Central air conditioner energy saving method and device based on Internet of Things and electronic equipment

Abstract

The invention relates to the technical field of the Internet of Things, in particular to a central air conditioner energy saving method and device based on the Internet of Things and electronic equipment. Building a priority coefficient according to the indoor temperature and the personnel density, and generating an emergency response strategy based on the priority coefficient; the air supply intensity is adjusted according to the collected carbon dioxide concentration and the mass concentration of the inhalable particles; optimizing refrigerating unit load distribution based on chilled water supply temperature, return water temperature and water pump flow; judging the equipment performance abnormity based on the load index of each time window in the monitoring period; based on the construction result of the priority coefficient in the management period and the judgment result of the performance abnormity of the equipment in each time window, sending a maintenance early warning to a user; and updating the maintenance early warning process according to the equipment current, the equipment lubricating oil viscosity and the freezing water conductivity collected in the management period. The operation efficiency of the central air conditioner is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a central air-conditioning energy-saving method, device and electronic equipment based on the Internet of Things. Background Art

[0002] As building sizes expand and energy conservation demands rise, central air conditioning systems face bottlenecks such as high energy consumption, poor adaptability to dynamic environments, and difficulty predicting equipment performance degradation. Traditional control methods, which often rely on fixed threshold start / stop strategies and single parameter adjustment strategies, struggle to cope with complex scenarios such as sudden changes in occupancy density, fluctuating air quality, and dynamic load variations.

[0003] Existing energy efficiency optimization technologies often rely on manual inspections or post-event maintenance, lacking the ability to diagnose hidden equipment failures (such as lubricant degradation and condenser scaling) early. Furthermore, multi-unit coordinated control and cold storage resource allocation lack a dynamic coupling mechanism with real-time environmental parameters, resulting in significant energy waste under certain operating conditions. Summary of the Invention

[0004] The object of the present invention is to provide a central air-conditioning energy-saving method, device and electronic equipment based on the Internet of Things to solve at least one of the problems existing in the prior art.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A central air-conditioning energy-saving method based on the Internet of Things, comprising:

[0007] Collect environmental parameters and equipment operating parameters;

[0008] Construct a priority coefficient based on indoor temperature and occupant density, and generate an emergency response strategy based on the priority coefficient;

[0009] An air cleanliness index is constructed based on the collected carbon dioxide concentration and inhalable particulate matter mass concentration, and the air supply intensity is adjusted based on the air cleanliness index;

[0010] Optimize refrigeration unit load distribution based on chilled water supply temperature, return water temperature and pump flow;

[0011] Construct an energy efficiency index based on the load index of each time window within the monitoring period, and judge the abnormality of equipment performance based on the energy efficiency index;

[0012] Based on the results of the priority coefficient construction within the management cycle and the judgment results of the equipment performance abnormality in each time window, the equipment attenuation index is constructed, and maintenance warnings are sent to users;

[0013] Update the maintenance warning process based on the equipment current, equipment lubricating oil viscosity and chilled water conductivity collected during the management cycle.

[0014] Optionally, a priority coefficient Y is constructed based on the indoor temperature Tr and the occupant density Pd, and the priority coefficient Y is compared with the risk thresholds u1 and u2 to generate an emergency response strategy. When Y is less than u1, the variable frequency water pump energy-saving mode is enabled. When Y is greater than or equal to u1 and less than u2, no emergency response strategy is adopted. When Y is greater than or equal to u2, the cooling supply to the non-medical area is cut off.

[0015] Optionally, an air cleanliness index C is constructed based on the collected carbon dioxide concentration Nc and the inhalable particulate matter concentration Nk, and the air cleanliness index C is compared with an air cleanliness index threshold c0. When C is greater than c0, the air supply intensity is adjusted to Fa.

[0016] Optionally, the load index L is calculated based on the chilled water supply temperature Ts, the return water temperature Th and the water pump flow Q. When the real-time load L is less than 0.7Lmax, a single variable-frequency compressor is operated. When the real-time load L is greater than or equal to 0.7Lmax and less than or equal to 1.1Lmax, the load distribution optimization of the refrigeration unit is not performed. When L is greater than 1.1Lmax, an overload warning is triggered and the cold storage tank is temporarily enabled to supplement the cooling capacity. Lmax is the rated capacity of the unit.

