Intelligent monitoring and analyzing system for operation energy consumption of household central air conditioner

Abstract

The invention belongs to the technical field of intelligent monitoring of operation energy consumption of air conditioners, and relates to an intelligent monitoring and analyzing system for operation energy consumption of household central air conditioners. According to the method, energy consumption data of independent operation of the air conditioner is accurately extracted from a household total electricity utilization power curve, a high-energy-consumption state is judged based on a historical reference, the correlation degree between the high-energy-consumption state and key operation characteristics is analyzed, then the diagnosis priority is generated in combination with outdoor temperature, short-term operation test verification is executed on a primary root, and the diagnosis efficiency is improved. And finally, generating an energy-saving regulation and control signal according to a verification result. The problems that in the prior art, air conditioner energy consumption is difficult to accurately strip from the total load, a high-energy-consumption source cannot be positioned, and energy-saving suggestions are difficult to take effect on the ground are solved. And the effects of improving the monitoring accuracy of the energy consumption of the air conditioner, accurately positioning a high-energy-consumption source and realizing effective closed-loop energy-saving optimization are achieved.

Description

Technical Field

[0001] This invention belongs to the field of intelligent monitoring technology for air conditioning operation energy consumption, and relates to an intelligent monitoring and analysis system for the operation energy consumption of a residential central air conditioning system. Background Technology

[0002] With the improvement of residents' living standards and increasingly stringent requirements for building energy conservation, the large-scale application and high-load operation of residential central air conditioning have led to increasingly prominent energy consumption issues. Currently, traditional energy consumption monitoring solutions mainly obtain overall household electricity consumption data through smart meters or power monitoring devices to form an electricity load curve. Some solutions attempt to combine device switching signals or simple time rules to make coarse-grained estimates of the power consumption of specific appliances. However, these methods have the following drawbacks: First, the total household electricity load is the result of the superposition of multiple appliances, and existing methods struggle to extract the true energy consumption of the air conditioner operating independently with high confidence from the complex and fluctuating total power curve. Simply relying on start / stop signals or fixed time periods is prone to misjudgment due to the occasional start / stop of other high-power appliances, leading to distorted air conditioning energy consumption data that cannot serve as a reliable basis for subsequent analysis.

[0003] Secondly, existing energy consumption assessments are mostly based on simple threshold comparisons or focus only on single statistical results such as total power consumption. Both are one-sided and result-oriented evaluation methods, lacking in-depth analysis of the high energy consumption process and its causes. This makes it difficult for the system to accurately determine whether a high energy consumption event is caused by a harsh external environment, decreased equipment energy efficiency, or poor user habits, thus failing to pinpoint the root cause of the problem.

[0004] Finally, even when abnormal energy consumption is identified, existing solutions typically only provide alarms for excessive energy consumption or simple statistical reports, at most offering generalized suggestions such as checking the equipment or raising the set temperature. These suggestions lack specific identification of the main causes of energy consumption in particular situations, and cannot be verified through controlled, low-intrusion testing to confirm the correctness of the hypothesized causes. This results in low feasibility and user adoption rates for the suggestions, making it difficult for energy-saving measures to be effectively implemented. Summary of the Invention

[0005] In view of this, in order to solve the problems mentioned in the background technology, a smart monitoring and analysis system for energy consumption of residential central air conditioning is proposed.

[0006] The objective of this invention can be achieved through the following technical solution: A smart monitoring and analysis system for energy consumption of a home central air conditioning system, comprising: an operation monitoring module: acquiring the total household power consumption curve and air conditioning start / stop signals within a monitoring period; based on the air conditioning start / stop signals, dividing the independent operating period of the air conditioning from the total household power consumption curve, and extracting the average power and cumulative energy consumption of the air conditioning within that period.

