A method and system for intelligent monitoring of the status of temperature control equipment

Abstract

This invention relates to the field of temperature controller data monitoring technology, specifically to an intelligent monitoring method and system for the status of temperature control equipment. First, current monitoring data, environmental sensor data, and historical data of the temperature control equipment are collected. Then, an environmental impact weight prediction model is used to process the current display monitoring data, environmental sensor data, and historical data in the current monitoring data to obtain the current environmental impact weight. Based on the current environmental impact weight and the current display monitoring data, a display status monitoring value is obtained. Based on the threshold judgment result of the display status monitoring value, the current control monitoring data in the current monitoring data is evaluated using a control monitoring standard value to obtain a control status monitoring value. Based on the threshold judgment result of the control status monitoring value, the current location information and current status monitoring information are input into the temperature control equipment location prediction model and optimized using a particle swarm optimization algorithm to obtain updated location information.

Description

Technical Field

[0001] This invention relates to the field of temperature controller data monitoring technology, specifically to a method and system for intelligent monitoring of the status of temperature control equipment. Background Technology

[0002] Smart thermostats integrate measurement, display, control, alarm, and recording functions, and are equipped with WiFi, OT communication protocols, and multiple communication interfaces. Smart thermostats that incorporate AI and voice control can achieve accurate temperature measurement and precise on-site and remote control of the temperature and working status of home appliances via mobile phone. The complex design and functions also bring great challenges to the monitoring of the status of temperature control equipment.

[0003] Currently, traditional methods for monitoring the status of temperature control equipment typically use current temperature monitoring data to verify and calibrate the status of the equipment, without incorporating historical environmental monitoring data to understand the impact of environmental changes on the monitoring of the equipment's status. Furthermore, traditional monitoring methods cannot adjust the location of the temperature control equipment based on its current status data, which reduces the accuracy and reliability of intelligent monitoring of the temperature control equipment's status.

[0004] To address this, a method and system for intelligent monitoring of the status of temperature control equipment is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for intelligent monitoring of the status of temperature control equipment. First, current monitoring data, environmental sensor data, and historical data of the temperature control equipment are collected. Then, an environmental impact weight prediction model is used to process the current display monitoring data, environmental sensor data, and historical data in the current monitoring data to obtain the current environmental impact weight. Based on the current environmental impact weight and the current display monitoring data, a display status monitoring value is obtained. Based on the threshold judgment result of the display status monitoring value, the current control monitoring data in the current monitoring data is evaluated using a control monitoring standard value to obtain a control status monitoring value. Based on the threshold judgment result of the control status monitoring value, the current location information and current status monitoring information are input into the temperature control equipment location prediction model and optimized using a particle swarm optimization algorithm to obtain updated location information.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for intelligent monitoring of the status of temperature control equipment, comprising:

[0008] Collect current monitoring data, environmental sensor data, historical display monitoring data, and historical environmental sensor data from temperature control equipment;

[0009] The historical environmental impact weight prediction model is trained using the historical display monitoring data and the historical environmental sensor data to obtain the historical environmental impact weights.

[0010] The current display monitoring data, the environmental sensing data, and the historical environmental impact weights are input into the pre-trained environmental impact weight prediction model to obtain the current environmental impact weights; the current display monitoring data are weighted using the current environmental impact weights to obtain the display status monitoring value.

[0011] The display status monitoring value is compared with a threshold. If it is lower than the display threshold, the display status monitoring value is set to 0 and the display abnormality information is output. Otherwise, the current control monitoring data in the current monitoring data is evaluated using the control monitoring standard value to obtain the control status monitoring value.

[0012] The control status monitoring value is compared with a threshold. If it is lower than the control threshold, the control status monitoring value is set to 0 and control abnormality information is output. Otherwise, the current location information and the current status monitoring information are input into the temperature control equipment location prediction model and optimized using the particle swarm optimization algorithm to obtain updated location information.

[0013] Furthermore, the current monitoring data includes: current display monitoring data and current control monitoring data; the environmental sensing data includes: ambient light sensing data and ambient temperature sensing data; the historical environmental sensing data includes: historical ambient light sensing data and historical ambient temperature sensing data.

[0014] Further, the current display monitoring data, the environmental sensing data, and the historical environmental impact weights are input into the pre-trained environmental impact weight prediction model to obtain the current environmental impact weights; the specific implementation process of using the current environmental impact weights to weight the current display monitoring data to obtain the display status monitoring value includes:

[0015] The current monitoring data, environmental sensor data and historical environmental impact weights are input into the pre-trained environmental impact weight prediction model to optimize and update the parameters, and the current environmental impact weights are obtained.

[0016] The current environmental impact weights include: the impact weights on temperature display, humidity display, and power display.

