Control method of building energy equipment, server, medium and storage product

Abstract

The invention provides a control method of building energy equipment, a server, a medium and a storage product, and relates to the field of intelligent control. The method comprises the steps of firstly obtaining operation data of energy equipment in a plurality of buildings under different environment parameter combinations, establishing an equipment optimal state prediction model according to the operation data, then obtaining operation environment parameters of target equipment by utilizing an environment sensor in a target building, and inputting the operation environment parameters into the model to obtain optimal operation parameters of the target equipment under the environment. And then obtaining the current operation parameter of the target equipment, judging whether the deviation between the current operation parameter and the optimal operation parameter exceeds a preset threshold value or not, if so, generating an equipment regulation and control instruction according to the deviation, and finally sending the instruction to the target equipment to adjust the operation parameter, thereby ensuring the efficient operation of the equipment.

Description

Technical Field

[0001] The present application relates to the field of intelligent control, and in particular to a control method, server, medium and storage product for building energy equipment. Background Art

[0002] As the scale of buildings continues to expand and their functions become increasingly complex, the problem of building energy consumption has become increasingly prominent, and energy management of equipment in buildings is crucial to energy conservation and emission reduction.

[0003] In existing methods, energy devices are often controlled according to a fixed schedule. For example, in buildings such as office buildings, shopping malls, and hotels, lighting in public areas (such as corridors, stairwells, elevator halls, toilets, etc.) is usually turned on and off according to a fixed schedule.

[0004] However, traditional methods often ignore the impact of environmental factors on equipment when adjusting equipment operation to save energy, which will cause energy waste to a certain extent. For example, many public areas of buildings have sufficient natural light near windows for most of the day, but due to fixed lighting schedules, lamps are still turned on, resulting in a large amount of electricity waste. Summary of the invention

[0005] The present application provides a control method, server, medium and storage product for building energy equipment, which are used to optimize the operating status of building energy equipment, improve energy utilization efficiency, and reduce energy waste and equipment loss.

[0006] In a first aspect, the present application provides a control method for building energy equipment, the method comprising: obtaining operating data of various types of energy equipment in multiple buildings under different environmental parameter combinations; establishing an equipment optimal state prediction model based on the operating data and the corresponding environmental parameter combination; obtaining the operating environment parameters of the target equipment through multiple environmental sensors pre-installed in the target building; inputting the operating environment parameters into the equipment optimal state prediction model to obtain the optimal operating parameters of the target equipment under the operating environment parameters; after obtaining the current operating parameters of the target equipment, determining whether the parameter deviation between the current operating parameters and the optimal operating parameters exceeds a preset deviation threshold; if so, generating corresponding equipment control instructions based on the parameter deviation; and sending the equipment control instructions to the target equipment to enable the target equipment to adjust the operating parameters.

[0007] By adopting the above technical solution, the optimal operating state can be accurately determined for the target equipment, avoiding inefficient operation of the equipment. When it is determined that the deviation between the current operating parameters and the optimal operating parameters exceeds the threshold, a control instruction is generated, which can adjust the equipment operating parameters in time to ensure that the equipment always operates in a state close to the optimal state, thereby effectively improving the equipment operating efficiency, reducing energy consumption, and extending the service life of the equipment.

[0008] In combination with some embodiments of the first aspect, in some embodiments, after the step of obtaining the operating environment parameters of the target device through multiple environmental sensors pre-installed in the target building, it also includes: determining a redundant environmental measurement value through a redundant environmental sensor, which is a sensor pre-installed as a backup for the environmental sensor; when the deviation between the redundant environmental measurement value and the operating environment parameter exceeds a preset maximum deviation threshold, marking the environmental sensor as an abnormal sensor; and calculating an average value based on the data measured by the redundant environmental sensor as the current operating environment parameter.

[0009] By adopting the above technical solution, it is possible to detect abnormalities of environmental sensors in a timely manner, ensure the accuracy of the acquired operating environment parameters, and thus make the optimal operating parameters output by the equipment optimal state prediction model more reliable, make the equipment control instructions more accurate, ensure stable and efficient operation of the equipment, and reduce equipment control errors caused by sensor failures.

[0010] In combination with some embodiments of the first aspect, in some embodiments, after the step of sending the device control instruction to the target device so that the target device adjusts the operating parameters, it also includes: determining that the target device currently reaches the optimal operating parameters; comparing and analyzing the current optimal operating parameters with the historical optimal operating parameters of the same type of device under the operating environment parameters; if the difference between the current optimal operating parameters and the historical optimal operating parameters exceeds the preset threshold of the parameters, it is determined that the target device has a potential fault; after determining the location information of the target device, sending a fault prompt information containing the potential fault and the location information to the management end.

[0011] By adopting the above technical solutions, potential equipment problems can be discovered in a timely manner, intervention can be made in the early stages of failure, the impact of equipment failure on the building energy system can be reduced, and maintenance costs can be reduced. At the same time, energy waste caused by equipment failure and interference with the operation of other equipment can be avoided.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after the step of sending the device control instruction to the target device so that the target device adjusts the operating parameters, it also includes: after receiving the viewing instruction sent by the management end, sending the consumption data and energy usage of the target devices in different areas and different energy types in the target building to the management end.

