Energy consumption control method, system and equipment for multi-split air conditioning system and storage medium

Abstract

The invention discloses a multi-split system energy consumption control method, system and device and a storage medium, and relates to the technical field of heating and ventilation, and the method comprises the steps that operation parameters, reference parameters and environment parameters of a multi-split system are obtained, and the multi-split system is matched with a target detection area; determining an actual energy consumption sequence according to the operation parameters; calculating a predicted energy consumption sequence of a target time period after the current moment according to the actual energy consumption sequence and the environmental parameters; and according to the fact that a difference value sequence between the predicted energy consumption sequence and the actual energy consumption sequence is within a set early warning range, the reference parameters and / or the operation parameters are / is adjusted, so that the total energy consumption of the multi-split system in the target monitoring area is within a set reference range. A multi-split system energy consumption monitoring system is constructed, and operation parameters or reference quotas are actively adjusted before a hyperbranched risk occurs by predicting difference early warning between an energy consumption sequence and actual energy consumption. The problem of response lag caused by dependence on static rules traditionally is solved, and the real-time performance and accuracy of energy consumption control are improved.

Description

Technical Field

[0001] The present invention relates to the field of heating and ventilation, and in particular to a method, system, device and storage medium for controlling energy consumption of a multi-split system. Background Art

[0002] Multi-split air conditioning systems, with their efficient and convenient installation advantages, have become the mainstream solution for regulating heat and humidity in modern building spaces. Current technologies primarily manage energy consumption through overall energy consumption monitoring and power regulation. This technology captures total system energy consumption data and, when energy consumption exceeds standards, implements basic control measures such as reducing cooling and heating power.

[0003] The existing management methods have problems such as extensive monitoring (unable to subdivide energy consumption by equipment, time periods and scenarios), simple regulation (only overall power reduction at the expense of comfort), and lack of a commitment mechanism (no scientific target setting and dynamic adjustment capabilities). These problems lead to low efficiency in energy consumption optimization, easily lead to operation and maintenance disputes, and make it difficult to meet low-carbon energy-saving needs. Summary of the Invention

[0004] The primary objective of this invention is to provide a method, system, device, and storage medium for controlling energy consumption in a multi-split system. By collecting operating parameters, environmental parameters, and baseline parameters in real time, a dynamic monitoring system for multi-split system energy consumption is established. By providing early warnings based on discrepancies between predicted energy consumption sequences and actual energy consumption, operating parameters or baseline quotas can be proactively adjusted before the risk of overspending occurs. This addresses the response lag caused by traditional methods relying on static rules, improving the real-time and accuracy of energy consumption control.

[0005] In order to achieve the above objectives, the embodiments of the present application provide the following technical solutions:

[0006] According to a first aspect of an embodiment of the present application, a method for controlling energy consumption of a multi-split system is provided, the method comprising:

[0007] Obtaining operating parameters, baseline parameters, and environmental parameters of a multi-connected system, wherein the multi-connected system matches a target detection area;

[0008] determining an actual energy consumption sequence based on the operating parameters;

[0009] Determining a predicted energy consumption sequence for a target period after a current moment based on the actual energy consumption sequence and the environmental parameters;

[0010] According to the difference value sequence between the predicted energy consumption sequence and the actual energy consumption sequence being within the set warning range, the benchmark parameters and / or operating parameters are adjusted so that the total energy consumption of the multi-connected system in the target monitoring area is within the set benchmark range.

[0011] Optionally, the benchmark parameters include a benchmark energy consumption quota and a quota weight, and the quota weight includes a time period-level allocation weight;

[0012] Adjusting the benchmark parameters includes:

[0013] Determine an actual energy consumption overspending sequence based on the difference between the actual energy consumption sequence and the benchmark energy consumption quota, and determine the time period corresponding to the actual energy consumption overspending sequence as the overspending time period, and determine the time period outside the overspending time period as the non-overspending time period;

[0014] Calculating a time period quota reduction amount according to the time period level allocation weight and the actual energy consumption overrun sequence;

[0015] The time period quota reduction amount is allocated according to the time period level weight of the non-overspending time period, and the baseline energy consumption quota of the non-overspending time period is adjusted.

[0016] Optionally, the quota weight further includes a regional allocation weight; and adjusting the benchmark parameter further includes:

[0017] Determining the target monitoring area as an overspending area, and determining the monitoring area outside the overspending area as a non-overspending area;

[0018] Calculating a regional quota reduction amount based on the regional allocation weight and the actual energy consumption overrun sequence;

[0019] The regional quota reduction amount is adjusted according to the regional level allocation weight of the non-overspending area, and the baseline energy consumption quota of the non-overspending area is adjusted.

[0020] Optionally, it also includes:

[0021] Generating an initial baseline energy consumption quota based on historical energy consumption data of the target monitoring area;

[0022] According to the energy consumption target value and the set constraint conditions set by the user, the initial benchmark energy consumption quota is revised to obtain a revised initial benchmark energy consumption quota;

[0023] Determining a compensation coefficient based on the energy efficiency attributes and climate zoning standards of the multi-split system in the target monitoring area, and adjusting the revised initial baseline energy consumption quota based on the compensation coefficient to obtain a baseline energy consumption quota;

[0024] The region-level allocation weight and the time period-level allocation weight are determined according to the functional attributes, time period characteristics and / or user settings of the target monitoring area.

[0025] Optionally, the environmental parameters include the indoor temperature, outdoor temperature of the VRF system, and personnel flow status in the target detection area; the operating parameters include indoor unit wind speed gear, compressor load rate, set temperature, and cooling and heating differential; and adjusting the operating parameters includes:

[0026] Calculating a wind speed adjustment coefficient based on the temperature difference between the indoor temperature and the set temperature and the compressor load rate, and adjusting the wind speed gear of the indoor unit according to the wind speed adjustment system;

[0027] adjusting the set temperature according to the indoor and outdoor temperature difference between the indoor temperature and the outdoor temperature;

[0028] The cooling and heating hysteresis is updated according to the attributes of the target monitoring area and the personnel flow status.

[0029] Optionally, the operating parameter includes a compressor operating frequency; and adjusting the operating parameter includes:

[0030] According to the personnel flow state in the target monitoring area being in an unmanned state and lasting for a first predetermined time, adjusting the operating frequency of the compressor to operate within a first adjustment range;

[0031] According to the duration reaching a second predetermined duration, adjusting the operating frequency of the compressor from the first adjustment range to a second adjustment range;

[0032] According to the duration exceeding the maximum allowable duration, the operating frequency of the compressor is limited to operating within a set minimum frequency range; wherein the frequency value of the minimum value of the first adjustment range is greater than or equal to the frequency value of the maximum value of the second adjustment range, and the frequency value of the minimum value of the second adjustment range is greater than or equal to the maximum value of the minimum frequency range.

[0033] Optionally, the set warning range includes a first warning range, a second warning range, and a third warning range; wherein the maximum value of the first warning range is less than or equal to the minimum value of the second warning range, and the maximum value of the second warning range is less than or equal to the minimum value of the third warning range;

[0034] According to the difference value sequence between the predicted energy consumption sequence and the actual energy consumption sequence being within the set warning range, adjusting the benchmark parameter and / or operating parameter includes:

[0035] adjusting the operating parameters according to the difference value sequence being within the first warning range;

[0036] According to the difference value sequence being in the second warning range, adjusting the benchmark parameter;

[0037] According to the difference value sequence being in the third warning range, the reference parameter and the operating parameter are adjusted.

[0038] According to a second aspect of an embodiment of the present application, there is provided a multi-split system energy consumption control system, the system comprising:

[0039] a data acquisition module for acquiring operating parameters, baseline parameters, and environmental parameters of a multi-connected system matched to a target detection area;

[0040] An actual energy consumption sequence determination module, configured to determine an actual energy consumption sequence according to the operating parameters;

[0041] A predicted energy consumption sequence determination module, configured to determine a predicted energy consumption sequence for a target period after a current moment based on the actual energy consumption sequence and the environmental parameters;

[0042] The control module is used to adjust the benchmark parameters and / or operating parameters according to the difference value sequence between the predicted energy consumption sequence and the actual energy consumption sequence being within the set warning range, so that the total energy consumption of the multi-connected system in the target monitoring area is within the set benchmark range.