[0017] The current temperature display data, current humidity display data, and current power display data are obtained from the current display monitoring data;

[0018] The difference between the current temperature display data, the current humidity display data, and the current power display data and the actual temperature data, the actual humidity data, and the actual power data are calculated to obtain the current temperature display deviation value, the current humidity display deviation value, and the current power display deviation value;

[0019] The current temperature display deviation, current humidity display deviation, and current power display deviation are evaluated using the current environmental influence weight to obtain the display status monitoring value.

[0020] Furthermore, the specific process of evaluating the current control monitoring data in the current monitoring data using control monitoring standard values ​​to obtain control status monitoring values ​​includes:

[0021] Obtain current control and monitoring data and control and monitoring standard values;

[0022] The current control monitoring data includes: current control mode data, current control anomaly data, current control delay data, and current control temperature data;

[0023] Based on the current control mode data and the current control anomaly data, the control mode coefficient and the control anomaly coefficient are obtained;

[0024] The control mode coefficient, the control anomaly coefficient, the current control delay data, and the current control temperature data are evaluated to obtain the control status monitoring value.

[0025] Furthermore, the specific implementation process of inputting the current location information and current status monitoring information into the temperature control equipment location prediction model and optimizing it using the particle swarm optimization algorithm to obtain updated location information includes:

[0026] Acquire historical location information, historical status monitoring information, current location information, and current status monitoring information;

[0027] The historical status monitoring information includes historical display status monitoring values ​​and historical control status monitoring values; the current status monitoring information includes current display status monitoring values ​​and current control status monitoring values.

[0028] The temperature control equipment location prediction model is trained using the historical location information and the historical status monitoring information to obtain a pre-trained equipment location prediction model;

[0029] The current location information and current status monitoring information are input into the pre-trained device location prediction model to obtain the predicted location information;

[0030] The predicted position information is set as the initial particle position, with the optimization objectives of maximizing the display state monitoring value and maximizing the control state monitoring value. The particle swarm algorithm is used for iteration to obtain the updated position information.

[0031] Furthermore, the specific implementation process of obtaining updated position information through iterative particle swarm optimization includes:

[0032] The optimization objectives of the particle swarm optimization algorithm are set to maximize the displayed state monitoring value and maximize the control state monitoring value.

[0033] A group of particles is randomly generated, and an initial position and initial velocity are set for each particle to form an initial particle swarm; at the same time, the global optimal position and optimal fitness are set; wherein, the initial position is set to the predicted position information output by the pre-trained device position prediction model;

[0034] The particle's velocity and position are updated using a particle swarm optimization algorithm based on the particle's current position, current velocity, historical best position, and global best position.

[0035] After each position update, the particle's fitness value is calculated, and the particle's local optimal position and global optimal position are updated based on the fitness value. Then, it is determined whether the current iteration number exceeds the preset maximum iteration number. If it does, the optimization process is terminated, and the adjustment variable value corresponding to the global optimal position is output, that is, the position information is updated.

[0036] A temperature control equipment status intelligent monitoring system includes: a system control module, a data acquisition module, a display status monitoring module, a control status monitoring module, a temperature control equipment position prediction module, and an output module;

[0037] The system control module is used to control the system's start, pause, and stop.

[0038] The data acquisition module is used to acquire the current monitoring data, environmental sensor data, historical display monitoring data, and historical environmental sensor data of the temperature control device;

[0039] The display status monitoring module is used to weight the current display monitoring data using the current environmental impact weight output by the environmental impact weight prediction model to obtain the display status monitoring value and compare it with the display threshold.

[0040] The control status monitoring module is used to evaluate the current control monitoring data using control monitoring standard values, obtain control status monitoring values, and compare control thresholds.

[0041] The temperature control device location prediction module is used to process data using a temperature control device location prediction model and a particle swarm algorithm to obtain updated location information.

[0042] The output module is used to output the error information and the updated location information.

[0043] Furthermore, the specific implementation process of the display status monitoring module for weighting the current display monitoring data using the current environmental impact weight output by the environmental impact weight prediction model to obtain the display status monitoring value includes:

[0044] The environmental impact weight prediction model was trained using historical display monitoring data and historical environmental sensor data to obtain historical environmental impact weights.

[0045] The current monitoring data, environmental sensor data, and the historical environmental impact weights are input into the environmental impact weight prediction model to optimize and update the parameters, thereby obtaining the current environmental impact weights.

[0046] The difference between the current temperature display data, current humidity display data, and current power display data and the actual temperature data, actual humidity data, and actual power data are calculated to obtain the current temperature display deviation value, current humidity display deviation value, and current power display deviation value;

[0047] The current temperature display deviation, current humidity display deviation, and current power display deviation are evaluated using the current environmental influence weight to obtain the display status monitoring value.