[0013] By adopting the above technical solutions, it will be easier for managers to fully understand the energy consumption of energy equipment in the building, help to formulate more reasonable energy management strategies, timely discover abnormal energy usage areas or equipment, further optimize energy distribution and use, improve energy utilization efficiency, and realize refined management of building energy.

[0014] In combination with some embodiments of the first aspect, in some embodiments, if it exceeds, the step of generating a corresponding device control instruction according to the parameter deviation specifically includes: the adjustment amplitude Δu in the device control instruction is calculated by the following formula: Where Δx represents the parameter deviation, K p is the proportionality coefficient, K i is the integration coefficient, K d is the differential coefficient, K p , K i and K d It is obtained in advance through experiments based on the physical characteristics of the target device. The device control instruction is used to control the target device to adjust according to the preset frequency and adjustment range to achieve the optimal operating state.

[0015] By adopting the above technical solution, the target equipment can be flexibly adjusted according to the adjustment range to avoid overly aggressive or conservative regulation, so that the equipment can be smoothly and efficiently adjusted to the optimal operating state.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the step of sending the device control instruction to the target device so that the target device adjusts the operating parameters, it also includes: continuously monitoring the water and electricity usage data of the target device; if the abnormal fluctuation of the water and electricity usage data within the preset time exceeds the set abnormal fluctuation range, analyzing the relationship between the current operating parameters of the target device and the optimal operating parameters; if the current operating parameters do not match the optimal operating parameters, determining whether the current operating parameters are within the preset normal operation threshold of the device; if the current operating parameters of the target device are within the normal operation threshold of the device, determining that the target device is operating normally, and determining that there is a fault in the water and electricity supply equipment, which is used to provide water and electricity to the target device; sending an alarm message to the user end that the water and electricity supply equipment is suspected of having a fault.

[0017] By adopting the above technical solution, it is possible to promptly detect abnormal water and electricity usage, accurately determine whether it is an equipment problem or a water and electricity supply problem, ensure the stability of water and electricity supply, avoid affecting equipment operation due to water and electricity problems, and reduce equipment damage and energy waste caused by water and electricity anomalies.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of continuously monitoring the water and electricity usage data of the target device, it also includes: if the water and electricity usage data exceeds the water and electricity usage threshold of the target device, the power of the target device is adjusted or the set water-using equipment is shut down according to the weight data of the target device and a preset adjustment plan. The weight data is determined in advance after a comprehensive assessment of the importance level, operating priority and energy consumption ratio of the target device. The adjustment plan includes prioritizing shutting down the target device whose weight is less than a first preset weight threshold according to the weight data, while maintaining the normal operation of the target device whose weight is greater than the second preset weight threshold, and reducing the power of the target device whose weight is greater than the first preset weight threshold and less than the second preset weight threshold, and the first preset weight threshold is less than the second preset weight threshold.

[0019] By adopting the above technical solutions, excessive consumption of water and electricity resources can be avoided. On the premise of ensuring the stability of the overall energy system of the building, priority can be given to ensuring the water and electricity supply of important equipment, and limited water and electricity resources can be reasonably allocated to ensure orderly operation of equipment in the building, reducing safety hazards and equipment failures caused by water and electricity overload.

[0020] In a second aspect, the present application provides a server, comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the server to execute the method described in the first aspect and any possible implementation of the first aspect.

[0021] In a third aspect, the present application provides a computer-readable storage medium comprising instructions, which, when executed on a server, causes the server to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, the present application provides a computer program product, which, when executed on a server, enables the server to execute the method described in the first aspect and any possible implementation manner of the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The technical means of obtaining energy equipment operation data to establish an optimal state prediction model, using environmental sensors to obtain parameters and comparing deviations to generate control instructions are adopted. Therefore, the technical problem that the equipment operation parameters in the existing technology are difficult to adjust to the optimal state in time, resulting in low efficiency and energy waste is effectively solved, thereby achieving the technical effect of improving equipment operation efficiency, reducing energy consumption and extending equipment service life.

[0024] 2. Since the technical means of comparing the current parameters with the historical optimal parameters after the equipment adjusts the operating parameters to determine potential faults and send fault prompt information to the management end is adopted, the technical problem that it is difficult to detect potential equipment faults in time in the existing technology, resulting in high maintenance costs, energy waste and interference with other equipment is effectively solved, thereby achieving the technical effect of early intervention in equipment problems, reducing maintenance costs, reducing energy waste and avoiding interference with the operation of other equipment.

[0025] 3. The technical means of continuously monitoring water and electricity usage data after the equipment adjusts its operating parameters, judging equipment or water and electricity supply problems based on abnormal fluctuations and generating control instructions has been adopted. Therefore, the technical problem that it is difficult to accurately judge the cause of water and electricity abnormalities in the existing technology, which leads to equipment damage and energy waste, has been effectively solved, thereby achieving the technical effect of ensuring stable water and electricity supply and reducing equipment damage and energy waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a control method of a building energy device in an embodiment of the present application; Figure 2 is another flow chart of the control method of the building energy equipment in the embodiment of the present application; Figure 3 It is a schematic diagram of a physical device structure of a server in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0028] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0029] For ease of understanding, the following is a description of the process of the method provided by this implementation. Figure 1, which is a flow chart of a method for controlling building energy equipment in an embodiment of the present application.

