Remote monitoring system and method based on central air conditioner

Abstract

The invention relates to the technical field of control systems, in particular to a remote monitoring system and method based on a central air conditioner, and the system comprises a causal chain analysis module, a dynamic weight adjustment module, a multi-level response module, a multi-level data analysis module and a priority control module. According to the method, the abnormal state recognition capability is improved by analyzing the change of the operating parameters of the central air conditioner, accurately positioning the abnormal path and screening the key parameters, the resource allocation is optimized by combining dynamic weight adjustment and real-time load data, the resource waste and uneven distribution are avoided, the regulation and control flexibility is enhanced by the grading threshold matching response action, and the control accuracy is improved. Regional data analysis defines the resource optimization direction, priority dynamic adjustment is combined with real-time analysis, key instruction quick response is achieved, and the adaptability and accuracy of a system in a complex environment are optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of control systems, and in particular to a remote monitoring system and method based on central air conditioning. Background Art

[0002] The field of remote monitoring technology includes systematic technologies for real-time monitoring of the operating status of central air conditioners, data collection and processing, remote control, and central air conditioner status diagnosis. The core content of this technical field includes the acquisition and transmission of hardware central air conditioner signals, network remote control based on communication protocols, and the generation of analysis and execution strategies for the collected data. The overall architecture of the remote monitoring system usually consists of terminal acquisition central air conditioners, communication interfaces, data management platforms, and user operation terminals. The central air conditioner operating status data is collected through the hardware layer, and the data is transmitted to the central control platform using network protocols. The platform generates control commands based on preset strategies and sends them to the central air conditioner, thereby achieving comprehensive monitoring and management of the target central air conditioner operation. This technology is widely used in many fields such as industrial automation, energy management, intelligent buildings, and central air conditioner operation and maintenance, and is constantly developing in a more efficient and accurate direction.

[0003] Among them, the remote monitoring system based on central air conditioning refers to a technical solution for monitoring and regulating the operating status of central air conditioning through dedicated data collection central air conditioning and remote communication network. This technical solution covers the following technical contents: collecting the core parameters of central air conditioning operation through multiple types of sensors installed on the central air conditioning (such as temperature sensors, humidity sensors, airflow pressure sensors, etc.); transmitting the collected data to the remote server based on TCP / IP protocol or other communication protocols; analyzing and judging the collected data through preset calculation and data analysis rules, generating control instructions including temperature adjustment, wind speed control and switch status adjustment; transmitting the instructions to the execution central air conditioning of the central air conditioning through remote communication to complete the adjustment of its operating status. The system is implemented as a whole with a distributed sensing and centralized management architecture, and specifically uses a variety of technical means such as network communication, data transmission and command execution to coordinate the remote monitoring and adjustment of central air conditioning.

[0004] Existing technologies mostly rely on single parameter fluctuation judgment in the analysis of abnormal states, lacking in-depth analysis of the complex relationship between parameters, which can easily lead to inaccurate positioning of abnormal states. Resource allocation strategies are mainly based on static rules, which are difficult to cope with the demand for real-time changes in load during operation, and may cause insufficient resource supply or overload in some areas. The generation of response instructions fails to fully combine the current operating status and abnormal conditions, and the single strategy processing may reduce the pertinence and effectiveness of regulation. Insufficient analysis of the correlation between regional resource allocation and central air-conditioning load status results in the inability of resource optimization schemes to accurately reflect the operating characteristics of central air-conditioning. Under high-load environments, the system cannot dynamically adjust the priority ranking of key instructions, which may cause delayed execution of key tasks or confusion in the execution order, affecting the overall central air-conditioning operation efficiency and reliability. These problems limit the applicability and execution efficiency of existing technologies in complex and changeable application scenarios. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a remote monitoring system and method based on central air conditioning.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A remote monitoring system based on central air conditioning comprises: The causal chain analysis module collects the central air-conditioning operating parameters, analyzes the change amplitude and trend of each parameter, sets the change amplitude threshold, screens the parameters with large change amplitude, analyzes the abnormal path according to the parameter correlation, locates the nodes with large change intensity in the path, extracts the nodes and their associated data, and generates the abnormal path data set; The dynamic weight adjustment module extracts the central air-conditioning load change rate based on the abnormal path data set, analyzes the difference between the fluctuation range and the current state, reallocates the weight values ​​of key parameters, records the trend of weight changes, and generates optimized parameter weight distribution data; The multi-level response module sets the threshold range of the operation status classification parameters based on the optimized parameter weight distribution data, matches the corresponding response actions, calculates the range value of the key parameter fluctuation impact, extracts and combines the adjustment instructions, and generates an operation adjustment instruction set; The multi-level data analysis module analyzes the regional operation data and the central air conditioning distribution ratio based on the operation adjustment instruction set, calculates the regional resource consumption deviation value, marks the area with large load distribution deviation, records the resource allocation characteristics of each area, and generates optimized area list data; The priority control module analyzes the difference between the load state and the operation mode based on the optimization area list data and the current resource pressure, dynamically adjusts the instruction priority, analyzes the operation optimization instructions according to the priority, and generates execution instruction sequence data.