[0007] Energy consumption assessment module: Based on the average power and cumulative energy consumption of the air conditioner, it determines whether the air conditioner is in a high energy consumption state by comparison; when it is determined to be in a high energy consumption state, it extracts three operating characteristic values ​​of the air conditioner in the corresponding time period: the actual operating energy efficiency ratio, the deviation value between the set temperature and the room temperature, and the daily cumulative running time of the air conditioner, and calculates the correlation between each operating characteristic value and the high energy consumption state.

[0008] Diagnostic optimization module: Based on various correlations and the current outdoor temperature, it generates a diagnostic priority list for high energy consumption root causes; according to the diagnostic priority list, it performs preset short-term operation tests on the primary diagnostic root causes in the list, verifies the diagnostic conclusions based on the changes in energy consumption before and after the test, and generates energy-saving control signals accordingly.

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention effectively solves the technical problem of accurately identifying the independent operation period of air conditioners in complex household power environments by using a dual criterion identification method based on the air conditioner start-stop signal and power curve change characteristics. This method uses the comparison of the moving average of the absolute value of the first difference with the benchmark rate of change, combined with the running time screening condition, to improve the accuracy of air conditioner energy consumption data extraction, avoid misjudgment caused by interference from other electrical appliances, and make the monitoring results have engineering application value.

[0010] (2) This invention solves the technical bottleneck of difficulty in locating the cause of high energy consumption by constructing a dynamic evaluation mechanism based on historical energy consumption benchmarks and combining statistical correlation analysis of three operating characteristic values ​​and energy consumption indicators. The system can quantitatively evaluate the contribution of factors such as decreased energy efficiency ratio, improper temperature control settings, or overuse to energy consumption, and dynamically generate diagnostic priorities based on outdoor temperature, thus upgrading energy consumption anomaly analysis from a single threshold judgment to a multi-dimensional correlation analysis, which greatly improves the accuracy and scientific nature of the diagnostic results.

[0011] (3) This invention overcomes the technical deficiency of traditional solutions that only provide generalized suggestions and are difficult to implement by designing a closed-loop optimization mechanism for short-term operation testing and energy consumption change verification. The system performs controllable tests on the primary diagnostic root causes, verifies the diagnostic conclusions based on measured energy consumption changes, and dynamically adjusts the optimization strategy to generate targeted energy-saving control signals. This method improves the feasibility of energy-saving measures and the user adoption rate, enabling energy consumption optimization to truly translate into actual energy-saving effects. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram showing the connection of each module in a smart monitoring and analysis system for energy consumption of a home central air conditioning system according to the present invention.

[0014] Figure 2 This is a flowchart of the method for dividing the independent operation segment of the air conditioner in this invention.

[0015] Figure 3 This is a flowchart illustrating the process of determining whether an air conditioner is in a high-energy-consumption state in this invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figure 1 As shown, the present invention provides a smart monitoring and analysis system for energy consumption of home central air conditioning, including: an operation monitoring module, an energy consumption assessment module, and a diagnosis and optimization module. The connection relationship between the modules is as follows: the operation monitoring module and the energy consumption assessment module are connected, and the energy consumption assessment module and the diagnosis and optimization module are connected.

[0018] Operation monitoring module: acquires the total household power consumption curve and air conditioner start / stop signals within the monitoring period; based on the air conditioner start / stop signals, it divides the independent operating period of the air conditioner from the total household power consumption curve and extracts the average power and cumulative energy consumption of the air conditioner within that period.

[0019] Considering that the total household power consumption includes the combined energy consumption of various loads such as air conditioners, lighting, and household appliances, directly using the total power consumption data for air conditioner energy consumption analysis will result in data distortion due to interference from other loads, making it impossible to accurately obtain the energy consumption characteristics of the air conditioner when it is running alone.

[0020] Meanwhile, the air conditioner start / stop signal is the core basis for defining its operating status. Based on this signal, the possible operating periods of the air conditioner can be initially identified, providing a prerequisite for the subsequent accurate division of the air conditioner's independent operating periods.