[0030] S101. Obtaining operation data of various energy equipment in multiple buildings under different combinations of environmental parameters; The server first needs to collect the operating data of various energy equipment in multiple buildings, such as air conditioners, lighting, elevators, etc. under different combinations of environmental parameters. Specific technical means may include: the server is connected to the environmental monitoring system and equipment control system in each building, and real-time data on environmental parameters such as temperature, humidity, and light collected by sensors in the building, as well as real-time operating status data of various energy equipment, such as the cooling capacity and air volume of air conditioners, the switch status and brightness of lighting, etc., and the energy consumption of energy equipment can also be collected through energy consumption data collectors. The server calls the historical data interface of the building control system to extract the operating data of various energy equipment in the building at different periods in the past period of time. For buildings where it is difficult to obtain equipment operating data in real time, the server can set up a data collection device, install it near the energy equipment, collect equipment operating status data in real time, and send it to the server through the network. The server can also obtain the original data of building energy consumption from the energy management department, and obtain equipment operating parameters by combining other building data processing and analysis. By collecting equipment operating data through multiple channels, the server can obtain a richer and more comprehensive equipment operating data set as the basis for establishing an accurate optimal state prediction model.

[0031] S102, establishing an equipment optimal state prediction model based on the operation data and corresponding environmental parameters; After obtaining enough equipment operation data, the server needs to establish an optimal state prediction model for equipment operation based on these data and the corresponding environmental parameters. Specific technical means may include: the server preprocesses the collected environmental parameter data, including cleaning, integration, formatting, etc., to obtain standardized structured data, the server matches the corresponding equipment operation data with the environmental data, and establishes a relationship model between the operation data and the environmental parameters. The server determines the optimal operating state range of each device under different environments based on historical data, which serves as sample data for training the optimal state prediction model. The server uses machine learning algorithms such as deep learning to train the equipment optimal state prediction model. It can also integrate expert experience for model training. The server uses cross-validation, grid search and other methods to adjust parameters during the training process to ensure model accuracy. And track model performance indicators and continuously optimize the model. The server can establish optimal state prediction sub-models for different types and brands of equipment, and develop model compression methods to reduce the model size. The server uses version control tools to manage model iterations to ensure traceability and consider compatibility with old model versions.

[0032] S103. Obtain the operating environment parameters of the target device through multiple environmental sensors pre-installed in the target building; the server is connected to the environmental monitoring system in the target building, which includes multiple types of environmental sensors, such as temperature sensors, humidity sensors, light sensors, etc. These sensors are pre-arranged in different areas of the building to monitor the environmental parameters in the building in real time. The server obtains the location information of the target device, determines the area where it is located, and then determines the environmental sensor that has the greatest impact on the target device based on the area. For example, if the target device is a lighting device in the lobby on the first floor of the building, the temperature sensor, humidity sensor and light sensor in the lobby area on the first floor are determined as key environmental sensors. The server obtains the real-time monitoring data of these key environmental sensors from the environmental monitoring system as the operating environment parameters of the target device.

[0033] Considering that the error of a single sensor may be large, the server can also select multiple sensors of the same type in the same area and use the data mean or median to reduce the error. The server continuously monitors the status of the sensor. If a sensor is offline or returns erroneous data, it will promptly obtain data from redundant sensors as an alternative. The server will also regularly verify the accuracy of sensor data. If the sensor error is large, the sensor will be replaced. The server can use blockchain and other technologies to record sensor data to ensure that the data cannot be tampered with. In order to improve the comprehensiveness of environmental parameter collection, the server can also combine the design structure data of the target building to establish a sensor selection optimization model. The model comprehensively considers multiple factors such as area, distance, and occlusion, and intelligently determines the key environmental sensor combination that affects the target device.

[0034] In some embodiments, in the target building, the server identifies multiple environmental sensors of the same type, such as temperature sensors, humidity sensors, etc. These sensors are located at different locations near the target device and are used to monitor the operating environment. The server designates several of the sensors as primary sensors responsible for obtaining the operating environment parameters of the device, and the remaining sensors are set as redundant sensors. After obtaining the environmental measurement values ​​of the primary sensors, the server synchronously obtains the measurement data of the redundant sensors as redundant environmental measurement values. The server performs denoising and quality assessment on multiple redundant environmental measurement values, filters out abnormal data, and obtains a valid set of redundant measurement values. The server calculates the average or median value of the redundant measurement values ​​and compares them with the measurement values ​​of the primary sensor. If the deviation between the two exceeds the maximum deviation threshold pre-configured by the server, it is determined that the measurement of the primary sensor is abnormal. The server marks the primary sensor as an abnormal sensor and switches the redundant sensor as a backup primary sensor.

[0035] After determining that the main sensor is abnormal, the server marks it as "abnormal state" and records the abnormal event, including abnormal sensor number, abnormal time, abnormal data and other information. At the same time, a sensor abnormality alarm is sent, the server switches the sensor state, and updates the standby redundant sensor to the main sensor to continue to obtain operating environment parameters. After the abnormal sensor is locked, the server only uses redundant environmental sensors to collect operating environment data. The server obtains the measurement values ​​of the redundant sensors, performs denoising, filters out invalid measurement values, and calculates the arithmetic mean of the processed valid redundant measurement value set. The calculated average value is used as the current environmental parameter and provided to the device status prediction model. Through the mutual backup design of redundant sensors and main sensors, environmental parameter acquisition is more reliable and single point failure problems are avoided.