[0007] As a further solution of the present invention, the abnormal path data set includes parameter change amplitude values, abnormal path node locations and node association strength information; the optimized parameter weight distribution data includes reallocated parameter weight values, distribution diagrams after weight adjustment and weight evolution trend records; the operation adjustment instruction set includes preset response actions, dynamic parameter adjustment instructions and load change adjustment plans; the optimized area list data includes area load deviation identifiers, area resource allocation characteristic indicators and area resource consumption distribution information; the execution instruction sequence data includes an instruction sorting priority list, an optimized operation instruction list and load adjustment instruction analysis results.

[0008] As a further solution of the present invention, the causal chain analysis module includes a parameter screening submodule, a path positioning submodule and a data generation submodule; The parameter screening submodule extracts the change data of each parameter based on the central air-conditioning operation parameters, builds a parameter fluctuation analysis benchmark, calculates the fluctuation range and trend of each parameter, sets the threshold range based on the fluctuation range distribution value, screens the parameters whose fluctuation range exceeds the set range, and generates a key parameter set; Based on the key parameter set, the path positioning submodule analyzes the correlation matrix between parameters, identifies the parameter paths with obvious correlation, tracks the parameter nodes with abnormal changes in the path, selects the nodes with significant change intensity according to the distribution law of the parameter change intensity, extracts the nodes and their associated data, and generates abnormal path node data; The data generation submodule parses the multi-dimensional data of nodes and associated parameters based on the abnormal path node data, constructs path data, and integrates the node parameters and their associated data sets to generate an abnormal path data set.

[0009] As a further solution of the present invention, the dynamic weight adjustment module includes a load rate extraction submodule, a fluctuation difference analysis submodule and a weight distribution optimization submodule; The load rate extraction submodule extracts the time series data of the load parameters based on the abnormal path data set, calculates the load change rate value for the differentiated time intervals, analyzes the rate change trend and constructs a trend model, classifies and labels the load rate according to the model data, and generates load change rate data; The fluctuation difference analysis submodule extracts the current central air-conditioning operation state parameters and the historical operation state parameters based on the load change rate data, calculates the difference value, constructs a statistical distribution diagram of the fluctuation range, matches the degree of operation state deviation according to the distribution diagram data, identifies the deviation amount of the load state change, and generates load state difference data; The weight distribution optimization submodule establishes a corresponding relationship between the weight value of the key parameter and the deviation based on the load state difference data, recalculates the parameter weight value, records the time series of the weight change value during the adjustment process, analyzes the optimized weight distribution relationship, and generates optimized parameter weight distribution data.

[0010] As a further solution of the present invention, the difference value calculation formula is specifically: ; Among them, D 3 Represents the difference value, R 3i represents the actual value of the central air-conditioning operating state parameter at the i-th time point, H 3i represents the reference value of the historical operating status parameter at the i-th time point, W 3i represents the weight factor at the i-th time point, n 3 Represents the total number of sampling time points.

[0011] As a further solution of the present invention, the multi-level response module includes a hierarchical parameter setting submodule, a fluctuation range calculation submodule, and an adjustment instruction generation submodule; The hierarchical parameter setting submodule extracts the weight values ​​of key parameters based on the optimized parameter weight distribution data, establishes an operating state parameter model in combination with the central air-conditioning operating state data, sets the hierarchical intervals of the operating state for the parameters in the model, generates a hierarchical threshold range table based on the interval range, and obtains an operating state hierarchical threshold table; The fluctuation range calculation submodule extracts the real-time fluctuation data of key parameters based on the operation status classification threshold table, constructs a parameter fluctuation time series model, calculates the deviation degree between the fluctuation data and the classification threshold range, analyzes the impact of the fluctuation on the operation status of the central air conditioner, and generates parameter fluctuation impact range data; The adjustment instruction generation submodule extracts the central air-conditioning operation parameter adjustment rules based on the parameter fluctuation influence range data, analyzes the correlation between the influence range of the fluctuation data and the adjustment rules, identifies the adjustment instruction sequence, integrates the instruction sequence and optimizes its execution logic, and generates an operation adjustment instruction set.

[0012] As a further solution of the present invention, the calculation formula of the deviation degree value is specifically: ; Among them, P 4 Represents the deviation value, V 4j represents the jth sampling point value of real-time fluctuation data, B 4j represents the reference value of the jth sampling point in the classification threshold range, W 4j Represents the weight factor of the jth sampling point, n 4 Represents the total number of sampling points.

[0013] As a further solution of the present invention, the multi-level data analysis module includes a data distribution analysis submodule, a resource deviation calculation submodule and an optimization list generation submodule; The data distribution analysis submodule extracts the operation data of each area based on the operation adjustment instruction set, analyzes the central air conditioning distribution ratio, constructs a mapping matrix between the operation data and the central air conditioning distribution, calculates the ratio value of the regional central air conditioning resource distribution according to the mapping relationship, and generates the regional central air conditioning distribution data; The resource deviation calculation submodule extracts the regional resource consumption value based on the regional central air conditioning distribution data, compares the central air conditioning distribution ratio, calculates the deviation between the resource consumption data and the central air conditioning ratio, analyzes the regional distribution characteristics of the deviation, marks the area where the deviation exceeds the threshold, and generates regional resource deviation data; The optimization list generation submodule classifies and analyzes the resource allocation characteristics of the marked area based on the regional resource deviation data, reorganizes the regional resource allocation data in combination with the central air-conditioning distribution and resource consumption characteristics, optimizes resource allocation and generates optimized regional list data.