[0017] Optionally, the equipment performance coefficient NXi of the i-th time window is constructed according to the load index Li in the i-th time window in the monitoring period and the equipment input power Pin collected in the i-th time window, and the equipment performance coefficient NXi of the i-th time window is compared with the equipment performance coefficient threshold n0. If NXi is less than n0, it is determined that the equipment performance in the i-th time window of the current monitoring period is abnormal; otherwise, it is determined that the equipment performance in the i-th time window of the current monitoring period is normal.

[0018] When the equipment performance is abnormal for three consecutive time windows within the monitoring period, a condenser cleaning warning will be sent to the user.

[0019] Optionally, an equipment attenuation index is constructed based on the construction result of the priority coefficient within the management period and the judgment result of the abnormality of the equipment performance in each time window, and a maintenance warning is sent to the user.

[0020] Specifically, the duration during which the priority coefficient Y is greater than the risk threshold u2 within the statistical management cycle is recorded as Ty, and the equipment attenuation index Sj is constructed based on Ty and the judgment results of the abnormality of equipment performance in each time window, and the equipment attenuation index Sj is compared with the attenuation index threshold s1. When Sj is less than or equal to s1, no maintenance warning is sent to the user, otherwise, a maintenance warning is sent to the user.

[0021] Optionally, the duration during which the device current a0 collected during the statistical management period is greater than the preset current a1 is recorded as A; when the ratio of A to Tg is less than or equal to the preset abnormality ratio β, the update coefficient is set to 1; otherwise, the update coefficient is set to (1+0.1×A / Tg).

[0022] Optionally, an abnormality coefficient YC is constructed based on the equipment lubricating oil viscosity ND and the chilled water conductivity DD collected during the management period, and the equipment attenuation coefficient is updated based on the abnormality coefficient YC and the update coefficient to update the maintenance warning process.

[0023] According to another aspect of the present application, a central air-conditioning energy-saving device based on the Internet of Things is provided, comprising:

[0024] Collection unit, used to collect environmental parameters and equipment operating parameters;

[0025] A priority analysis unit is used to construct a priority coefficient based on indoor temperature and occupant density, and generate an emergency response strategy based on the priority coefficient;

[0026] An intensity adjustment unit is used to construct an air cleanliness index based on the collected carbon dioxide concentration and inhalable particulate matter mass concentration, and adjust the air supply intensity based on the air cleanliness index;

[0027] Load optimization unit, used to optimize the load distribution of the refrigeration unit based on the chilled water supply temperature, return water temperature and water pump flow;

[0028] A performance judgment unit is used to construct an energy efficiency index based on the load index of each time window within the monitoring period, and to judge the abnormality of equipment performance based on the energy efficiency index;

[0029] Maintenance warning unit, which is used to construct the equipment attenuation index based on the construction results of the priority coefficient within the management cycle and the judgment results of equipment performance abnormality in each time window, and send maintenance warnings to users;

[0030] The update unit is used to update the maintenance warning process based on the equipment current, equipment lubricating oil viscosity and chilled water conductivity collected during the management cycle.

[0031] According to another aspect of the present application, an electronic device is provided, comprising:

[0032] one or more processors;

[0033] a storage device for storing one or more programs;

[0034] When the one or more programs are executed by the one or more processors, the one or more processors implement the central air-conditioning energy-saving method based on the Internet of Things.