[0048] Furthermore, the specific implementation process of the control status monitoring module for evaluating the current control monitoring data using control monitoring standard values ​​to obtain control status monitoring values ​​includes:

[0049] Obtain current control and monitoring data and control and monitoring standard values;

[0050] The current control monitoring data includes: current control mode data, current control anomaly data, current control delay data, and current control temperature data;

[0051] Based on the current control mode data and the current control anomaly data, the control mode coefficient and the control anomaly coefficient are obtained;

[0052] The control mode coefficient, the control anomaly coefficient, the current control delay data, and the current control temperature data are evaluated to obtain the control status monitoring value.

[0053] Furthermore, the specific implementation process of the temperature control equipment location prediction module for processing data using a temperature control equipment location prediction model and a particle swarm optimization algorithm to obtain updated location information includes:

[0054] Acquire historical location information, historical status monitoring information, current location information, and current status monitoring information;

[0055] The historical status monitoring information includes historical display status monitoring values ​​and historical control status monitoring values; the current status monitoring information includes current display status monitoring values ​​and current control status monitoring values.

[0056] The temperature control equipment location prediction model is trained using the historical location information and the historical status monitoring information to obtain a pre-trained equipment location prediction model;

[0057] The current location information and current status monitoring information are input into the pre-trained device location prediction model to obtain the predicted location information;

[0058] The predicted position information is set as the initial particle position, with the optimization objectives of maximizing the display state monitoring value and maximizing the control state monitoring value. The particle swarm algorithm is used for iteration to obtain the updated position information.

[0059] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0060] 1. This invention proposes a display status monitoring function for monitoring the display status of temperature control equipment. This function first trains an environmental impact weight prediction model using historical display monitoring data and historical environmental sensor data to obtain historical environmental impact weights. Then, it uses the current environmental impact weights output by the environmental impact weight prediction model to weight the current display monitoring data, obtaining a display status monitoring value. This display status monitoring value reflects the degree of interference from changes in ambient light and temperature on the display status of the temperature control equipment, thereby effectively improving the accuracy and reliability of intelligent monitoring of the temperature control equipment status.

[0061] 2. This invention proposes a control status monitoring function for monitoring the control status of temperature control equipment. This function obtains control mode coefficients and control anomaly coefficients by collecting current control mode data and current control anomaly data. Then, it evaluates the current control delay data and current control temperature data by combining the control mode coefficients and the control anomaly coefficients to obtain the control status monitoring value. This function can effectively monitor various dimensions of temperature control equipment based on the control status monitoring value, thereby effectively improving the accuracy and reliability of intelligent monitoring of the temperature control equipment status.

[0062] 3. This invention proposes a temperature control equipment position prediction function to adjust the position of the temperature control equipment based on real-time monitoring information. This function uses a pre-trained equipment position prediction model trained with historical position information and historical status monitoring information to predict the position of the temperature control equipment and obtain predicted position information. Then, the function uses a particle swarm optimization algorithm to iteratively optimize the predicted position information to obtain updated position information. The position information obtained by this function is less affected by environmental interference and can achieve stable control performance, thereby further improving the accuracy and reliability of intelligent monitoring of the temperature control equipment status. Attached Figure Description

[0063] Figure 1 This is a flowchart illustrating an intelligent monitoring method for the status of a temperature control device according to the present invention.

[0064] Figure 2 This is a schematic diagram of the environmental impact weight prediction model of the present invention;

[0065] Figure 3 This is a schematic diagram of the structure of an intelligent monitoring system for the status of temperature control equipment according to the present invention. Detailed Implementation

[0066] 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.

[0067] Example 1

[0068] Smart thermostats integrate measurement, display, control, alarm, and recording functions, and are equipped with WiFi, OT communication protocols, and multiple communication interfaces. Smart thermostats that incorporate AI and voice control can achieve accurate temperature measurement and precise on-site and remote control of the temperature and working status of home appliances via mobile phone. The complex design and functions also bring great challenges to the monitoring of the status of temperature control equipment.

[0069] To improve the user experience of smart thermostats, a smart thermostat manufacturer has introduced a smart monitoring method for the status of temperature control equipment provided by this invention. The process can be referenced below. Figure 1 The specific implementation method is as follows:

[0070] First, collect the current monitoring data, environmental sensor data, historical display monitoring data, and historical environmental sensor data of the temperature control equipment;

[0071] Furthermore, the current monitoring data includes: current display monitoring data and current control monitoring data; the environmental sensing data includes: ambient light sensing data and ambient temperature sensing data; the historical environmental sensing data includes: historical ambient light sensing data and historical ambient temperature sensing data.

[0072] Furthermore, the parameter values ​​of the currently displayed monitoring data and environmental sensor data are shown in Table 1.

[0073] Table 1. Parameter data of currently displayed monitoring data and environmental sensor data

[0074]

[0075] In this embodiment, historical environmental sensor data is used to train an environmental impact weight prediction model to predict the changing trends of past environmental factors on the display data; current monitoring data and environmental sensor data provide a strong data foundation for display status monitoring and control status monitoring.

