Methods, devices, and systems for controlling electromechanical equipment in multiple areas of a building.

Abstract

This application relates to a method, apparatus, and system for controlling electromechanical equipment in multiple areas of a building. The method includes: acquiring the current energy consumption value of the electromechanical equipment under a data center group control system; acquiring a comparison result between the energy consumption value and a target energy consumption value, and determining a corresponding target data model based on the comparison result and the operating parameters of the electromechanical equipment; wherein, the target data model is used to generate a control logic combination to control the energy consumption value of the electromechanical equipment under the data center group control system to meet preset conditions; the control logic combination represents the combination of control parameters of each electromechanical device under the data center group control system; comparing the energy consumption values ​​of electromechanical equipment in any two areas of multiple areas based on the target data model, and adjusting the target data model corresponding to the higher energy consumption value based on the target data model corresponding to the lower energy consumption value. This application solves the problem that existing building automation systems all adopt a single-system regional control method, resulting in poor automatic control performance.

Description

Technical Field

[0001] This application relates to the field of electromechanical equipment control, and in particular to a method, device and system for controlling electromechanical equipment in multiple areas of a building. Background Technology

[0002] Existing building automation systems, such as data center group control systems, involve a single group control system collecting data from various terminal units, sensors, and actuators via a main control module. This data is then programmed into the main controller to generate control logic, which is subsequently sent to the terminal units for execution. The host computer software then sends modification commands to the main control module to change the terminal equipment, such as adjusting pump or main unit frequencies, causing the system to adjust accordingly for automatic control. However, this existing single-system, regional control method has limited operational data acquisition and requires pre-defined fixed logic. The system gradually reaches an optimal operating state through manual parameter adjustments during operation, resulting in low efficiency and poor automatic control performance. Summary of the Invention

[0003] This application provides a method, device, and system for controlling electromechanical equipment in multiple areas of a building, in order to solve the problem that existing building automation systems all adopt a single-system regional control method, resulting in poor automatic control performance.

[0004] In a first aspect, this application provides a method for controlling electromechanical equipment in multiple areas of a building, comprising: obtaining the current energy consumption value of electromechanical equipment under a data center group control system, wherein one data center group control system corresponds to one area; obtaining a comparison result between the energy consumption value and a target energy consumption value, and determining a corresponding target data model based on the comparison result and the operating parameters of the electromechanical equipment; wherein the target data model is used to generate a control logic combination to control the energy consumption value of the electromechanical equipment under the data center group control system to meet preset conditions; the control logic combination represents the combination of control parameters of each electromechanical equipment under the data center group control system; comparing the energy consumption values ​​of electromechanical equipment in any two areas of the multiple areas based on the target data model, and adjusting the target data model corresponding to the higher energy consumption value based on the target data model corresponding to the lower energy consumption value, wherein the electromechanical equipment in the two areas is the same.

[0005] Secondly, this application provides a control device for electromechanical equipment in multiple areas of a building, comprising: an acquisition module for acquiring the current energy consumption value of electromechanical equipment under a data center group control system, wherein one data center group control system corresponds to one area; a processing module for acquiring a comparison result between the energy consumption value and a target energy consumption value, and determining a corresponding target data model based on the comparison result and the operating parameters of the electromechanical equipment; wherein the target data model is used to generate a control logic combination to control the energy consumption value of the electromechanical equipment under the data center group control system to meet preset conditions; the control logic combination represents the control parameter combination of each electromechanical equipment under the data center group control system; and an adjustment module for comparing the energy consumption values ​​of electromechanical equipment in any two areas of the multiple areas based on the target data model, and adjusting the target data model corresponding to the higher energy consumption value based on the target data model corresponding to the lower energy consumption value, wherein the electromechanical equipment in the two areas is the same.

[0006] Thirdly, this application provides a control system for electromechanical equipment in multiple areas of a building, including the device described in the second aspect.

[0007] Fourthly, this application provides an electronic device, comprising: at least one communication interface; at least one bus connected to the at least one communication interface; at least one processor connected to the at least one bus; and at least one memory connected to the at least one bus, wherein the processor is configured to execute the method for controlling electromechanical equipment in multiple areas of a building as described in the first aspect of this application.

[0008] Fifthly, this application also provides a computer storage medium storing computer-executable instructions, which are used to execute the method for controlling electromechanical equipment in multiple areas of a building as described in the first aspect of this application.