[0035] The beneficial effects of the present invention are as follows: by constructing an adaptive decision-making system with multi-dimensional parameter fusion, the overall performance of the system is significantly improved. By utilizing the holographic perception of environmental parameters and equipment status, the precise matching of air-conditioning loads and the intelligent scheduling of cold storage resources are achieved, reducing ineffective energy consumption; based on the zoned cooling control of the dynamic priority strategy, the stability of key areas is prioritized in sudden high-load scenarios, thereby enhancing the emergency resilience of the system; the air supply mode is optimized by combining the air quality index with the nonlinear adjustment function, taking into account both indoor comfort and energy-saving goals; through the equipment attenuation warning model driven by historical data, the performance degradation of the compressor and the abnormality of the lubrication system are identified in advance, and the periodic maintenance is upgraded to predictive maintenance, reducing the sudden failure rate; the innovative introduction of the collaborative calibration mechanism of multiple physical quantities such as current and viscosity enables the maintenance strategy to automatically adapt to the actual wear status of the equipment and extend the life of key components. This method effectively solves the problems of delayed response and extensive energy efficiency management of traditional systems, taking into account the long-term needs of refined operation and maintenance and low-carbon operation, and is suitable for complex scenarios such as medical and commercial that have high requirements for cooling reliability and energy efficiency sensitivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 these drawings without creative work.

[0037] Figure 1 This is a flow chart of the central air-conditioning energy-saving method based on the Internet of Things in this embodiment.

[0038] Figure 2 Schematic diagram of the flowchart of the early warning update method of this embodiment.

[0039] Figure 3 This is a structural diagram of the central air-conditioning energy-saving device based on the Internet of Things in this embodiment.

[0040] Figure 4 Schematic diagram of the structure of the electronic device of this embodiment. DETAILED DESCRIPTION

[0041] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the following detailed description is illustrative rather than restrictive and should not be used to limit the scope of protection of the present invention.

[0042] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0043] Specifically, this embodiment is applied to a central air-conditioning system equipped with dual compressors, and is particularly suitable for places with high personnel mobility, complex environmental requirements and sensitivity to energy efficiency. It is mainly aimed at the coordinated control of refrigeration units, sudden load response and refined energy consumption management scenarios, and supports dynamic linkage between variable frequency compressors and cold storage equipment.

[0044] See also Figure 1 As shown, it is a flow chart of the central air-conditioning energy-saving method based on the Internet of Things in this embodiment, including:

[0045] Step S101 , collecting environmental parameters and equipment operating parameters, the environmental parameters including indoor temperature, carbon dioxide concentration, inhalable particulate matter mass concentration and human density, the equipment operating parameters including chilled water supply temperature, return water temperature, water pump flow, equipment current, equipment lubricating oil viscosity and chilled water conductivity.

[0046] Specifically, a comprehensive perception network is built through multi-dimensional parameter collection, enabling real-time digital mapping of environmental conditions and equipment operating conditions. This not only provides a data foundation for subsequent analysis, but also improves monitoring accuracy through multi-source heterogeneous sensor fusion, preventing single parameter deviations from influencing overall decision-making.

[0047] For example, in this embodiment, the indoor temperature can be collected by a high-precision temperature and humidity sensor, the carbon dioxide concentration can be collected by an NDIR (non-dispersive infrared) sensor, the mass concentration of inhalable particulate matter can be collected by a laser scattering particulate matter sensor, and the inhalable particulate matter is PM2.5. The density of people can be collected by camera + AI number recognition technology, the supply temperature and return temperature of chilled water can be collected by a temperature sensor, the water pump flow can be collected by an electromagnetic flowmeter or an ultrasonic flowmeter installed in the chilled water main pipeline, the viscosity of the lubricating oil can be collected by an online viscosity sensor, the compressor and motor circuit can be monitored by a clamp current sensor or a Hall effect sensor to collect the equipment current, and the conductivity of the chilled water can be collected by embedding a conductivity sensor in the water circulation system; this embodiment does not specifically limit the method of collecting the above data, and those skilled in the art can freely set it according to their needs.

[0048] Please continue reading Figure 1 As shown, the central air-conditioning energy-saving method based on the Internet of Things also includes:

[0049] Step S102: construct a priority coefficient according to the indoor temperature and the occupant density, and generate an emergency response strategy based on the priority coefficient.