[0043] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect above.

[0044] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which computer-readable instructions are stored. The computer-readable instructions can be executed by a processor to implement the method described in the first aspect above.

[0045] In summary, the embodiments of the present application provide a method, system, device and storage medium for controlling energy consumption of a multi-split system. By real-time collection of operating parameters, baseline parameters and environmental parameters of the multi-split system in the target monitoring area; determining the actual energy consumption sequence according to the operating parameters; calculating the predicted energy consumption sequence of the target time period after the current moment according to the actual energy consumption sequence and the environmental parameters; and adjusting the baseline parameters and / or operating parameters according to the difference value sequence between the predicted energy consumption sequence and the actual energy consumption sequence being within the set warning range, so that the total energy consumption of the multi-split system in the target monitoring area is within the set benchmark range. By real-time collection of operating parameters, environmental parameters and baseline parameters, a dynamic monitoring system for energy consumption of a multi-split system is constructed, and by warning of the difference between the predicted energy consumption sequence and the actual energy consumption, the operating parameters or benchmark quotas are actively adjusted before the risk of overspending occurs. This solves the problem of response lag caused by the traditional method relying on static rules, and improves the real-time and accuracy of energy consumption control. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0047] The structures, proportions, sizes, etc. illustrated in this specification are intended solely to complement the contents disclosed herein and to facilitate understanding and reading by persons skilled in the art. They are not intended to limit the conditions under which the present invention may be implemented and therefore have no substantive technical significance. Any structural modifications, changes in proportions, or adjustments in sizes, without affecting the efficacy and objectives of the present invention, shall remain within the scope of the technical contents disclosed herein.

[0048] Figure 1 A schematic diagram of a method for controlling energy consumption of a multi-split system provided in an embodiment of the present application;

[0049] Figure 2 A schematic diagram of the front-end input configuration architecture and relationships provided in an embodiment of the present application;

[0050] Figure 3 A schematic diagram of the relationship between control content and methods provided in the embodiments of this application;

[0051] Figure 4 A schematic diagram of an energy consumption control system for a multi-split system provided in an embodiment of the present application;

[0052] Figure 5 A structural diagram of an electronic device provided in an embodiment of the present application is shown;

[0053] Figure 6 A diagram showing a computer-readable storage medium provided in an embodiment of the present application.

[0054] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0056] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0057] In addition, the terms "first," "second," and so on, used in this disclosure are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referenced. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this disclosure, "plurality" means at least two, such as two or three, unless otherwise specifically defined.

[0058] In the present invention, unless otherwise specified or limited, the terms "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can mean fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will be able to understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0059] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in this field can implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0060] Figure 1A method for controlling energy consumption of a multi-split system provided in an embodiment of the present application is shown, the method comprising:

[0061] Step 101: Acquire operating parameters, baseline parameters, and environmental parameters of a multi-connected system, wherein the multi-connected system matches a target detection area;

[0062] Step 102: determining an actual energy consumption sequence according to the operating parameters;

[0063] Step 103: determining a predicted energy consumption sequence for a target period after the current moment based on the actual energy consumption sequence and the environmental parameters;

[0064] Step 104: Based on the difference value sequence between the predicted energy consumption sequence and the actual energy consumption sequence being within the set warning range, the benchmark parameters and / or operating parameters are adjusted so that the total energy consumption of the multi-connected system in the target monitoring area is within the set benchmark range.

[0065] In one possible implementation, the benchmark parameters include a benchmark energy consumption quota and a quota weight, and the quota weight includes a time period allocation weight. In step 104, adjusting the benchmark parameters includes:

[0066] According to the difference between the actual energy consumption sequence and the benchmark energy consumption quota, the actual energy consumption overspending sequence is determined, and the time period corresponding to the actual energy consumption overspending sequence is determined as the overspending time period, and the time period outside the overspending time period is determined as the non-overspending time period; the time period quota reduction amount is calculated according to the time period-level allocation weight and the actual energy consumption overspending sequence; the time period quota reduction amount is adjusted according to the time period-level allocation weight of the non-overspending time period, and the benchmark energy consumption quota of the non-overspending time period is adjusted.

[0067] By identifying overspending periods and calculating quota reductions, the reductions are then redistributed to periods within the quota range based on weights. This addresses the rigidity of time-based energy consumption allocation under traditional fixed quota models (e.g., overspending during the day and waste at night), enabling flexible scheduling and global balance of energy resources across time. By introducing time-based allocation weights as a basis for redistribution (e.g., high weights for peak periods and low weights for off-peak periods), quota adjustments are aligned with business priorities, avoiding energy demand conflicts caused by "one-size-fits-all" reductions and enhancing the acceptability and operability of quota adjustments.

[0068] For example, in an office building's VLC system, if the monitoring indicates overspending during the afternoon hours of 12:00 PM to 2:00 PM (intensive personnel and high equipment load), actual energy consumption exceeds the quota by 20%. The non-overspending period is from 8:00 PM to 6:00 AM (on-duty mode only), with actual energy consumption falling 30% below the quota. The following actions are taken: calculate the midday overspending (e.g., 50 kWh); allocate the overspending based on the nighttime weight, increase the nighttime quota to 115% of the original value; and start the nighttime cooling equipment early to store energy for the next day's afternoon. This ensures that the midday overspending is offset by the nighttime redundancy quota, keeping total energy consumption within the baseline range.

[0069] In a possible implementation, the quota weights further include regional allocation weights; and in step 104, adjusting the benchmark parameters further includes:

[0070] The target monitoring area is determined as the overspending area, and the monitoring area outside the overspending area is determined as the non-overspending area; the regional quota reduction amount is calculated according to the regional-level allocation weight and the actual energy consumption overspending sequence; the regional quota reduction amount is adjusted according to the regional-level allocation weight of the non-overspending area, and the baseline energy consumption quota of the non-overspending area is adjusted.

[0071] By identifying overspending areas and calculating quota reductions, the reductions are dynamically allocated to non-overspending areas based on regional weights, solving the imbalance in regional energy consumption distribution under the traditional fixed quota model (such as overspending in core areas and waste in peripheral areas), and achieving on-demand flow and cross-regional collaborative optimization of energy consumption resources in the spatial dimension. Based on regional allocation weights (such as production workshop weight > storage area weight), it ensures that high-priority areas receive more compensation when quotas are redistributed, avoids the risk of critical business interruption caused by "egalitarian" adjustments, and maintains the functional integrity of the system. By linking the quota reductions in overspending areas with the quota increases in non-overspending areas, a "spatial energy consumption transfer" effect is formed, converting local redundant quotas into globally available resources, and improving the overall energy efficiency of the system.

[0072] For example, in a factory with a multi-split system, monitoring indicates that the overspending area is the production workshop (equipment-intensive), with actual energy consumption exceeding the quota by 25%. The non-overspending area is the raw material warehouse (low constant temperature requirements), with actual energy consumption falling 40% below the quota. The following actions are performed: calculate the workshop overspending amount (e.g., 200 kWh); distribute the overspending amount by warehouse area weight, increasing each warehouse's quota to 130% of the original value; and increase the warehouse's air conditioning power to share the workshop load. This offsets the workshop overspending with the warehouse's redundant quota, maintaining total energy consumption within the baseline range and maintaining workshop production.

[0073] In a possible implementation, the reference parameter is determined according to the following steps:

[0074] An initial baseline energy consumption quota is generated based on the historical energy consumption data of the target monitoring area; the initial baseline energy consumption quota is corrected according to the energy consumption target value and the set constraints set by the user to obtain a corrected initial baseline energy consumption quota; a compensation coefficient is calculated based on the energy efficiency attributes and climate zoning standards of the multi-connected system of the target monitoring area, and the corrected initial baseline energy consumption quota is adjusted according to the compensation coefficient to obtain a baseline energy consumption quota; the regional-level allocation weight and the time-period-level allocation weight are determined based on the functional attributes, time-period characteristics and / or user settings of the target monitoring area.

[0075] Through a four-dimensional calibration mechanism combining historical data, user goals, energy efficiency attributes, and climate characteristics, this approach addresses the static and unrealistic nature of traditional benchmark energy consumption quotas, building a dynamic benchmark model that conforms to both objective operating rules and subjective management needs. A collaborative decision-making mechanism based on functional attributes (spatial dimension), time period characteristics (temporal dimension), and user settings (management dimension) generates a dynamically evolving regional and time period weighting system, addressing the rigid and single nature of traditional weight allocation.