[0009] Compared with the prior art, the technical solution provided in this application has the following advantages: In this application embodiment, after obtaining the energy consumption values ​​of electromechanical equipment in each data center group control system, the corresponding target data model is determined based on the comparison result between the energy consumption value and the target energy consumption value. That is, by comparing the energy consumption values ​​of electromechanical equipment in the same data center group control system vertically, a better target data model for the current data center group control system is obtained. Furthermore, a horizontal comparison can also be made, that is, by comparing the energy consumption values ​​of multiple data center group control systems with the same electromechanical equipment, and adjusting the target data model corresponding to the higher energy consumption value based on the target data model corresponding to the lower current energy consumption value. It can be seen that this application embodiment does not adopt a single-system regional control method as in related technologies, but can obtain the corresponding target data model after cross-system horizontal and vertical comparisons to achieve the lowest energy consumption value and further save energy. Attached Figure Description

[0010] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0013] Figure 1 A flowchart illustrating a method for controlling electromechanical equipment in multiple areas of a building, provided as an embodiment of this application;

[0014] Figure 2 This is a schematic diagram of vertical energy-saving regulation optimization provided in the embodiments of this application;

[0015] Figure 3 This is a schematic diagram of cross-server control of a building automation system with cloud-based adaptive energy-saving control provided in an embodiment of this application;

[0016] Figure 4 This is a schematic diagram of the horizontal energy-saving regulation optimization provided in the embodiments of this application;

[0017] Figure 5 A schematic diagram of a control device for electromechanical equipment in multiple areas of a building, provided as an embodiment of this application;

[0018] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] The following disclosure provides numerous different embodiments or examples for implementing various structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the scope of the invention. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0021] To address the problem that existing building automation systems typically employ single-system, regional control methods, resulting in poor automatic control performance, this application provides a method for controlling electromechanical equipment in multiple areas of a building, such as... Figure 1 As shown, the steps of this method include:

[0022] Step 101: Obtain the current energy consumption value of electromechanical equipment under the data center group control system, where one data center group control system corresponds to one area;

[0023] The building in this application embodiment can be a smart office building, smart factory, smart park, smart hospital, etc., and the building includes multiple areas, each area corresponding to a data center group control system. Each data center group control system includes multiple electromechanical devices, such as servers, lighting equipment, security equipment, air conditioning equipment, communication equipment, etc. The energy consumption value refers to the total energy consumption value of all electromechanical devices in the current data center group control system.

[0024] Step 102: Obtain the comparison results between the energy consumption value and the target energy consumption value, and determine the corresponding target data model based on the comparison results and the operating parameters of the electromechanical equipment; wherein, the target data model is used to generate control logic combination to control the energy consumption value of the electromechanical equipment under the computer room group control system to meet the preset conditions; the control logic combination represents the combination of control parameters of each electromechanical equipment under the computer room group control system;

[0025] In response, if the current energy consumption value is lower than the target energy consumption value, the data model determined based on the current operating parameters of the electromechanical equipment becomes the target data model; if the current energy consumption value is higher than the target energy consumption value, the current operating parameters need to be adjusted before generating the corresponding target data model. Therefore, the data model in this embodiment serves to compare the energy consumption data of electromechanical equipment in the data center group control system, such as generator sets, valves, pumps, and sensors, under different conditions and operating states. This allows for the acquisition of a better data source for control logic, namely the control logic combination generated from the target data model. This combination can control the energy consumption of the electromechanical equipment in the data center group control system to the expected value, thereby achieving energy conservation.

[0026] Step 103: Based on the target data model, compare the energy consumption values ​​of electromechanical equipment in any two regions among multiple regions, and adjust the target data model corresponding to the higher energy consumption value based on the target data model corresponding to the lower energy consumption value. The electromechanical equipment in any two regions is the same.

[0027] Step 102 above compares the energy consumption values ​​of node devices in the same data center group control system to obtain the target data model, which is to control the electromechanical equipment in the same data center group control system vertically. However, in actual application scenarios, there may be multiple data center group control systems, and the electromechanical equipment in these multiple data center group control systems is the same. Therefore, in this embodiment, a horizontal comparison can also be performed. For data center group control systems with the same electromechanical equipment, it is determined whether their energy consumption values ​​are the same under the same conditions. If they are different, it indicates that the target data model of the data center group control system with higher energy consumption needs to be adjusted. Specifically, it can be adjusted based on the target data model of the data center group control system with lower energy consumption.

