts. First, the cooling load demand of 2,093,500 is added to the cooling load deviation of 1,693,500, resulting in a divider denominator of 3,787,000. Setting the electrical power of 500,000 as the numerator, performing a division operation (500,000 divided by 3,787,000) yields an output quotient of approximately 0.1320. After truncating with a mask to retain four digits, the real-time energy efficiency ratio is obtained as 0.1320.

[0029] The cross-boundary coding judgment submodule collects the preset standard energy efficiency ratio critical value and the tolerance limit value for the real-time energy efficiency ratio. It compares and judges the real-time energy efficiency ratio with the preset standard energy efficiency ratio critical value. If the real-time energy efficiency ratio is greater than the preset standard energy efficiency ratio critical value, it calls the tolerance limit value and performs a second comparison between the real-time energy efficiency ratio and the tolerance limit value. It extracts the cross-boundary trigger signal from the second comparison and converts the cross-boundary trigger signal into a status coding sequence to establish an excess energy consumption status identifier. The system monitors the real-time energy efficiency ratio and extracts the preset standard energy efficiency ratio threshold value based on the equipment's factory test calibration and the tolerance limit value calculated based on equipment aging from the baseline library. The first-stage hardware comparator is activated to compare the real-time energy efficiency ratio with the standard threshold value. If the former is greater than the latter, a trigger pulse is output to activate the second-stage comparator, which compares the real-time energy efficiency ratio with the tolerance limit value a second time. If the limit is exceeded again, an interrupt signal is generated, and the cross-limit trigger signal is extracted and sent to the state machine encoder. This encoder converts the signal into a hexadecimal state code sequence containing a timestamp and an identifier, ultimately establishing an excess energy consumption status indicator.

[0030] Table 1: Configuration Table for Anomaly Judgment Boundaries Real-time energy efficiency ratio 0.1320 Preset standard energy efficiency ratio threshold 0.1000 Tolerance Limit Limit 0.1250 As shown in Table 1, the system extracts a preset standard energy efficiency ratio critical value of 0.1000 and a tolerance limit value of 0.1250. The current real-time energy efficiency ratio is 0.1320. The first-level comparison determines that 0.1320 is greater than 0.1000, activating the alarm link. The second level compares 0.1320 with the tolerance limit of 1250 a second time. Since 0.1320 is still greater than 0.1250, the second-level comparator output is interrupted, extracting the cross-limit trigger signal. The encoder queries the dictionary table to convert it into a status code sequence packet, formally establishing the equipment's excess energy consumption status identifier.

[0031] Specifically, such as Figure 2 , 6 As shown, the temperature offset extraction module includes: The difference extraction and conversion submodule, for the excess energy consumption status indicator, obtains the return water temperature of the pipeline network and the preset target return water temperature, sets the return water temperature of the pipeline network as the minuend, sets the preset target return water temperature as the subtrahend, performs a subtraction calculation operation by subtracting the subtrahend from the subtrahend, extracts the difference parameter output by the subtraction calculation, collects the temperature conversion proportional coefficient constant, performs a multiplication operation operation between the difference parameter and the temperature conversion proportional coefficient constant, and obtains the control deviation data; Through IoT access, when an excessive energy consumption status indicator is detected, this module sends a command to the main pipeline node to obtain the latest pipeline return water temperature and reads the preset target return water temperature issued by the engineer from the configuration file. A subtractor unit is configured, setting the pipeline return water temperature as the minuend and the preset target return water temperature as the subtrahend, and performing subtraction to extract the difference parameter. Subsequently, the temperature conversion proportional coefficient constant obtained through grid optimization is read. The multiplier array is activated, multiplying the difference parameter by this conversion proportional constant to amplify it, achieving an effective mapping from the physical temperature scale to a dimensionless adjustment quantity, thereby obtaining control deviation data. The pipeline return water temperature obtained under abnormal operating conditions is set to 13.5 degrees Celsius, the preset target return water temperature is set to 12 degrees Celsius, and the temperature conversion proportional coefficient constant calibrated in the external storage area is 4. The pipeline return water temperature of 13.5 degrees Celsius is configured as the minuend, and the preset target return water temperature of 12 degrees Celsius is configured as the subtrahend; the subtractor performs subtraction to obtain a difference parameter of 1.5 degrees Celsius. The difference parameter 1.5 is then multiplied by the gain factor 4 in the multiplier to obtain an amplification result of 6, and finally the control deviation data 6 is obtained.