[0076] For example, extracting energy consumption data from the past three years to generate an initial baseline, and then adjusting the initial baseline if the user sets a 15% energy efficiency target for the current season, will result in a correction. The compensation coefficient for high-efficiency magnetic levitation units is 0.9, for older centrifugal units is 1.2, for severely cold regions (Harbin) is 1.3, and for hot-summer, warm-winter regions (Shenzhen) is 0.8. Regional weightings are: luxury stores (1.5) > dining areas (1.2) > parking lots (0.7), and time-of-day weightings are: peak business hours (1.4) > nighttime hours (0.6). Dynamic benchmarks are established based on physical laws (climate / energy efficiency), management needs (user goals), and operational characteristics (historical data). This ensures that the energy quota system is environmentally adaptable (climate compensation), equipment-aware (energy efficiency compensation), and management-compatible (user goals). This provides a high-precision benchmark for subsequent dynamic adjustments (such as overspending transfers), fundamentally improving the effectiveness of energy consumption control.

[0077] In one possible implementation, the environmental parameters include the indoor temperature and outdoor temperature of the VRF system and the personnel flow status in the target detection area; the operating parameters include the indoor unit wind speed gear, the compressor load rate, the set temperature, and the cooling and heating differential; in step 103, adjusting the operating parameters includes:

[0078] The wind speed adjustment coefficient is calculated based on the temperature difference between the indoor temperature and the set temperature, and the compressor load rate, and the wind speed gear of the indoor unit is adjusted according to the wind speed adjustment system; the set temperature is adjusted based on the indoor and outdoor temperature difference between the indoor temperature and the outdoor temperature; the cooling and heating return difference is updated according to the attributes of the monitoring area and the personnel flow status.

[0079] Wind speed coefficient = f (temperature difference, load rate) to avoid waste of high wind speed under low load; set temperature correction = g (indoor and outdoor temperature difference) to utilize natural cold and heat sources; hysteresis update = h (regional attributes, personnel status) to relax temperature control accuracy as needed. Form an energy-saving control chain that links "equipment load-environmental conditions-usage requirements". The temperature difference / load rate in traditional existing technologies independently controls the wind speed, which is prone to conflict (such as insufficient wind speed when the load is low but the temperature difference is large). This application establishes a dual-factor weight function to dynamically balance cooling requirements and energy consumption constraints. Predict the heat load trend through the indoor and outdoor temperature difference, adjust the set temperature in advance (such as pre-cooling before the high temperature at noon), and automatically switch to hysteresis mode based on regional attributes (office / warehouse) and personnel status (high / low mobility).

[0080] The wind speed adjustment coefficient is calculated through the collaborative calculation of the dual factors of temperature difference and compressor load rate to solve the problems of over-regulation or response lag in traditional single temperature control, and to achieve precise adaptation of wind speed gears while ensuring thermal comfort, thus avoiding energy waste caused by high wind speed at low load. The set temperature is dynamically corrected based on the temperature difference between indoor and outdoor, breaking through the limitation of fixed temperature setting value being out of touch with the environment (such as maintaining low temperature cooling when it is cool outside), and using natural cold / heat sources to reduce mechanical cooling and heating loads, thus achieving energy conservation and consumption reduction driven by environmental conditions. The cooling and heating hysteresis is dynamically adjusted in combination with regional attributes (such as functional area type) and personnel flow status to solve the problem of ineffective energy consumption of the traditional fixed hysteresis mode in low-usage areas (such as frequent start and stop of unmanned warehouses), and the equipment sleep cycle is extended through differentiated hysteresis settings.

[0081] In a possible implementation, the operating parameter includes a compressor operating frequency; in step 103, adjusting the operating parameter includes:

[0082] According to the personnel flow status in the monitoring area being in an unmanned state and the duration thereof reaching a first predetermined duration, the operating frequency of the compressor is adjusted to operate within a first adjustment range; according to the duration thereof reaching a second predetermined duration, the operating frequency of the compressor is adjusted from the first adjustment range to operate within a second adjustment range; according to the duration thereof exceeding the maximum allowed duration, the operating frequency of the compressor is limited to operate within a set minimum frequency range; wherein the frequency value of the minimum value of the first adjustment range is greater than or equal to the frequency value of the maximum value of the second adjustment range, and the frequency value of the minimum value of the second adjustment range is greater than or equal to the maximum value of the lowest frequency range.

[0083] By using a strictly decreasing frequency range constraint (first range ≥ second range ≥ lowest range), we ensure: maintaining basic temperature control capabilities in the initial period of no-man's land (first level); further reducing energy consumption during medium- to long-term periods of no-man's land (second level); ensuring safety requirements such as equipment antifreeze at the lowest frequency for extremely long periods of no-man's land (third level); and avoiding equipment damage or high energy consumption caused by "cliff-like" shutdowns. The three-level frequency adjustment mechanism triggered by duration is used to solve the energy waste problem of traditional air conditioners maintaining full-frequency operation in unmanned areas. On the premise of ensuring the safe operation of the equipment, the compressor load is reduced in a step-by-step manner as the length of unmanned time increases, realizing refined and decreasing control of energy consumption. Taking the personnel flow status and duration as the core control parameters, the compressor frequency is dynamically matched to the actual use needs of the space, solving the problem of the disconnection between traditional schedule control (such as fixed night-time frequency reduction) and the actual occupancy status.

[0084] For example, after a meeting in a conference room, when no one is in control, the compressor frequency is initially 45Hz (full frequency 50Hz); first-level triggering (no one for 30 minutes): the frequency drops to 35-40Hz (first range); second-level triggering (no one for 2 hours): the frequency drops to 25-30Hz (second range); extreme triggering (no one for 4 hours): the frequency is limited to 15Hz (lowest range); compared with continuous full-frequency operation, 4 hours saves 62% of energy consumption; maintaining low-frequency operation can prevent pipe freezing (in winter) or lubricating oil deposition.

[0085] In one possible embodiment, the set warning range includes a first warning range, a second warning range and a third warning range; wherein the maximum value of the first warning range is less than or equal to the minimum value of the second warning range, and the maximum value of the second warning range is less than or equal to the minimum value of the third warning range; in step 103, according to the difference value sequence between the predicted energy consumption sequence and the actual energy consumption sequence being in the set warning range, the benchmark parameters and / or operating parameters are adjusted, including: according to the difference value sequence being in the first warning range, the operating parameters are adjusted; according to the difference value sequence being in the second warning range, the benchmark parameters are adjusted; according to the difference value sequence being in the third warning range, the benchmark parameters and operating parameters are adjusted.

[0086] Through a strictly increasing three-level warning range (first < second < third), a differentiated energy consumption abnormality response strategy is established to solve the pain points of "over-intervention" or "insufficient response" in traditional single threshold control, and achieve: mild deviation: only optimize equipment operating parameters (such as wind speed / frequency, etc.); moderate deviation: correct energy consumption quota benchmark; severe deviation: global parameter linkage adjustment.

[0087] In a possible implementation, calculating the predicted energy consumption sequence for the target period after the current moment based on the actual energy consumption sequence and the environmental parameters may include:

[0088] Meteorological parameters and building thermal parameters are input into the simulation software to generate hourly cooling and heating load curves throughout the year. Combined with the partial load performance data of the multi-split air-conditioning system, the cooling and heating load curves are converted into target energy consumption values for each time period. The long-short-term memory neural network model is called to calculate the predicted energy consumption sequence for the target time period after the current moment.

[0089] In a possible implementation, the method further includes: correcting the prediction parameters of the long short-term memory neural network model based on the execution results of the operation parameter optimization or operation control adjustment; and iteratively updating the calculation results of the predicted energy consumption sequence using the corrected prediction parameters.

[0090] In a possible implementation, the method further includes: identifying an inefficient indoor unit or a faulty outdoor unit and generating a device tag; and optimizing operating parameters or adjusting operating control for the tagged device.