[0021] In one specific embodiment, discrete data of the total household power consumption within a monitoring period are collected by a power monitoring device deployed in the main circuit of the household power distribution. The data is preprocessed to form a curve of the total household power consumption. At the same time, the start and stop signals of the air conditioner are collected by the air conditioner controller or a dedicated sensor. The data preprocessing is a technology well known to those skilled in the art and will not be further limited or described.

[0022] Please see Figure 2As shown, based on the level changes of the air conditioner start and stop signals, the start and end times of air conditioner operation are determined, and candidate running segments are generated.

[0023] During the candidate running period, with a preset sampling interval of 1 minute to 5 minutes, the first-order difference absolute value sequence of the total household power consumption curve is calculated. This sequence reflects the degree of power change between adjacent sampling points and is used to quantify the fluctuation characteristics of the electrical load.

[0024] The moving average of the first-order difference absolute value sequence within the sliding time window is calculated to characterize the local smoothing level of the fluctuation of total household electricity consumption in the current candidate period. The arithmetic mean of the first-order difference absolute values ​​of the total household electricity consumption curve within the monitoring period is calculated as the benchmark rate of change. This benchmark rate of change represents the overall average intensity of the fluctuation of household electricity load within the monitoring period, providing a reference benchmark for judging whether the local fluctuations are stable.

[0025] The duration of the sliding time window is determined to be 3 to 10 minutes based on the fluctuation characteristics of household electricity load.

[0026] If the moving average is less than the baseline rate of change, and the duration of the candidate operating period is greater than the average duration of all candidate operating periods generated based on the air conditioner start-stop signal within the monitoring period, it indicates that the total household power consumption fluctuates smoothly during the candidate period and the air conditioner operates stably. This eliminates interference caused by short-term switching of other high-power appliances or frequent start-stop of the air conditioner. Thus, it can be determined that the air conditioner is the dominant power load during this period, and the candidate operating period is confirmed as an independent operating period. Otherwise, the operating period is a non-independent operating period.

[0027] Within a confirmed independent operating period, based on the discrete sampling point sequence of the total household power consumption curve, the arithmetic mean of the power values ​​of all sampling points is calculated as the average power of the air conditioner, which is used to characterize the typical power level of the air conditioner during this period. The cumulative energy consumption is calculated by multiplying the power values ​​of the discrete sampling points with the preset sampling interval, which is used to reflect the total electrical energy consumed by the air conditioner during this period.

[0028] Energy consumption assessment module: Based on the average power and cumulative energy consumption of the air conditioner, it determines whether the air conditioner is in a high energy consumption state by comparison; when it is determined to be in a high energy consumption state, it extracts three operating characteristic values ​​of the air conditioner in the corresponding time period: the actual operating energy efficiency ratio, the deviation value between the set temperature and the room temperature, and the daily cumulative running time of the air conditioner, and calculates the correlation between each operating characteristic value and the high energy consumption state.

[0029] Considering that the energy consumption level of residential central air conditioning is affected by various factors such as operating conditions and set parameters, it is impossible to determine whether it is in an unreasonable high energy consumption state based on a single energy consumption data. Therefore, it is necessary to establish a relative benchmark for comparative evaluation.

[0030] Meanwhile, once a high energy consumption state is confirmed, it is necessary to identify which operational characteristics are the key factors leading to high energy consumption in order to provide direction for subsequent accurate diagnosis. In addition, the correlation between operational characteristic values ​​and high energy consumption state directly determines the diagnostic priority, and the accuracy of the correlation calculation will affect the diagnostic efficiency and energy-saving control effect. Therefore, the system needs to complete the determination of high energy consumption state and correlation analysis.

[0031] Please see Figure 3 As shown, in one specific embodiment, determining whether the air conditioner is in a high energy consumption state includes: calculating the arithmetic mean of the average power of the air conditioner during all confirmed independent operating periods within the monitoring period as a power benchmark, and the arithmetic mean of the cumulative energy consumption as an energy consumption benchmark.