[0076] The historical environmental impact weight prediction model is trained using the historical display monitoring data and the historical environmental sensor data to obtain the historical environmental impact weights.

[0077] The current display monitoring data, the environmental sensing data, and the historical environmental impact weights are input into the pre-trained environmental impact weight prediction model to obtain the current environmental impact weights; the current display monitoring data are weighted using the current environmental impact weights to obtain the display status monitoring value.

[0078] Furthermore, the specific implementation process of displaying the status monitoring value includes:

[0079] The current monitoring data, environmental sensor data and historical environmental impact weights are input into the pre-trained environmental impact weight prediction model to optimize and update the parameters, and the current environmental impact weights are obtained.

[0080] The current environmental impact weights include: the impact weights on temperature display, humidity display, and power display.

[0081] The structure of the environmental impact weight prediction model in this embodiment can be found in the following reference. Figure 2 The system comprises: an input layer, a CNN-LSTM feature extraction layer, a feature merging layer, an environmental impact weight prediction layer, and an output layer. The input layer maps the current monitoring data, environmental sensing data, and historical environmental impact weights into the feature space for further processing. The CNN-LSTM feature extraction layer extracts the temporal features of the LSTM and the spatial features of the CNN. The feature merging layer merges the temporal and spatial features. The environmental impact weight prediction layer uses a multilayer perceptron to obtain the weight prediction results. The output layer outputs the current environmental impact weights through a linear activation function.

[0082] The current temperature display data, current humidity display data, and current power display data are obtained from the current display monitoring data;

[0083] The difference between the current temperature display data, the current humidity display data, and the current power display data and the actual temperature data, the actual humidity data, and the actual power data are calculated to obtain the current temperature display deviation value, the current humidity display deviation value, and the current power display deviation value;

[0084] The current temperature display deviation, current humidity display deviation, and current power display deviation are evaluated using the current environmental influence weight to obtain the display status monitoring value.

[0085] Furthermore, the formula for calculating the display status monitoring value is as follows:

[0086] ;

[0087] Wherein, XSJC is the display status monitoring value, which is in the range of [0, 1] and the larger the value, the more normal the temperature control device display is; N is the number of times sample data is collected; To influence the weight of temperature display; This displays the current temperature data from the i-th acquisition. This is actual temperature data; To influence the weight of humidity display; This represents the current humidity display data collected for the i-th time. This is actual humidity data; To influence the weighting of power display; This represents the current power display data from the i-th acquisition. This is the actual power data.

[0088] Furthermore, the weighting of temperature display is affected. Influence on humidity display weight And the weight that affects power display The weight values ​​are obtained from the environmental impact weight prediction model and are between [0, 1].

[0089] Furthermore, the four sets of current display data collected from the 8th to 11th times were used to calculate the display status monitoring value to obtain the temperature control device display status monitoring results, as shown in Table 2 below, and the display threshold was set to 0.9.

[0090] Table 2. Monitoring Results of Temperature Control Equipment Display Status

[0091]

[0092] This embodiment proposes a display status monitoring function for monitoring the display status of temperature control equipment. This function first trains an environmental impact weight prediction model using historical display monitoring data and historical environmental sensor data to obtain historical environmental impact weights. Then, it uses the current environmental impact weights output by the environmental impact weight prediction model to weight the current display monitoring data, obtaining a display status monitoring value. This display status monitoring value reflects the degree of interference from changes in ambient light and temperature on the display status of the temperature control equipment, thereby effectively improving the accuracy and reliability of intelligent monitoring of the temperature control equipment status.

[0093] The display status monitoring value is compared with a threshold. If it is lower than the display threshold, the display status monitoring value is set to 0 and the display abnormality information is output. Otherwise, the current control monitoring data in the current monitoring data is evaluated using the control monitoring standard value to obtain the control status monitoring value.

[0094] Furthermore, the specific process of evaluating the current control monitoring data in the current monitoring data using control monitoring standard values ​​to obtain control status monitoring values ​​includes:

[0095] Obtain current control and monitoring data and control and monitoring standard values;

[0096] The current control monitoring data includes: current control mode data, current control anomaly data, current control delay data, and current control temperature data; the current control mode data represents the indoor temperature data under different control modes, and the current control anomaly data represents the abnormal deviation value between the target temperature and the actual temperature.

[0097] Based on the current control mode data and the current control anomaly data, the control mode coefficient and the control anomaly coefficient are obtained;

[0098] The control mode coefficient, the control anomaly coefficient, the current control delay data, and the current control temperature data are evaluated to obtain the control status monitoring value.

[0099] Furthermore, the control mode coefficient and control anomaly coefficient are expressed as follows:

[0100] ;in, For the control mode coefficient, This represents the change in indoor temperature when the temperature control device is turned on and off. Set the temperature for the target. To control the outlier coefficient, This is the actual temperature. The allowable temperature deviation range; the control mode coefficient represents the degree of influence of the temperature control equipment on indoor temperature changes under different working modes; the control anomaly coefficient represents the temperature control performance of the temperature control equipment under abnormal working conditions.