[0032] The adjustment parameter generation submodule collects preset adjustment extreme value parameters, compares the control deviation data with the preset adjustment extreme value parameters, and if the control deviation data is greater than the preset adjustment extreme value parameters, the preset adjustment extreme value parameters are extracted as boundary output items; if they are not greater, the control deviation data are used as in-situ output items. The boundary output items and in-situ output items are summarized and the data frame format conversion and adjustment operation is performed to obtain the device control offset. The system reads preset adjustment extreme value parameters from the tamper-proof storage area to prevent over-adjustment. A dual-track limiting control circuit is constructed, and the control deviation data is compared with the preset adjustment extreme value parameters. If the absolute value of the deviation data is greater than the adjustment extreme value, the relay forcibly closes the safety protection channel, blocking the over-limit value, and extracts the preset adjustment extreme value parameter as the boundary output item; if it is not greater, the pass-through channel is maintained, and the original deviation data is transmitted as the in-situ output item. After summarizing the output items, the serialized microcode is called to attach a check bit and perform frame format conversion, finally encapsulating the compliant device control offset.

[0033] Table 2: Execution Table of Limiting Protection Judgment Logic Control deviation data 6 Preset adjustment of extreme parameters 5 Boundary output items 5 As shown in Table 2, the control deviation data acquired by the front-end is 6, and the preset adjustment extreme value parameter in the safety storage area is set to 5. Comparing the two inputs to the limiting dual-track circuit, the detection of an out-of-bounds characteristic (6 > 5) triggers a relay to forcibly close the safety protection channel, blocking the transmission of data 6. The extreme value parameter 5 is extracted and used as the boundary output item. If the deviation data is 4, it is transmitted along the direct channel. After extracting the boundary item 5, it is pushed into the convergence buffer, and a cyclic redundancy check is added to package it, generating a device control offset of 5 that can be sent out.

[0034] Specifically, such as Figure 2 , 7 As shown, the instruction issuing module includes: The temperature parameter synthesis submodule calls the device control offset, extracts the air conditioning unit's outlet water reference temperature, sets the outlet water reference temperature as the addend, sets the device control offset as the augend, performs a combined addition operation on the addend and augend, extracts the sum of the addition operation, collects the preset operating temperature limit parameters, compares the sum with the limit parameters, discards sums that exceed the limit, retains sums within the range and performs format synchronization operation, and establishes the outlet water target temperature. The system intercepts the encapsulated device control offset and extracts the factory-set air conditioning unit outlet water reference temperature from the configuration register. A digital adder is configured, setting the outlet water reference temperature as the addend and the unpacked control offset as the augend. A combined addition operation is performed to extract the output sum. To avoid exceeding the freezing point or shutdown threshold due to accumulation, the underlying boundary monitoring process is called to read the preset operating temperature limit parameters (including overheat and antifreeze threshold arrays). An interval checker compares the sum with the limit parameters; if the sum exceeds the limit, it is discarded; if it falls within the safety window, it is retained. A floating-point alignment conversion is then performed to construct the final outlet water target temperature. The extracted air conditioning unit outlet water reference temperature is set to 7 degrees Celsius, and the captured and parsed device control offset is 5. The reference temperature 7 is assigned as the addend, and the offset 5 is assigned as the augend. A combined addition operation (7+5) is performed to obtain a sum of 12 degrees Celsius. The preset operating temperature limit parameters define the safety red line interval as [5, 15] degrees Celsius. The detector found that 12 completely fell within the safe range, so the system allowed and retained the value 12. After being sealed in a box using floating-point complement, a reliable target water temperature of 12 degrees Celsius was established.

