Control load distributed control method and system based on user random behaviors

Abstract

The invention belongs to the technical field of power systems, and discloses a control load distributed control method and system based on user random behaviors. The method comprises the following steps: dividing a building into a plurality of temperature control areas, and generating a first control reference sequence based on user information and environment information of the temperature control areas, whether a control reference sequence with the similarity with the first control reference sequence larger than a first preset value exists in the historical operation data table or not is judged, if yes, the control reference sequence corresponding to the maximum similarity value is defined as a second control reference sequence, a first temperature control instruction is generated based on an air conditioner operation mode corresponding to the second control reference sequence, and if not, a second temperature control instruction is generated. And generating a second temperature control instruction based on the user information and the temperature control mode corresponding to the temperature control area, periodically judging whether feedback information of the user is received or not, and if yes, generating a third temperature control instruction of the temperature control area based on the feedback information. According to the technical scheme, intelligent and personalized control over the building temperature control load can be achieved, and the user satisfaction degree is improved while the building energy efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and in particular relates to a method and system for controlling load distribution based on random behavior of users. Background Art

[0002] In modern buildings, especially large commercial buildings and smart homes, temperature control systems are crucial for enhancing living and working comfort. However, traditional temperature control methods, often based on preset fixed schedules or simple sensor feedback, ignore the diversity and randomness of user behavior and lack the precision to adjust the building's temperature control load. Therefore, how to achieve intelligent and distributed control of building temperature control loads based on user random behavior and building energy conditions has become a key issue in the field of building energy conservation and optimization.

[0003] Similar prior art includes Chinese patent application publication number CN117190280A, which discloses a distributed building heating control method and system. This method obtains environmental information and building structural information at each air outlet within the building; determines a corresponding proportionality coefficient based on the relationship between the environmental information and building structural information at each air outlet and the heating supply; calculates the heating area of each air outlet based on the obtained proportionality coefficient; and adjusts the heat load at the corresponding air outlet based on the calculated heating area. This method can regulate the heat load at the air outlet to prevent excessively high or low temperatures in certain areas, but it lacks consideration for the user conditions within the control area. Another Chinese patent application, publication number CN118705732A, discloses a method and system for intelligent zoning temperature control of central air conditioning. This method utilizes BIM to obtain a detailed floor plan of a building, divide it into zones, and determine the temperature control requirements for each zone based on usage requirements, orientation, and floor level. Temperature sensors, humidity sensors, and CO2 sensors are installed in each zone to obtain environmental data. Indoor occupancy detection sensors are installed at key locations to detect the number of people in the zone and their activity level. The sensors periodically collect environmental data and transmit it to a central control system via a wireless network. Based on sensor data and historical data, a temperature control model is established for each zone. A machine learning algorithm is used to predict future temperature changes. Based on the regional temperature control model and the user's preset comfort requirements, an intelligent temperature control strategy is formulated. The central control system converts the temperature control strategy into specific control instructions and sends them to the air conditioning equipment in the zone. Upon receiving the control instructions, the equipment adjusts the cooling and heating power, wind speed, and wind direction. This method comprehensively considers the user situation within the control area and lacks analysis of individual user differences.

[0004] Therefore, it is an urgent problem to provide a distributed control method and system for controlling loads based on random behavior of users, so as to improve user satisfaction while improving building energy efficiency and enhance the automation level of building temperature control load control. Summary of the Invention

[0005] In response to the above-mentioned technical problems, the present invention provides a method and system for distributed control of control loads based on random behavior of users.

[0006] In a first aspect, the present invention provides a method for distributed control of control load based on random user behavior, the method comprising the following steps: Step 1: Divide the building into multiple temperature control zones based on its physical structure; Step 2: Obtain user information and environmental information of any temperature control area, and generate a first control reference sequence based on the user information and environmental information; Step 3: Determine whether there is a control reference sequence in the historical operation data table whose similarity to the first control reference sequence is greater than a first preset value. If so, proceed to step 4; if not, proceed to step 5. Step 4: Define the control reference sequence corresponding to the maximum similarity as a second control reference sequence, use the air conditioning operation mode corresponding to the second control reference sequence as the first air conditioning operation mode of any temperature control zone, generate a first temperature control instruction based on the first air conditioning operation mode, and send it to the corresponding air conditioning equipment; Step 5: Based on the identification information of any temperature control zone, a corresponding first temperature control mode is obtained; based on the user information and the first temperature control mode, a second air conditioning operation mode is generated; based on the second air conditioning operation mode, a second temperature control instruction is generated and sent to the corresponding air conditioning device; Step 6: Periodically determine whether user feedback information is received. If so, proceed to step 7. If not, store the current air-conditioning operation mode in the historical operation data table in correspondence with the first control reference sequence. Step 7: Generate a third air-conditioning operation mode for any temperature control area based on the feedback information, generate a third temperature control instruction based on the third air-conditioning operation mode, and send it to the corresponding air-conditioning equipment, and then store the third air-conditioning operation mode in the historical operation data table corresponding to the first control reference sequence.

[0007] Specifically, the first environmental information is collected by a data collection device, and the method for obtaining the environmental information is: Obtaining a device identification of the data acquisition device, and extracting a calibration function corresponding to the data acquisition device from a calibration function table based on the device identification; Obtaining a first environmental value and a collection time in the first environmental information, calculating a calibration value based on a calibration function, the first environmental value, and the collection time, and using the sum of the calibration value and the first environmental value as a second environmental value of the data acquisition device; Environmental information is generated based on the device identification, the second environmental value, and the collection time.