[0036] S104, inputting the operating environment parameters into the device optimal state prediction model to obtain the optimal operating parameters of the target device under the operating environment parameters; The server inputs the acquired operating environment parameters of the target device, such as temperature, humidity and light data, into the pre-trained device optimal state prediction model, which can be a model independently established for the target device or a general model established for the target device category. The server inputs the operating environment parameters, and the model, after multiple layers of calculation, gives the optimal operating parameters of the target device in the current environment at the output layer, such as the optimal setting temperature and air volume of the air conditioner, the optimal brightness of the lighting, etc. The server corrects the model output results based on expert experience to avoid model prediction deviations. If the environmental parameters change significantly, the server uses incremental learning to quickly adjust the model to ensure prediction accuracy. To improve the robustness of the model, the server uses Monte Carlo algorithms to generate more comprehensive environmental parameter samples, expand the model training sample library, and enhance the model's ability to adapt to more complex environments. The server regularly uses newly collected data to update the sample library and model to continuously optimize the model. The server also develops lightweight model compression methods to reduce prediction delays and improve real-time control response speed.

[0037] S105, after obtaining the current operating parameters of the target device, determining whether a parameter deviation between the current operating parameters and the optimal operating parameters exceeds a preset deviation threshold; The server connects to the control system of the target device and obtains the current operating parameter data of the target device in real time, such as the output temperature, static pressure, air volume and other parameters of the air conditioner. After obtaining the parameters, the server uses the parameter mapping model established by the machine learning algorithm to filter out the impact of the device's own state changes on the parameters, and obtains parameters that reflect the degree of matching between the device and the environment. The server obtains the probability distribution model of the normal operating parameters of the device based on historical data training, and compares it with the optimal operating parameters to determine whether the current parameters are within the normal range. If it is not within the normal range, the server compares the difference between the optimal operating parameters output by the model and the current operating parameter data based on the parameter deviation threshold set by expert experience. If the difference exceeds the deviation threshold, it is determined that the device needs to be adjusted.

[0038] If the parameter deviation exceeds the preset deviation threshold, step S106 is executed; If the parameter deviation does not exceed the preset deviation threshold, step S107 is executed.

[0039] S106, if exceeded, generating corresponding equipment control instructions according to the parameter deviation; When the server determines that the deviation between the current operating parameters of the target device and the optimal operating parameters exceeds the preset deviation threshold, it will start the device control instruction generation process. Specifically, the server receives the deviation data of the optimal parameters output by the model, and determines the deviation between it and the current operating parameter data. According to the deviation characteristics and the operating characteristics of the device, the server selects a suitable one from a plurality of pre-set control strategies. The control strategy includes the control amplitude of the target device. If the control amplitude is Δu, it can be obtained as follows: Where Δx represents the parameter deviation, K p is the proportionality coefficient, K i is the integration coefficient, K d is the differential coefficient, K p , K i and K d It is obtained in advance through experiments based on the physical characteristics of the target device. The device control instructions are used to control the target device to adjust according to the preset frequency and adjustment range to achieve the best operating state. The experiment mentioned here may include obtaining the device response data under different operating conditions in advance. During the experiment, the input parameters of the equipment (such as control signals, environmental conditions, etc.) are gradually changed, and the output parameters of the equipment (such as operating status, energy consumption, etc.) are accurately measured at the same time, and the operating data of the equipment under various working conditions are recorded, including the actual response of the equipment under different parameter deviations. During the experiment, a large amount of equipment operation data is collected, including parameter deviation Δx, the corresponding actual output changes of the equipment, and dynamic response data during the adjustment process. Based on the experimental data and the expected control performance requirements of the equipment, K is preliminarily determined. p , K i, K d Then, through multiple experiments and simulations, the coefficient values ​​are continuously adjusted, the control effect of the equipment is observed, and the performance indicators of the system are evaluated, such as steady-state error, overshoot, adjustment time, etc.

[0040] In some embodiments, the control amplitude can also be obtained as follows: Δu = K1×(Δx)2×sgn(Δx)+K2×∫(Δx)3dt+K3×e^|Δx|×dΔx / dt, where sgn(Δx) represents the sign function of the deviation, e represents the base of the natural logarithm, and K1, K2, and K3 are coefficients obtained from experiments on the physical characteristics of the device. This formula maintains the accuracy of control and increases the ability to respond quickly to large deviations by introducing square terms, cubic integrals, and exponential differentials. The server determines the control strategy of the target device based on the control amplitude, generates specific device control instructions, and then writes the device control instructions in a specific format and communication protocol to ensure that the target device can accurately understand and execute them.

[0041] S107: Send the device control instruction to the target device so that the target device adjusts the operating parameters.

[0042] After generating the device control instructions, the server needs to send the instructions to the corresponding target devices accurately and timely to adjust the device operating parameters. This process involves efficient communication with the target device control system and effective management of instruction sending and execution, ensuring that the device can adjust the operating parameters according to the expected control strategy and ensure the stable operation of the entire building energy system.