[0014] As a further solution of the present invention, the priority control module includes a resource pressure assessment submodule, a load difference analysis submodule and an instruction priority adjustment submodule; The resource pressure assessment submodule extracts resource consumption related parameters based on the optimized regional inventory data, calculates regional resource utilization, builds a mapping model of resource allocation and pressure, analyzes the matching between resource distribution data and the mapping model, assesses regional pressure differences, and generates resource pressure data; The load difference analysis submodule extracts the dynamic parameters of the load state and establishes a state distribution model based on the resource pressure data, compares the current operation mode with the state distribution model, calculates the deviation value, identifies the matching difference of the operation mode based on the deviation value distribution characteristics, and generates load state difference data; The instruction priority adjustment submodule calls the adjustment instructions in the optimization area list data based on the load state difference data, builds a priority sorting model according to the instruction relevance, adjusts the instruction priority sorting and optimizes the execution logic based on the load difference analysis results, and generates execution instruction sequence data.

[0015] A remote monitoring method based on central air conditioning comprises the following steps: S1: By collecting the central air-conditioning operating parameters, analyzing the amplitude and trend of each parameter change, setting the change amplitude threshold, screening the parameters with large change amplitude, analyzing the abnormal path according to the parameter correlation, locating the nodes with large change intensity in the path, and generating the abnormal path data set; S2: Based on the abnormal path data set, extract the central air-conditioning load change rate, analyze the difference between the fluctuation range and the current state, record the trend of weight change, and generate optimized parameter weight distribution data; S3: Based on the optimized parameter weight distribution data, set the threshold range of the operation status classification parameter, match the corresponding response action, calculate the range value of the key parameter fluctuation impact, and generate an operation adjustment instruction set; S4: Based on the operation adjustment instruction set, the regional operation data and the central air conditioning distribution ratio are analyzed, the regional resource consumption deviation value is calculated, the resource allocation characteristics of each region are recorded, and the optimized regional list data is generated; S5: Based on the optimization area list data and in combination with the current resource pressure, the difference between the load state and the operation mode is analyzed, the instruction priority is dynamically adjusted, and the execution instruction sequence data is generated.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by analyzing the changes in the operating parameters of the central air-conditioning, the abnormal path is accurately located and the key parameters are screened, the ability to identify abnormal states is improved, dynamic weight adjustment is combined with real-time load data to optimize resource allocation, avoid resource waste and uneven distribution, hierarchical threshold matching response actions enhance the flexibility of regulation, regional data analysis clarifies the direction of resource optimization, dynamic priority adjustment is combined with real-time analysis, rapid response to key instructions is achieved, and the adaptability and accuracy of the system in complex environments are optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use 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 ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 is a system flow chart of the present invention; Figure 2 It is a submodule flow chart of the present invention; Figure 3 It is a flow chart of the cause-effect chain analysis module of the present invention; Figure 4 This is a flow chart of the dynamic weight adjustment module of the present invention; Figure 5 is a flow chart of a multi-stage response module of the present invention; Figure 6 It is a flow chart of the multi-level data parsing module of the present invention; Figure 7 It is a flow chart of the priority control module of the present invention; Figure 8 The figure is a flow chart of the steps of the method of the present invention. DETAILED DESCRIPTION

[0019] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0020] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0021] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0022] In the embodiments of the present invention, sometimes the subscripts such as W 1 It may be written in non-subscript form such as W1. When the difference is not emphasized, the meaning is the same.

[0023] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0024] See also Figure 1 and Figure 2 , a remote monitoring system based on central air conditioning includes: The causal chain analysis module collects the central air-conditioning operating parameters, analyzes the change amplitude and trend of each parameter, sets the change amplitude threshold, filters the parameters with large change amplitude, analyzes the abnormal path according to the parameter correlation, locates the nodes with large change intensity in the path, extracts the nodes and their associated data, and generates the abnormal path data set; The dynamic weight adjustment module extracts the central air-conditioning load change rate based on the abnormal path data set, analyzes the difference between the fluctuation range and the current state, reallocates the weight values ​​of key parameters, records the trend of weight changes, and generates optimized parameter weight distribution data; The multi-level response module sets the threshold range of the operating status classification parameters based on the optimized parameter weight distribution data, matches the corresponding response actions, calculates the range value affected by the fluctuation of key parameters, extracts and combines the adjustment instructions, and generates an operating adjustment instruction set; The multi-level data analysis module analyzes the regional operation data and the central air conditioning distribution ratio based on the operation adjustment instruction set, calculates the regional resource consumption deviation value, marks the area with large load distribution deviation, records the resource allocation characteristics of each area, and generates optimized area list data; The priority control module is based on the optimization area list data, combined with the current resource pressure, analyzes the difference between the load status and the operation mode, dynamically adjusts the instruction priority, runs the optimization instructions according to the priority analysis, and generates the execution instruction sequence data.