[0101] Furthermore, the formula for calculating the control status monitoring value is as follows:

[0102] ;

[0103] Among them, KZJC is the control status monitoring value, which is in the range of [0, 1] and the larger the value, the more normal the temperature control equipment is. For the control mode coefficient, The control anomaly coefficient is represented by KZPG, the control status evaluation value, and M represents the number of data collections. To control the delay weight; This represents the current control delay data collected in the j-th iteration; To control the delay threshold; To control temperature weighting; This represents the current control temperature data collected in the j-th acquisition. To control the temperature threshold.

[0104] In this embodiment, both the control delay weight and the control temperature weight are set to 0.5; the control delay threshold and the control temperature threshold are related to the model and performance of the temperature control equipment and can be adjusted according to the actual situation.

[0105] Furthermore, the control status monitoring results of the temperature control equipment were obtained by using the calculation formula of the control status monitoring value from the four sets of current control monitoring data collected from the 23rd to the 26th times, as shown in Table 3 below, and the control threshold was set to 0.85.

[0106] Table 3. Monitoring Results of Temperature Control Equipment Display Status

[0107]

[0108] This embodiment proposes a control status monitoring function for monitoring the control status of temperature control equipment. This function obtains control mode coefficients and control anomaly coefficients by collecting current control mode data and current control anomaly data. Then, it evaluates current control delay data and current control temperature data by combining the control mode coefficients and control anomaly coefficients to obtain a control status monitoring value. This function can effectively monitor various dimensions of temperature control equipment based on the control status monitoring value, thereby effectively improving the accuracy and reliability of intelligent monitoring of the temperature control equipment status.

[0109] The control status monitoring value is compared with a threshold. If it is lower than the control threshold, the control status monitoring value is set to 0 and control abnormality information is output. Otherwise, the current location information and the current status monitoring information are input into the temperature control equipment location prediction model and optimized using the particle swarm optimization algorithm to obtain updated location information.

[0110] Furthermore, the specific implementation process of inputting the current location information and current status monitoring information into the temperature control equipment location prediction model and optimizing it using the particle swarm optimization algorithm to obtain updated location information includes:

[0111] Acquire historical location information, historical status monitoring information, current location information, and current status monitoring information;

[0112] The historical status monitoring information includes historical display status monitoring values ​​and historical control status monitoring values; the current status monitoring information includes current display status monitoring values ​​and current control status monitoring values.

[0113] The temperature control equipment location prediction model is trained using the historical location information and the historical status monitoring information to obtain a pre-trained equipment location prediction model;

[0114] The current location information and current status monitoring information are input into the pre-trained device location prediction model to obtain the predicted location information;

[0115] The predicted position information is set as the initial particle position, and the particle swarm optimization algorithm is used to iterate and obtain the updated position information with the goal of maximizing the state monitoring information.

[0116] This embodiment proposes a temperature control device location prediction function to adjust the position of the temperature control device based on real-time monitoring information. This function uses a pre-trained device location prediction model trained with historical location information and historical status monitoring information to predict the position of the temperature control device and obtain predicted position information. Then, the function uses a particle swarm optimization algorithm to iteratively optimize the predicted position information to obtain updated position information. The position information obtained by this function is less affected by environmental interference and can achieve stable control performance, thereby further improving the accuracy and reliability of intelligent monitoring of the temperature control device status.

[0117] This embodiment proposes an intelligent monitoring method for the status of temperature control equipment. By using the display status monitoring value obtained from the current environmental influence weight and the current display monitoring data, as well as the control status monitoring value obtained from the current control monitoring data, the monitoring time for different equipment states can be effectively shortened, improving the accuracy and reliability of intelligent monitoring of the temperature control equipment status. Simultaneously, based on the threshold judgment result of the control status monitoring value, the prediction results of the temperature control equipment location prediction model and the iterative optimization of the particle swarm optimization algorithm are used to obtain updated location information. This improves the stability of the temperature control equipment's operating status, thereby enhancing the user experience.

[0118] Example 2

[0119] This embodiment provides an intelligent monitoring system for the status of temperature control equipment, the system structure of which is as follows: Figure 3 As shown, the specific implementation method is as follows:

[0120] The system control module is used to control the system's start, pause, and stop.

[0121] The data acquisition module is used to acquire the current monitoring data, environmental sensor data, historical display monitoring data, and historical environmental sensor data of the temperature control equipment;

[0122] The display status monitoring module is used to weight the current display monitoring data using the current environmental impact weight output by the environmental impact weight prediction model to obtain the display status monitoring value and compare it with the display threshold; the display status monitoring module includes a display status monitoring unit and a judgment unit.