[0050] Specifically, the priority coefficient Y is constructed based on the indoor temperature Tr and the occupant density Pd. The expression of Y is:

[0051] Where w1 is the temperature weight, w2 is the personnel density weight, w1+w2=1, Te is the set temperature, △T is the temperature difference threshold, and Pc is the personnel density threshold;

[0052] The priority coefficient Y is compared with the risk thresholds u1 and u2 to generate an emergency response strategy. When Y is less than u1, the variable frequency water pump energy-saving mode is enabled. When Y is greater than or equal to u1 and less than u2, no emergency response strategy is adopted. When Y is greater than or equal to u2, the cooling supply to non-medical areas is cut off.

[0053] Specifically, a weighted fusion mechanism is introduced to dynamically assess environmental risk levels, breaking through the mechanical nature of traditional threshold control. In particular, in scenarios such as public healthcare, a strategy of cutting off cooling supply to non-medical areas prioritizes stable cooling in key areas, balancing energy conservation and safety needs and enhancing system resilience.

[0054] For example, in this embodiment, the temperature weight can be set to 0.7, the personnel density weight can be set to 0.3, the temperature difference threshold is 3°C, and the personnel density threshold can be set to 0.5 people / m 2 , the risk threshold u1 can be set to 0.3, and u2 can be set to 0.5; in this embodiment, no specific limitation is imposed on the setting of the above data, and those skilled in the art can freely set it according to needs.

[0055] Please continue reading Figure 1 As shown, the central air-conditioning energy-saving method based on the Internet of Things also includes:

[0056] In step S103 , an air cleanliness index is constructed according to the collected carbon dioxide concentration and the mass concentration of inhalable particulate matter, and the air supply intensity is adjusted based on the air cleanliness index.

[0057] Specifically, step S103 constructs an air cleanliness index C based on the collected carbon dioxide concentration Nc and the inhalable particulate matter concentration Nk. The expression of C is: C = x1 × Nc / N1 + x2 × Nk / N2; where x1 is the carbon dioxide weight, x2 is the particulate matter weight, x1 + x2 = 1, N1 is the carbon dioxide concentration threshold, and N2 is the particulate matter concentration threshold;

[0058] Compare the air cleanliness index C with the air cleanliness index threshold c0. When C is greater than c0, adjust the air supply intensity to Fa, Fa = Fb × [1 + α × tanh (C - c0)]. Otherwise, do not adjust the air supply intensity.

[0059] Where Fb is the reference air supply intensity and α is the wind speed correction factor.

[0060] Specifically, the system dynamically adjusts ventilation volume based on the air quality index, improving indoor air quality while avoiding the excessive energy consumption associated with fixed air volume settings. Controlling air supply intensity through a nonlinear function balances adjustment sensitivity with system stability, preventing equipment wear caused by frequent starts and stops.

[0061] For example, in this embodiment, the carbon dioxide weight can be set to 0.6, the particulate matter weight can be set to 0.4, the carbon dioxide concentration threshold can be set to 1500ppm, and the particulate matter concentration threshold can be set to 50μg / m 3 , the cleanliness index threshold can be set to 0.38, and the baseline air supply intensity can be set to 15L / (s·m 2 ), the wind speed correction factor can be set to 0.12; in this embodiment, no specific limitation is imposed on the setting of the above data, and those skilled in the art can freely set it according to their needs.

[0062] Please continue reading Figure 1 As shown, the central air-conditioning energy-saving method based on the Internet of Things also includes:

[0063] Step S104: Optimize the load distribution of the refrigeration unit based on the chilled water supply temperature, return water temperature and water pump flow.

[0064] Specifically, the load index L is calculated based on the chilled water supply temperature Ts, the return water temperature Th and the water pump flow Q, L = ρ × Cp × Q × (Th-Ts) / 3600; ρ is the water density, cp is the specific heat capacity;

[0065] When the real-time load L is less than 0.7Lmax, a single variable-frequency compressor is operated. When the real-time load L is greater than or equal to 0.7Lmax and less than or equal to 1.1Lmax, the load distribution optimization of the refrigeration unit is not performed. When L is greater than 1.1Lmax, an overload warning is triggered and the cold storage tank is temporarily enabled to supplement the cooling capacity. Lmax is the rated capacity of the unit.