[0043] In the above embodiments, a series of technical means are used to obtain the operating data of energy equipment under different environmental parameter combinations through multiple channels to establish an accurate prediction model, use widely distributed environmental sensors to obtain the operating environmental parameters of the target equipment, input the environmental parameters into the model to obtain the optimal operating parameters and compare the deviation with the current operating parameters obtained in real time to generate control instructions, and accurately send the instructions to the target equipment and continuously monitor the subsequent operating status. Therefore, it is possible to accurately grasp the equipment operation requirements and make timely adjustments.

[0044] In some embodiments, after sending the device control instruction to the target device, the server can continuously monitor the device operation status, track the key parameter values, and determine whether the parameters are adjusted to the optimal operation state. When the device operation parameters are monitored to enter the optimal parameter range predicted by the model, the server determines that the device has reached the optimal state. After reaching the optimal state, the server immediately re-acquires the real-time operation parameter data of the target device and records it in the parameter state database. The re-acquired parameter data will be used as the benchmark for subsequent potential fault detection. The server calls the parameter state database to extract the current operation parameters of the target device after it reaches the optimal state. The server determines the same type of device based on the device type, brand, model, production date and other information, and searches for the historical optimal operation parameter records of such devices in the historical data with similar environmental parameters. Statistical methods such as mean, median, variance, etc. are used to calculate the reasonable range of historical optimal parameters, and the current parameters of the target device are compared with the historical optimal parameter range one by one to analyze the differences. Based on the historical statistical data of the device, the server determines the reasonable fluctuation range of each parameter and sets the parameter threshold. Compare the difference between the current parameter and the historical best parameter to see if it exceeds the threshold. If the difference exceeds the threshold, it is determined that there is an abnormality. There is a high probability that the deviation of the parameter trend is caused by equipment failure. Based on the differences of multiple parameters, if most of the key parameters show abnormalities, it is confirmed that the target device has a potential fault. When the server detects a potential fault in the target device, it calls the positioning system to obtain the precise location of the device. The server encapsulates the fault prompt information, including: device number, device type, fault type, location coordinates, etc., and automatically sends the fault prompt information to the system administrator through email, text message, etc. After receiving the prompt, the management end can arrange personnel for inspection and maintenance as soon as possible to avoid the expansion of the accident.

[0045] In some embodiments, the server provides an interface for the management end to initiate a data viewing request. The management end selects the target building and specific area to be viewed on the interface. After receiving the viewing instruction containing the target building and area information, the server queries the database related to the building. The server locates the target equipment in different areas based on the area information, such as lighting and air conditioning in the office area, service facilities in the computer room, etc. The server extracts its historical operation data based on the equipment type, including energy consumption, usage time, switch status, etc. For different energy forms, the server classifies and counts the consumption of electricity, water, natural gas, etc., and summarizes the total energy consumption of each area. The server uses data structuring and visualization technology to uniformly make the equipment consumption data into tables and statistical charts according to the area and energy type, and sends the processed consumption data and usage to the management end through the data exchange interface pre-agreed with the management end. After receiving the data, the management end can intuitively understand the energy usage status of each area in the building for formulating energy-saving strategies.

[0046] After combining the above embodiments, the method provided by this embodiment is further described in more detail below. Figure 2 , is another flow chart of the control method of building energy equipment in an embodiment of the present application.

[0047] S201, continuously monitoring the water and electricity usage data of the target device; The server first establishes a stable communication connection with the water and electricity metering equipment in the building. These metering devices include smart meters, water meters, etc., which can collect water and electricity consumption data of the target equipment in real time. The communication method can use wired Ethernet, wireless Wi-Fi, ZigBee or other dedicated industrial communication protocols to ensure the accuracy and timeliness of data transmission. For example, for important equipment in large commercial buildings, the server is connected to a high-precision smart meter through industrial Ethernet to obtain power consumption data accurate to the minute level; for some equipment that is more dispersed and has lower real-time requirements, such as some lighting systems or small office equipment, a wireless ZigBee network can be used to connect water meters or electricity meters, which can not only meet data collection needs, but also reduce wiring costs.