[0008] Specifically, the calibration function is generated as follows: Step 21: Perform a calibration test on the data acquisition device at a preset time interval, obtain the test results, and store the test results in a calibration information table corresponding to the device identifier, wherein the test results include actual values, measured values, and calibration time; Step 22: Upon receiving the test result, determine whether the number of all test results corresponding to the device identifier in the calibration information table is 1. If so, calculate an initial calibration value based on the test result, assign the initial calibration value to the calibration function, and store the calibration function in the calibration function table corresponding to the device identifier. If not, proceed to step 23. Step 23: Extract all test results corresponding to the device identifier from the calibration information table, perform machine learning on all test results to generate a new calibration function, and update the calibration function table based on the new calibration function.

[0009] Specifically, step 2 includes: obtaining air-conditioning equipment information of any temperature-controlled area, and dividing any temperature-controlled area into multiple sub-areas based on the air-conditioning equipment information.

[0010] Specifically, user information includes the number of users and user location information. The temperature control mode is generated as follows: Step 511: extract any temperature control area, and divide the air conditioning setting conditions of any temperature control area into N types based on user information; Step 512: Extract any air conditioning setting condition and obtain the building structure information corresponding to the air conditioning setting condition; generate M fourth air conditioning operation modes based on the air conditioning setting condition and the building structure information according to a preset standard, wherein the fourth air conditioning operation mode includes the temperature setting and wind speed setting of the air conditioning equipment associated with the sub-area corresponding to the air conditioning setting condition; Step 513: Calculate the energy consumption reduction rate corresponding to any fourth air-conditioning operation mode; Step 514: After traversing all fourth air-conditioning operation modes, sort all fourth air-conditioning operation modes in descending order of energy consumption reduction rate; Step 515: After traversing all air conditioning setting conditions, group the fourth air conditioning operation modes with the same sequence number in different air conditioning setting conditions into one group; Step 516: assign identifiers to all the fourth air-conditioning operation modes after grouping, and then generate K temperature control modes based on the air-conditioning operation modes after the identifiers are assigned.

[0011] Specifically, before step 5, the first temperature control mode of any temperature control area is set by the management personnel, or the first temperature control mode of any temperature control area is set dynamically according to a time period.

[0012] Specifically, the user information includes a user ID, and step 7 includes: Step 71: Define the user who sends the feedback information as a first user, and calculate the somatosensory parameter value of any first user based on the personal information and environmental information of any first user; Step 72: Based on the user identifier of any first user, obtain a first somatosensory value distribution table of any first user from the historical somatosensory data table; Step 73: Based on the feedback information and the somatosensory parameter value of any first user, determine whether the specific somatosensory interval in the first somatosensory value distribution table has changed; if so, update the first somatosensory value distribution table based on the feedback information and the somatosensory parameter value; Step 74: After traversing all first users, extract the somatosensory value distribution table of all users in any temperature control area, and cluster all users based on specific somatosensory intervals into multiple user groups; Step 75: Extract any user group, calculate a first average value of the lower limit values of the specific somatosensory interval and a second average value of the upper limit values of the specific somatosensory interval for all users in the user group, and generate a group-specific somatosensory interval based on the first average value and the second average value; Step 76: After traversing all user groups, generate region-specific somatosensory intervals based on group-specific somatosensory intervals; Step 77: Based on the region-specific somatosensory interval, the current air-conditioning operation mode is learned to generate a third air-conditioning operation mode, so that the somatosensory parameter values of all users fall within the region-specific somatosensory interval.

[0013] Specifically, step 76 includes: Determine whether there are overlapping areas among all group-specific somatosensory intervals. If so, use the overlapping areas as the region-specific somatosensory intervals. If not, obtain the number of users in each user group, set the weight parameters of each user group based on the number of users, and perform weighted averaging on the upper and lower limits of the group-specific somatosensory intervals based on the weight parameters to generate the region-specific somatosensory intervals.

[0014] In a second aspect, the present invention further provides a load control distributed control system based on user random behavior, the system comprising: a region division module, a data acquisition module, an information judgment module, a first control module, a second control module, an adjustment judgment module, and a control adjustment module; The zone division module is used to obtain a detailed floor plan of the building and divide the building into multiple temperature-controlled zones based on the building's physical structure; a data acquisition module, configured to extract any temperature-controlled area, obtain user information and environmental information of any temperature-controlled area, and generate a first control reference sequence based on the user information and environmental information; an information determination module, configured to traverse a historical operation data table according to the first control reference sequence, and determine whether there is a control reference sequence in the historical operation data table whose similarity to the first control reference sequence is greater than a first preset value; if so, enter the first control module; if not, enter the second control module; a first control module, configured to define a control reference sequence corresponding to the maximum similarity value as a second control reference sequence, use the air conditioning operation mode corresponding to the second control reference sequence as a first air conditioning operation mode for any temperature control zone, generate a first temperature control instruction based on the first air conditioning operation mode, and send the first temperature control instruction to the corresponding air conditioning device; a second control module, configured to obtain a first temperature control mode corresponding to any temperature control zone based on identification information of any temperature control zone, generate a second air-conditioning operation mode for any temperature control zone based on user information and the first temperature control mode, generate a second temperature control instruction based on the second air-conditioning operation mode, and send the second temperature control instruction to the corresponding air-conditioning device; an adjustment judgment module, configured to periodically judge whether user feedback information has been received; if not, to store the current air-conditioning operation mode in the historical operation data table in correspondence with the first control reference sequence; and if so, to enter the control adjustment module, wherein the current air-conditioning operation mode is the first air-conditioning operation mode or the second air-conditioning operation mode; A control adjustment module is used to generate a third air-conditioning operation mode for any temperature-controlled area based on feedback information, generate a third temperature control instruction based on the third air-conditioning operation mode, send the third temperature control instruction to the corresponding air-conditioning equipment, and then store the third air-conditioning operation mode in the historical operation data table corresponding to the first control reference sequence.