[0028] Through steps 101 to 103 above, in this embodiment, after obtaining the energy consumption values ​​of electromechanical equipment in each data center group control system, the corresponding target data model is determined based on the comparison result between the energy consumption values ​​and the target energy consumption values. That is, by vertically comparing the energy consumption values ​​of electromechanical equipment in the same data center group control system, a better target data model for the current data center group control system is obtained. Furthermore, a horizontal comparison can also be made, that is, comparing the energy consumption values ​​of multiple data center group control systems with the same electromechanical equipment, and adjusting the target data model corresponding to the higher energy consumption value based on the target data model corresponding to the lower current energy consumption value. It can be seen that this embodiment does not adopt a single-system regional control method as in related technologies, but rather obtains the corresponding target data model after cross-system horizontal and vertical comparisons to achieve the lowest energy consumption value and further save energy.

[0029] In this embodiment of the application, the method for obtaining the current energy consumption value of electromechanical equipment under the data center group control system involved in step 101 above may further include:

[0030] Step 11: Obtain the operating parameters of each electromechanical device in the data center group control system;

[0031] Step 12: Determine the energy consumption value based on the operating parameters of the electromechanical equipment.

[0032] In specific examples, electromechanical equipment can include servers, lighting equipment, security equipment, air conditioning equipment, communication equipment, and so on. Different application scenarios require different electromechanical equipment, and the energy consumption values ​​of the corresponding equipment can be obtained based on actual needs.

[0033] In this embodiment, if the current energy consumption value is lower than the target energy consumption value, it indicates that the current operating parameters of the electromechanical equipment are feasible, meaning that the corresponding target data model can be generated according to the current operating parameters of the electromechanical equipment. If the current energy consumption value is higher than the target energy consumption value, it indicates that the current operating parameters of the electromechanical equipment need to be adjusted, and need to be adjusted to the expected value. Based on this, the method of determining the corresponding target data model based on the comparison results and the operating parameters of the electromechanical equipment involved in step 102 above can further include:

[0034] Step 21: If the comparison result shows that the energy consumption is less than or equal to the target energy consumption value, generate the corresponding first target data model based on the current operating parameters of the electromechanical equipment.

[0035] Step 22: If the comparison result shows that the energy consumption is greater than the target energy consumption value, adjust the operating parameters of the electromechanical equipment and generate a second target data model based on the adjusted operating parameters. The second target data model generates a control logic combination to control the energy consumption value of the electromechanical equipment under the computer room group control system to be lower than the current energy consumption value.

[0036] As can be seen, in this embodiment, vertical data energy-saving adjustments can be performed, that is, comparing the energy consumption value of the data center group control system with its own until a value that meets expectations is found, thereby generating a corresponding target data model. Furthermore, the method of adjusting the operating parameters of electromechanical equipment and generating a second target data model based on the adjusted operating parameters, involved in step 22 above, can further include:

[0037] Step 31: Adjust the operating parameters of the electromechanical equipment and generate a first data model based on the adjusted operating parameters. The first data model generates a control logic combination to control the first energy consumption value of the electromechanical equipment under the computer room group control system to be lower than the current energy consumption value.

[0038] Step 32: Adjust the operating parameters of the electromechanical equipment again, and generate a second data model based on the adjusted operating parameters. The second data model generates a control logic combination to control the second energy consumption value of the electromechanical equipment under the computer room group control system to be lower than the first energy consumption value.

[0039] Step 33: Determine the second target data model from the second data model.