[0048] In some embodiments, if the water and electricity usage data exceeds the water and electricity usage threshold of the target device, the power of the target device is adjusted or the set water-using device is turned off according to the weight data of the target device and the preset adjustment scheme. Specifically, the server continuously obtains real-time water and electricity usage data from the water and electricity metering device (such as smart meter, water meter) connected to the target device. The server sets the water and electricity usage threshold of the target device in advance based on factors such as the historical water and electricity usage data, equipment performance, and building energy management strategy of the target device. This threshold is a judgment standard for determining when the power or operating status of the device needs to be adjusted to avoid excessive consumption of water and electricity resources or safety problems that may be caused by overload. The server compares the water and electricity usage data obtained in real time with the set water and electricity usage threshold. If it is found that the water and electricity usage data exceeds the corresponding threshold at a certain monitoring time point, the server will start the subsequent processing flow based on the weight data and the preset adjustment scheme. The server classifies the target equipment according to its importance in the overall operation of the building. The factors considered in determining the importance level include the criticality of the function undertaken by the equipment to the normal operation of the building, the impact of equipment failure on the activities of people and business development in the building, etc. These can be the levels set by the staff. For example, for hospital buildings, life support systems (such as ventilators, heart monitors, etc.) are extremely important level equipment because they are directly related to the life safety of patients; while ordinary lighting equipment is relatively less important. In addition to the importance level, the server also considers the operation priority of the target equipment. The factors determining the operation priority include the timeliness requirements of the equipment operation, the urgency of the business or area served by the equipment, etc. For example, in an office building, the server room equipment that is performing urgent data processing tasks has a higher operation priority because interrupting its operation may cause data loss or business interruption; while the lighting equipment in the corridor has a lower operation priority during non-office hours. The server can also analyze the proportion of the target equipment in the total energy consumption of the building. Energy consumption percentage refers to the ratio of the total energy consumption of the target device in a specific time period to the total energy consumption of all devices in the building in the same time period. It intuitively reflects the share of the device in the overall energy consumption of the building. Devices with a high energy consumption percentage have a greater impact on building energy management, and their weights are correspondingly higher. The specific process of obtaining the energy consumption percentage is as follows: Install high-precision metering equipment, such as smart electricity meters, water meters, gas meters, etc., on each energy supply branch line in the building to accurately measure the energy consumption data of each device or device group. These metering devices can record energy usage in real time or periodically, and transmit the data to the server or energy management system. Determine the appropriate data collection cycle. According to the type of equipment and energy management needs, you can choose to collect data at the minute level, hour level, day level or other time intervals.After each data collection cycle, the server or energy management system summarizes and counts the collected energy consumption data to calculate the total energy consumption of each target device in that cycle. At the same time, the system calculates the total energy consumption of all devices in the building in the same collection cycle. Based on the above statistical data, the energy consumption percentage of the target device is calculated using the following formula: Energy consumption percentage = total energy consumption of the target device / total energy consumption of all devices in the building × 100%.

[0049] In addition, the server can pre-set a first preset weight threshold and a second preset weight threshold, which are used to divide different device processing strategies. The first preset weight threshold is low and is used to filter out relatively unimportant devices that can be shut down first; the second preset weight threshold is high and is used to determine key devices that must maintain normal operation. The server divides the devices into three categories according to the predetermined target device weight data: devices with weights less than the first preset weight threshold, devices with weights greater than the second preset weight threshold, and devices with weights between the two. Taking an office building as an example, assuming that the weight of a small fan device is 0.2 (less than 0.3), it is a device that can be shut down first; the weight of the key server device in the server room is 0.8 (greater than 0.7), which is a device that must maintain normal operation; and the weight of the air conditioning equipment in the ordinary office area is 0.5 (between 0.3 and 0.7), which is a device that needs to adjust power. For devices with weights less than the first preset weight threshold, the server sends a control instruction to directly shut down these devices to quickly reduce water and electricity consumption. For devices with weights greater than the second preset weight threshold, the server ensures that they continue to operate normally and do not perform power adjustment or shutdown operations to ensure the normal implementation of key functions of the building. For devices whose weights are between the first preset weight threshold and the second preset weight threshold, the server reduces their power according to the preset power adjustment strategy. The power adjustment strategy can be set according to the device type and specific needs, such as reducing the device power by a certain proportion (such as 20%, 30%, etc.), or dynamically adjusting the power according to the operating status and environmental parameters of the device. After executing the adjustment plan, the server continuously monitors the water and electricity usage data of the target device and the overall energy supply and demand in the building. If it is found that the water and electricity usage still exceeds the threshold or other abnormal conditions occur, the server can further optimize the adjustment plan according to the new situation, such as further reducing the power of intermediate weight devices, re-evaluating device weights and adjusting the list of devices to be shut down or kept running.

[0050] S202: If the abnormal fluctuation of the water and electricity usage data within the preset time exceeds the set abnormal fluctuation range, the relationship between the current operating parameters of the target device and the optimal operating parameters is analyzed; The server continuously acquires the water and electricity consumption data of the target device through the communication connection established with the water and electricity metering devices (such as smart meters and water meters) in the building. These metering devices record the water and electricity usage at certain time intervals (such as every minute, every hour, etc.) and transmit the data to the server. The server pre-sets the abnormal fluctuation range of water and electricity usage data and the preset time for judgment. The abnormal fluctuation range can be obtained by statistical analysis based on the historical water and electricity usage data of the device, and the preset time is determined based on the operating characteristics of the device and actual needs.

[0051] After receiving the real-time water and electricity usage data, the server will calculate the fluctuation of the water and electricity usage data within the preset time and determine whether it exceeds the set abnormal fluctuation range. The fluctuation calculation can be performed in a variety of ways, which are not limited here, such as calculating statistical indicators such as the variance and standard deviation of the data, and comparing them with the preset threshold. If the abnormal fluctuation of the water and electricity usage data within the preset time exceeds the set abnormal fluctuation range, the server continues to analyze the relationship between the current operating parameters and the optimal operating parameters of the target device. The server first obtains the current operating parameters of the target device, which may include the power, temperature, pressure, flow, etc. of the device, and the specific parameters depend on the type of device. At the same time, the server obtains the optimal operating parameters of the target device under the current environmental parameters from the previously established device optimal state prediction model. Then, the server compares the current operating parameters with the optimal operating parameters and analyzes the degree of difference between the two, including the deviation direction (whether it is higher or lower than the optimal value) and the deviation size of each parameter. This analysis process may involve data normalization processing to more accurately compare the differences between different parameters.