[0066] Specifically, the thermodynamic characteristics of the water cycle are combined to accurately quantify real-time loads, enabling coordinated control of unit start-up and shutdown strategies and cold storage equipment. Low-load operation is driven by variable-frequency compressors, reducing losses from frequent start-up and shutdown of fixed-frequency equipment. Cold storage tanks are used to smooth peak loads, extending the life of primary equipment.

[0067] For example, in this embodiment, ρ is 1000 kg / m 3 , cp is the specific heat capacity 4.18kJ / kg·℃.

[0068] Please continue reading Figure 1 As shown, the central air-conditioning energy-saving method based on the Internet of Things also includes:

[0069] Step S105 : constructing an energy efficiency index based on the load index of each time window within the monitoring period, and judging the abnormality of equipment performance according to the energy efficiency index.

[0070] Specifically, the equipment performance coefficient NXi of the i-th time window is constructed according to the load index Li in the i-th time window during the monitoring period and the equipment input power Pin collected in the i-th time window, NXi = Li × 3600 / (Pin × γ), where γ is the preset performance coefficient;

[0071] Compare the device performance coefficient NXi of the i-th time window with the device performance coefficient threshold n0. If NXi is less than n0, the device performance in the i-th time window of the current monitoring period is determined to be abnormal. Otherwise, the device performance in the i-th time window of the current monitoring period is determined to be normal.

[0072] When the equipment performance is abnormal for three consecutive time windows within the monitoring period, a condenser cleaning warning will be sent to the user.

[0073] Specifically, the energy efficiency index continuously tracks equipment operating performance and identifies potential performance degradation trends. This provides early warning of issues like condenser scaling, shifting reactive maintenance to preventive maintenance, reducing the risk of unexpected downtime while ensuring the system remains in an efficient operating range for the long term.

[0074] For example, in this embodiment, the preset performance coefficient can be set to 3.5, and the device performance coefficient threshold can be set to 0.8×γ; this embodiment does not specifically limit the setting of the above data, and those skilled in the art can freely set it according to needs.

[0075] Specifically, in this embodiment, the device input power can be collected by a smart meter.

[0076] Please continue reading Figure 1 As shown, the central air-conditioning energy-saving method based on the Internet of Things also includes:

[0077] Step S106: construct an equipment attenuation index based on the result of constructing the priority coefficient within the management period and the result of judging the abnormality of equipment performance in each time window, and send a maintenance warning to the user.

[0078] Specifically, the duration of time during which the priority coefficient Y is greater than the risk threshold u2 within the management cycle is counted and recorded as Ty. Based on Ty and the judgment results of the abnormal performance of the equipment in each time window, the equipment attenuation index Sj is constructed. The expression of Sj is Sj = z1×ln(5×Ty / Tg+1) / ln6+z2×Mn / Mz;

[0079] Where z1 is the first weight, z2 is the second weight, z1 + z2 = 1, Tg is the duration of the management period, Mn is the number of time windows with abnormal device performance within the management period, and Mz is the total number of time windows within the management period.

[0080] The equipment attenuation index Sj is compared with the attenuation index threshold s1. When Sj is less than or equal to s1, no maintenance warning is sent to the user. Otherwise, a maintenance warning is sent to the user.

[0081] Specifically, we uniformly assess historical equipment load pressure and abnormality frequency to quantify equipment health. Time window statistics enhance the reliability of trend forecasts, helping operations and maintenance personnel develop targeted maintenance plans and reduce resource waste caused by over- or under-maintenance.

[0082] For example, in this embodiment, the first weight can be set to 0.2, the second weight can be set to 0.8, and the attenuation index threshold can be set to 0.36; this embodiment does not specifically limit the setting of the above data, and those skilled in the art can freely set it according to needs.

[0083] Please continue reading Figure 1 As shown, the central air-conditioning energy-saving method based on the Internet of Things also includes:

[0084] Step S107 : updating the maintenance warning process based on the equipment current, equipment lubricating oil viscosity, and chilled water conductivity collected during the management period.