[0091] In one possible implementation, configuring monitoring zones and operating logic rules includes: dividing the time intervals into cooling season, heating season, and transition season according to the climate zoning standards and historical meteorological data of the target building's location; dividing the multi-split indoor units into multiple logical monitoring zones based on the similarity of cooling and heating demands in the building's functional areas; and setting differentiated operating parameters for each monitoring zone, including temperature setting range, maximum simultaneous opening rate, and operating mode switching rules.

[0092] The energy consumption control method for a multi-split system provided in the embodiments of the present application is applicable to the multi-split air conditioning system provided in the embodiments of the present application, wherein the multi-split air conditioning system includes a multi-split outdoor unit, a refrigerant manifold, a multi-split indoor unit, and other major equipment. The energy consumption control method is implemented by:

[0093] The first aspect: operation cycle and partition configuration

[0094] 1. Climate division: Based on the HVAC climate division of the building location (severe cold / cold / hot summer and cold winter, etc.), combined with the meteorological correction data of the past three years, the time range of the cooling season, heating season and transition season is dynamically defined.

[0095] 2. Division of monitoring areas: Divide the control areas according to the building functions (such as office areas, meeting rooms, and public areas), and configure the operation logic differently according to the cooling and heating demand characteristics of each area.

[0096] Based on user-required target energy consumption information, local HVAC climate zoning conditions, and building operational characteristics, the cooling, heating, and transition seasons are divided into different time zones, along with different indoor unit monitoring and control zones. The system also defines the periodic operation repetition logic for each monitoring zone, including system start and stop times, system operating mode, set temperature ranges for each VRF indoor unit, and the maximum simultaneous operating rate of each indoor unit. This refined setting, based on the building's HVAC operational characteristics, provides a clear target framework for subsequent energy consumption management.

[0097] Based on actual building usage, monitor the actual energy consumption of designated zones (such as office areas, conference rooms, and rest areas). Energy consumption characteristics vary depending on factors such as frequency of use and occupancy density. Zoning management allows for customized energy management strategies tailored to each area's characteristics. For example, office areas, which are densely populated during work hours and have higher requirements for temperature and ventilation, can have their air conditioning energy consumption controls relaxed, while energy consumption is strictly controlled during non-working hours. Office areas operate in cooling / heating mode from 8:00 AM to 7:00 PM on weekdays, within a temperature range of 24-26°C and with a maximum indoor unit usage rate of ≤85%. Conference rooms, on the other hand, are pre-cooled or pre-heated before use, and air conditioning output is automatically adjusted based on occupancy during use, saving energy. Conference rooms are pre-cooled or pre-heated at scheduled times, with air conditioning output adjusted in real time based on occupancy density. Furthermore, energy consumption limits are relaxed for public areas such as corridors and elevators during peak hours, while switching to low-load operation during off-peak hours. During rush hour, when traffic is high, air conditioning energy control can be relaxed; during most other times, low-load operation can be used to significantly reduce energy consumption.

[0098] The second aspect: load simulation and target energy consumption calculation

[0099] Input parameters: Meteorological data: Typical annual meteorological data + meteorological correction parameters from the past three years; Building parameters: Shape coefficient, window-to-wall ratio, business characteristics, altitude, and prevailing wind direction. Simulation process: The software simulates hourly cooling and heating loads for 8,760 hours throughout the year, generating a dynamic load curve. This is then converted into system energy consumption values by combining the partial-load operating characteristics of the multi-connected system (brand / model performance curves). Target energy consumption templates are generated for each time period (daily, weekly, and monthly), and compensation mechanisms for extreme weather conditions are implemented (e.g., a correction factor of 1.2 for a high temperature of 40°C). For example, the target daily energy consumption for an office area in summer is determined through simulation to be X kWh.

[0100] Based on the typical meteorological year of the user's building location, combined with meteorological corrections for neighboring years, and its typical building appearance (shape coefficient, window-to-wall ratio, architectural form, etc.), load simulation software is used to conduct 8,760 hours of load simulation throughout the year. Combined with the partial load operation of the multi-split unit, performance simulation is used to accurately convert the cooling and heating loads into system energy consumption, thereby generating a scientific and reasonable target energy consumption. This target energy consumption determination method takes into account the actual operating characteristics of the multi-split air conditioning system under different operating conditions, making the target energy consumption more accurate and instructive. For example, by simulating the actual partial load operation and energy consumption of the multi-split unit under different outdoor temperatures and indoor load conditions, the target energy consumption values for each time period are comprehensively derived.

[0101] The third aspect: energy consumption monitoring and responsibility tracing

[0102] 1. Energy consumption classification: Contract performance energy consumption refers to energy consumption that complies with the operating conditions (time period, temperature, and opening rate) stipulated in the contract; breach of contract energy consumption refers to abnormal energy consumption that exceeds the contract constraints (such as overtime operation and temperature violations).

[0103] 2. Calculate the energy consumption contribution of each indoor unit based on the distribution of cooling and heating capacity through refrigerant flow metering technology. Generate a ranking of equipment with default energy consumption and locate the root cause of the anomaly. For example, if an indoor unit experiences a surge in default energy consumption due to a set temperature that is too low, the system will automatically flag the issue and recommend corrective measures.

[0104] 3. Diagnosis of inefficient equipment: Outdoor unit: monitors heat dissipation efficiency (condensing temperature - ambient temperature > 8°C for 2 hours to be considered abnormal); indoor unit: detects long-term under-temperature (set temperature - return air temperature > 3°C for 4 hours); Processing flow: The diagnosis results automatically generate a maintenance work order, which is tracked and processed until the loop is closed.

[0105] Based on the contracted usage conditions for the multi-split air conditioning system, energy consumption is broken down into those within and outside the specified range. Energy consumption outside the specified range is further refined. First, based on intelligent calculations and measurement of the evaporation and condensation heat exchange of the refrigerant in the indoor units, electricity is accurately allocated based on the amount of cooling and heating energy. This allows for a ranking of the energy consumption of each multi-split indoor unit within and outside the specified range, and identifies those units that are operating abnormally, providing important explanations and supporting evidence for contract performance. For example, if analysis reveals an abnormally high energy consumption for a particular indoor unit during a specific time period, further investigation can be conducted to identify the underlying cause, such as excessive cooling or heating settings, or prolonged operation without shutting down. This provides the user with clear remedial action and guidance to help them rectify system usage. Furthermore, energy consumption monitoring in each configured zone supports granular viewing and management, meeting the needs of zoned management based on actual building operations. High-energy-consuming indoor units can be identified and their causes can be analyzed in depth (e.g., performance degradation, high load caused by prolonged door and window openings, or unusual outdoor weather conditions), enabling comprehensive and refined management of system energy consumption and rapid identification of anomalies.

[0106] Fourthly, hierarchical energy consumption control strategy

[0107] 1. Early warning mechanism: Monthly warning: triggered when the cumulative energy consumption reaches 80% of the target value in the month, and adjustment suggestions are pushed; contract performance risk warning: triggers the highest level of warning when it is predicted that the target cannot be met before the end of the contract period.

[0108] 2. Control strategy (increasing by intervention intensity): Local optimization: adjusting the indoor unit's wind speed and temperature differential (reducing the number of starts and stops); intelligent linkage: human-sensing start and stop (switching to standby mode after 15 minutes of absence); system-level control: limiting compressor frequency and predictively shutting down equipment in non-core areas.

[0109] The method of the present invention uses data visualization to monitor target energy consumption and actual energy consumption in real time, and uses a variety of control methods of multi-split air-conditioning systems to manage and control actual energy consumption. Among them, the difference between actual energy consumption and target energy consumption is monitored in real time according to the logic of daily calculation using visual analysis charts. Once the actual energy consumption exceeds the target energy consumption, the software algorithm will immediately activate the internal target energy consumption quota allocation redefinition mechanism. At the same time, when there is a significant risk of achieving the target energy consumption, a series of targeted energy consumption control measures will be accurately activated through big data analysis and intelligent control algorithms without significantly affecting comfort and thermal sensations. For example, the indoor unit wind speed control intelligently adjusts the wind speed according to the indoor temperature and personnel activities, reducing energy consumption while ensuring comfort; set temperature control, fine-tune the set temperature of each indoor unit to make it operate within a reasonable range; cooling and heating differential reset, optimize the system's standby operation switching logic, and reduce unnecessary energy waste; human sensing start and stop or temperature adjustment, using human body sensing technology, automatically reduce energy consumption or shut down the equipment when no one is detected; temporary system frequency limiting, during peak power consumption or high energy consumption, appropriately limit the operating frequency of key components such as compressors to reduce overall energy consumption.