[0123] Furthermore, the specific process by which the display status monitoring unit weights the current environmental impacts output by the environmental impact weight prediction model to obtain the display status monitoring value includes:

[0124] The environmental impact weight prediction model was trained using historical display monitoring data and historical environmental sensor data to obtain historical environmental impact weights.

[0125] The current monitoring data, environmental sensor data, and the historical environmental impact weights are input into the environmental impact weight prediction model to optimize and update the parameters, thereby obtaining the current environmental impact weights.

[0126] The difference between the current temperature display data, current humidity display data, and current power display data and the actual temperature data, actual humidity data, and actual power data are calculated to obtain the current temperature display deviation value, current humidity display deviation value, and current power display deviation value;

[0127] The current temperature display deviation, current humidity display deviation, and current power display deviation are evaluated using the current environmental influence weight to obtain the display status monitoring value.

[0128] Furthermore, the display status monitoring unit uses the four sets of current display data collected from the 42nd to the 45th times and combines them with the calculation formula of the display status monitoring value to obtain the monitoring results of the display status monitoring unit, as shown in Table 4 below.

[0129] Table 4. Monitoring Results of Display Status Monitoring Unit

[0130]

[0131] Furthermore, the judgment unit is used to compare the display status monitoring value with the display threshold.

[0132] The control status monitoring module is used to evaluate the current control monitoring data using control monitoring standard values, obtain control status monitoring values, and compare them with control thresholds; the control status monitoring module includes a control status monitoring unit and a judgment unit;

[0133] Furthermore, the control status monitoring unit evaluates the current control monitoring data using control monitoring standard values ​​to obtain control status monitoring values. The specific implementation process includes:

[0134] Obtain current control and monitoring data and control and monitoring standard values;

[0135] The current control monitoring data includes: current control mode data, current control anomaly data, current control delay data, and current control temperature data;

[0136] Based on the current control mode data and the current control anomaly data, the control mode coefficient and the control anomaly coefficient are obtained;

[0137] The control mode coefficient, the control anomaly coefficient, the current control delay data, and the current control temperature data are evaluated to obtain the control status monitoring value.

[0138] Furthermore, the control status monitoring unit uses the four sets of current control monitoring data collected from the 84th to 87th times and combines them with the calculation formula of the control status monitoring value to obtain the monitoring results of the control status monitoring unit, as shown in Table 5 below.

[0139] Table 5. Monitoring Results of Control Status Monitoring Unit

[0140]

[0141] Furthermore, the judgment unit is used to compare the control status monitoring value with the control threshold.

[0142] The temperature control equipment location prediction module is used to process data using a temperature control equipment location prediction model and a particle swarm optimization algorithm to obtain updated location information. The temperature control equipment location prediction module includes a temperature control equipment location prediction unit and a location optimization unit.

[0143] Furthermore, the specific implementation process of the temperature control equipment position prediction unit obtaining predicted position information by using the temperature control equipment position prediction model includes:

[0144] Acquire historical location information, historical status monitoring information, current location information, and current status monitoring information;

[0145] The historical status monitoring information includes historical display status monitoring values ​​and historical control status monitoring values; the current status monitoring information includes current display status monitoring values ​​and current control status monitoring values.

[0146] The temperature control equipment location prediction model is trained using the historical location information and the historical status monitoring information to obtain a pre-trained equipment location prediction model;

[0147] The current location information and current status monitoring information are input into the pre-trained device location prediction model to obtain the predicted location information.

[0148] Furthermore, the location optimization unit uses the particle swarm optimization algorithm for iterative processing to obtain updated location information, which includes:

[0149] The optimization objectives of the particle swarm optimization algorithm are set to maximize the displayed state monitoring value and maximize the control state monitoring value.

[0150] A group of particles is randomly generated, and each particle is assigned an initial position and an initial velocity to form an initial particle swarm; at the same time, a global optimal position and an optimal fitness are set; wherein, the initial position is set to the predicted position information output by the pre-trained device position prediction model;

[0151] The particle's velocity and position are updated using a particle swarm optimization algorithm based on the particle's current position, current velocity, historical best position, and global best position.

[0152] After each position update, the particle's fitness value is calculated, and the particle's local optimal position and global optimal position are updated based on the fitness value. Then, it is determined whether the current iteration number exceeds the preset maximum iteration number. If it does, the optimization process is terminated, and the adjustment variable value corresponding to the global optimal position is output, that is, the position information is updated.

[0153] The output module is used to output exception information and update location information.