[0085] See also Figure 2 As shown, the early warning update method includes:

[0086] Step S201 : determining an update coefficient based on the device current collected during a management period.

[0087] Specifically, the duration of time during which the device current a0 collected during the statistical management period is greater than the preset current a1 is recorded as A; when the ratio of A to Tg is less than or equal to the preset abnormality ratio β, the update coefficient is set to 1; otherwise, the update coefficient is set to (1+0.1×A / Tg).

[0088] Specifically, the system dynamically adjusts the sensitivity of the early warning model based on the device's current status, addressing the difficulty of traditional fixed thresholds in adapting to load fluctuations. By assessing the duration of current overlimits, the system's stress is correlated with the cumulative risk effect and the rate of device aging. This enables adaptive optimization of the early warning mechanism, preventing potential risks such as short-term overloads falsely triggering maintenance instructions or long-term, hidden overloads that go undetected.

[0089] For example, in this embodiment, the preset abnormality ratio can be set to 0.1, and the preset current can be set to 90% of the rated current of the equipment; this embodiment does not specifically limit the setting of the preset abnormality ratio, and those skilled in the art can freely set it according to needs.

[0090] See also Figure 2 As shown, the early warning update method further includes:

[0091] Step S202 : updating the maintenance warning process based on the equipment lubricating oil viscosity, chilled water conductivity, and update coefficient collected during the management period.

[0092] Specifically, the anomaly coefficient YC is constructed based on the equipment lubricating oil viscosity ND and chilled water conductivity DD collected during the management period. YC = v1 × (ND-NB) / NB + v2 × DD / DB, where v1 is the viscosity weight, v2 is the conductivity weight, v1+v2=1, NB is the viscosity threshold, and DB is the conductivity threshold.

[0093] The device attenuation coefficient is updated based on the abnormal coefficient YC and the update coefficient. The attenuation index threshold is updated to s2, and s2 = s1 × [1 + η × lg (5 × update coefficient × YC + 1) / lg6] is set, where η is the update factor.

[0094] Specifically, the health assessment system incorporates slowly evolving fault characteristics such as lubricant degradation and water contamination, overcoming the limitations of single current parameter diagnosis. Through the weighted fusion of multiple physical parameters (viscosity and conductivity), the system accurately captures the changing trends of equipment friction loss and corrosion, enhancing the ability to predict complex faults such as mechanical wear and water system scaling. Combined with the dynamic correction threshold of the update coefficient, the early warning strategy is compatible with both conventional aging patterns and sudden abnormal interference, forming a multi-dimensional, progressive health management closed loop.

[0095] For example, in this embodiment, the viscosity weight can be set to 0.45, the conductivity weight can be set to 0.55, the viscosity threshold can be set to 1.5 times the initial viscosity of the equipment lubricating oil, the conductivity threshold can be set to 1000 μS / cm, and the update factor can be set to 0.1; this embodiment does not specifically limit the setting of the above data, and those skilled in the art can freely set it according to needs.

[0096] For example, in this embodiment, the time window can be set to 1 minute, the monitoring period can be set to 1 hour, and the management period can be set to 7 days, 15 days, etc.; this embodiment does not specifically limit the above settings, and those skilled in the art can freely set them according to their needs.

[0097] See also Figure 3 As shown, the central air-conditioning energy-saving device based on the Internet of Things includes:

[0098] Collection unit, used to collect environmental parameters and equipment operating parameters;

[0099] A priority analysis unit is used to construct a priority coefficient based on indoor temperature and occupant density, and generate an emergency response strategy based on the priority coefficient;

[0100] An intensity adjustment unit is used to construct an air cleanliness index based on the collected carbon dioxide concentration and inhalable particulate matter mass concentration, and adjust the air supply intensity based on the air cleanliness index;

[0101] Load optimization unit, used to optimize the load distribution of the refrigeration unit based on the chilled water supply temperature, return water temperature and water pump flow;

[0102] A performance judgment unit is used to construct an energy efficiency index based on the load index of each time window within the monitoring period, and to judge the abnormality of equipment performance based on the energy efficiency index;

[0103] Maintenance warning unit, which is used to construct the equipment attenuation index based on the construction results of the priority coefficient within the management cycle and the judgment results of equipment performance abnormality in each time window, and send maintenance warnings to users;

[0104] The update unit is used to update the maintenance warning process based on the equipment current, equipment lubricating oil viscosity and chilled water conductivity collected during the management cycle.