[0052] S203: If the current operating parameter does not match the optimal operating parameter, determine whether the current operating parameter is within a preset normal operation threshold of the device; After determining that the current operating parameters do not match the optimal operating parameters, the server obtains the normal operating thresholds of the target device pre-set. These thresholds are determined based on factors such as the technical specifications of the device, the safe operating range, and historical operating data, and cover various key indicators of the device operating parameters, such as the upper and lower limits of power, the safe range of temperature, etc. The server compares the current operating parameters of the target device with the corresponding normal thresholds one by one, and for each parameter, determines whether it is within the range specified by the normal threshold.

[0053] If it is not within the normal operation threshold of the device, execute step S204; If the device is within the normal operation threshold, execute step S205.

[0054] S204, determining that the target device has a fault, and sending device fault prompt information to the user terminal; When it is determined in S203 that the current operating parameters of the target device are not within the preset normal threshold of the device operation, the server determines that the target device has a fault based on this. This judgment is a comprehensive consideration of the comparison results of the various operating parameters of the device with the normal threshold, which may involve multiple key parameters exceeding the normal range at the same time or a key parameter seriously deviating from the normal range. The server generates detailed device fault prompt information based on the relevant information of the target device, which includes the device number, device type, fault occurrence time, current abnormal value of the operating parameter and possible fault cause (based on the pre-set fault diagnosis rules and knowledge base, the server can preliminarily infer the cause of the fault based on the abnormal situation of the operating parameter). The server selects an appropriate method to send the device fault prompt information to the user end according to the pre-configured communication settings. The communication method may include SMS, email, system pop-up push, mobile application push and other forms to ensure that relevant personnel can receive fault notifications in time. The server sends the generated device fault prompt information to the target user end accurately according to the selected sending method.

[0055] S205, determining that the target device is working normally, and determining that there is a fault in the water and electricity supply device, which is used to provide water and electricity to the target device; After judging in S203 that the current operating parameters of the target device are within the preset normal threshold of the device operation, the server will confirm the operating status of the target device again to ensure that the previous judgment is accurate. This may involve further monitoring and analysis of the device operating parameters to see if there are potential abnormal trends or short-term fluctuations that do not exceed the normal threshold. Since the water and electricity usage data fluctuate abnormally (as judged in S202), but the target device's own operating parameters are normal, the server begins to analyze the relationship between the water and electricity supply equipment and the operation of the target device. The server takes into account that the operating status of the water and electricity supply equipment may affect the water and electricity usage data of the target device. Even if the target device itself can adapt to the fluctuation of water and electricity supply within a certain range, abnormal water and electricity supply may still be the cause of abnormal water and electricity usage data. For example, the voltage instability of the water and electricity supply equipment may cause fluctuations in the power measurement of the target device, but the control and protection mechanism of the target device itself enables it to continue to work normally. However, this abnormal water and electricity supply situation needs to be detected and processed to avoid potential damage to the long-term operation of the equipment or affect the normal operation of other related equipment.

[0056] Based on the above analysis, the server determines that there is a fault in the water and electricity supply equipment. This judgment is made after excluding the possibility of the target equipment's own fault and combining it with the abnormal fluctuation of the water and electricity usage data. The failure of the water and electricity supply equipment may include abnormal water supply pipeline pressure, unstable supply voltage or frequency, and inaccurate measurement caused by the failure of the water and electricity metering equipment itself. For example, if the server detects that the water and electricity usage data of multiple devices in the entire building area have similar abnormal fluctuations, and the operating parameters of the target equipment are normal, it is likely that the common part of the water and electricity supply system (such as the main water supply pump, distribution room equipment, etc.) has a fault. The server records the fault-related information of the water and electricity supply equipment, including the time of the fault, the scope of the water and electricity supply equipment that may be involved (such as the specific distribution room, water supply pipeline branch, etc.), and the characteristics of the abnormally fluctuating water and electricity data (such as the voltage fluctuation range, abnormal changes in water flow, etc.). This information will be used for further troubleshooting and diagnosis in the future, and it will also help managers understand the full picture of the fault and provide a basis for formulating maintenance plans.

[0057] S206: Send an alarm message to the user indicating that the water and electricity supply equipment is suspected to have a fault.

[0058] The server generates detailed alarm information based on the determined fault conditions of the water and electricity supply equipment. The alarm information includes relevant identification information of the water and electricity supply equipment (such as equipment number, location, etc.), description of the fault type (such as voltage instability, abnormal water pressure, etc.), time of fault occurrence, and assessment of the possible impact on the building energy system (for example, which areas may be affected by the normal operation of equipment, whether there are safety hazards, etc.). The server determines the user end that receives the suspected fault alarm information of the water and electricity supply equipment, which usually includes the terminal equipment of relevant personnel such as building facility management personnel and water and electricity maintenance personnel.

[0059] In the embodiment of the present application, due to the continuous monitoring of the target equipment's water and electricity usage data, the equipment power is adjusted or the water-using equipment is shut down according to the water and electricity usage threshold, the equipment or water and electricity supply problems are judged according to the water and electricity fluctuations and the corresponding control instructions are generated, as well as a series of technical means for subsequent continuous monitoring, it is possible to comprehensively and timely grasp the water and electricity related information of the building energy equipment and make precise adjustments. It effectively solves the problems in the prior art that it is difficult to grasp the dynamics of water and electricity usage in real time, cannot effectively deal with water and electricity anomalies, unreasonable allocation of water and electricity resources, and difficult to ensure the stability of water and electricity supply, thereby achieving the technical effect of optimizing water and electricity resource allocation, improving energy utilization efficiency, reducing equipment failure rate, and ensuring the stable operation of the building energy system.