[0110] In summary, this application has constructed a complete, scientific and accurate energy consumption control system for multi-split air-conditioning systems, which has effectively solved the energy consumption management problems existing in the existing technology, determined the management and control model and serialized means to achieve the energy consumption operation and maintenance commitments of the multi-split air-conditioning system with users, promoted the effective fulfillment of the energy consumption operation and maintenance commitments reached with users, and promoted the development of multi-split air-conditioning systems towards low-carbon, energy-saving and efficient operation.

[0111] The energy consumption control method for a multi-split system provided in an embodiment of the present application is described in detail below with reference to the accompanying drawings.

[0112] Figure 2This is a schematic diagram of the pre-input configuration architecture and relationships relied upon by the energy consumption control method for a multi-split air conditioning system provided in an embodiment of the present application. This can be, but is not limited to, executed by a cloud server device, such as an edge server with sufficient computing resources and deployed locally within the multi-split air conditioning system. As shown in the figure, the pre-input configuration architecture and relationships relied upon by the energy consumption control method for a multi-split air conditioning system include the following three aspects:

[0113] The first aspect is the energy consumption commitment contract information configuration module: establishing a contractual constraint framework for energy consumption management and converting contract terms into quantifiable operating parameters.

[0114] 1. Climate zoning and seasonal division

[0115] The HVAC season type is determined based on national standards (five major divisions: severe cold / cold / hot summer and cold winter) and meteorological degree days. The HVAC season type includes cooling season, heating season and transition season. The transition season time period boundaries are dynamically adjusted through historical data.

[0116] Operators communicate and negotiate with users to precisely define the timeframes for the cooling, heating, and transition seasons, as well as different zones for monitoring and control of indoor units, based on local HVAC climate zoning conditions and building operational characteristics. For example, in a specific region, based on its HVAC climate zoning, specific geographical characteristics (such as altitude and topography), and the special requirements of building types (such as those in industrial and other areas with special thermal and humid environments), the summer cooling season is defined as May 15th to September 30th, the transition season is defined as October 1st to November 15th and March 15th to May 14th, and the heating season is defined as November 16th of the current year to March 14th of the following year.

[0117] HVAC climate zones can be categorized according to relevant national standards into severely cold regions, cold regions, hot summer and cold winter regions, hot summer and warm winter regions, and temperate regions. These zones can be further divided based on meteorological degree days. Based on the zoning of a building's location, the timeframes for the cooling, heating, and transition seasons can be preliminarily determined. However, it's important to note that the final determination of these timeframes must be based on the actual operational characteristics of the user's building.

[0118] 2. Monitoring area division

[0119] 2.1 Functional zones are defined as follows: Core Area: open office area (high staff density and heavy equipment load); Intermittent Area: conference room (frequency of use drives energy consumption model); Public Area: corridor / lobby (dynamically regulated by foot traffic). Detailed monitoring zoning is performed within the building, subdivided into open office areas, independent offices, conference rooms, etc.; public areas are divided into corridors, lobbies, etc. The establishment of monitoring zones should take into account the similarity of heating and cooling demand patterns in each area. For example, in an open office area with dense staffing and frequent equipment use, its energy consumption model focuses on the impact of staff number and office equipment usage on energy consumption; while conference rooms primarily consider factors such as meeting frequency and duration.

[0120] 2.2 Categorize areas by similarity in cooling and heating demands, and configure differentiated operating logic. The definition of monitoring zones needs to be based on the actual operational characteristics of the building. Different monitoring zones have significantly different operating requirements and cooling and heating demands for the VRF units. Therefore, tailoring the operating logic specified in the contract is crucial to ultimately fulfilling the energy consumption and operation and maintenance commitments. For example, office areas typically have higher cooling and heating demands, and the operating hours of the AC system units are longer. Public areas, on the other hand, typically have temporary cooling and heating demands, and the operating hours of the AC system units, including the management and control methods, can be more flexible.

[0121] 3. Operation parameter configuration

[0122] Determine the periodic operating logic for each monitoring zone, including system start and stop times, operating mode, temperature range for each indoor unit, and the maximum simultaneous operating rate of indoor units. For example, during the cooling season, a monitoring zone might start the system at 8:00 AM on weekdays and shut it down at 7:00 PM; and at 9:00 AM on weekends and shut it down at 6:00 PM. Set the operating mode to either cooling or dehumidification, with each indoor unit temperature set within a range of 24-26°C. Repeat these steps to complete the operational configuration for each monitoring zone.

[0123] Secondly, the monitoring partition and associated internal machine configuration module: building device topology relationships and special scenario exemption mechanisms. Establishing a flexible balance between energy-saving goals and special needs to avoid one-size-fits-all management and control. Specifically including:

[0124] 1. Device association mapping

[0125] A one-to-many relationship table between zones and indoor units is established based on physical installation locations to establish mapping rules. Dynamic binding is then achieved using device codes (such as MAC addresses). Based on the regional air conditioning installation situation, each monitoring zone is associated with a corresponding indoor unit. This achieves matching between monitoring zones and indoor units.

[0126] 2. Special internal machine mark (special target equipment)

[0127] For equipment in scenarios with special heat and humidity requirements (such as laboratories / constant temperature rooms / areas near toilets), strong intervention measures (such as frequency limiting and forced shutdown) are not taken, and energy consumption data is not included in the performance statistics. Special indoor units can be marked as special target indoor units. The multi-split air-conditioning system to which the indoor units in this area belong does not participate in the control measures with stricter control means, and is not included in the energy consumption scope of energy consumption operation and maintenance commitment performance. The definition of a special indoor unit can be a specific target office, or a place with special heat and humidity environment requirements, such as an area near a toilet.

[0128] Third, the energy consumption simulation module generates target energy consumption: This module generates scientific energy consumption benchmarks through digital simulation. This module integrates building thermal characteristics with equipment operating characteristics, breaking through the limitations of traditional static threshold methods. Using a professional software system that includes building cooling and heating load simulation and multi-split air conditioning system energy consumption simulation based on actual test conditions for specific models, the process from load simulation to energy consumption simulation and deriving target energy consumption is completed. Specifically, this includes:

[0129] 1. Input data layer

[0130] The input data includes meteorological data and building parameters; the meteorological data includes: typical meteorological year + actual measurement corrections in the past three years (temperature / humidity / wind speed); the building parameters include: shape coefficient (building compactness), window-to-wall ratio (lighting and heat loss), and business characteristics.

[0131] The professional software system simulates the building's heating and cooling loads by inputting typical and recent meteorological parameters, building form factor, window-to-wall ratio, structural form, building type, region, altitude, and prevailing wind direction. It then outputs hourly heating and cooling load simulation results for 8,760 hours throughout the year.

[0132] In the professional software system, by configuring the brand, model, nameplate and key performance parameters of the multi-split air-conditioning system, and combining the operating characteristic curve and operating time distribution of the multi-split air-conditioning system under the actual partial load operating conditions under the above-mentioned load simulation calculation results, the energy consumption of the multi-split air-conditioning system under different operating conditions is simulated and calculated.

[0133] 2. Simulation calculation layer

[0134] 2.1 Load simulation: Output hourly cooling and heating load curves for 8760 hours throughout the year; 2.2 Energy consumption simulation: Based on the input multi-split performance parameters (brand / model / partial load characteristic curve), the load is converted into equipment-level energy consumption values.

[0135] 3. Target Generation Layer

[0136] 3.1 Dynamic template: Generates target energy consumption values at daily / weekly / monthly granularity; 3.2 Fault-tolerance mechanism: Extreme weather compensation (such as a 40°C high temperature correction coefficient) and partial load operation energy consumption calculation (energy efficiency simulation at sub-full load). Through multiple simulations and optimizations, target energy consumption values for cooling and heating are generated for different time periods, and summarized to form the cooling and heating and total target energy consumption of the entire multi-split air conditioning system in different time periods. Target energy consumption is imported and automatically calculated using target energy consumption templates that meet the control needs at different time granularity. For example, after simulation analysis, it was determined that the target energy consumption for a certain office area on a certain day in the summer cooling season is X kWh.