[0105] The central air-conditioning energy-saving device based on the Internet of Things provided in the embodiment of the present application can execute the central air-conditioning energy-saving method based on the Internet of Things provided in any embodiment of the present application, and has the corresponding functional modules and beneficial effects of the execution method.

[0106] See also Figure 4 As shown, it is a structural diagram of an electronic device in this embodiment. The electronic device in the embodiment of the present invention may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), wearable electronic devices, etc., as well as fixed terminals such as digital TVs, desktop computers, smart home devices, etc. Figure 4 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0107] like Figure 4 As shown, the electronic device includes: a processor 501, a memory 502, a communication interface 503 and a system bus 504. The processor includes at least one of a central processing unit (CPU), a graphics processing unit (GPU) or a field programmable gate array (FPGA), and is configured to call computer programs and data stored in the memory and generate control instructions; the memory includes a random access memory (RAM) and / or a non-volatile memory (NVM), and the NVM includes a flash memory, a solid-state drive (SSD) or a combination thereof, which is used to store computer programs, process intermediate data and historical data sets; the communication interface includes a wired communication module and a wireless communication module, the wired communication module supports Ethernet or RS-485 protocol for connecting to a sensor network; the wireless communication module supports LoRa, 5G or satellite communication protocol for transmitting processing results to a remote server; the system bus adopts a PCI Express or AXI bus architecture to achieve high-speed data exchange and clock synchronization between the processor, memory and communication interface.

[0108] This embodiment further provides a computer-readable storage medium, which physically stores computer-executable instructions. When the instructions are transmitted to the processing unit via the integrated circuit substrate, they are packaged and processed through the data channel of the bus system and then solidified into the non-volatile storage area of ​​the storage module. The executable instructions are configured to implement the complete technical solution described in the method for the central air-conditioning energy-saving device based on the Internet of Things when executed by the processor.

[0109] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the implementation methods of the present invention. For ordinary technicians in this field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the scope of protection of the present invention.

Claims

1. A central air conditioning energy saving method based on the Internet of Things, characterized in that: include: Collect environmental parameters and equipment operating parameters; Construct a priority coefficient based on indoor temperature and occupant density, and generate an emergency response strategy based on the priority coefficient; An air cleanliness index is constructed based on the collected carbon dioxide concentration and inhalable particulate matter mass concentration, and the air supply intensity is adjusted based on the air cleanliness index; Optimize refrigeration unit load distribution based on chilled water supply temperature, return water temperature and pump flow; Construct an energy efficiency index based on the load index of each time window within the monitoring period, and judge the abnormality of equipment performance based on the energy efficiency index; Based on the results of the priority coefficient construction within the management cycle and the judgment results of the equipment performance abnormality in each time window, the equipment attenuation index is constructed, and maintenance warnings are sent to users; Update the maintenance warning process based on the equipment current, equipment lubricating oil viscosity and chilled water conductivity collected during the management cycle.

2. The central air-conditioning energy-saving method based on the Internet of Things according to claim 1, characterized in that: A priority coefficient Y is constructed based on the indoor temperature Tr and the occupant density Pd, and is compared with the risk thresholds u1 and u2 to generate an emergency response strategy. When Y is less than u1, the variable frequency water pump energy-saving mode is enabled. When Y is greater than or equal to u1 and less than u2, no emergency response strategy is adopted. When Y is greater than or equal to u2, the cooling supply to non-medical areas is cut off.

3. The central air-conditioning energy-saving method based on the Internet of Things according to claim 2, characterized in that: The air cleanliness index C is constructed based on the collected carbon dioxide concentration Nc and the inhalable particulate matter concentration Nk, and the air cleanliness index C is compared with the air cleanliness index threshold c0. When C is greater than c0, the air supply intensity is adjusted to Fa.