[0015] Compared with the prior art, the beneficial effects of the present invention are at least as follows: 1. Generate a control reference sequence based on user information and environmental information. By judging the similarity of historical data, the air conditioning operation mode is intelligently set according to historical operation data or preset temperature control mode. This realizes intelligent selection of air conditioning operation mode, improves the system's operating efficiency, and intelligently controls the building's temperature control load.

[0016] 2. By subdividing the temperature control area and generating multiple air-conditioning operation modes based on data such as the number of users and location information, the operation of the air-conditioning equipment can be accurately controlled. The management personnel can select the temperature control mode according to needs or dynamically set the temperature control mode according to time periods. This improves the personalized setting of the temperature control area or the adaptability to environmental changes in different time periods. While paying attention to user comfort, the energy efficiency of the temperature control equipment is also considered.

[0017] 3. Periodically judge user feedback, dynamically adjust the air-conditioning operation mode according to user feedback information, realize real-time temperature control adjustment, accurately respond to user needs, improve user satisfaction, and have a high degree of automation.

[0018] 4. Collecting environmental information through data acquisition equipment and calibrating it in combination with calibration functions can ensure the accuracy and reliability of the environmental information obtained, make decisions based on accurate data, and improve the accuracy of control. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only embodiments of the present invention. Those skilled in the art can also derive other drawings based on the provided drawings without inventive effort. Figure 1 This is a flow chart of the distributed control method for controlling load based on random behavior of users according to the present invention; Figure 2 This is a modular schematic diagram of the distributed control system for controlling loads based on random user behavior according to the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the specific embodiments described herein are only used to explain the present invention and are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0021] It should be noted that if there are descriptions involving "first," "second," etc. in the embodiments of the present invention, such descriptions are for descriptive purposes only and should not be understood as indicating or implying their relative importance or implicitly indicating the number of the technical features indicated. Therefore, features specified as "first" or "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions of the various embodiments may be combined with each other, but this must be based on the premise that they can be implemented by a person of ordinary skill in the art. If the combination of technical solutions is mutually inconsistent or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0022] Figure 1 FIG. 1 is a flow chart of an embodiment of a method for distributed control of load based on random user behavior provided by the present invention. The flow chart specifically includes the following steps: Step 1: Divide the building into multiple temperature control zones based on its physical structure.

[0023] Specifically, a detailed floor plan of the building is obtained, and independent building spaces are divided into temperature-controlled areas based on the physical structure of the building. The types of independent building spaces are corridors, rooms, stairs, etc.

[0024] Step 2: Obtain user information and environmental information of any temperature control area, and generate a first control reference sequence based on the user information and environmental information.

[0025] Exemplarily, the environmental information includes temperature, humidity, sunshine, air flow rate, etc.

[0026] User information includes user identification and location. For example, user identification is obtained through smart cards, mobile devices (such as mobile phone apps), wearable devices, or biometric systems (such as facial recognition) to uniquely identify the user. Indoor positioning technologies (such as Bluetooth beacons, Wi-Fi positioning, infrared sensors, or cameras) or image recognition are used to track the user's location within the temperature-controlled area in real time. Sensors within the area (such as infrared thermal imaging, people counters, and cameras combined with AI analysis) are used to count the number of users in the current temperature-controlled area. Other information (such as activity level and clothing thickness) may be supplemented through wearable devices (to monitor heart rate and exercise status), environmental sensors (to infer clothing based on temperature feedback), or manual user input (such as app feedback). User information acquisition methods can be selected and combined based on the actual application scenario and system design to meet the system's requirements for user information.

[0027] Specifically, the first environmental information is collected by a data collection device, and the method for obtaining the environmental information is: Obtaining a device identification of the data acquisition device, and extracting a calibration function corresponding to the data acquisition device from a calibration function table based on the device identification; Obtaining a first environmental value and a collection time in the first environmental information, calculating a calibration value based on a calibration function, the first environmental value, and the collection time, and using the sum of the calibration value and the first environmental value as a second environmental value of the data acquisition device; Environmental information is generated based on the device identification, the second environmental value, and the collection time.

[0028] Due to differences in manufacturer, model, and hardware, errors inevitably exist between the measured values collected by data acquisition equipment and the actual values. Aging due to long-term use can also cause errors between the measured values collected by data acquisition equipment and the actual values. To achieve precise control, the accuracy of environmental information must be guaranteed. Therefore, before controlling the air conditioner's operating mode, calibration is performed to eliminate or reduce equipment measurement errors.

[0029] Preferably, the usage time of the data acquisition device is acquired based on the acquisition time, and the usage time and the first environmental value are input into a calibration function to acquire a calibration value.

[0030] Specifically, the calibration function is generated as follows: Step 21: Perform a calibration test on the data acquisition device at a preset time interval, obtain the test results, and store the test results in a calibration information table corresponding to the device identification, wherein the test results include actual values, measured values, and calibration time.

[0031] Step 22: When the test results are received, determine whether the number of all test results corresponding to the device identifier in the calibration information table is 1. If so, calculate the initial calibration value based on the test results, assign the initial calibration value to the calibration function, and store the calibration function in the calibration function table corresponding to the device identifier. If not, proceed to step 23.

[0032] Step 23: Extract all test results corresponding to the device identifier from the calibration information table, perform machine learning on all test results to generate a new calibration function, and update the calibration function table based on the new calibration function.

[0033] The preset time interval is set according to the experience of those skilled in the art or according to the actual application scenario, and the embodiments of the present application do not limit this. Preferably, the preset time interval is set according to the application environment of the data acquisition device.