[0025] The abnormal path data set includes parameter change amplitude values, abnormal path node locations, and node association strength information. The optimized parameter weight distribution data includes the reallocated parameter weight values, the distribution diagram after weight adjustment, and the weight evolution trend record. The operation adjustment instruction set includes preset response actions, dynamic parameter adjustment instructions, and load change adjustment plans. The optimized area list data includes area load deviation identification, area resource allocation characteristic indicators, and area resource consumption distribution information. The execution instruction sequence data includes the instruction sorting priority list, the optimization operation instruction list, and the load adjustment instruction analysis results.

[0026] See also Figure 3 and Figure 2 ,The causal chain analysis module includes parameter screening submodule, path positioning submodule and data generation submodule; The parameter screening submodule extracts the change data of each parameter based on the central air-conditioning operation parameters, builds a parameter fluctuation analysis benchmark, calculates the fluctuation range and trend of each parameter, sets the threshold range based on the fluctuation range distribution value, screens the parameters whose fluctuation range exceeds the set range, and generates a key parameter set; The extracted operating data is partitioned and analyzed, and the fluctuation data of different parameter time periods are discretized using the interval division method. The frequency distribution statistics of the change interval of each parameter are performed, and the numerical amplitude in each interval is recorded. The fluctuation amplitude of each interval is calculated by applying the difference calculation formula to the statistical results of the interval data. The upper and lower limits of the fluctuation amplitude are screened by setting fixed thresholds, and the screened data constitute the key parameter set.

[0027] The path positioning submodule is based on the key parameter set. By analyzing the correlation matrix between parameters, it identifies the parameter paths with obvious correlation, tracks the parameter nodes with abnormal changes in the path, selects the nodes with significant change intensity according to the distribution law of parameter change intensity, extracts the nodes and their associated data, and generates abnormal path node data; Based on the key parameter set analysis, the correlation matrix between parameters is calculated according to the formula ; Calculate the correlation between parameters.

[0028] In the formula, R represents the correlation coefficient between parameters, P i and Q i are the values ​​of the two parameters at the i-th time point, and is the mean of the corresponding parameter, and n is the number of time points.

[0029] The correlation is calculated by dividing the covariance of the parameters at the time point by the product of the standard deviation. The covariance reflects the degree to which the two parameters change together. The standard deviation is used to standardize the distribution of the parameters. By monitoring the change data of a central air-conditioning operating parameter in a day, assuming that the sampling points are 1440 points, the mean of parameter A is 35, and the mean of parameter B is 45, the calculation formula is as follows: ; Substituting the calculated results into the formula, the correlation matrix value is obtained.

[0030] The data generation submodule parses the multi-dimensional data of nodes and associated parameters based on the abnormal path node data, constructs the path data, and integrates the node parameters and their associated data sets to generate the abnormal path data set; The multidimensional data of nodes and associated parameters are parsed through abnormal path node data. The correlation evaluation method between nodes is adopted to structure the data into a network model. The shortest path is calculated by the association relationship between path nodes. The evaluation basis of node strength is the sum of the weights of each associated path. Nodes with high strength are extracted and data integration is performed to finally generate an abnormal path data set.

[0031] See also Figure 4 and Figure 2 ,The dynamic weight adjustment module includes a load rate extraction submodule, a fluctuation difference analysis submodule, and a weight distribution optimization submodule; The load rate extraction submodule extracts the time series data of load parameters based on the abnormal path data set, calculates the load change rate value for differentiated time intervals, analyzes the rate change trend and builds a trend model, classifies and labels the load rate according to the model data, and generates load change rate data; The discrete time series analysis method is applied to divide the extracted data into differentiated time intervals. By calculating the difference in load value changes in each time interval, the load change rate is calculated using the formula and the change rate is trend fitted. The time series data is divided into fixed intervals, and the rate values ​​in the interval are classified and labeled according to the rate change fitting model to generate load change rate data.

[0032] The fluctuation difference analysis submodule extracts the current central air-conditioning operating state parameters and historical operating state parameters based on the load change rate data, calculates the difference value, constructs a statistical distribution diagram of the fluctuation range, matches the operating state deviation degree according to the distribution diagram data, identifies the deviation amount of the load state change, and generates load state difference data; The specific formula for calculating the difference value is: ; Among them, D 3 Represents the difference value, R 3i represents the actual value of the central air-conditioning operating state parameter at the i-th time point, H 3i represents the reference value of the historical operating status parameter at the i-th time point, W 3i represents the weight factor at the i-th time point, n 3 Represents the total number of sampling time points.

[0033] The formula is used to calculate the difference value. The deviation value between the current central air-conditioning operating status parameters and the historical operating status parameters is calculated by weighted calculation, and the deviation value is normalized to obtain the comprehensive difference value.

[0034] Formula parameter description: D 3 is the final difference value, indicating the overall deviation between the current state and the historical state; R 3i is the current central air-conditioning operating status parameter value at the i-th time point, which is collected in real time by the central air-conditioning operating data monitoring system. The unit is the measurement unit of the operating parameter, such as temperature (℃) and power (kW); H 3i is the historical operating status reference value at the i-th time point, which is calculated by the statistical mean of historical data, such as the historical temperature mean and the historical power mean; W 3i is the weight factor of the i-th time point. The weight value is evaluated according to the importance of the time point in the central air-conditioning operation cycle. The calculation of the weight value is related to the priority of the operation task, and the value range is 0 to 1; n 3 is the total number of sampling time points, which is obtained by dividing the central air-conditioning operation cycle, and the unit is the number of time points; The summation symbol indicates that the deviation values ​​at all time points are accumulated, the absolute value symbol indicates that the deviation value is forward processed, and the square root symbol indicates that the final deviation value is normalized.