[0035] The speed control signal conversion submodule collects the state transition mapping table for the target outlet water temperature, uses the target outlet water temperature as the index item to perform internal table matching and extraction, obtains the corresponding pulse duty cycle parameter, constructs a level alternating waveform sequence based on the pulse duty cycle parameter, converts the level alternating waveform sequence into hardware level pulses, reads the compressor communication protocol specification parameters, performs frame data encapsulation and packaging operation on the hardware level pulses, and obtains the frequency conversion speed control command. The system reads the target outlet water temperature variable and retrieves a pre-recorded state transition mapping table from flash memory. This table maps the nonlinear relationship between the temperature request target and the inverter duty cycle. Based on the addressing mechanism, the target outlet water temperature is used as an index to perform matching and extraction in the table, and the corresponding pulse duty cycle parameter is decoded. A pulse width modulation waveform generator is configured to construct an alternating high and low level waveform sequence based on the duty cycle parameter and convert it into a real hardware pulse control flow. Finally, the compressor communication protocol specification parameters are retrieved, and protocol frame headers and footers and error correction codes are appended to the pulse flow for splicing operations, packaging and generating the variable frequency speed control command issued to the actuator.

[0036] Table 3: Pulse Waveform Conversion Mapping Configuration Table Target water temperature 12 Match the corresponding pulse duty cycle parameter 60% As shown in Table 3, the target effluent temperature is set to 12 degrees Celsius. The value 12 is used as an index in the flash memory state transition mapping table for matching. After locking onto the number 12, the algorithm engine extracts its associated payload and decodes it to obtain a matching pulse duty cycle parameter of 60%. This 60% duty cycle parameter is then fed into the waveform generator to construct an alternating waveform sequence of high level for the first 60 milliseconds and low level for the last 40 milliseconds within a 100-millisecond clock cycle. Finally, the slave address and baud rate, among other protocol parameters, are retrieved from the protocol stack. Frame data encapsulation and splicing are then performed on the pulse stream to generate a variable frequency speed control command, which is then sent to the interface.

[0037] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of protection of the described technical solutions.

Claims

1. The online monitoring and intelligent analysis system for public building energy consumption based on Internet of Things, characterized in that, The system includes: The building heat loss module obtains the indoor and outdoor temperatures and wall thermal resistance of public buildings, calculates the weighted cumulative result of outdoor temperature increment and extracts the difference between it and indoor temperature, and establishes the theoretical heat loss rate. The load monitoring module collects the instantaneous flow rate of chilled water and the supply and return water temperatures of the public building pipe network. It performs a differential extraction operation on the supply and return water temperatures to obtain temperature difference data. It then combines the temperature difference data with the instantaneous flow rate of chilled water to perform a proportional calculation to obtain the building's cooling load demand. The energy efficiency anomaly determination module calls the theoretical heat loss rate and the building cooling load demand, monitors the power of the central air conditioning unit of the public building, performs a difference extraction operation on the building cooling load demand and the theoretical heat loss rate to obtain cooling load deviation data, calculates the ratio of the power of the central air conditioning unit to the building cooling load demand to extract the real-time energy efficiency ratio, and compares it with the preset standard energy efficiency ratio critical value and tolerance limit value to generate an excess energy consumption status indicator. The temperature offset extraction module, based on the excess energy consumption status identifier, obtains the return water temperature of the pipeline network and the preset target return water temperature, extracts the temperature difference to convert control deviation data, and obtains the equipment control offset. The instruction sending module extracts the reference temperature of the air conditioner's outlet water and merges it with the execution value of the device control offset to establish the target temperature of the outlet water. The target temperature of the outlet water is then converted into a level pulse and sent to the compressor to obtain the variable frequency speed control instruction. 2.The online monitoring and intelligent analysis system for energy consumption of public buildings based on Internet of Things according to claim 1, characterized in that, The theoretical heat loss rate includes conduction loss rate, convection loss rate, and radiation loss rate. The building cooling load demand includes the cooling load of the building envelope, the cooling load of personnel, and the cooling load of equipment. The excess energy consumption status indicators include a yellow warning code for slight over-limit, a red warning code for severe energy consumption, and a black code for equipment failure. The equipment control offset includes the host power adjustment difference, the water pump speed correction value, and the fan start / stop threshold compensation. The variable frequency speed control command includes the given parameter of operating frequency, the preset value of acceleration time, and the torque limit characteristic quantity.