[0034] The calibration value of the same data acquisition device may be different when measuring different actual values, and the measurement error will become larger as the usage time goes by. Therefore, the independent variables of the calibration function obtained by training the test results are the measurement value, usage time, etc., and the dependent variable is the calibration value (i.e., the error value).

[0035] According to the technical solution of the present invention, by comparing test results at different time points and using machine learning techniques to generate an accurate calibration function, the impact of the basic characteristics of the data acquisition device and time changes on the measured values can be reduced. Regular calibration testing and updating of the calibration function can maintain the long-term accuracy of the measured data. Over time, the calibration function becomes more accurate, thereby improving the control effect of the entire temperature control system. Furthermore, different data acquisition devices may have different error characteristics. By customizing the calibration function for each data acquisition device, the adaptability and flexibility of the system can be improved.

[0036] Step 3: Determine whether there is a control reference sequence in the historical operation data table whose similarity to the first control reference sequence is greater than a first preset value. If so, proceed to step 4; if not, proceed to step 5.

[0037] Specifically, the historical operation data table is traversed based on the first control reference sequence to determine whether there is a control reference sequence in the historical operation data table whose similarity to the first control reference sequence is greater than a first preset value.

[0038] The first preset value is set according to the experience of those skilled in the art or according to actual application scenarios, and is not limited in the embodiments of the present application.

[0039] Step 4: Define the control reference sequence corresponding to the maximum similarity as the second control reference sequence, use the air-conditioning operation mode corresponding to the second control reference sequence as the first air-conditioning operation mode of any temperature control area, generate a first temperature control instruction based on the first air-conditioning operation mode, and send it to the corresponding air-conditioning equipment.

[0040] If a control reference sequence with a similarity greater than a first preset value exists, it indicates that the historical operation data table contains an operating environment that is highly similar to the current user information and environmental information, indicating that the user's needs may be consistent. The corresponding historical air conditioning operation mode is then used as the current primary air conditioning operation mode for the temperature-controlled area. This air conditioning operation mode includes the air conditioning equipment identifier, set temperature, wind speed, and operating time. This achieves intelligent adjustment of the air conditioning operation mode, a high degree of automation, and improved temperature control accuracy and efficiency.

[0041] Step 5: Obtain the corresponding first temperature control mode based on the identification information of any temperature control area, generate a second air conditioning operation mode based on the user information and the first temperature control mode, generate a second temperature control instruction based on the second air conditioning operation mode, and send it to the corresponding air conditioning equipment.

[0042] Specifically, the first temperature control mode is a temperature control mode corresponding to any temperature control area.

[0043] Specifically, step 2 includes: obtaining air-conditioning equipment information of any temperature-controlled area, and dividing any temperature-controlled area into multiple sub-areas based on the air-conditioning equipment information.

[0044] If the temperature control area is large, multiple air outlets are set up to ensure better temperature control. Each air outlet corresponds to an air conditioning device. In this case, the temperature control area is divided into multiple sub-areas based on the location of the air conditioning device. By dividing the temperature control area into smaller sub-areas, more refined temperature control can be performed on each sub-area, which can reduce unnecessary energy consumption.

[0045] Specifically, user information includes the number of users and user location information. The temperature control mode is generated as follows: Step 511: extract any temperature control area, and divide the air conditioning setting conditions of any temperature control area into N types based on user information.

[0046] Step 512: Extract any air conditioning setting condition and obtain the building structure information corresponding to any air conditioning setting condition; generate M fourth air conditioning operation modes according to preset standards based on any air conditioning setting condition and the building structure information, wherein the fourth air conditioning operation mode includes the temperature setting and wind speed setting of the air conditioning equipment related to the sub-area corresponding to any air conditioning setting condition.

[0047] Step 513: Calculate the energy consumption reduction rate corresponding to any fourth air-conditioning operation mode.

[0048] Step 514: After traversing all fourth air-conditioning operation modes, sort all fourth air-conditioning operation modes in descending order of energy consumption reduction rate.

[0049] Step 515: After traversing all air-conditioning setting conditions, the fourth air-conditioning operation modes with the same sequence number in different air-conditioning setting conditions are grouped together.

[0050] Step 516: assign identifiers to all the fourth air-conditioning operation modes after grouping, and then generate K temperature control modes based on the air-conditioning operation modes after the identifiers are assigned.

[0051] For example, the temperature control area is divided into four sub-areas in the shape of a square: the upper left sub-area is A1, the upper right sub-area is A2, the lower left sub-area is A3, and the lower right sub-area is A4. The maximum capacity of any sub-area is 10 people. When dividing air conditioning control conditions based on user information, a user located in sub-area A1 with 0 to 3 people is used as one air conditioning setting condition; a user located in sub-area A1 with 4 to 10 people is used as another air conditioning setting condition; ..., a user located in sub-area A4 with 0 to 3 people is used as one air conditioning setting condition; and a user located in sub-area A4 with 4 to 10 people is used as another air conditioning setting condition. In other words, the air conditioning setting conditions for the temperature control area can be divided into eight types.

[0052] In a cooling environment, if there are few people in the sub-area, the heat generated is small, and the energy consumption required to reach the target temperature is small; if there are many people in the sub-area, the heat generated is large, and the energy consumption required to reach the target temperature is large; if there are windows or doors in the sub-area, the cold air in the sub-area will overflow, the insulation effect is low, and the energy consumption required to reach the target temperature is large. Based on the above reasons, setting the air conditioning operation mode of the temperature control area based on the air conditioning setting conditions and building structure information can generate a temperature control mode that better meets the actual needs of users, thereby improving user satisfaction.