[0035] Specific calculation process: The monitoring cycle of central air conditioning operating parameters is one day, and the sampling time point is every hour within 24 hours, so the number of sampling points is n. 3 =24. Assume that the operating state parameters of the central air conditioner on a certain day are (Unit: °C), historical operating status parameters are (Unit: °C), the weight factor of the time point is .

[0036] Calculate the deviation value item by item: ; ; ; ; ; Add up all the deviations: ; Calculate the normalized difference value: ; The result shows that the comprehensive deviation value between the current central air-conditioning operating status and the historical status is 1.13, which reflects the overall deviation degree of the central air-conditioning operating status and can be used to evaluate the deviation trend or abnormal situation of the operating status.

[0037] The weight distribution optimization submodule establishes a corresponding relationship between the weight value of the key parameter and the deviation based on the load state difference data, recalculates the parameter weight value, records the time series of the weight change value during the adjustment process, analyzes the optimized weight distribution relationship, and generates optimized parameter weight distribution data; The weight value is dynamically adjusted in combination with the statistical distribution of the deviation, and the weighted distribution update model is applied to record the weight change value during the adjustment process. The strength relationship between the parameters is calculated by association, and the optimized parameter distribution results are extracted from the updated weight value, ultimately forming the optimized parameter weight distribution data.

[0038] See also Figure 5 and Figure 2 ,The multi-level response module includes a hierarchical parameter setting submodule, a fluctuation range calculation submodule, and an adjustment instruction generation submodule; The hierarchical parameter setting submodule extracts the weight values ​​of key parameters based on the optimized parameter weight distribution data, establishes an operating status parameter model in combination with the central air-conditioning operating status data, sets the hierarchical intervals of the operating status for the parameters in the model, generates a hierarchical threshold range table based on the interval range, and obtains an operating status hierarchical threshold table; An operating status parameter model is established by combining the real-time operating status data of the central air conditioner. The model uses a grading method to divide the parameter operating status into several intervals, calculates the upper and lower limits of the parameters for each interval, sets the grading threshold to generate a grading threshold range table, and forms the final operating status grading threshold table.

[0039] The fluctuation range calculation submodule extracts the real-time fluctuation data of key parameters based on the operation status classification threshold table, builds a parameter fluctuation time series model, calculates the deviation between the fluctuation data and the classification threshold range, analyzes the impact of the fluctuation on the operation status of the central air conditioner, and generates parameter fluctuation impact range data; The specific calculation formula for the deviation degree is: ; Among them, P 4 Represents the deviation value, V 4j represents the jth sampling point value of real-time fluctuation data, B 4j represents the reference value of the jth sampling point in the classification threshold range, W 4j Represents the weight factor of the jth sampling point, n 4 Represents the total number of sampling points.

[0040] The formula is used to calculate the degree of deviation between the fluctuation data and the classification threshold range, and the square root normalization method of weighted absolute deviation is used to quantify the impact of fluctuations on the operating status of the central air conditioner.

[0041] Formula parameter description: P 4 is the deviation degree value, which indicates the average deviation value between the real-time fluctuation data and the classification threshold range; V 4j is the real-time fluctuation data value of the jth sampling point, collected by the real-time monitoring system, such as the reading of the temperature sensor, in degrees Celsius; B 4j is the reference value of the jth sampling point in the classification threshold range, which is obtained based on historical data statistics and is usually the median or average value of the classification interval; W 4j is the weight factor of the jth sampling point, reflecting the importance of the sampling point in the calculation process. The weight factor is usually set based on the criticality of the central air-conditioning operation during the period of the sampling point, ranging from 0 to 1; n 4 is the total number of sampling points, which indicates the number of sampling points involved in the calculation process, usually obtained by multiplying the running time by the sampling frequency; The absolute value symbol indicates taking the absolute value of the data difference to avoid distortion of the deviation result caused by the offset of positive and negative values. The fraction symbol indicates normalization of the accumulated value, and the square root symbol is further processed into the final deviation value.

[0042] Specific calculation process: The sampling duration is one day, the sampling frequency is once per hour, and the number of sampling points is n 4 =24. The real-time fluctuation data for a certain day is (Unit: °C), the reference value of the classification threshold is (Unit: °C), the weight factor is .

[0043] Calculate the deviation value item by item: ; ; ; ; ; Add up the deviations: ; Calculate the normalized deviation value: ; The result shows that the deviation degree value is 1.12, which reflects the matching degree between the fluctuation level of the current central air-conditioning operation status parameters and the classification threshold range, and provides a quantitative basis for the evaluation of the central air-conditioning operation status.

[0044] The adjustment instruction generation submodule extracts the central air-conditioning operation parameter adjustment rules based on the parameter fluctuation influence range data, analyzes the correlation between the influence range of the fluctuation data and the adjustment rules, identifies the adjustment instruction sequence, integrates the instruction sequence and optimizes its execution logic, and generates an operation adjustment instruction set; By analyzing the impact of fluctuation data on the operating status and the applicability of adjustment rules, the adjustment instruction sequence is identified in combination with the central air-conditioning operation logic. The instruction sequence is reordered through a logical optimization method, unnecessary instructions are eliminated and integrated and optimized, and finally an operation adjustment instruction set is generated.