3. The Internet of Things-based online monitoring and intelligent analysis system for energy consumption in public buildings according to claim 1, characterized in that, The building heat dissipation module includes: The temperature weighted accumulation submodule obtains the indoor and outdoor temperatures and wall thermal resistance of public buildings. It extracts the meteorological temperature values ​​corresponding to each time node for the outdoor temperature, collects the time-incrementing sequence values, performs single-item multiplication operations on the meteorological temperature values ​​and the time-incrementing sequence values ​​in the order of time nodes, collects the output values ​​of the multiplication operations and performs an addition operation to generate the outdoor temperature weighted accumulation value. The temperature difference extraction submodule calls the outdoor temperature increment weighted cumulative value and the indoor temperature, determines the relationship between the two values. If the outdoor temperature increment weighted cumulative value is greater than the indoor temperature, the indoor temperature is subtracted from the outdoor temperature increment weighted cumulative value and the one-way calculated difference is output. If it is not greater, the outdoor temperature increment weighted cumulative value is subtracted from the indoor temperature and the reverse calculated difference is output to obtain the indoor and outdoor temperature difference. The heat loss rate calculation submodule, based on the indoor-outdoor temperature difference and the wall thermal resistance, inputs the wall thermal resistance into the reciprocal operation term and outputs the reciprocal conversion value, collects the external surface area data of the public building, performs continuous multiplication operation on the indoor-outdoor temperature difference and the reciprocal conversion value with the external surface area data of the public building, extracts the continuous multiplication operation term output result and performs synchronous conversion and adjustment processing of dimensional state quantities to establish the theoretical heat loss rate.

4. The Internet of Things-based online monitoring and intelligent analysis system for energy consumption in public buildings according to claim 3, characterized in that, The load monitoring module includes: The data separation and extraction submodule collects the instantaneous flow rate and supply and return water temperature of chilled water in the public building pipe network. It performs local node caching operation on the instantaneous flow rate of chilled water in the public building pipe network, extracts the supply side temperature reading value within the supply and return water temperature, reads the return side temperature reading value within the supply and return water temperature, compares the arrangement sequence of the supply side temperature reading value and the return side temperature reading value, sets the reading value at the top of the sequence as the minuend, and sets the reading value at the bottom of the sequence as the subtrahend, to obtain the supply and return water temperature separation value. The temperature difference calculation submodule calls the supply and return water temperature separation value, reads the minuend and subtrahend, subtracts the subtrahend from the minuend to perform a difference extraction operation, records the difference value output by the difference extraction operation, determines the positive and negative range characteristics of the difference value, removes the difference value in the negative range, retains the difference value in the positive range, and performs a data format synchronization conversion operation on the retained difference value in the positive range to obtain the supply and return water temperature difference data. The load ratio calculation submodule, based on the supply and return water temperature difference data and the instantaneous flow rate of chilled water buffered at the local node, uses the supply and return water temperature difference data as the base term for multiplication and the instantaneous flow rate of chilled water buffered at the local node as the multiplier term. It performs a proportional product operation on the base term and the multiplier term, extracts the output value of the proportional product operation, collects the water specific heat capacity constant parameter, and multiplies the output value of the proportional product operation by the water specific heat capacity constant parameter to generate the building cooling load demand.

5. The Internet of Things-based online monitoring and intelligent analysis system for energy consumption in public buildings according to claim 4, characterized in that, The energy efficiency anomaly detection module includes: The difference parameter extraction submodule calls the theoretical heat loss rate and the building cooling load demand, monitors the power of the central air conditioning unit of the public building, and stores the monitored power in the local cache node. It performs a difference extraction operation on the building cooling load demand and the theoretical heat loss rate, takes the building cooling load demand as the minuend, sets the theoretical heat loss rate as the subtrahend, performs numerical subtraction, extracts the difference parameter from the numerical subtraction operation, and obtains the cooling load deviation data. The power demand calculation submodule reads the electrical power in the local cache node based on the cooling load deviation data, calls the building cooling load demand, adds the building cooling load demand to the cooling load deviation data as the denominator, sets the electrical power in the local cache node as the numerator, performs a division operation on the numerator and denominator, extracts the division operation output quotient value, and obtains the real-time energy efficiency ratio. The cross-boundary coding determination submodule collects a preset standard energy efficiency ratio threshold value and a tolerance limit value for the real-time energy efficiency ratio. It compares and determines the real-time energy efficiency ratio with the preset standard energy efficiency ratio threshold value. If the real-time energy efficiency ratio is greater than the preset standard energy efficiency ratio threshold value, it calls the tolerance limit value and performs a second comparison between the real-time energy efficiency ratio and the tolerance limit value. It extracts the cross-boundary trigger signal from the second comparison and converts the cross-boundary trigger signal into a status coding sequence to establish an excess energy consumption status identifier.