[0032] The average power of the air conditioner in the current independent operating segment to be judged is compared with the power benchmark, and the cumulative energy consumption of the current independent operating segment is compared with the energy consumption benchmark.

[0033] When the average power of the air conditioner is greater than the power benchmark and the cumulative energy consumption is greater than the energy consumption benchmark, it means that the air conditioner is not only operating at a power level higher than the typical level, but also that the total electricity consumption during this period is also beyond the normal range. It shows abnormality in both instantaneous intensity and cumulative total amount. Therefore, it is determined that the air conditioner is in a high energy consumption state. Conversely, it is determined that the air conditioner is in a non-high energy consumption state.

[0034] Furthermore, the method for obtaining the correlation between the various operating characteristic values ​​and the high energy consumption state is as follows: collect all independent operating segments that are determined to be in a high energy consumption state within the monitoring period to form a high energy consumption sample set.

[0035] For each sample in the high-energy-consumption sample set, extract its corresponding three operating characteristic values: the actual operating energy efficiency ratio during that period, the deviation between the set temperature and the room temperature, and the cumulative daily operating time of the air conditioner.

[0036] These three operational characteristics together constitute the core dimensions affecting air conditioner energy consumption: the actual operating energy efficiency ratio directly reflects the performance degradation or operating efficiency of the equipment itself; the deviation between the set temperature and the room temperature reflects the degree of matching between the user's temperature control setting and actual needs, directly affecting the compressor load; and the cumulative daily running time of the air conditioner represents the user's usage habits and the accumulated time of continuous system load. These three factors are analyzed from the perspectives of equipment status, setting rationality, and user behavior to address the root causes of high energy consumption.

[0037] The method for obtaining the actual operating energy efficiency ratio is as follows: directly obtain the cooling or heating data during independent operation by means of the air conditioning communication interface or by measuring the temperature difference and air volume at the air inlet and outlet, and simultaneously obtain the cumulative power consumption during the period, calculate the ratio of cooling or heating capacity to cumulative power consumption, and obtain the actual operating energy efficiency ratio.

[0038] Since high energy consumption may manifest as high instantaneous power or energy accumulation due to long-term operation, a single indicator cannot fully characterize it. Therefore, the average power of the air conditioner and the cumulative energy consumption are used as energy consumption characterization indicators. The statistical correlation coefficient between each operating characteristic value and each energy consumption characterization indicator is calculated. This coefficient represents the strength and direction of the linear relationship between the operating characteristic value and the energy consumption indicator. The larger the absolute value, the stronger the statistical correlation.

[0039] Based on the absolute value of the statistical correlation coefficient, the correlation between each operational characteristic value and the high energy consumption state is determined. Specifically, for each operational characteristic value, the absolute value of its statistical correlation coefficient with the average power of the air conditioner and the absolute value of its statistical correlation coefficient with the cumulative energy consumption are obtained. The statistical correlation coefficients can be calculated using statistical methods such as Pearson correlation coefficient and Spearman's rank correlation coefficient, which will not be further limited or elaborated upon.

[0040] For each operational characteristic value, the absolute values ​​of its two corresponding statistical correlation coefficients are compared. Since the purpose of the correlation is to measure the maximum potential correlation strength between the characteristic and the overall state of high energy consumption, in order not to underestimate its impact, the larger one is selected as the correlation between the operational characteristic value and the state of high energy consumption.

[0041] Diagnostic optimization module: Based on various correlations and the current outdoor temperature, it generates a diagnostic priority list for high energy consumption root causes; according to the diagnostic priority list, it performs preset short-term operation tests on the primary diagnostic root causes in the list, verifies the diagnostic conclusions based on the changes in energy consumption before and after the test, and generates energy-saving control signals accordingly.