[0045] See also Figure 6 and Figure 2 ,The multi-level data parsing module includes a data distribution parsing submodule, a resource deviation calculation submodule, and an optimization list generation submodule; The data distribution analysis submodule extracts the operation data of each area based on the operation adjustment instruction set, analyzes the central air conditioning distribution ratio, constructs a mapping matrix between the operation data and the central air conditioning distribution, calculates the ratio value of the regional central air conditioning resource distribution based on the mapping relationship, and generates the regional central air conditioning distribution data; The distribution ratio of central air conditioners is extracted by analyzing the regional operation characteristics, the operation data is classified according to the central air conditioner category and function, and a mapping relationship matrix between regional operation data and central air conditioner distribution is constructed. The resource allocation ratio is calculated based on the distribution ratio of central air conditioners in the matrix and the correlation of operation parameter distribution, and finally the regional central air conditioner distribution data is generated.

[0046] The resource deviation calculation submodule extracts the regional resource consumption value based on the regional central air conditioning distribution data, compares the central air conditioning distribution ratio, calculates the deviation between the resource consumption data and the central air conditioning ratio, analyzes the regional distribution characteristics of the deviation, marks the area where the deviation exceeds the threshold, and generates regional resource deviation data; Based on the regional central air conditioning distribution data, the regional resource consumption value is extracted according to the formula ; Calculate the deviation between resource consumption data and central air conditioning ratio.

[0047] Where P is the deviation, U j is the jth data point of the regional resource consumption value, V j is the jth data point of the central air conditioning ratio, and k is the number of data points in the region.

[0048] The deviation is calculated by averaging the square of the difference between resource consumption data and the central air conditioning ratio, which quantifies the degree of match between resource consumption and central air conditioning distribution. , the central air conditioning ratio is , the number of data points is 3, and the calculation is as follows: ; ; ; Substituting the above results into the formula: ; The result shows that the deviation is 18, which represents the degree of deviation between resource consumption and central air-conditioning distribution in the region, providing data support for further optimization.

[0049] The optimization list generation submodule classifies and analyzes the resource allocation characteristics of the marked area based on the regional resource deviation data, combines the central air conditioning distribution and resource consumption characteristics, reorganizes the regional resource allocation data, optimizes resource allocation and generates optimized regional list data; By analyzing the relationship between resource consumption characteristics and central air-conditioning distribution, optimization features are extracted, and the resource allocation data of each region is reorganized. The proportion of central air-conditioning distribution in each region is readjusted to the optimal state. The adjusted allocation strategy is integrated according to the central air-conditioning operation requirements, and finally the optimized regional list data is generated.

[0050] See also Figure 7 and Figure 2 ,The priority control module includes a resource pressure assessment submodule, a load difference analysis submodule, and an instruction priority adjustment submodule; The resource pressure assessment submodule extracts resource consumption-related parameters based on the optimized regional inventory data, calculates regional resource utilization, builds a mapping model of resource allocation and pressure, analyzes the matching between resource distribution data and the mapping model, evaluates regional pressure differences, and generates resource pressure data; Extract resource consumption related parameters, monitor the real-time resource consumption of central air conditioners in each area, analyze the numerical changes in regional resource utilization, compare the resource consumption ratio of each area with the available central air conditioning capacity, and build a mapping model of regional resource allocation and pressure. Calculate the resource utilization difference based on the model and generate resource pressure data for each area.

[0051] The load difference analysis submodule extracts the dynamic parameters of the load state and establishes a state distribution model based on the resource pressure data, compares the current operation mode with the state distribution model, calculates the deviation value, and identifies the matching difference of the operation mode based on the deviation value distribution characteristics to generate load state difference data; Extract the dynamic parameters of the load state and establish the state distribution model. According to the formula ; Calculate the deviation value.

[0052] Where D 2 is the deviation value, S 2i is the i-th parameter value of the current operating mode, M 2i is the i-th parameter value of the state distribution model, n 2 is the total number of parameters.

[0053] The deviation value is calculated by the average of the square difference between the operating mode parameters and the state distribution model parameters. Assume that the current operating parameters of a central air conditioner are , the state distribution model parameters are , the total number of parameters is 3, that is . The calculation is as follows: ; ; ; Substituting the above results into the formula: ; The result shows that the deviation value is 4, which reflects the average deviation between the current operating mode and the state distribution model, and provides data support for identifying the matching differences of the operating modes.

[0054] The instruction priority adjustment submodule calls the adjustment instructions in the optimization area list data based on the load status difference data, builds a priority sorting model according to the instruction relevance, adjusts the instruction priority sorting and optimizes the execution logic based on the load difference analysis results, and generates execution instruction sequence data; Call the adjustment instructions in the optimization area inventory data, build a priority sorting model by analyzing the correlation of the instructions, use the difference analysis results to adjust the execution priority of the instructions, reorder the instructions, and determine the efficient sorting order of the execution sequence in combination with the optimized logical model, and finally generate the optimized execution instruction sequence data.