[0053] Exemplarily, for air-conditioning setting condition B1 (users are located in sub-area A1 and the number of people is 0 to 3), there are walls on two sides and open spaces on two sides, and six fourth air-conditioning operation modes are generated according to preset standards: a1, setting the target temperature of the air-conditioning equipment corresponding to sub-area A1 to 24°C; a2, setting the target temperature of the air-conditioning equipment corresponding to sub-area A1 to 26°C; a3, setting the target temperature of the air-conditioning equipment corresponding to sub-area A1 to 24°C and 26°C in a cycle according to a preset period; a4, setting the target temperature of the air-conditioning equipment corresponding to sub-area A1 to 25°C, and setting the target temperature of the air-conditioning equipment corresponding to sub-area A2 to 26°C; a5, setting the target temperature of the air-conditioning equipment corresponding to sub-area A1 to 25°C, and setting the target temperature of the air-conditioning equipment corresponding to sub-area A3 to 26°C; a6, setting the target temperature of the air-conditioning equipment corresponding to sub-area A1 to 26°C, setting the target temperature of the air-conditioning equipment corresponding to sub-area A2 to 27°C, and setting the target temperature of the air-conditioning equipment corresponding to sub-area A3 to 27°C. Preferably, the target temperature of each air-conditioning device in a4, a5 and a6 can also be set periodically and dynamically; while setting the target temperature, the wind speed can also be set.

[0054] The calculation formula for the energy consumption reduction rate R corresponding to the fourth air-conditioning operation mode i is: , Among them, E t is the total energy consumption in the baseline control mode, E i is the energy consumption in the fourth air-conditioning operation mode i.

[0055] Exemplarily, the above-mentioned reference control mode is that all air-conditioning equipment in the temperature control area operates according to a preset target temperature and a preset wind speed.

[0056] Exemplarily, there are two air-conditioning setting conditions B2 and B3. The result of sorting all the fourth air-conditioning operating modes corresponding to the air-conditioning setting condition B2 is (c1, c2, c3), and the result of sorting all the fourth air-conditioning operating modes corresponding to the air-conditioning setting condition B3 is (d1, d2, d3). c1 and d1 are divided into a group (identified as E1), c2 and d2 are divided into a group (identified as E2), and c3 and d3 are divided into a group (identified as E3). Then, 3 temperature control modes are included. The air-conditioning operating modes corresponding to the first temperature control mode E1 are c1 and d1, the air-conditioning operating modes corresponding to the second temperature control mode E2 are c2 and d2, and the air-conditioning operating modes corresponding to the third temperature control mode E3 are c3 and d3.

[0057] Energy saving and comfort are opposites. When energy saving is high, comfort is relatively low, and when energy saving is low, comfort is relatively high. By sorting all fourth air-conditioning operating modes in descending order of energy consumption reduction rate, users can understand the energy saving of various air-conditioning operating modes and choose the appropriate air-conditioning operating mode according to their own needs. Grouping air-conditioning operating modes with the same serial number can simplify the management of temperature control strategies, because the air-conditioning operating modes in each group have similar energy consumption characteristics and temperature control effects, which helps to unify the temperature control strategies under different air-conditioning setting conditions, making the temperature control of the entire building more coordinated and consistent. Assigning identifiers to the grouped air-conditioning operating modes helps to standardize the management of temperature control modes, so that each mode can be uniquely identified and called, increasing flexibility. Users can choose the most appropriate mode for temperature control according to actual needs.

[0058] When generating the second air-conditioning operation mode of any temperature control area based on user information and the first temperature control mode, the user information includes the number of users and user location information, etc. The corresponding air-conditioning setting conditions can be obtained by analyzing the user information. Each air-conditioning setting condition corresponds to multiple fourth air-conditioning operation modes. The second air-conditioning operation mode can be selected based on the first temperature control mode.

[0059] Preferably, the user information may also include user activities (such as sitting still, exercising) and the like.

[0060] Specifically, before step 5, the first temperature control mode of any temperature control area is set by the management personnel, or the first temperature control mode of any temperature control area is set dynamically according to a time period.

[0061] The manager of any temperature-controlled zone is responsible for that zone. Each zone manager can select the appropriate temperature control mode based on their needs (focusing on energy savings or comfort). Different temperature control modes can be set for each zone based on its function, purpose, or user importance, enabling refined control of the air conditioning equipment in that zone.

[0062] The temperature control mode can also be set dynamically according to the time period. For example, during non-working hours, there are fewer people working, so a temperature control mode with a high energy consumption reduction rate is selected. During working hours, there are more people working, so a temperature control mode with a low energy consumption reduction rate is selected.

[0063] According to the technical solution of the present invention, a personalized temperature control mode can be generated, energy consumption can be controlled more accurately, and energy use can be optimized.

[0064] Step 6: Periodically determine whether user feedback information is received. If so, proceed to step 7. If not, store the current air-conditioning operation mode in the historical operation data table in correspondence with the first control reference sequence.

[0065] Specifically, the current air-conditioning operation mode is the first air-conditioning operation mode or the second air-conditioning operation mode.

[0066] For example, user feedback information may include whether the ambient temperature is high, low, or suitable. The user's perception of ambient temperature may change over time or due to changes in the thickness of the user's clothing. If user feedback information is received, the air conditioner operating mode needs to be adjusted based on the user's actual situation.

[0067] Step 7: Generate a third air-conditioning operation mode for any temperature control area based on the feedback information, generate a third temperature control instruction based on the third air-conditioning operation mode, and send it to the corresponding air-conditioning equipment, and then store the third air-conditioning operation mode in the historical operation data table corresponding to the first control reference sequence.