[0154] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent monitoring of the status of temperature control equipment, characterized in that, include: Collect current monitoring data, environmental sensor data, historical display monitoring data, and historical environmental sensor data from temperature control equipment; The historical environmental impact weight prediction model is trained using the historical display monitoring data and the historical environmental sensor data to obtain the historical environmental impact weights. The current display monitoring data, the environmental sensing data, and the historical environmental impact weights are input into the pre-trained environmental impact weight prediction model to obtain the current environmental impact weights; the current display monitoring data are weighted using the current environmental impact weights to obtain the display status monitoring value. The display status monitoring value is compared with a threshold. If it is lower than the display threshold, the display status monitoring value is set to 0 and display abnormality information is output. Otherwise, the current control monitoring data in the current monitoring data is evaluated using the control monitoring standard value to obtain the control status monitoring value; The specific process of obtaining control status monitoring values ​​includes: Obtain current control and monitoring data and control and monitoring standard values; The current control monitoring data includes: current control mode data, current control anomaly data, current control delay data, and current control temperature data; the current control mode data represents the indoor temperature data under different control modes, and the current control anomaly data represents the abnormal deviation value between the target temperature and the actual temperature. Based on the current control mode data and the current control anomaly data, the control mode coefficient and the control anomaly coefficient are obtained; The control mode coefficient, the control anomaly coefficient, the current control delay data, and the current control temperature data are evaluated to obtain the control status monitoring value; Furthermore, the control mode coefficient and control anomaly coefficient are expressed as follows: and ;in, For the control mode coefficient, This represents the change in indoor temperature when the temperature control device is turned on and off. Set the temperature for the target. To control the outlier coefficient, This is the actual temperature. The allowable temperature deviation range; the control mode coefficient represents the degree of influence of the temperature control equipment on indoor temperature changes under different operating modes; the control anomaly coefficient represents the temperature control performance of the temperature control equipment under abnormal operating conditions; Furthermore, the formula for calculating the control status monitoring value is as follows: in, This is a control status monitoring value, which is in the range of [0, 1] and the larger the value, the more normal the temperature control equipment is. For the control mode coefficient, To control the abnormal coefficient; M represents the control state evaluation value; M represents the number of data collection sessions. To control the delay weight; This represents the current control delay data collected in the j-th iteration; To control the delay threshold; To control temperature weighting; This represents the current control temperature data collected for the j-th time. To control the temperature threshold; The control status monitoring value is compared with a threshold. If it is lower than the control threshold, the control status monitoring value is set to 0 and control abnormality information is output. Otherwise, the current location information and the current status monitoring information are input into the temperature control equipment location prediction model and optimized using the particle swarm optimization algorithm to obtain updated location information.

2. The intelligent monitoring method for the status of temperature control equipment according to claim 1, characterized in that, The current monitoring data includes: currently displayed monitoring data and currently controlled monitoring data; the environmental sensing data includes: ambient light sensing data and ambient temperature sensing data; the historical environmental sensing data includes: historical ambient light sensing data and historical ambient temperature sensing data.

3. The intelligent monitoring method for the status of temperature control equipment according to claim 1, characterized in that, The currently displayed monitoring data, the environmental sensing data, and the historical environmental impact weights are input into the pre-trained environmental impact weight prediction model to obtain the current environmental impact weights. The specific implementation process of weighting the current display monitoring data using the current environmental influence weights to obtain the display status monitoring value includes: The current monitoring data, environmental sensor data and historical environmental impact weights are input into the pre-trained environmental impact weight prediction model to optimize and update the parameters, and the current environmental impact weights are obtained. The current environmental impact weights include: the impact weights on temperature display, humidity display, and power display. The current temperature display data, current humidity display data, and current power display data are obtained from the current display monitoring data; The difference between the current temperature display data, the current humidity display data, and the current power display data and the actual temperature data, the actual humidity data, and the actual power data are calculated to obtain the current temperature display deviation value, the current humidity display deviation value, and the current power display deviation value; The current temperature display deviation, current humidity display deviation, and current power display deviation are evaluated using the current environmental influence weight to obtain the display status monitoring value.

4. The intelligent monitoring method for the status of temperature control equipment according to claim 1, characterized in that, The specific process of inputting the current location information and current status monitoring information into the temperature control equipment location prediction model and optimizing it using the particle swarm optimization algorithm to obtain updated location information includes: Acquire historical location information, historical status monitoring information, current location information, and current status monitoring information; The historical status monitoring information includes historical display status monitoring values ​​and historical control status monitoring values; the current status monitoring information includes current display status monitoring values ​​and current control status monitoring values. The temperature control equipment location prediction model is trained using the historical location information and the historical status monitoring information to obtain a pre-trained equipment location prediction model; The current location information and current status monitoring information are input into the pre-trained device location prediction model to obtain the predicted location information; The predicted position information is set as the initial particle position, with the optimization objectives of maximizing the display state monitoring value and maximizing the control state monitoring value. The particle swarm algorithm is used for iteration to obtain the updated position information.