[0042] Outdoor temperature is a key environmental factor affecting the energy consumption of air conditioning operation. It directly determines the size of the system's heat load and operating conditions, thereby changing the weight of factors such as temperature setting deviation, equipment operating efficiency, and usage time on total energy consumption. Under different outdoor temperatures, the main causes of high energy consumption may differ. If diagnosis is made directly by sorting by correlation without considering outdoor temperature, the diagnostic efficiency may be reduced.

[0043] Meanwhile, the accuracy of identifying the root causes of high energy consumption needs to be verified through actual operation tests. Reliability of diagnostic conclusions cannot be guaranteed solely based on correlation analysis. Furthermore, energy-saving control signals need to have specific and executable parameters to achieve precise optimization of the air conditioning operating status. Therefore, a closed-loop process of prioritization, testing and verification, and parameter solidification is required to complete the diagnostic optimization.

[0044] In one specific embodiment, the method for obtaining the diagnostic priority list is as follows: the current outdoor temperature is collected by a temperature sensor, the average outdoor temperature within the monitoring period is calculated as a reference outdoor temperature, and the current outdoor temperature is compared with the reference outdoor temperature.

[0045] If the current outdoor temperature is higher than the reference outdoor temperature, for example, if the current temperature is higher than the average temperature of the monitoring period, it indicates that the air conditioning system is under a significantly increased external heat load. Under this condition, since the compressor load rate is sensitive to the temperature setting, the deviation between the set temperature and the actual indoor temperature becomes the dominant factor affecting overall energy consumption. Therefore, the deviation between the set temperature and the room temperature is placed at the top of the diagnostic priority list, and the remaining two operating characteristic values ​​are sorted from high to low according to their correlation.

[0046] If the current outdoor temperature is less than or equal to the reference outdoor temperature, it means that the outdoor ambient temperature is within the normal or mild range. The high energy consumption of the air conditioner is more likely due to low equipment operating efficiency or user usage habits. The three operating characteristic values ​​are sorted from high to low according to their correlation to generate a diagnostic priority list. This list is used to guide the system to check and verify potential high energy consumption sources one by one in order of probability.

[0047] Furthermore, the step of performing a preset short-term operational test on the primary diagnostic root cause in the diagnostic priority list includes: identifying the primary diagnostic root cause from the diagnostic priority list, wherein the primary diagnostic root cause is one of three operational characteristic values.

[0048] Based on the primary diagnostic root cause, the corresponding test actions are performed as follows: If the primary diagnostic root cause is the deviation between the set temperature and the room temperature, it indicates that the preliminary diagnosis is that the unreasonable temperature setting is the main cause of high energy consumption. Then, the preset temperature offset is increased or decreased based on the current air conditioner set temperature (the preset temperature offset is usually +1℃ to +2℃ in cooling mode and -1℃ to -2℃ in heating mode), and the operation is maintained within the preset test window.

[0049] If the primary diagnostic root cause is the daily cumulative running time of the air conditioner, it means that the preliminary diagnosis is that the excessive running time of the air conditioner is the main cause of high energy consumption. In this case, the current daily cumulative running time of the air conditioner will be reduced by a preset time offset (usually by 0.5 hours to 1 hour), and the operation will be maintained within the preset test window.

[0050] If the primary diagnostic root cause is the actual operating energy efficiency ratio, it indicates that the initial diagnosis is that the low operating efficiency of the air conditioning equipment itself is the main cause of high energy consumption. In this case, the air conditioning operating mode should be switched to the preset energy-saving mode (such as the low-frequency operation mode of the inverter air conditioner, the alternating operation mode of the air supply mode and the cooling / heating mode, etc.), and the operation should be maintained within the preset test window.

[0051] Within the preset test window, the average power consumption during the independent operation of the air conditioner is calculated based on the total household power consumption curve as the test energy consumption benchmark.

[0052] The average power during the baseline period before the test is obtained as a comparison benchmark. The baseline period before the test is the independent operation period of the adjacent air conditioner before the test.