[0068] Specifically, the user information includes a user ID, and step 7 includes: Step 71: Define the user who sends the feedback information as a first user, and calculate the somatosensory parameter value of any first user based on the personal information and environmental information of any first user.

[0069] Step 72: Based on the user identifier of any first user, obtain a first somatosensory value distribution table of any first user from the historical somatosensory data table.

[0070] Step 73: Based on the feedback information and somatosensory parameter value of any first user, determine whether the specific somatosensory interval in the first somatosensory value distribution table has changed; if so, update the first somatosensory value distribution table based on the feedback information and somatosensory parameter value.

[0071] Step 74: After traversing all first users, extract the somatosensory value distribution table of all users in any temperature control area, and cluster all users based on specific somatosensory intervals into multiple user groups.

[0072] Step 75: Extract any user group, calculate a first average value of the lower limit values of the specific somatosensory interval and a second average value of the upper limit values of the specific somatosensory interval for all users in the user group, and generate a group specific somatosensory interval based on the first average value and the second average value.

[0073] Step 76: After traversing all user groups, generate region-specific somatosensory intervals based on the group-specific somatosensory intervals.

[0074] Step 77: Based on the region-specific somatosensory interval, the current air-conditioning operation mode is learned to generate a third air-conditioning operation mode, so that the somatosensory parameter values of all users fall within the region-specific somatosensory interval.

[0075] The above-mentioned specific somatosensory range is a somatosensory parameter range in which the user feels the ambient temperature is suitable.

[0076] The somatosensory parameter value is based on the principle of human thermal balance, taking into account factors such as air temperature, average radiant temperature, relative humidity, air velocity, human activity level and clothing conditions, and is used to predict the average thermal comfort feeling of the human body in a specific environment. For example, according to the empirical function Calculate the human body parameter values, where x1 is the human metabolic rate, x2 is the thermal resistance of the clothing, x3 is the air temperature, x4 is the average radiant temperature, x5 is the relative humidity, and x6 is the air velocity.

[0077] The human metabolic rate can be estimated by using wearable devices or sensors to monitor the user's physiological parameters, such as heart rate and skin temperature. The user's activity level can also be determined by analyzing the user's movement pattern (such as through accelerometer data) and adjusting the metabolic rate accordingly. Clothing thermal resistance can be estimated based on sensor data of ambient temperature and user activity level. The type of clothing worn by the user can also be obtained (image recognition or user input), and the system estimates the clothing thermal resistance based on the clothing type. In addition, preset default values can be used. These values are set based on average data for the general population. For example, the metabolic rate in a sedentary state is usually set to 1.1 met (metabolic unit), while the typical indoor clothing thermal resistance may be set to 0.6 clo (clothing unit).

[0078] The user's somatosensory value distribution table in the historical somatosensory data table is generated based on the user's historical somatosensory parameter values and historical feedback information. For example, for user Y1, their somatosensory value distribution table is [(-3, z1), low ambient temperature; (z1, z2), suitable ambient temperature; (z2, 3), high ambient temperature], and for user Y2, their somatosensory value distribution table is [(-3, z3), low ambient temperature; (z3, z4), suitable ambient temperature; (z4, 3), high ambient temperature].

[0079] If a user's ambient temperature perception changes (for example, a user previously reported "ambient temperature comfortable" at a certain PMV value but now reports "ambient temperature high"), the newly calculated sensory parameter value is compared with historical data to re-determine the boundaries between different ambient temperature sensations (such as "cold," "comfortable," and "hot"). For example, suppose user Y3 experiences "ambient temperature comfortable" in an environment with a temperature of 22°C and 50% humidity in the morning. The calculated sensory parameter value is 0.5, and this sensory parameter value and the user's perception are stored in the historical sensory data table. In the afternoon, in an environment with a temperature of 22°C and 50% humidity, the user reports "ambient temperature high." The recalculated sensory parameter value remains 0.5. However, due to the change in user Y3's comfort preference, the boundaries between different ambient temperature sensations need to be adjusted. For example, the suitable ambient temperature range in the morning was [-0.6, 0.6], while the suitable ambient temperature range in the afternoon was adjusted to [-0.9, 0.4].

[0080] After updating the somatosensory value distribution table of all users in the temperature-controlled area, all users are clustered based on specific somatosensory intervals and divided into multiple user groups to identify user groups with similar somatosensory needs; then, group-specific somatosensory intervals are generated to determine the community sense needs of each user group; then, based on the group-specific somatosensory intervals, area-specific somatosensory intervals are generated to obtain the community sense needs of all users in the entire temperature-controlled area; finally, based on this community sense need and reinforcement learning of the current air-conditioning operation mode, the environmental conditions are changed to a range that everyone feels comfortable.

[0081] According to the technical solution of the present invention, personalized temperature control services can be provided for different user groups to improve user satisfaction. By dynamically adjusting the boundary values of the user's ambient temperature perception range to optimize the body perception range, unnecessary energy consumption can be reduced while ensuring user comfort.

[0082] Specifically, step 76 includes: Determine whether there are overlapping areas among all group-specific somatosensory intervals. If so, use the overlapping areas as the region-specific somatosensory intervals. If not, obtain the number of users in each user group, set the weight parameters of each user group based on the number of users, and perform weighted averaging on the upper and lower limits of the group-specific somatosensory intervals based on the weight parameters to generate the region-specific somatosensory intervals.

[0083] The overlapping areas of group-specific somatosensory intervals represent the common needs of different user groups. If there are no overlapping areas, generating region-specific somatosensory intervals by obtaining the number of users in each user group and setting weight parameters based on the number of users can make the final somatosensory intervals more representative of the needs and preferences of the majority of users, better balance the needs of different user groups, and achieve fairer resource allocation among different user groups, which helps to improve the efficiency of the entire building temperature control system and user satisfaction.