[0055] See also Figure 8 , a remote monitoring method based on central air conditioning, comprising the following steps: S1: By collecting the central air-conditioning operating parameters, analyzing the amplitude and trend of each parameter change, setting the change amplitude threshold, screening the parameters with large change amplitude, analyzing the abnormal path according to the parameter correlation, locating the nodes with large change intensity in the path, and generating the abnormal path data set; S2: Based on the abnormal path data set, extract the central air-conditioning load change rate, analyze the difference between the fluctuation range and the current state, record the trend of weight change, and generate optimized parameter weight distribution data; S3: Based on the optimized parameter weight distribution data, set the threshold range of the operation status classification parameters, match the corresponding response actions, calculate the range value of the key parameter fluctuation impact, and generate an operation adjustment instruction set; S4: Based on the operation adjustment instruction set, the regional operation data and the central air conditioning distribution ratio are analyzed, the regional resource consumption deviation value is calculated, the resource allocation characteristics of each region are recorded, and the optimized regional inventory data is generated; S5: Based on the optimization area list data and combined with the current resource pressure, the difference between the load status and the operation mode is analyzed, the instruction priority is dynamically adjusted, and the execution instruction sequence data is generated.

[0056] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A remote monitoring system based on central air conditioning, characterized in that: The system comprises: The causal chain analysis module collects the central air-conditioning operating parameters, analyzes the change amplitude and trend of each parameter, sets the change amplitude threshold, filters the parameters with large change amplitude, analyzes the abnormal path according to the parameter correlation, locates the nodes with large change intensity in the path, extracts the nodes and their associated data, and generates the abnormal path data set; The dynamic weight adjustment module extracts the central air-conditioning load change rate based on the abnormal path data set, analyzes the difference between the fluctuation range and the current state, reallocates the weight values ​​of key parameters, records the trend of weight changes, and generates optimized parameter weight distribution data; The multi-level response module sets the threshold range of the operation status classification parameters based on the optimized parameter weight distribution data, matches the corresponding response actions, calculates the range value of the key parameter fluctuation impact, extracts and combines the adjustment instructions, and generates an operation adjustment instruction set; The multi-level data analysis module analyzes the regional operation data and the central air conditioning distribution ratio based on the operation adjustment instruction set, calculates the regional resource consumption deviation value, marks the area with large load distribution deviation, records the resource allocation characteristics of each area, and generates optimized area list data; The priority control module analyzes the difference between the load state and the operation mode based on the optimization area list data and the current resource pressure, dynamically adjusts the instruction priority, analyzes the operation optimization instructions according to the priority, and generates execution instruction sequence data.

2. The remote monitoring system based on central air conditioning according to claim 1 is characterized in that: The abnormal path data set includes parameter change amplitude values, abnormal path node locations and node association strength information; the optimized parameter weight distribution data includes reallocated parameter weight values, distribution diagrams after weight adjustment and weight evolution trend records; the operation adjustment instruction set includes preset response actions, dynamic parameter adjustment instructions and load change adjustment plans; the optimized area list data includes area load deviation identifiers, area resource allocation characteristic indicators and area resource consumption distribution information; the execution instruction sequence data includes an instruction sorting priority list, an optimized operation instruction list and load adjustment instruction analysis results.

3. The remote monitoring system based on central air conditioning according to claim 1 is characterized in that: The causal chain analysis module includes a parameter screening submodule, a path positioning submodule and a data generation submodule; The parameter screening submodule extracts the change data of each parameter based on the central air-conditioning operation parameters, builds a parameter fluctuation analysis benchmark, calculates the fluctuation range and trend of each parameter, sets the threshold range based on the fluctuation range distribution value, screens the parameters whose fluctuation range exceeds the set range, and generates a key parameter set; Based on the key parameter set, the path positioning submodule analyzes the correlation matrix between parameters, identifies the parameter paths with obvious correlation, tracks the parameter nodes with abnormal changes in the path, selects the nodes with significant change intensity according to the distribution law of the parameter change intensity, extracts the nodes and their associated data, and generates abnormal path node data; The data generation submodule parses the multi-dimensional data of nodes and associated parameters based on the abnormal path node data, constructs path data, and integrates the node parameters and their associated data sets to generate an abnormal path data set.

4. The remote monitoring system based on central air conditioning according to claim 1 is characterized in that: The dynamic weight adjustment module includes a load rate extraction submodule, a fluctuation difference analysis submodule and a weight distribution optimization submodule; The load rate extraction submodule extracts the time series data of the load parameters based on the abnormal path data set, calculates the load change rate value for the differentiated time intervals, analyzes the rate change trend and constructs a trend model, classifies and labels the load rate according to the model data, and generates load change rate data; The fluctuation difference analysis submodule extracts the current central air-conditioning operation state parameters and the historical operation state parameters based on the load change rate data, calculates the difference value, constructs a statistical distribution diagram of the fluctuation range, matches the degree of operation state deviation according to the distribution diagram data, identifies the deviation amount of the load state change, and generates load state difference data; The weight distribution optimization submodule establishes a corresponding relationship between the weight value of the key parameter and the deviation based on the load state difference data, recalculates the parameter weight value, records the time series of the weight change value during the adjustment process, analyzes the optimized weight distribution relationship, and generates optimized parameter weight distribution data.