[0053] It should be noted that the preceding adjacent independent air conditioner operation period must satisfy the following conditions: the outdoor temperature difference with the preset test window is within a preset range, and the indoor set temperature is the same. If this condition is not met, the previous period is selected until the condition is met. The preset range can be set to within ±2℃, and as another implementation method, the implementer can set it according to the actual situation.

[0054] The difference between the test energy consumption baseline and the comparison baseline is calculated as the change in energy consumption before and after the test.

[0055] Furthermore, the step of verifying the diagnostic conclusion based on the change in energy consumption before and after the test, and generating an energy-saving control signal accordingly, includes: extracting the calculated change in energy consumption before and after the test; when the change in energy consumption before and after the test is less than zero, it indicates that after the adjustment for the primary diagnostic root cause is performed, the average operating power of the air conditioner has indeed decreased, proving that the diagnostic root cause is effective, and determining that the diagnostic conclusion of the primary diagnostic root cause is effective.

[0056] Based on the verified diagnostic conclusions, the adjustment parameters executed within the test window are solidified into permanent operating parameters, generating energy-saving control signals.

[0057] The energy-saving control signal is an instruction set containing specific control parameters. The specific control parameters are determined based on the verified primary diagnostic root cause type: if the primary diagnostic root cause is the deviation between the set temperature and the room temperature, then the specific control parameter is the set temperature value adjusted within the test window.

[0058] If the primary diagnostic cause is the cumulative daily operating time of the air conditioner, then the specific control parameter is the upper limit of the daily operating time adjusted within the test window.

[0059] If the primary diagnostic cause is the actual operating energy efficiency ratio, then the specific control parameter is the preset energy-saving mode identifier switched within the test window.

[0060] The energy-saving control signal is sent to the air conditioner controller through the communication interface to continuously execute the specific control parameters.

[0061] Since the aforementioned short-term tests have verified the energy-saving effect of this adjustment parameter under the current operating conditions, in order to make the energy-saving effect sustainable, this instruction will be generated and issued to guide the air conditioner to automatically operate according to this optimized parameter under similar conditions in the future.

[0062] When the change in energy consumption before and after the test is greater than or equal to zero, it indicates that the adjustment measures taken have not produced the expected energy-saving effect. The root cause of the diagnosis may not be the main cause of the current high energy consumption state, or the adjustment range may be inappropriate. The diagnosis conclusion of the primary root cause is invalid, and the primary root cause of the diagnosis is removed from the diagnosis priority list.

[0063] Based on the correlation of the remaining operational feature values ​​and the current outdoor temperature, a new diagnostic priority list is generated as the updated diagnostic priority list, and short-term operational tests are re-executed based on the updated diagnostic priority list.

[0064] It should be noted that if the updated diagnostic priority list is empty, or all running feature values ​​have been verified as invalid, the diagnostic optimization process will stop and a prompt message will be output.

[0065] In summary, this invention first uses an operation monitoring module to accurately divide the independent operating periods of the air conditioner based on start / stop signals and power curve fluctuation analysis, and extracts the average power and cumulative energy consumption; then, an energy consumption assessment module uses a dynamic benchmark to determine high energy consumption states, and analyzes the correlation coefficients of actual operating energy efficiency ratio, set temperature deviation, and daily cumulative operating time to quantify their correlation; next, a diagnostic optimization module uses the current outdoor temperature to generate a diagnostic priority list, and performs short-term operating tests on the primary root causes in sequence to verify their effectiveness; finally, based on the verification results, energy-saving control instructions are generated and solidified.

[0066] This system automates the entire process from precise data extraction and intelligent state assessment to root cause closed-loop diagnosis and automatic generation of optimization strategies, improving the accuracy of high-energy-consumption problem diagnosis and the effectiveness of control measures.