6. The Internet of Things-based online monitoring and intelligent analysis system for energy consumption in public buildings according to claim 5, characterized in that, Set the real-time energy efficiency ratio as the data to be compared and set the tolerance limit value as the benchmark data; if the data to be compared is greater than the benchmark data, generate a first-level cross-limit instruction and configure the first-level cross-limit instruction as a cross-limit trigger signal. If the data to be compared is equal to or less than the benchmark data, a secondary cross-boundary instruction is generated and configured as a cross-boundary trigger signal.

7. The Internet of Things-based online monitoring and intelligent analysis system for energy consumption of public buildings according to claim 5, characterized in that, The temperature offset extraction module includes: The difference extraction and conversion submodule, for the excess energy consumption status identifier, obtains the pipe network return water temperature and the preset target return water temperature, sets the pipe network return water temperature as the minuend, sets the preset target return water temperature as the subtrahend, performs a subtraction calculation operation by subtracting the subtrahend from the subtrahend, extracts the difference parameter output by the subtraction calculation, collects the temperature conversion proportional coefficient constant, performs a multiplication operation operation between the difference parameter and the temperature conversion proportional coefficient constant, and obtains the control deviation data; The adjustment parameter generation submodule collects preset adjustment extreme value parameters, compares the control deviation data with the preset adjustment extreme value parameters, and if the control deviation data is greater than the preset adjustment extreme value parameters, the preset adjustment extreme value parameters are extracted as boundary output items; if they are not greater, the control deviation data are used as in-situ output items. The boundary output items and in-situ output items are summarized, and a data frame format conversion and adjustment operation is performed to obtain the device control offset.

8. The Internet of Things-based online monitoring and intelligent analysis system for energy consumption in public buildings according to claim 7, characterized in that, Create a pre-defined blank matrix, write the boundary output items and the original output items into the pre-defined blank matrix, and extract the full-load data matrix; Collect the preset frame header code sequence, preset terminal address, and preset frame tail code sequence; Set the full-load data matrix as the core segment of the payload; Set the preset frame header code sequence as the start bit, set the preset terminal address as the address bit, and set the preset frame tail code sequence as the stop bit.

9. The Internet of Things-based online monitoring and intelligent analysis system for energy consumption of public buildings according to claim 7, characterized in that, The instruction issuing module includes: The temperature parameter synthesis submodule calls the device control offset, extracts the air conditioning unit's outlet water reference temperature, sets the outlet water reference temperature as the addend, sets the device control offset as the augend, performs a combined addition operation on the addend and augend, extracts the sum of the addition operation, collects the preset operating temperature limit parameters, compares the sum with the limit parameters, discards sums that exceed the limit, retains sums within the range and performs a format synchronization operation, and establishes the outlet water target temperature. The speed control signal conversion submodule collects the state transition mapping table for the target outlet water temperature, uses the target outlet water temperature as an index item to perform internal table matching extraction, obtains the corresponding pulse duty cycle parameter, constructs a level alternating waveform sequence based on the pulse duty cycle parameter, converts the level alternating waveform sequence into hardware level pulses, reads the compressor communication protocol specification parameters, performs frame data encapsulation and packaging operation on the hardware level pulses, and obtains the frequency conversion speed control command.

10. The Internet of Things-based online monitoring and intelligent analysis system for energy consumption of public buildings according to claim 9, characterized in that, Obtain the preset period constant, multiply the pulse duty cycle parameter with the preset period constant, and extract the high-level duration parameter; Subtract the high-level duration parameter from the preset period constant to obtain the low-level duration parameter by subtraction; perform cyclic splicing according to the order of the high-level duration parameter and the low-level duration parameter to generate an alternating level waveform sequence.

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