[0137] The specialized software system for load simulation and energy consumption simulation of multi-split air conditioning systems falls outside the scope of this invention. It incorporates multiple building models, meteorological parameter models, and multi-split air conditioning system product model and performance parameter models, specifically designed for simulating building cooling and heating loads and energy consumption. To achieve realistic and accurate target energy consumption, it is crucial for specialized software to accurately calculate the actual energy efficiency, rated cooling and heating capacity, and extreme cooling and heating demand fluctuations caused by extreme weather fluctuations. For example, in most cases, multi-split air conditioning systems do not operate at 100% load, i.e., at rated output, so partial-load energy consumption analysis and calculation are necessary. Furthermore, the possibility of extreme summer temperatures exceeding 40°C and high humidity, as well as the possibility of significant warming and cold snaps during transitional seasons, must be considered.

[0138] Contract configuration provides a framework for both legal and technical constraints on energy management. Device mapping enables precise "zone-to-device" control, while special target mechanisms address specific scenario requirements. The simulation engine generates dynamic target values through digital twins, providing a scientific benchmark for monitoring and regulation. This complete technical closed loop of "contract agreement → device control → scientific verification" addresses the issues of extensive traditional energy management and the difficulty of tracing responsibilities.

[0139] Figure 3 This is a schematic diagram of the relationship between the monitoring and control content and methods of the energy consumption control method for the multi-split air-conditioning system provided in the embodiment of the present application. The inefficient equipment operation diagnosis module provided in this embodiment is a description of the diagnostic content of the inefficient operation of the multi-split air-conditioning system. It is not limited to other abnormal conditions that may significantly or slightly affect the efficient operation of the multi-split air-conditioning system, such as refrigerant leakage, compressor bearing wear failure, etc. In addition, the multi-split air-conditioning system management and control method provided in this embodiment is a description of common control means. It is not limited to other system-level control methods such as model predictive control.

[0140] As shown in the figure, the specific content and methods of monitoring and controlling the energy consumption control method of the multi-split air conditioning system of the present invention include the following parts:

[0141] Part 1, energy consumption monitoring and analysis module: visual monitoring of energy consumption data.

[0142] 1. Data collection: The energy consumption data of each indoor unit (such as electric power and refrigerant flow) is obtained in real time through the data interface; a special energy consumption monitoring function module is provided to obtain the energy consumption data of each indoor unit of the multi-split air-conditioning system stored in the data warehouse of the data center in real time through the data interface.

[0143] 2. Macro-display: A line chart shows the cumulative energy consumption trend (over the contract period) of "target vs. actual"; a bar chart compares energy consumption differences at the monthly / daily level (with red and green colors indicating exceeding / meeting the target).

[0144] Utilizing visualization technology, actual energy consumption and target energy consumption are displayed daily in intuitive charts, such as line charts and bar charts. For example, the software module interface displays the trend of cumulative target energy consumption and actual energy consumption during the contract period in the form of a line chart, while the comparison of actual energy consumption and target energy consumption at the monthly and daily levels is displayed in the form of a bar chart. This allows operators to quickly identify the degree of surplus or deficit between actual energy consumption and target energy consumption.

[0145] 3. Micro-source tracing: Determine the energy consumption proportion of each partition (locate high-energy-consuming areas); determine the energy consumption ranking of individual internal machines (identify abnormal equipment).

[0146] Detailed energy consumption data query and analysis functions are provided for each monitoring zone and indoor unit. For example, the energy consumption breakdown and percentage of each monitoring zone are displayed in the form of a Sankey diagram, the energy consumption trend changes of each monitoring zone are displayed in the form of a line chart, and the energy consumption ranking of each indoor unit is displayed in the form of a bar chart. This allows operators to conveniently view the real-time energy consumption, cumulative energy consumption, historical energy consumption trends, energy consumption percentage and other information of each monitoring zone and indoor unit, so as to carry out targeted management and optimization. For example, through the software interface, it was found that the energy consumption in the public area of a certain floor has increased abnormally recently. By analyzing the detailed data, it was found that this was due to the long-term opening of windows in the corridor, resulting in drafts, causing the indoor units of the multi-split air conditioning system to operate at high load for a long time, resulting in high energy consumption in this monitoring zone, so that timely adjustments can be made. Abnormal energy consumption in a public area → Analysis found that the long-term opening of windows has caused high load on the indoor units → Close the windows or adjust the operation strategy.

[0147] The second part is the energy consumption contract relationship decomposition module: division of contract energy consumption responsibilities.

[0148] 1. Data classification: Contractual energy consumption: operating time periods and temperature setting values that comply with the contract; non-contractual energy consumption: operating behaviors that exceed the contract constraints (such as overtime start-up and temperature violations).

[0149] Based on the contracted usage conditions, energy consumption data for the VRF systems' internal units is broken down into those within and outside the contracted range. Using data analysis algorithms, detailed time-based statistics and rankings are visually analyzed for energy consumption outside the contracted range for each VRF unit. For example, the software's functional module interface displays bar charts showing energy consumption within and outside the contracted range over different time periods, and a ranked bar chart showing the ranking of each unit's energy consumption outside the contracted range.

[0150] 2. Root Cause Analysis: Time-based statistics: Analyze the energy consumption distribution of defaults by hour, day, or week. Equipment Ranking: Generate a list of default energy consumption rankings for indoor units. Link equipment operation logs (such as setting parameters and start / stop records) to pinpoint the specific cause of the default.

[0151] Operations personnel can analyze data to identify the cause of significant energy consumption violations. Example scenario: A certain indoor unit ranks first in energy consumption outside of the contract → Check the logs to find out that it is running overtime → Investigate whether it is due to human error or equipment failure. If it is found that the energy consumption of an indoor unit in a multi-split air conditioning system ranks first outside the set range for a certain period of time, further in-depth analysis of the indoor unit's operation logs, setting parameters, and other information can be used to identify the root cause of the indoor unit's operating violation. For example, if it is found that the indoor unit is on for much longer than the time set in the contract, analyze whether it was forgotten to be turned off or was turned on outside the set time for a specific reason.

[0152] In the second part, the contract setting scope includes all the configuration contents based on the contract information for setting the usage of the multi-split air-conditioning system described in the content of the present invention and the first aspect of this embodiment.

[0153] The third part is the energy consumption over-standard warning module: dynamic warning and quota management. The energy consumption over-standard warning module provides a series of target energy consumption and actual energy consumption monitoring and management functions.

[0154] 1. Dynamic quota: When actual energy consumption exceeds the target, the remaining quota is reallocated by region / time period (for example, if area A exceeds the target, the quota of area B will be reduced).

[0155] Dynamic allocation of target energy consumption quotas: When actual energy consumption exceeds the target, the target energy consumption quota is redistributed and defined based on historical energy consumption data, real-time environmental parameters, system operating status, and other multi-dimensional information. For example, if energy consumption in a certain area is found to be higher due to an increase in the number of people participating in an activity that day, the monitoring algorithm will appropriately adjust the target energy consumption in that area and reduce the quotas in other areas with lower energy consumption.

[0156] In the third section, the target energy consumption quota will be readjusted when actual energy consumption exceeds the target and the remaining target energy consumption quota is less than the pre-determined target energy consumption. For example, extreme summer heat causes energy consumption overruns → dynamic reductions in non-core area quotas → prioritizing office comfort. If actual energy consumption exceeds the target energy consumption during the contract period of June to August, the target energy consumption quota for the remainder of the contract period, such as September to December, will be reallocated to various time periods and monitoring zones. In more stringent circumstances, allocation will not be limited to individual VRF units, allowing for refined target energy consumption management.

[0157] 2. Three-level early warning: monthly warning + contract period warning + performance risk warning, and automatic push reports (including energy consumption trends and root cause analysis) to operation and maintenance personnel.

[0158] Monthly warnings are triggered when the target percentage is reached in the current month; contract period warnings are triggered when the target cannot be reached before the end of the contract; and contract performance risk warnings are triggered by combining weather forecasts with historical data to set a date in advance.