[0084] As a preferred technical solution of the present invention, in step 6 or step 7, when the air conditioning operation mode is stored in the historical operation data table corresponding to the first control reference sequence, the original control parameter sequence corresponding to the air conditioning operation mode and the various parameter values of the first control reference sequence are averaged, and the control parameter sequence obtained after the averaging is stored corresponding to the air conditioning operation mode.

[0085] Based on this, the amount of data storage can be reduced while making the stored control parameter sequence more stable and reliable, improving the quality of historical operation data and enhancing the robustness in the face of environmental changes and user behavior changes.

[0086] Figure 2 The figure shows a schematic diagram of the structure of an embodiment of the distributed control system for controlling load based on random behavior of users provided by the present invention. Figure 2 As shown, the system includes: a region division module 10 , a data acquisition module 20 , an information judgment module 30 , a first control module 40 , a second control module 50 , an adjustment judgment module 60 and a control adjustment module 70 .

[0087] The area division module 10 is used to obtain a detailed floor plan of the building and divide the building into multiple temperature control areas based on the physical structure of the building.

[0088] The data acquisition module 20 is used to extract any temperature control area, obtain user information and environmental information of any temperature control area, and generate a first control reference sequence based on the user information and environmental information.

[0089] The information judgment module 30 is used to traverse the historical operation data table according to the first control reference sequence and determine whether there is a control reference sequence in the historical operation data table whose similarity with the first control reference sequence is greater than a first preset value. If so, the first control module 40 is entered; if not, the second control module 50 is entered.

[0090] The first control module 40 is used to define the control reference sequence corresponding to the maximum similarity value as the second control reference sequence, use the air-conditioning operation mode corresponding to the second control reference sequence as the first air-conditioning operation mode of any temperature control area, generate a first temperature control instruction based on the first air-conditioning operation mode, and send the first temperature control instruction to the corresponding air-conditioning equipment.

[0091] The second control module 50 is used to obtain the first temperature control mode corresponding to any temperature control area based on the identification information of any temperature control area, generate a second air-conditioning operation mode for any temperature control area based on user information and the first temperature control mode, generate a second temperature control instruction based on the second air-conditioning operation mode, and send the second temperature control instruction to the corresponding air-conditioning equipment.

[0092] The adjustment determination module 60 is configured to periodically determine whether user feedback information has been received. If not, the current air conditioning operation mode is stored in the historical operation data table in correspondence with the first control reference sequence. If so, the control adjustment module 70 is executed, where the current air conditioning operation mode is the first air conditioning operation mode or the second air conditioning operation mode. The control adjustment module 70 is used to generate a third air-conditioning operation mode for any temperature control area based on the feedback information, generate a third temperature control instruction based on the third air-conditioning operation mode, and send the third temperature control instruction to the corresponding air-conditioning equipment, and then store the third air-conditioning operation mode in the historical operation data table corresponding to the first control reference sequence.

[0093] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0094] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The above-described program can be stored in a non-volatile computer-readable storage medium. When executed, the program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0095] The above embodiments merely represent preferred implementations of the present invention. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention.

Claims

1. A distributed control method for control load based on user random behavior, characterized in that: The steps include: Step 1: Divide the building into multiple temperature control zones based on its physical structure; Step 2: Obtain user information and environmental information of any temperature control zone, and generate a first control reference sequence based on the user information and the environmental information; Step 3: Determine whether there is a control reference sequence in the historical operation data table whose similarity to the first control reference sequence is greater than a first preset value. If so, proceed to step 4; if not, proceed to step 5. Step 4: Define the control reference sequence corresponding to the maximum similarity as a second control reference sequence, use the air conditioning operation mode corresponding to the second control reference sequence as the first air conditioning operation mode of any temperature control zone, generate a first temperature control instruction based on the first air conditioning operation mode, and send it to the corresponding air conditioning device; Step 5: acquiring a corresponding first temperature control mode based on the identification information of any temperature control zone, generating a second air conditioning operation mode based on the user information and the first temperature control mode, generating a second temperature control instruction based on the second air conditioning operation mode, and sending the instruction to the corresponding air conditioning device; Step 6: Periodically determine whether user feedback information is received. If so, proceed to step 7. If not, store the current air-conditioning operation mode in the historical operation data table in correspondence with the first control reference sequence. Step 7: Generate a third air-conditioning operation mode for any of the temperature-controlled areas based on the feedback information, generate a third temperature control instruction based on the third air-conditioning operation mode, and send it to the corresponding air-conditioning equipment, and then store the third air-conditioning operation mode in the historical operation data table in correspondence with the first control reference sequence.

2. The method according to claim 1, characterized in that The first environmental information is collected by a data collection device, and the method for obtaining the environmental information is as follows: Obtaining a device identification of the data acquisition device, and extracting a calibration function corresponding to the data acquisition device from a calibration function table based on the device identification; Obtaining a first environmental value and an acquisition time in the first environmental information, calculating a calibration value based on the calibration function, the first environmental value, and the acquisition time, and using the sum of the calibration value and the first environmental value as a second environmental value of the data acquisition device; The environmental information is generated based on the device identification, the second environmental value, and the collection time.

3. The method according to claim 2, characterized in that The calibration function is generated as follows: Step 21: performing a calibration test on the data acquisition device at a preset time interval, obtaining a test result, and storing the test result in a calibration information table corresponding to the device identifier, wherein the test result includes an actual value, a measured value, and a calibration time; Step 22: upon receiving the test result, determining whether the number of all test results corresponding to the device identifier in the calibration information table is 1; if so, calculating an initial calibration value based on the test result, assigning the initial calibration value to the calibration function, and storing the calibration function in the calibration function table corresponding to the device identifier; if not, proceeding to step 23; Step 23: extract all the test results corresponding to the device identification from the calibration information table, perform machine learning on all the test results to generate a new calibration function, and update the calibration function table based on the new calibration function.