[0067] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0068] Those skilled in the art will recognize that the algorithmic steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0069] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0070] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0071] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart monitoring and analysis system for energy consumption of residential central air conditioning, characterized in that, include: Run the monitoring module to obtain the total household power consumption curve and air conditioner start / stop signals within the monitoring period; Based on the air conditioner start / stop signal, the independent operating period of the air conditioner is divided from the total household power consumption curve, and the average power and cumulative energy consumption of the air conditioner during the period are extracted. The energy consumption assessment module determines whether the air conditioner is in a high energy consumption state by comparing the average power and cumulative energy consumption of the air conditioner. When it is determined to be in a high energy consumption state, it extracts three operating characteristic values ​​of the air conditioner in the corresponding time period: the actual operating energy efficiency ratio, the deviation between the set temperature and the room temperature, and the cumulative daily running time of the air conditioner, and calculates the correlation between each operating characteristic value and the high energy consumption state. The diagnostic optimization module generates a diagnostic priority list for high energy consumption sources based on various correlations and the current outdoor temperature. According to the diagnostic priority list, it performs preset short-term operation tests on the primary diagnostic sources in the list, verifies the diagnostic conclusions based on the changes in energy consumption before and after the test, and generates energy-saving control signals accordingly.

2. The intelligent monitoring and analysis system for energy consumption of a residential central air conditioning system as described in claim 1, characterized in that, The method for dividing the independent operation period of the air conditioner is as follows: Based on the air conditioner start and stop signals, the start and end times of air conditioner operation are determined, and candidate operation segments are generated. During the candidate runtime period, the first-order difference absolute value sequence of the total household power consumption curve is calculated at a preset sampling interval; Calculate the moving average of the first-order difference absolute value sequence within the sliding time window, and calculate the arithmetic mean of the first-order difference absolute values ​​of the total household power consumption curve within the monitoring period as the baseline rate of change. If the moving average is less than the baseline rate of change, and the duration of the candidate running segment is greater than the average duration of all candidate running segments generated based on the air conditioning start / stop signal within the monitoring period, then the candidate running segment is identified as an independent running segment.

3. The intelligent monitoring and analysis system for energy consumption of residential central air conditioning as described in claim 1, characterized in that, The extraction of the average power and cumulative energy consumption of the air conditioners during this period includes: Within a confirmed independent operating period, the arithmetic mean of the power values ​​of all sampling points is calculated as the average power of the air conditioner based on the discrete sampling point sequence of the total household power consumption curve. Within a confirmed independent operating period, the cumulative energy consumption is calculated by multiplying the power values ​​of discrete sampling points with the preset sampling interval.

4. The intelligent monitoring and analysis system for energy consumption of a residential central air conditioning system as described in claim 1, characterized in that, The determination of whether the air conditioner is in a high-energy-consumption state includes: The arithmetic mean of the average power of the air conditioners during all confirmed independent operating periods within the monitoring period is used as the power benchmark, and the arithmetic mean of the cumulative energy consumption is used as the energy consumption benchmark. The average power of the air conditioner in the current independent operating segment to be judged is compared with the power benchmark, and the cumulative energy consumption of the current independent operating segment is compared with the energy consumption benchmark. When the average power of the air conditioner is greater than the power benchmark and the cumulative energy consumption is greater than the energy consumption benchmark, the air conditioner is determined to be in a high energy consumption state.

5. The intelligent monitoring and analysis system for energy consumption of a residential central air conditioning system as described in claim 1, characterized in that, The method for obtaining the correlation between the various operational characteristic values ​​and the high energy consumption state is as follows: Collect all independent running segments that are identified as high-energy-consumption states within the monitoring period to form a high-energy-consumption sample set; For each sample in the high-energy-consumption sample set, extract its corresponding three operating characteristic values: the actual operating energy efficiency ratio during that period, the deviation between the set temperature and the room temperature, and the cumulative daily operating time of the air conditioner. Using the average power and cumulative energy consumption of air conditioners as energy consumption indicators, the statistical correlation coefficient between each operational characteristic value and each energy consumption indicator is calculated. The correlation between each operational characteristic value and the high energy consumption state is determined based on the absolute value of the statistical correlation coefficient.