5. The remote monitoring system based on central air conditioning according to claim 4 is characterized in that: The specific calculation formula of the difference value is: ； Among them, D3 represents the difference value, R 3i represents the actual value of the central air-conditioning operating state parameter at the i-th time point, H 3i represents the reference value of the historical operating status parameter at the i-th time point, W 3i represents the weight factor of the i-th time point, and n3 represents the total number of sampling time points.

6. The remote monitoring system based on central air conditioning according to claim 1 is characterized in that: The multi-level response module includes a hierarchical parameter setting submodule, a fluctuation range calculation submodule and an adjustment instruction generation submodule; The hierarchical parameter setting submodule extracts the weight values ​​of key parameters based on the optimized parameter weight distribution data, establishes an operating state parameter model in combination with the central air-conditioning operating state data, sets the hierarchical intervals of the operating state for the parameters in the model, generates a hierarchical threshold range table based on the interval range, and obtains an operating state hierarchical threshold table; The fluctuation range calculation submodule extracts the real-time fluctuation data of key parameters based on the operation status classification threshold table, constructs a parameter fluctuation time series model, calculates the deviation degree between the fluctuation data and the classification threshold range, analyzes the impact of the fluctuation on the operation status of the central air conditioner, and generates parameter fluctuation impact range data; The adjustment instruction generation submodule extracts the central air-conditioning operation parameter adjustment rules based on the parameter fluctuation influence range data, analyzes the correlation between the influence range of the fluctuation data and the adjustment rules, identifies the adjustment instruction sequence, integrates the instruction sequence and optimizes its execution logic, and generates an operation adjustment instruction set.

7. The remote monitoring system based on central air conditioning according to claim 6 is characterized in that: The calculation formula of the deviation degree value is specifically: ； Among them, P4 represents the deviation value, V 4j represents the jth sampling point value of real-time fluctuation data, B 4j represents the reference value of the jth sampling point in the classification threshold range, W 4j represents the weight factor of the jth sampling point, and n4 represents the total number of sampling points.

8. The remote monitoring system based on central air conditioning according to claim 1 is characterized in that: The multi-level data analysis module includes a data distribution analysis submodule, a resource deviation calculation submodule and an optimization list generation submodule; The data distribution analysis submodule extracts the operation data of each area based on the operation adjustment instruction set, analyzes the central air conditioning distribution ratio, constructs a mapping matrix between the operation data and the central air conditioning distribution, calculates the ratio value of the regional central air conditioning resource distribution according to the mapping relationship, and generates the regional central air conditioning distribution data; The resource deviation calculation submodule extracts the regional resource consumption value based on the regional central air conditioning distribution data, compares the central air conditioning distribution ratio, calculates the deviation between the resource consumption data and the central air conditioning ratio, analyzes the regional distribution characteristics of the deviation, marks the area where the deviation exceeds the threshold, and generates regional resource deviation data; The optimization list generation submodule classifies and analyzes the resource allocation characteristics of the marked area based on the regional resource deviation data, reorganizes the regional resource allocation data in combination with the central air-conditioning distribution and resource consumption characteristics, optimizes resource allocation and generates optimized regional list data.

9. The remote monitoring system based on central air conditioning according to claim 1 is characterized in that: The priority control module includes a resource pressure assessment submodule, a load difference analysis submodule and an instruction priority adjustment submodule; The resource pressure assessment submodule extracts resource consumption related parameters based on the optimized regional inventory data, calculates regional resource utilization, builds a mapping model of resource allocation and pressure, analyzes the matching between resource distribution data and the mapping model, assesses regional pressure differences, and generates resource pressure data; The load difference analysis submodule extracts the dynamic parameters of the load state and establishes a state distribution model based on the resource pressure data, compares the current operation mode with the state distribution model, calculates the deviation value, identifies the matching difference of the operation mode based on the deviation value distribution characteristics, and generates load state difference data; The instruction priority adjustment submodule calls the adjustment instructions in the optimization area list data based on the load state difference data, builds a priority sorting model according to the instruction relevance, adjusts the instruction priority sorting and optimizes the execution logic based on the load difference analysis results, and generates execution instruction sequence data.

10. A remote monitoring method based on central air conditioning, characterized in that: According to any one of claims 1 to 9, a remote monitoring system based on a central air conditioner is implemented, comprising the following steps: S1: By collecting the central air-conditioning operating parameters, analyzing the amplitude and trend of each parameter change, setting the change amplitude threshold, screening the parameters with large change amplitude, analyzing the abnormal path according to the parameter correlation, locating the nodes with large change intensity in the path, and generating the abnormal path data set; S2: Based on the abnormal path data set, extract the central air-conditioning load change rate, analyze the difference between the fluctuation range and the current state, record the trend of weight change, and generate optimized parameter weight distribution data; S3: Based on the optimized parameter weight distribution data, set the threshold range of the operation status classification parameter, match the corresponding response action, calculate the range value of the key parameter fluctuation impact, and generate an operation adjustment instruction set; S4: Based on the operation adjustment instruction set, the regional operation data and the central air conditioning distribution ratio are analyzed, the regional resource consumption deviation value is calculated, the resource allocation characteristics of each region are recorded, and the optimized regional list data is generated; S5: Based on the optimization area list data and in combination with the current resource pressure, the difference between the load state and the operation mode is analyzed, the instruction priority is dynamically adjusted, and the execution instruction sequence data is generated.

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