[0040] In this regard, in specific examples, such as Figure 2As shown, taking an air conditioning system as an example, the initial control mode of the air conditioning system records the operating status of the unit, the refrigeration system equipment, the cooling system, terminal sealing panels, fans, valves, and other equipment. Taking a data center group control system as an example, the data center energy efficiency value and energy consumption value are the final output data. The data involved in data center operation includes unit parameters such as start / stop, mode, and operating frequency; water pumps: start, stop, and operating frequency; valves: opening degree and opening feedback; sensors: pressure sensors, temperature sensors, and humidity sensors; and meters: water meters, electricity meters, and heat meters. An initial data model can be generated using the above parameters, and the energy consumption data P of the initial data model is recorded. The initial model energy consumption data is compared with the user's target energy consumption data P. When P ≤ P0... 目 Once this adjustment is complete and the energy consumption data meets the set target value, P is set as the target energy consumption value. When P ≥ Ptarget, the system will self-learn the logic between each device in the initial logical data model and generate data model 1. The data model generates control logic combinations to regulate the operating status of the entire system's devices and records the energy consumption data as P1, satisfying P1 ≤ Ptarget. At this point, the system uses P1 as the energy consumption target value and continues to perform logic optimization learning to generate data model 2. It then generates control logic combinations to regulate the devices, records the energy consumption P2, and compares the results. This process is repeated until Pn ≤ Pn-1, achieving continuous optimized operation of the air conditioning system and meeting the continuous energy-saving target.

[0041] The above explains the horizontal data comparison in the embodiments of this application. The vertical data comparison will be explained below. Based on this, the method in step 103 above, which involves comparing the energy consumption values ​​of electromechanical equipment in any two regions based on the target data model, and adjusting the target data model corresponding to the higher energy consumption value based on the target data model corresponding to the lower energy consumption value, can further include:

[0042] Step 41: Identify areas with identical electromechanical equipment across multiple regions;

[0043] Step 42: Select any two areas from the areas with the same electromechanical equipment;

[0044] Step 43: Compare the energy consumption values ​​of electromechanical equipment in any two regions until the region with the same electromechanical equipment is identified as having the lowest energy consumption value.

[0045] Step 44: Based on the target data model corresponding to the area with the lowest energy consumption value, adjust the target data models corresponding to other areas of electromechanical equipment.

[0046] It is evident that for buildings with multiple areas, the electromechanical equipment in the data center group control system may also share the same areas. For these areas with identical equipment, a longitudinal comparison can be used to determine which area has the lowest energy consumption. This allows for the adjustment of the target data model corresponding to higher energy consumption based on the target data model corresponding to the area with the lowest energy consumption. Furthermore, the method for adjusting other target data models corresponding to the same areas of electromechanical equipment, based on the target data model corresponding to the area with the lowest energy consumption, as mentioned in step 44 above, can further include:

[0047] Step 51: Compare the first control logic combination generated by the target data model corresponding to the region with the lowest energy consumption value with the second control logic combination generated by other target data models.

[0048] Step 52: Determine the different control parameters in the first control logic combination and the second control logic combination;

[0049] Step 53: Adjust other target data models based on different control parameters.

[0050] In a specific example, within the air conditioning room control logic combination, the outlet water temperature is the same as T1, the equipment parameters are identical, the operating frequency of the water pump in system 1 is X1 Hz, the main unit's operating frequency is X2, and the energy consumption is P1. The operating frequency of the water pump in system 2 is Y1 Hz, the main unit's operating frequency is Y2, and the energy consumption is P2. Since P2 < P1, system 2 horizontally adjusts system 1, changing the equipment's operating state to meet energy consumption optimization requirements. It can be seen that this is a comparison coefficient of the operating frequencies of the main pumps and main units of the two systems, horizontally adjusting them to match the operating frequency of system 1 before operation.

[0051] Furthermore, in this embodiment, vertical and horizontal data comparisons can be performed simultaneously. That is, after the production data model is compared vertically, it is compared with the parameters and energy consumption data of the same devices in another system across servers to form a comparison ratio. At this time, the horizontal ratio is input into the vertical logic group, and the resulting energy consumption data is compared. Since the energy consumption data of the original vertically generated control logic combination is better than the newly generated logic control combination, if it is not better, it will be run according to the vertically generated control logic combination until a ratio of better energy consumption data appears.

[0052] The present application will be explained and described below with reference to specific embodiments, which provide a cloud-based adaptive energy-saving control building automation system, such as... Figure 3As shown, the system includes: data center group control, terminal centralized control, photovoltaic energy storage scheduling, intelligent lighting, and other systems. It is not limited to the control of a single system but includes cross-system linkage control logic. Data from each region is stored separately in the cloud using their respective control logic units, and cross-server data fusion is performed to form a multi-system aggregated big data cloud platform, providing a unified source of regional control data. The integrated cloud server includes the following modules:

[0053] The system operating parameter and energy consumption data comparison unit compares the operating status of electromechanical equipment (including various energy-consuming equipment within buildings) in different areas with their related energy consumption data to form various data models. The data models are used to compare the energy consumption data of generator sets, valves, pumps, and sensors under different conditions and operating states, serving as a data source for generating better control logic.