5. The intelligent monitoring method for the status of temperature control equipment according to claim 4, characterized in that, The specific implementation process of obtaining updated position information through iterative particle swarm optimization includes: The optimization objectives of the particle swarm optimization algorithm are set to maximize the displayed state monitoring value and maximize the control state monitoring value. A group of particles is randomly generated, and an initial position and initial velocity are set for each particle to form an initial particle swarm; at the same time, the global optimal position and optimal fitness are set; wherein, the initial position is set to the predicted position information output by the pre-trained device position prediction model; The particle's velocity and position are updated using a particle swarm optimization algorithm based on the particle's current position, current velocity, historical best position, and global best position. After each position update, the particle's fitness value is calculated, and the particle's local optimal position and global optimal position are updated based on the fitness value. Then, it is determined whether the current iteration number exceeds the preset maximum iteration number. If it does, the optimization process is terminated, and the adjustment variable value corresponding to the global optimal position is output, that is, the position information is updated.

6. A temperature control equipment status intelligent monitoring system, characterized in that, include: The system includes a control module, a data acquisition module, a display status monitoring module, a control status monitoring module, a temperature control equipment location prediction module, and an output module. The system control module is used to control the system's start, pause, and stop. The data acquisition module is used to acquire the current monitoring data, environmental sensor data, historical display monitoring data, and historical environmental sensor data of the temperature control device; The display status monitoring module is used to weight the current display monitoring data using the current environmental impact weight output by the environmental impact weight prediction model to obtain the display status monitoring value and compare it with the display threshold. The control status monitoring module is used to evaluate the current control monitoring data using control monitoring standard values, obtain control status monitoring values, and compare them with control thresholds; the specific implementation process for obtaining the control status monitoring values ​​includes: Obtain current control and monitoring data and control and monitoring standard values; The current control monitoring data includes: current control mode data, current control anomaly data, current control delay data, and current control temperature data; the current control mode data represents the indoor temperature data under different control modes, and the current control anomaly data represents the abnormal deviation value between the target temperature and the actual temperature. Based on the current control mode data and the current control anomaly data, the control mode coefficient and the control anomaly coefficient are obtained; The control mode coefficient, the control anomaly coefficient, the current control delay data, and the current control temperature data are evaluated to obtain the control status monitoring value; Furthermore, the control mode coefficient and control anomaly coefficient are expressed as follows: and ;in, For the control mode coefficient, This represents the change in indoor temperature when the temperature control device is turned on and off. Set the temperature for the target. To control the outlier coefficient, This is the actual temperature. The allowable temperature deviation range; the control mode coefficient represents the degree of influence of the temperature control equipment on indoor temperature changes under different operating modes; the control anomaly coefficient represents the temperature control performance of the temperature control equipment under abnormal operating conditions; Furthermore, the formula for calculating the control status monitoring value is as follows: in, This is a control status monitoring value, which is in the range of [0, 1] and the larger the value, the more normal the temperature control equipment is. For the control mode coefficient, To control the abnormal coefficient; M represents the control state evaluation value; M represents the number of data collection sessions. To control the delay weight; This represents the current control delay data collected in the j-th iteration; To control the delay threshold; To control temperature weighting; This represents the current control temperature data collected for the j-th time. To control the temperature threshold; The temperature control equipment location prediction module is used to process data using a temperature control equipment location prediction model and a particle swarm optimization algorithm. Get updated location information; The output module is used to output the error information and the updated location information.

7. The intelligent monitoring system for the status of temperature control equipment according to claim 6, characterized in that, The specific implementation process of the display status monitoring module for weighting the current display monitoring data using the current environmental impact weight output by the environmental impact weight prediction model to obtain the display status monitoring value includes: The environmental impact weight prediction model was trained using historical display monitoring data and historical environmental sensor data to obtain historical environmental impact weights. The current monitoring data, environmental sensor data, and the historical environmental impact weights are input into the environmental impact weight prediction model to optimize and update the parameters, thereby obtaining the current environmental impact weights. The difference between the current temperature display data, current humidity display data, and current power display data and the actual temperature data, actual humidity data, and actual power data are calculated to obtain the current temperature display deviation value, current humidity display deviation value, and current power display deviation value; The current temperature display deviation, current humidity display deviation, and current power display deviation are evaluated using the current environmental influence weight to obtain the display status monitoring value.

8. The intelligent monitoring system for the status of temperature control equipment according to claim 6, characterized in that, The temperature control equipment location prediction module is used to process data using a temperature control equipment location prediction model and a particle swarm optimization algorithm to obtain updated location information. The specific implementation process includes: Acquire historical location information, historical status monitoring information, current location information, and current status monitoring information; The historical status monitoring information includes historical display status monitoring values ​​and historical control status monitoring values; the current status monitoring information includes current display status monitoring values ​​and current control status monitoring values. The temperature control equipment location prediction model is trained using the historical location information and the historical status monitoring information to obtain a pre-trained equipment location prediction model; The current location information and current status monitoring information are input into the pre-trained device location prediction model to obtain the predicted location information; The predicted position information is set as the initial particle position, with the optimization objectives of maximizing the display state monitoring value and maximizing the control state monitoring value. The particle swarm algorithm is used for iteration to obtain the updated position information.

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