[0159] 1. Monthly and Contract Period Alerts: The system analyzes overall system energy consumption data on a monthly and contract period basis. When actual energy consumption reaches a certain percentage of the target energy consumption (e.g., 80%) for the month, the system automatically triggers a monthly alert. This alert is promptly notified to operators via software interface displays, text messages, and push notifications on internal enterprise collaboration platforms, informing them that current energy consumption is approaching the target. The system also provides relevant energy consumption data reports so that operators can take timely action.

[0160] 2. Contract performance risk warning: During the contract period, the monitoring algorithm continuously tracks energy consumption. When it is estimated that the actual energy consumption may exceed the target energy consumption by the end of the contract period, and it is difficult to complete the energy consumption and operation commitments in the remaining time according to the current energy consumption trend, the contract period warning is triggered. The warning is also notified to the operating personnel in a variety of ways, and a detailed energy consumption analysis report is provided, including the energy consumption of each time period and each partition, as well as the predicted energy consumption trend, to help the operating personnel formulate more stringent energy consumption management and control strategies to ensure that the energy consumption and operation commitments can be achieved in the end. The contract performance risk warning occurs when the monitoring algorithm is based on the current actual energy consumption and the remaining amount of target energy consumption, the remaining contract performance time, and the energy consumption of the remaining contract performance time predicted by combining meteorological parameters and actual building usage. The final warning is obtained when there is a risk in contract performance. It is the highest level warning for achieving contractually committed energy consumption performance by the method described in the present invention.

[0161] Fourthly, the inefficient equipment diagnosis module: equipment health status monitoring and maintenance closed loop.

[0162] The inefficient equipment diagnosis module obtains data through the data interface and the diagnosis algorithm deployed in the data algorithm center diagnoses poor heat dissipation of the multi-split outdoor unit and long-term failure of the multi-split indoor unit to reach the temperature.

[0163] 1. Diagnostic scope: Outdoor unit problems: poor heat dissipation (abnormal condensing temperature), decreased compressor efficiency; indoor unit problems: long-term temperature failure (deviation between set temperature and return air temperature >3°C for 4 hours).

[0164] 2. Diagnostic tool: Parameter curve analysis: Displays core parameters (such as outdoor unit ambient temperature and indoor unit return air temperature) over the past seven days; automatic work order generation: diagnostic results → maintenance suggestions → work order dispatch → closed-loop processing.

[0165] Diagnostic analysis of poor heat dissipation of multi-split outdoor units: For the multi-split outdoor unit equipment under monitoring and control, the system presents in real time the poor heat dissipation of the outdoor unit diagnosed by the diagnostic algorithm running in the data algorithm, and provides trend curves of core parameters such as outdoor dry-bulb temperature, outdoor unit ambient temperature, condensing saturation temperature, condenser tube temperature, etc. for the outdoor unit in the past day to week for analysis by operation and maintenance personnel. The diagnostic results are automatically generated into active work orders through the work order system, which detail the possible root causes of the problem and are transferred to maintenance engineers for timely processing. Timely closed loops reduce the time of low-energy-efficiency operation of equipment. For example, an alarm for poor heat dissipation of an outdoor unit → check the abnormal condensing temperature curve → maintenance personnel clean the heat sink.

[0166] Diagnostic analysis of long-term under-temperature conditions of multi-split indoor units: For the multi-split indoor unit equipment under monitoring and control, the diagnostic algorithm running in the data algorithm is used to present in real time the long-term under-temperature conditions of the indoor units, and trend curves of core parameters such as the outdoor dry-bulb temperature, indoor set temperature, indoor return air temperature, and corrected room temperature of the indoor unit for the past day to week are provided for analysis by operation and maintenance personnel. Similarly, the diagnostic results automatically generate active work orders through the work order system, detailing the possible root causes of the problem, and are transferred to maintenance engineers for timely processing. Timely closed loop to reduce the time of inefficient operation of equipment. The inefficient operation of multi-split air-conditioning systems is not limited to the above two types of diagnostic analysis. It may also include: refrigerant leakage, compressor bearing wear failure, electronic expansion valve throttling blockage, etc. The diagnostic analysis content will be continuously iterated and updated in the method system of the present invention.

[0167] Fifthly, energy consumption control execution module: hierarchical energy-saving strategy execution.

[0168] Energy consumption control includes a series of regulatory measures to reduce the energy consumption of VRF systems. Control algorithms deployed in the data algorithm center ensure that these measures are executed appropriately and at the appropriate time. Typically, specific energy consumption control measures are dynamically activated based on actual energy consumption, target energy consumption, and contract performance. These energy consumption control measures, which vary in intensity, such as wind speed control, sensor-activated start / stop, or temperature adjustment, are implemented throughout the energy consumption and maintenance commitment contract period, where conditions permit.

[0169] Implementation methods include (in increasing order of intervention intensity): 1. Local adjustment: wind speed reduction (reducing fan energy consumption by 5-10%); temperature hysteresis adjustment (reducing the number of starts and stops); 2. Intelligent linkage: human-sensing start and stop: switching to standby mode when no one is present; temperature offset: fine-tuning the set value (±1°C) during transitional seasons; 3. System-level control: compressor frequency limiting: limiting frequency during peak power consumption (reducing energy consumption by 20-30%); predictive shutdown: shutting down equipment in non-core areas in advance based on weather forecasts. For example, after a meeting, when no one is present, the cooling set point temperature will be automatically increased after 15 minutes to save standby energy. Specifically including:

[0170] 1. Indoor unit fan speed control: By sensing the indoor temperature and setting stability, the system dynamically and intermittently lowers the fan speed of the V-type indoor unit, reducing fan energy consumption and, therefore, energy consumption of the V-type indoor unit. For example, when the temperature reaches 25.5°C and the interval between cooling and heating differentials is long, the fan speed is adjusted from high to medium or low.

[0171] 2. Indoor unit set temperature control: An intelligent analysis and control algorithm fine-tunes the indoor unit set temperature based on factors such as the indoor and outdoor temperature difference and occupant activity. For example, during transitional seasons, when outdoor temperatures are favorable, the cooling set temperature can be appropriately raised or the heating set temperature lowered, minimizing energy consumption while ensuring comfort.

[0172] 3. Internal cooling and heating differential settings: Reset the cooling and heating differential based on the usage characteristics and energy consumption of different areas. For areas with low personnel flow, increase the differential setting appropriately to reduce the number of system starts and stops and reduce energy consumption. For areas with higher temperature accuracy requirements, high personnel flow, and high requirements for thermal and humidity environments, keep the differential setting small.

[0173] 4. Indoor unit occupancy start / stop or temperature adjustment: A occupancy sensor is installed indoors and linked to the multi-split indoor units. When no activity is detected for a certain period of time, the indoor units are automatically powered down or shut down, or the cooling or heating setpoints are gradually increased or decreased over time, as required. For example, if no activity occurs within 15 minutes after a meeting in a conference room, the air conditioner automatically switches to low-energy standby mode.

[0174] 5. Temporary frequency limiting of indoor and outdoor unit systems: When the energy consumption of the entire system is too high or it is at a peak power consumption period, intelligent analysis and control will be used to send instructions to each multi-split outdoor unit to temporarily limit the operating frequency of key components such as the compressor, so as to reduce the overall energy consumption of the multi-split air-conditioning system.

[0175] The control and implementation measures in this section are not limited to the several measures provided in this embodiment. Depending on the operation life cycle stage of the multi-split air-conditioning system, with sufficient data accumulation, the model predictive control algorithm is also an important control means.

[0176] In summary, the embodiment of the present application provides a method for controlling energy consumption of a multi-split system, by obtaining the operating parameters, baseline parameters and environmental parameters of the multi-split system, the multi-split system is matched with the target detection area; the actual energy consumption sequence is determined according to the operating parameters; the predicted energy consumption sequence of the target time period after the current moment is calculated according to the actual energy consumption sequence and the environmental parameters; according to the difference value sequence between the predicted energy consumption sequence and the actual energy consumption sequence being within the set warning range, the baseline parameters and / or operating parameters are adjusted so that the total energy consumption of the multi-split system in the target monitoring area is within the set benchmark range. By collecting operating parameters, environmental parameters and baseline parameters in real time, a dynamic monitoring system for the energy consumption of the multi-split system is constructed, and by warning of the difference between the predicted energy consumption sequence and the actual energy consumption, the operating parameters or benchmark quotas are actively adjusted before the risk of overspending occurs. This solves the problem of response lag caused by the traditional method relying on static rules, and improves the real-time and accuracy of energy consumption control.