[0054] Data storage unit: Used to store the basic data model of each region and the data model generated after cross-server comparison. In the existing data center group control system, one data center corresponds to one server, thus the data model of each system is independently segmented.

[0055] In this embodiment, "cross-service" refers to enabling network communication between various independent data center group control systems, allowing data exchange between the two systems. Specifically, this can be achieved by using different cloud service data models, horizontally comparing data from the same devices in each stage under the same energy consumption, and then adjusting each parameter individually to obtain Padjusted < Poriginal, thus forming cross-service data adjustment. Furthermore, the basic data model continuously changes over time, constantly adjusting Padjusted < Poriginal to continuously optimize the energy-saving effect of each service system.

[0056] Logic self-learning unit: This unit collects and statistically analyzes the execution of logic control modules in various cloud regions to identify and learn the data. It then performs systematic statistical analysis based on different system and equipment parameters. The purpose of this statistical analysis is to obtain the data correlations between the unit, actuators, and sensors under the current control logic, in order to establish a data model.

[0057] Specifically, this can be achieved by: recording the operating parameters of each link in the system according to the initial control logic, and then, according to the set energy-saving target, providing the system energy consumption P, and changing the dynamic parameters such as the running time, temperature, pressure, frequency, and rate of each device in the system to achieve energy-saving stability of the system. Then, based on the usage demand of the terminal cooling capacity, the energy consumption data is obtained in reverse, and the equipment parameters are adjusted in a positive direction, thus cyclically adjusting the system.

[0058] Control logic generation unit: Used to generate a more energy-efficient set of control logic by collecting, comparing, analyzing, and learning data models from the above units.

[0059] In this specific example, the current air conditioning unit operates at 80Hz, requiring an outlet water temperature of 17 degrees Celsius. The water pump operates at 50Hz, requiring a terminal temperature of 24 degrees Celsius. When the air conditioning terminal temperature is increased by one degree Celsius, or the outdoor temperature decreases, data model analysis shows that the air conditioning unit's operating frequency of 70Hz can meet the outlet water temperature requirements and satisfy the terminal usage. Therefore, a new control logic group is generated, and the air conditioning unit operates at 70Hz to achieve energy savings.

[0060] Adaptive matching execution unit: Used to automatically identify and execute the generated control logic set based on regional characteristics. Specifically, the regional characteristic self-identification and execution means that the generated control logic combination includes the equipment of the unit and each actuator, each device in each region, and the location of each device. The logic group uses unique identifiers such as device ID information, data type, and signal transmission method as data tags, and then distributes the data to the end-user for execution via hardware devices such as gateways.

[0061] Based on this, vertical data energy-saving regulation and optimization, such as Figure 2 As shown, taking an air conditioning system as an example, the initial control mode of the air conditioning system records the operating status of the unit, the refrigeration system equipment, the cooling system, terminal sealing panels, fans, valves, and other equipment. Taking a data center group control system as an example, the data center energy efficiency value and energy consumption value are the final output data. The data involved in data center operation includes unit parameters such as start / stop, mode, and operating frequency; water pumps: start, stop, and operating frequency; valves: opening degree and opening feedback; sensors: pressure sensors, temperature sensors, and humidity sensors; and meters: water meters, electricity meters, and heat meters. An initial data model can be generated using the above parameters, and the energy consumption data P of the initial data model is recorded. The initial model energy consumption data is compared with the user's target energy consumption data P. When P ≤ P0... 目 Once this adjustment is complete and the energy consumption data meets the set target value, P is set as the target energy consumption value. When P ≥ Ptarget, the system will self-learn the logic between each device in the initial logical data model and generate data model 1. The data model generates control logic combinations to regulate the operating status of the entire system's devices and records the energy consumption data as P1, satisfying P1 ≤ Ptarget. At this point, the system uses P1 as the energy consumption target value and continues to perform logic optimization learning to generate data model 2. It then generates control logic combinations to regulate the devices, records the energy consumption P2, and compares the results. This process is repeated until Pn ≤ Pn-1, achieving continuous optimized operation of the air conditioning system and meeting the continuous energy-saving target.