6. The intelligent monitoring and analysis system for energy consumption of a residential central air conditioning system as described in claim 5, characterized in that, The determination of the correlation between each operational characteristic value and the high energy consumption state based on the absolute value of the statistical correlation coefficient includes: For each operational characteristic value, the absolute value of its statistical correlation coefficient with the average power of the air conditioner and the absolute value of its statistical correlation coefficient with the cumulative energy consumption are obtained. For each operational characteristic value, compare the absolute values ​​of its two corresponding statistical correlation coefficients, and select the larger one as the correlation degree between the operational characteristic value and the high energy consumption state.

7. The intelligent monitoring and analysis system for energy consumption of residential central air conditioning as described in claim 1, characterized in that, The method for obtaining the diagnostic priority list is as follows: Obtain the current outdoor temperature and the average outdoor temperature within the monitoring period as the reference outdoor temperature, and compare the current outdoor temperature with the reference outdoor temperature. If the current outdoor temperature is higher than the reference outdoor temperature, the deviation between the set temperature and the room temperature will be placed at the top of the diagnostic priority list, and the remaining two operating characteristic values ​​will be sorted from high to low according to their correlation. If the current outdoor temperature is less than or equal to the reference outdoor temperature, the three operational characteristic values ​​will be sorted from high to low according to their correlation to generate a diagnostic priority list.

8. The intelligent monitoring and analysis system for energy consumption of a residential central air conditioning system as described in claim 1, characterized in that, The step of performing a preset short-term operational test on the primary diagnostic root causes in the diagnostic priority list includes: Identify the primary diagnostic root cause from the diagnostic priority list, wherein the primary diagnostic root cause is one of three operational characteristics; Based on the primary diagnostic root cause, perform the corresponding test actions as follows: If the primary diagnostic cause is the deviation between the set temperature and the room temperature, then increase or decrease the preset temperature offset based on the current air conditioner set temperature, and maintain operation within the preset test window; If the primary diagnostic root cause is the cumulative daily running time of the air conditioner, then reduce the current cumulative daily running time of the air conditioner by a preset time offset and maintain operation within the preset test window; If the primary diagnostic cause is the actual operating energy efficiency ratio, then switch the air conditioner's operating mode to the preset energy-saving mode and maintain operation within the preset test window; Within the preset test window, the average power consumption during the independent operation of the air conditioner is calculated based on the total household power consumption curve as the test energy consumption benchmark. The average power during the baseline period before the test is used as a comparison benchmark. The baseline period before the test is the independent operation period of the adjacent air conditioner before the test. The difference between the test energy consumption baseline and the comparison baseline is calculated as the change in energy consumption before and after the test.

9. The intelligent monitoring and analysis system for energy consumption of a residential central air conditioning system as described in claim 1, characterized in that, The process of verifying diagnostic conclusions based on changes in energy consumption before and after testing, and generating energy-saving control signals accordingly, includes: Extract the calculated change in energy consumption before and after the test. When the change in energy consumption before and after the test is less than zero, the diagnosis of the primary diagnostic root cause is deemed valid. Based on the verified diagnostic conclusions, the adjustment parameters executed within the test window are solidified into permanent operating parameters, generating energy-saving control signals. When the change in energy consumption before and after the test is greater than or equal to zero, the diagnosis conclusion of the primary diagnostic root cause is invalid and the primary diagnostic root cause is removed from the diagnostic priority list. Based on the correlation of the remaining operating feature values ​​and combined with the current outdoor temperature, a new diagnostic priority list is generated as the updated diagnostic priority list. The short-run tests will be re-executed based on the updated diagnostic priority list.

10. The intelligent monitoring and analysis system for energy consumption of a residential central air conditioning system as described in claim 9, characterized in that, The energy-saving control signal is a set of instructions containing specific control parameters.

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