[0177] Based on the same technical concept, the embodiment of the present application also provides a multi-connected system energy consumption control system, such as Figure 4 As shown, the system includes:

[0178] A data acquisition module 401 is used to obtain operating parameters, baseline parameters, and environmental parameters of a multi-connected system that matches a target detection area;

[0179] an actual energy consumption sequence determining module 402, configured to determine an actual energy consumption sequence according to the operating parameters;

[0180] The predicted energy consumption sequence determination module 403 is configured to determine the predicted energy consumption sequence for the target period after the current moment based on the actual energy consumption sequence and the environmental parameters;

[0181] The control module 404 is used to adjust the benchmark parameters and / or operating parameters according to the difference value sequence between the predicted energy consumption sequence and the actual energy consumption sequence being within the set warning range, so that the total energy consumption of the multi-connected system in the target monitoring area is within the set benchmark range.

[0182] The present application also provides an electronic device corresponding to the method provided in the above embodiment. Figure 5 , which shows an electronic device provided by some embodiments of the present application. The electronic device 20 may include: a processor 200, a memory 201, a bus 202, and a communication interface 203. The processor 200, the communication interface 203, and the memory 201 are connected via the bus 202. The memory 201 stores a computer program executable on the processor 200. When the processor 200 executes the computer program, it executes the method provided by any of the aforementioned embodiments of the present application.

[0183] The memory 201 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage. The system network element and at least one other network element are connected via at least one physical port (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0184] The bus 202 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 201 is used to store programs. The processor 200 executes the programs upon receiving execution instructions. The methods disclosed in any of the aforementioned embodiments of the present application may be applied to or implemented by the processor 200.

[0185] The processor 200 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor 200 or by software instructions. The above processor 200 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in conjunction with the embodiments of this application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory 201 , and the processor 200 reads the information in the memory 201 and completes the steps of the above method in combination with its hardware.

[0186] The electronic device provided in the embodiments of the present application and the method provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, operated or implemented by them.

[0187] The present application also provides a computer-readable storage medium corresponding to the method provided in the above embodiment. Figure 6 The computer-readable storage medium shown is a CD 30 on which a computer program (ie, a program product) is stored. When the computer program is run by a processor, the method provided by any of the aforementioned embodiments is executed.

[0188] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0189] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the method provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0190] It should be noted that the above embodiments illustrate rather than limit the present application, and that a person skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbols placed between brackets should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present application may be implemented by means of hardware comprising several different elements and by means of appropriately programmed computers. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

[0191] The above description is merely a preferred embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0192] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made by using the contents of the present invention description and drawings under the concept of the present invention, or direct / indirect application in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for controlling energy consumption of a multi-connected system, characterized in that: The method comprises: Obtaining operating parameters, baseline parameters, and environmental parameters of a multi-connected system, wherein the multi-connected system matches a target detection area; determining an actual energy consumption sequence based on the operating parameters; Determining a predicted energy consumption sequence for a target period after a current moment based on the actual energy consumption sequence and the environmental parameters; According to the difference value sequence between the predicted energy consumption sequence and the actual energy consumption sequence being within the set warning range, the benchmark parameters and / or operating parameters are adjusted so that the total energy consumption of the multi-connected system in the target monitoring area is within the set benchmark range.

2. The method according to claim 1, wherein The benchmark parameters include a benchmark energy consumption quota and a quota weight, wherein the quota weight includes a time period-level allocation weight; Adjusting the benchmark parameters includes: Determine an actual energy consumption overspending sequence based on the difference between the actual energy consumption sequence and the benchmark energy consumption quota, and determine the time period corresponding to the actual energy consumption overspending sequence as the overspending time period, and determine the time period outside the overspending time period as the non-overspending time period; Calculating a time period quota reduction amount according to the time period level allocation weight and the actual energy consumption overrun sequence; The time period quota reduction amount is allocated according to the time period level weight of the non-overspending time period, and the baseline energy consumption quota of the non-overspending time period is adjusted.

3. The method according to claim 2, wherein The quota weights also include regional allocation weights; Adjusting the reference parameters further includes: Determining the target monitoring area as an overspending area, and determining the monitoring area outside the overspending area as a non-overspending area; Calculating a regional quota reduction amount based on the regional allocation weight and the actual energy consumption overrun sequence; The regional quota reduction amount is adjusted according to the regional level allocation weight of the non-overspending area, and the baseline energy consumption quota of the non-overspending area is adjusted.

4. The method according to claim 2 or 3, wherein: Also includes: Generating an initial baseline energy consumption quota based on historical energy consumption data of the target monitoring area; According to the energy consumption target value and the set constraint conditions set by the user, the initial benchmark energy consumption quota is revised to obtain a revised initial benchmark energy consumption quota; Determining a compensation coefficient based on the energy efficiency attributes and climate zoning standards of the multi-split system in the target monitoring area, and adjusting the revised initial baseline energy consumption quota based on the compensation coefficient to obtain a baseline energy consumption quota; The region-level allocation weight and the time period-level allocation weight are determined according to the functional attributes, time period characteristics and / or user settings of the target monitoring area.

5. The method according to any one of claims 1 to 3, wherein The environmental parameters include the indoor temperature, outdoor temperature and personnel flow status of the multi-split system and the target detection area, and the operating parameters include the indoor unit wind speed gear, compressor load rate, set temperature and cooling and heating differential; Adjusting the operating parameters includes: Calculating a wind speed adjustment coefficient based on the temperature difference between the indoor temperature and the set temperature and the compressor load rate, and adjusting the wind speed gear of the indoor unit according to the wind speed adjustment system; adjusting the set temperature according to the indoor and outdoor temperature difference between the indoor temperature and the outdoor temperature; The cooling and heating hysteresis is updated according to the attributes of the target monitoring area and the personnel flow status.

6. The method according to claim 5, wherein The operating parameters include the operating frequency of the compressor; adjusting the operating parameters includes: According to the personnel flow state in the target monitoring area being in an unmanned state and lasting for a first predetermined time, adjusting the operating frequency of the compressor to operate within a first adjustment range; According to the duration reaching a second predetermined duration, adjusting the operating frequency of the compressor from the first adjustment range to a second adjustment range; According to the duration exceeding the maximum allowable duration, the operating frequency of the compressor is limited to operating within a set minimum frequency range; wherein the frequency value of the minimum value of the first adjustment range is greater than or equal to the frequency value of the maximum value of the second adjustment range, and the frequency value of the minimum value of the second adjustment range is greater than or equal to the maximum value of the minimum frequency range.

7. The method according to any one of claims 1 to 3, wherein: The set warning range includes a first warning range, a second warning range, and a third warning range; wherein the maximum value of the first warning range is less than or equal to the minimum value of the second warning range, and the maximum value of the second warning range is less than or equal to the minimum value of the third warning range; According to the difference value sequence between the predicted energy consumption sequence and the actual energy consumption sequence being within the set warning range, adjusting the benchmark parameter and / or operating parameter includes: adjusting the operating parameters according to the difference value sequence being within the first warning range; According to the difference value sequence being in the second warning range, adjusting the benchmark parameter; According to the difference value sequence being in the third warning range, the reference parameter and the operating parameter are adjusted.

8. A multi-connected system energy consumption control system, characterized in that: The system comprises: a data acquisition module for acquiring operating parameters, baseline parameters, and environmental parameters of a multi-connected system matched to a target detection area; An actual energy consumption sequence determination module, configured to determine an actual energy consumption sequence according to the operating parameters; A predicted energy consumption sequence determination module, configured to determine a predicted energy consumption sequence for a target period after a current moment based on the actual energy consumption sequence and the environmental parameters; The control module is used to adjust the benchmark parameters and / or operating parameters according to the difference value sequence between the predicted energy consumption sequence and the actual energy consumption sequence being within the set warning range, so that the total energy consumption of the multi-connected system in the target monitoring area is within the set benchmark range.

9. An electronic device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that Computer-readable instructions are stored thereon, and the computer-readable instructions can be executed by a processor to implement the method according to any one of claims 1 to 7.

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