[0062] Vertical data control relies on data comparison and learning based on a data model of a single region. However, this approach is limited by the number and type of equipment, as well as the amount of data, resulting in a single type of data model. Therefore, dynamic horizontal data comparison is crucial. Figure 4As shown, the device logic sets and data of each region are integrated through the regional clouds to form a cross-server data pool. Device operation data, corresponding energy consumption data, and operation logic from each cloud are compared horizontally to generate a big data AI model. This model optimizes and updates energy consumption data, and then vertically compares and matches control logic, adjusting changes to the regional clouds and finally distributing them to end devices for execution. It continuously monitors regional operation data and uploads it to the integrated data pool. The AI ​​self-learning big data model continuously generates more control logic modules as data sources become richer and more abundant. This iterative process allows the building automation system to continuously adapt to changes in building equipment over time, dynamically adjusting and optimizing building energy efficiency. Regarding the combination of vertical and horizontal approaches, in a specific example, each air conditioning room system includes unit equipment, execution equipment, and sensing equipment. When the system is automatically run through control logic, all equipment in a room forms a single data model. This model records the operating status and mode of each device, as well as the energy consumption and energy efficiency values ​​of the entire system. Vertically, data is compared along a time axis to dynamically adjust the status of individual devices or local system devices, monitoring energy consumption data to obtain a more energy-efficient combination of operation logic. Horizontal comparison data refers to comparing the differences in the states of the same equipment in two systems when the energy efficiency or energy consumption values ​​of two systems are the same during a certain operating period. By referring to the comparison data, a new data model is established, and then the energy consumption data is calculated in reverse for comparison, so as to obtain a more energy-efficient control logic combination.

[0063] This application addresses the problem in related technologies where the system is relatively independent, has limited dynamic data sources, lacks horizontal data comparison, and cannot adjust to environmental changes after initial system settings, thus failing to meet original energy-saving targets or causing energy-saving effects to gradually decrease over time. This application dynamically adjusts system operating data, using cloud-based big data comparison to adjust the judgment conditions and logic execution of system units, sensors, and actuators, ensuring system energy-saving stability and reducing interference from changes in local environment and system usage habits on overall system energy efficiency. Furthermore, it solves the problem in related technologies where a fixed set of control logic requires manual analysis, calculation, comparison, adjustment, and modification of control parameters and execution logic before being programmed into the logic control module. This is not only inefficient but also susceptible to human error, resulting in poor intelligent and automatic control and unmanaged energy consumption. This application, through multi-logic comparison and automatic modification, implements control over target energy-saving effects, eliminating human management factors and achieving AI-driven automatic regulation, meeting the intelligent energy-saving regulation needs of building automation systems.

[0064] Corresponding to the above Figure 1 This application provides a device for controlling electromechanical equipment in multiple areas of a building, such as... Figure 5As shown, the device includes:

[0065] The acquisition module 502 is used to acquire the current energy consumption value of electromechanical equipment under the data center group control system, wherein one data center group control system corresponds to one area;

[0066] The processing module 504 is used to obtain the comparison result between the energy consumption value and the target energy consumption value, and to determine the corresponding target data model based on the comparison result and the operating parameters of the electromechanical equipment; wherein, the target data model is used to generate control logic combination to control the energy consumption value of the electromechanical equipment under the computer room group control system to meet the preset conditions; the control logic combination represents the combination of control parameters of each electromechanical equipment under the computer room group control system.

[0067] The adjustment module 506 is used to compare the energy consumption values ​​of electromechanical equipment in any two regions among multiple regions based on the target data model, and to adjust the target data model corresponding to the higher energy consumption value based on the target data model corresponding to the lower energy consumption value, wherein the electromechanical equipment in any two regions is the same.

[0068] In this embodiment, after obtaining the energy consumption values ​​of electromechanical equipment in each data center group control system, a corresponding target data model is determined based on the comparison between the energy consumption values ​​and the target energy consumption values. That is, by vertically comparing the energy consumption values ​​of electromechanical equipment in the same data center group control system, a better target data model for the current data center group control system is obtained. Furthermore, a horizontal comparison can also be used, i.e., comparing the energy consumption values ​​of multiple data center group control systems with the same electromechanical equipment, and adjusting the target data model corresponding to the higher energy consumption value based on the target data model corresponding to the lower current energy consumption value. Therefore, this embodiment does not employ a single-system regional control method as in related technologies, but rather obtains the corresponding target data model after cross-system horizontal and vertical comparisons to achieve the lowest energy consumption value and further save energy.