4. The method according to claim 1, wherein The step 2 includes: obtaining air-conditioning equipment information of any temperature-controlled area, and dividing any temperature-controlled area into multiple sub-areas based on the air-conditioning equipment information.

5. The method according to claim 4, characterized in that The user information includes the number of users and user location information. The temperature control mode is generated as follows: Step 511: extract any of the temperature control areas, and divide the air conditioning setting conditions of any of the temperature control areas into N types based on the user information; Step 512: Extract any air conditioning setting condition, obtain building structure information corresponding to any of the air conditioning setting conditions, and generate M fourth air conditioning operation modes according to a preset standard based on any of the air conditioning setting conditions and the building structure information, wherein the fourth air conditioning operation modes include temperature settings and wind speed settings of the air conditioning equipment associated with the sub-area corresponding to any of the air conditioning setting conditions; Step 513: Calculate the energy consumption reduction rate corresponding to any fourth air-conditioning operation mode; Step 514: After traversing all fourth air-conditioning operation modes, sort all the fourth air-conditioning operation modes in descending order of the energy consumption reduction rate; Step 515: After traversing all air conditioning setting conditions, group the fourth air conditioning operation modes with the same sequence number in different air conditioning setting conditions into one group; Step 516: assign identifiers to all the fourth air-conditioning operation modes after grouping, and then generate K temperature control modes based on the air-conditioning operation modes after the identifiers are assigned.

6. The method according to claim 1, characterized in that Before step 5, the first temperature control mode of any of the temperature control areas is set by a management personnel, or the first temperature control mode of any of the temperature control areas is set dynamically according to a time period.

7. The method according to claim 1, characterized in that The user information includes a user identifier, and step 7 includes: Step 71: Define the user who sends the feedback information as a first user, and calculate a somatosensory parameter value of any first user based on the personal information of any first user and the environmental information; Step 72: Based on the user identifier of any of the first users, obtain a first somatosensory value distribution table of any of the first users from a historical somatosensory data table; Step 73: Based on the feedback information and the somatosensory parameter value of any of the first users, determine whether a specific somatosensory interval in the first somatosensory value distribution table has changed; if so, update the first somatosensory value distribution table based on the feedback information and the somatosensory parameter value; Step 74: After traversing all first users, extract the somatosensory value distribution table of all users in any temperature control area, and cluster all users based on the specific somatosensory intervals into multiple user groups; Step 75: Extract any user group, calculate a first average value of the lower limit values of the specific somatosensory interval and a second average value of the upper limit values of the specific somatosensory interval for all users in any user group, and generate a group-specific somatosensory interval based on the first average value and the second average value; Step 76: After traversing all user groups, generate a region-specific somatosensory interval based on the group-specific somatosensory interval; Step 77: Based on the region-specific somatosensory interval, the current air-conditioning operation mode is learned to generate the third air-conditioning operation mode, so that the somatosensory parameter values of all users fall within the region-specific somatosensory interval.

8. The method according to claim 7, characterized in that The step 76 includes: Determine whether there is an overlapping area among all group-specific somatosensory intervals. If so, use the overlapping area as the area-specific somatosensory interval. If not, obtain the number of users in each user group, set the weight parameter of each user group based on the number of users, and perform weighted averaging on the upper and lower limits of the group-specific somatosensory interval based on the weight parameter to generate the area-specific somatosensory interval.

9. A distributed control system for controlling loads based on random user behavior, for implementing the method according to any one of claims 1 to 8, characterized in that: include: An area division module, a data acquisition module, an information judgment module, a first control module, a second control module, an adjustment judgment module, and a control adjustment module; The zone division module is used to obtain a detailed floor plan of the building and divide the building into multiple temperature control zones based on the physical structure of the building; The data acquisition module is configured to extract any temperature control area, obtain user information and environmental information of any temperature control area, and generate a first control reference sequence based on the user information and the environmental information; The information judgment module is configured to traverse a historical operation data table according to the first control reference sequence, and determine whether there is a control reference sequence in the historical operation data table whose similarity with the first control reference sequence is greater than a first preset value; if so, enter the first control module; if not, enter the second control module; the first control module being configured to define the control reference sequence corresponding to the maximum similarity as a second control reference sequence, use the air conditioning operation mode corresponding to the second control reference sequence as the first air conditioning operation mode of any of the temperature control areas, generate a first temperature control instruction based on the first air conditioning operation mode, and send the first temperature control instruction to the corresponding air conditioning device; the second control module being configured to obtain a first temperature control mode corresponding to any of the temperature control zones based on identification information of any of the temperature control zones, generate a second air-conditioning operation mode for any of the temperature control zones based on the user information and the first temperature control mode, generate a second temperature control instruction based on the second air-conditioning operation mode, and send the second temperature control instruction to the corresponding air-conditioning device; The adjustment judgment module is used to periodically judge whether user feedback information is received; if not, store the current air-conditioning operation mode and the first control reference sequence in the historical operation data table in correspondence; if so, enter the control adjustment module, wherein the current air-conditioning operation mode is the first air-conditioning operation mode or the second air-conditioning operation mode; The control adjustment module is used to generate a third air-conditioning operation mode for any of the temperature-controlled areas based on the feedback information, generate a third temperature control instruction based on the third air-conditioning operation mode, and send the third temperature control instruction to the corresponding air-conditioning equipment, and then store the third air-conditioning operation mode in the historical operation data table in correspondence with the first control reference sequence.

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