[0060] The following describes the server in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , is a schematic diagram of a physical device structure of a server in an embodiment of the present application.

[0061] It should be noted that Figure 3 The structure of the server shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0062] like Figure 3 As shown, the server includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0063] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0064] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 309, and / or installed from a removable medium 311. When the computer program is executed by a central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0065] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0066] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0067] Specifically, the server of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the control method of the building energy equipment provided in the above embodiment is implemented.

[0068] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the server described in the above embodiment; or may exist independently without being assembled into the server. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the server, the server implements the control method of the building energy equipment provided in the above embodiment.

[0069] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0070] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0071] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A control method for building energy equipment, characterized in that: The method comprises: Obtain operating data of various energy equipment in multiple buildings under different combinations of environmental parameters; Establishing an equipment optimal state prediction model based on the operation data and corresponding environmental parameters; Acquire the operating environment parameters of the target device through multiple environmental sensors pre-installed in the target building; Inputting the operating environment parameters into the device optimal state prediction model to obtain the optimal operating parameters of the target device under the operating environment parameters; After obtaining the current operating parameters of the target device, determining whether a parameter deviation between the current operating parameters and the optimal operating parameters exceeds a preset deviation threshold; If it exceeds, a corresponding device control instruction is generated according to the parameter deviation; The device control instruction is sent to the target device so that the target device adjusts the operating parameters.

2. The method according to claim 1, characterized in that After the step of obtaining the operating environment parameters of the target device through a plurality of environmental sensors pre-installed in the target building, the method further includes: Determining a redundant environmental measurement value by means of a redundant environmental sensor, wherein the redundant environmental sensor is a sensor provided in advance as a backup for the environmental sensor; When the deviation between the redundant environmental measurement value and the operating environment parameter exceeds a preset maximum deviation threshold, marking the environmental sensor as an abnormal sensor; An average value is calculated based on the data measured by the redundant environmental sensors as the current operating environment parameter.

3. The method according to claim 1, characterized in that After the step of sending the device control instruction to the target device so that the target device adjusts the operating parameters, the method further includes: Determining that the target device currently reaches the optimal operating parameters; Compare and analyze the current optimal operating parameters with the historical optimal operating parameters of the same type of equipment under the operating environment parameters; If the difference between the current optimal operating parameter and the historical optimal operating parameter exceeds a preset parameter threshold, it is determined that the target device has a potential fault; After determining the location information of the target device, fault prompt information including the potential fault and the location information is sent to the management end.

4. The method according to claim 1, characterized in that: After the step of sending the device control instruction to the target device so that the target device adjusts the operating parameters, the method further includes: After receiving the viewing instruction sent by the management end, the consumption data and energy usage of target devices in different areas and different energy types in the target building are sent to the management end.

5. The method according to claim 1, characterized in that If it exceeds, the step of generating corresponding equipment control instructions according to the parameter deviation specifically includes: The adjustment amplitude Δu in the device control instruction is calculated by the following formula: Where Δx represents the parameter deviation, K p is the proportionality coefficient, K i is the integration coefficient, K d is the differential coefficient, K p , K i and K d It is obtained in advance through experiments based on the physical characteristics of the target device. The device control instruction is used to control the target device to adjust according to the preset frequency and adjustment range to achieve the best operating state.

6. The method according to claim 1, characterized in that After the step of sending the device control instruction to the target device so that the target device adjusts the operating parameters, the method further includes: Continuously monitor the water and electricity usage data of the target device; If the abnormal fluctuation of the water and electricity usage data within the preset time exceeds the set abnormal fluctuation range, analyzing the relationship between the current operating parameters of the target device and the optimal operating parameters; If the current operating parameters do not match the optimal operating parameters, determining whether the current operating parameters are within a preset threshold for normal operation of the device; If the current operating parameters of the target device are within the normal operating threshold of the device, it is determined that the target device is operating normally, and it is determined that there is a fault in the water and electricity supply equipment, which is used to provide water and electricity to the target device; an alarm message indicating that there is a suspected fault in the water and electricity supply equipment is sent to the user end.

7. The method according to claim 6, characterized in that After the step of continuously monitoring the water and electricity usage data of the target device, the method further includes: If the water and electricity usage data exceeds the water and electricity usage threshold of the target device, the power of the target device is adjusted or the set water-using equipment is shut down according to the weight data of the target device and a preset adjustment plan. The weight data is determined in advance after a comprehensive assessment of the importance level, operation priority and energy consumption ratio of the target device. The adjustment plan includes preferentially shutting down the target devices whose weights are less than a first preset weight threshold according to the weight data, while maintaining the normal operation of the target devices whose weights are greater than a second preset weight threshold, and reducing the power of the target devices whose weights are greater than the first preset weight threshold and less than the second preset weight threshold, and the first preset weight threshold is less than the second preset weight threshold.

8. A server, characterized in that: The server includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the server to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a server, the server is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a server, the server is caused to execute the method according to any one of claims 1 to 7.

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