[0069] In an optional embodiment of this application, the processing module in this application embodiment may further include: a first processing unit, configured to generate a corresponding first target data model based on the current operating parameters of the electromechanical equipment when the comparison result is that the energy consumption is less than or equal to the target energy consumption value; and a second processing unit, configured to adjust the operating parameters of the electromechanical equipment when the comparison result is that the energy consumption is greater than the target energy consumption value, and generate a second target data model based on the adjusted operating parameters, wherein the second target data model generates a control logic combination to control the energy consumption value of the electromechanical equipment under the data center group control system to be lower than the current energy consumption value.

[0070] In an optional embodiment of this application, the second processing unit in this application embodiment may further include: a first processing subunit, configured to adjust the operating parameters of the electromechanical equipment and generate a first data model based on the adjusted operating parameters, wherein the first data model generates a control logic combination to control the first energy consumption value of the electromechanical equipment under the data center group control system to be lower than the current energy consumption value; a second processing subunit, configured to adjust the operating parameters of the electromechanical equipment again and generate a second data model based on the adjusted operating parameters, wherein the second data model generates a control logic combination to control the second energy consumption value of the electromechanical equipment under the data center group control system to be lower than the first energy consumption value; and a first determining subunit, configured to determine the second data model to a second target data model.

[0071] In an optional embodiment of this application, the adjustment module may further include: a first determining unit, configured to determine regions with identical electromechanical equipment among multiple regions; a selecting unit, configured to select any two regions from the regions with identical electromechanical equipment; a processing unit, configured to compare the energy consumption values ​​of the electromechanical equipment in the two regions until the region with the lowest energy consumption value is determined from the regions with identical electromechanical equipment; and an adjustment unit, configured to adjust other target data models corresponding to the regions with identical electromechanical equipment based on the target data model corresponding to the region with the lowest energy consumption value.

[0072] In an optional embodiment of this application, the adjustment unit may further include: a comparison subunit, used to compare the first control logic combination generated by the target data model corresponding to the region with the lowest energy consumption value with the second control logic combination generated by other target data models; a second determination subunit, used to determine the different control parameters in the first control logic combination and the second control logic combination; and an adjustment subunit, used to adjust other target data models based on the different control parameters.

[0073] In an optional embodiment of this application, the acquisition module may further include: an acquisition unit, used to acquire the operating parameters of each electromechanical device under the data center group control system; and a second determination unit, used to determine the energy consumption value based on the operating parameters of the electromechanical device.

[0074] like Figure 6 As shown in the figure, this application provides an electronic device, including a processor 611, a communication interface 612, a memory 613, and a communication bus 614, wherein the processor 611, the communication interface 612, and the memory 613 communicate with each other through the communication bus 614.

[0075] Memory 613 is used to store computer programs;

[0076] In one embodiment of this application, when the processor 611 executes the program stored in the memory 613, it implements the method for controlling electromechanical equipment in multiple areas of a building provided in any of the aforementioned method embodiments. Its function is similar and will not be described again here.

[0077] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method for controlling electromechanical equipment in multiple areas of a building as provided in any of the foregoing method embodiments.

[0078] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0079] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0080] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0081] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for controlling electromechanical equipment in multiple areas of a building, characterized in that, include: Obtain the current energy consumption value of electromechanical equipment under the data center group control system, where one data center group control system corresponds to one area; The comparison results between the energy consumption value and the target energy consumption value are obtained, and a corresponding target data model is determined based on the comparison results and the operating parameters of the electromechanical equipment. The target data model is used to generate control logic combinations to ensure that the energy consumption value of the electromechanical equipment under the data center group control system meets preset conditions. The control logic combinations represent the control parameter combinations of each electromechanical device under the data center group control system. The data model is used to compare the energy consumption data of the electromechanical equipment in the data center group control system under different conditions and operating states, thereby obtaining a better data source for the control logic. Based on the target data model, the energy consumption values ​​of electromechanical equipment in any two regions of the multiple regions are compared, and the target data model corresponding to the higher energy consumption value is adjusted based on the target data model corresponding to the lower energy consumption value, wherein the electromechanical equipment in any two regions is the same.