4. The central air-conditioning energy-saving method based on the Internet of Things according to claim 3 is characterized in that: The load index L is calculated based on the chilled water supply temperature Ts, return water temperature Th and water pump flow Q. When the real-time load L is less than 0.7Lmax, a single variable-frequency compressor is operated. When the real-time load L is greater than or equal to 0.7Lmax and less than or equal to 1.1Lmax, the load distribution optimization of the refrigeration unit is not performed. When L is greater than 1.1Lmax, an overload warning is triggered and the cold storage tank is temporarily enabled to supplement the cooling capacity. Lmax is the rated capacity of the unit.

5. The central air-conditioning energy-saving method based on the Internet of Things according to claim 4 is characterized in that: The equipment performance coefficient NXi of the i-th time window is constructed based on the load index Li in the i-th time window within the monitoring period and the equipment input power Pin collected in the i-th time window. The equipment performance coefficient NXi of the i-th time window is compared with the equipment performance coefficient threshold n0. If NXi is less than n0, it is determined that the equipment performance in the i-th time window of the current monitoring period is abnormal. Otherwise, it is determined that the equipment performance in the i-th time window of the current monitoring period is normal. When the equipment performance is abnormal for three consecutive time windows within the monitoring period, a condenser cleaning warning will be sent to the user.

6. The central air-conditioning energy-saving method based on the Internet of Things according to claim 5, characterized in that: Based on the construction results of the priority coefficient within the management cycle and the judgment results of the equipment performance abnormality in each time window, the equipment attenuation index is constructed, and a maintenance warning is sent to the user. Specifically, the duration during which the priority coefficient Y is greater than the risk threshold u2 within the statistical management cycle is recorded as Ty, and the equipment attenuation index Sj is constructed based on Ty and the judgment results of the abnormality of equipment performance in each time window, and the equipment attenuation index Sj is compared with the attenuation index threshold s1. When Sj is less than or equal to s1, no maintenance warning is sent to the user, otherwise, a maintenance warning is sent to the user.

7. The central air-conditioning energy-saving method based on the Internet of Things according to claim 6, characterized in that: The duration of time during which the device current a0 collected during the statistical management period is greater than the preset current a1 is recorded as A. When the ratio of A to Tg is less than or equal to the preset abnormality ratio β, the update coefficient is set to 1; otherwise, the update coefficient is set to (1+0.1×A / Tg).

8. The central air-conditioning energy-saving method based on the Internet of Things according to claim 7, characterized in that: The abnormality coefficient YC is constructed based on the equipment lubricating oil viscosity ND and chilled water conductivity DD collected during the management cycle, and the equipment attenuation coefficient is updated based on the abnormality coefficient YC and the update coefficient to update the maintenance warning process.

9. A central air-conditioning energy-saving device based on the Internet of Things, characterized in that: include: Collection unit, used to collect environmental parameters and equipment operating parameters; A priority analysis unit is used to construct a priority coefficient based on indoor temperature and occupant density, and generate an emergency response strategy based on the priority coefficient; An intensity adjustment unit is used to construct an air cleanliness index based on the collected carbon dioxide concentration and inhalable particulate matter mass concentration, and adjust the air supply intensity based on the air cleanliness index; Load optimization unit, used to optimize the load distribution of the refrigeration unit based on the chilled water supply temperature, return water temperature and water pump flow; A performance judgment unit is used to construct an energy efficiency index based on the load index of each time window within the monitoring period, and to judge the abnormality of equipment performance based on the energy efficiency index; Maintenance warning unit, which is used to construct the equipment attenuation index based on the construction results of the priority coefficient within the management cycle and the judgment results of equipment performance abnormality in each time window, and send maintenance warnings to users; The update unit is used to update the maintenance warning process based on the equipment current, equipment lubricating oil viscosity and chilled water conductivity collected during the management cycle.

10. An electronic device, characterized in that: The electronic device comprises: one or more processors; a storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the central air-conditioning energy-saving method based on the Internet of Things as described in any one of claims 1-8.

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