2. The method according to claim 1, characterized in that, Based on the comparison results and the operating parameters of the electromechanical equipment, the corresponding target data model is determined, including: If the comparison result indicates that the energy consumption value is less than or equal to the target energy consumption value, a corresponding first target data model is generated based on the current operating parameters of the electromechanical equipment. If the comparison result shows that the energy consumption is greater than the target energy consumption value, the operating parameters of the electromechanical equipment are adjusted, and a second target data model is generated based on the adjusted operating parameters. The second target data model generates a control logic combination to control the energy consumption value of the electromechanical equipment under the data center group control system to be lower than the current energy consumption value.

3. The method according to claim 2, characterized in that, Adjusting the operating parameters of the electromechanical equipment and generating a second target data model based on the adjusted operating parameters includes: The operating parameters of the electromechanical equipment are adjusted, and a first data model is generated based on the adjusted operating parameters. The first data model generates a control logic combination to control the first energy consumption value of the electromechanical equipment under the data center group control system to be lower than the current energy consumption value. The operating parameters of the electromechanical equipment are adjusted again, and a second data model is generated based on the adjusted operating parameters. The second data model generates a combination of control logic to control the second energy consumption value of the electromechanical equipment under the data center group control system to be lower than the first energy consumption value. The second data model is used to determine the second target data model.

4. The method according to claim 1, characterized in that, Based on the target data model, the energy consumption values ​​of electromechanical equipment in any two of the multiple regions are compared, and the target data model corresponding to the higher energy consumption value is adjusted based on the target data model corresponding to the lower energy consumption value, including: Identify areas in the plurality of regions where the electromechanical equipment is identical; Select any two areas from the areas with the same electromechanical equipment; Compare the energy consumption values ​​of electromechanical equipment in any two regions until the region with the lowest energy consumption value is determined from the regions with the same electromechanical equipment. Based on the target data model corresponding to the region with the lowest energy consumption, the target data models corresponding to other regions of electromechanical equipment are adjusted.

5. The method according to claim 4, characterized in that, Based on the target data model corresponding to the area with the lowest energy consumption, adjustments are made to other target data models corresponding to the same area of ​​electromechanical equipment, including: The first control logic combination generated by the target data model corresponding to the region with the lowest energy consumption value is compared with the second control logic combination generated by other target data models. Identify the different control parameters in the first control logic combination and the second control logic combination; Adjust other target data models based on the different control parameters.

6. The method according to claim 1, characterized in that, Obtain the current energy consumption values ​​of electromechanical equipment under the data center group control system, including: Obtain the operating parameters of each electromechanical device under the data center group control system; The energy consumption value is determined based on the operating parameters of the electromechanical equipment.

7. A control device for electromechanical equipment in multiple areas of a building, characterized in that, include: The acquisition module is used to acquire the current energy consumption value of electromechanical equipment under the data center group control system, where one data center group control system corresponds to one area; The processing module is used to obtain the comparison result between the energy consumption value and the target energy consumption value, and to determine the corresponding target data model based on the comparison result and the operating parameters of the electromechanical equipment; wherein, the target data model is used to generate control logic combinations to control the energy consumption value of the electromechanical equipment under the data center group control system to meet preset conditions; the control logic combinations represent the control parameter combinations of each electromechanical equipment under the data center group control system; the function of the data model is to compare the energy consumption data of the electromechanical equipment in the data center group control system under different conditions and operating states, thereby obtaining a data source for better control logic; The adjustment module is used to compare the energy consumption values ​​of electromechanical equipment in any two of the multiple regions based on the target data model, and to adjust the target data model corresponding to the higher energy consumption value based on the target data model corresponding to the lower energy consumption value, wherein the electromechanical equipment in any two regions is the same.

8. A control system for electromechanical equipment in multiple areas of a building, characterized in that, Includes the apparatus as described in claim 7.

9. An electronic device, comprising: At least one communication interface; At least one bus connected to the at least one communication interface; At least one processor connected to the at least one bus; At least one memory connected to the at least one bus, wherein the processor is configured to execute the method for controlling electromechanical equipment in multiple areas of a building as described in any one of claims 1 to 6.

10. A computer storage medium storing computer-executable instructions, said computer-executable instructions being used to execute the method for controlling electromechanical equipment in multiple areas of a building as described in any one of claims 1 to 6.

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