CN107392465B - Operation management method and server for building electromechanical equipment - Google Patents
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Abstract
The application provides an operation management method and a server for building electromechanical equipment, wherein the method comprises the following steps: determining each attribute of the customized service object according to the input customized target parameter; acquiring current parameters of each attribute of an object; obtaining an adjusting strategy according to the customized target parameter and the current parameter and a preset algorithm, wherein the adjusting strategy comprises one or more adjusting instructions; and outputting a corresponding adjusting instruction to the building electromechanical equipment for controlling the object according to the adjusting strategy. The system provides quantifiable, customizable and verifiable integral service management of the building electromechanical system.
Description
Technical Field
The application relates to the technical field of control of building electromechanical systems, in particular to an operation management method and a server of building electromechanical equipment.
Background
At present, the property management industry of China is still in a relatively primary starting stage, the management method adopts a traditional 'people management mode', the work requirements are collected by a superior level, the work requirements are arranged to be executed by a subordinate level, and then the work requirements are checked by the superior level. Some property information management platforms appear in the market at present, but the processes of work deployment and execution are only moved to the information platform to be carried out, the property management mode is not changed essentially, and people still play a dominant role in management. The biggest disadvantage of this method is that it depends too much on subjective judgment of people, so that the actual service level is unstable, the excellent management experience is not easy to copy and expand, and a great deal of effort is also needed in supervision. In addition, the services provided by the property can only be described from the process in a subjective way without objective judgment standards, so that the industry enters a low-quality and low-price competition dilemma.
Electromechanical systems currently used in the field of auxiliary production, such as air conditioning systems in commercial or office buildings, lighting systems, heating and cooling systems in industry, heat dissipation systems in data rooms, and the like, are managed by property workers on the basis of fixed targets for operation management of the electromechanical systems; the customer does not have flexible enough option and cannot well meet the personalized service requirement.
Disclosure of Invention
In view of this, embodiments of the present application provide a method and a server for managing operation of building electromechanical devices, so as to solve the technical problem in the prior art that a user cannot implement personalized customization based on various services provided by the building electromechanical devices.
According to an aspect of an embodiment of the present application, there is provided an operation management method of a building electromechanical device, the method including: determining each attribute of the customized service object according to the input customized target parameter; acquiring current parameters of each attribute of the object; obtaining an adjusting strategy according to the customized target parameter and the current parameter according to a preset algorithm, wherein the adjusting strategy comprises one or more adjusting instructions; and outputting a corresponding adjusting instruction to the building electromechanical equipment for controlling the object according to the adjusting strategy.
According to another aspect of the embodiments of the present application, there is provided a server, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to: the operation management method of the building electromechanical device provided by the embodiment of the application is executed.
The beneficial effects of the embodiment of the application include: the method provides quantifiable, customizable and verifiable integral service management of the building electromechanical system, realizes completely unmanned management of automatic collection of demands and automatic analysis and decision from data, and compared with the existing management based on experience, the management and decision based on data analysis realizes more efficient and energy-saving electromechanical equipment operation.
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The above and other objects, features and advantages of the present application will become more apparent from the following description of embodiments thereof with reference to the accompanying drawings, in which:
FIG. 1 is an architectural diagram of an embodiment of the present application;
FIG. 2 is a schematic diagram of actions performed based on algorithms and data prediction in an embodiment of the present application;
FIG. 3 is a schematic view illustrating the adjustment of the environment of the internal space of the building according to the embodiment of the present application;
FIG. 4 is a schematic diagram of estimating an operating cost through multiple models in an embodiment of the present application.
Detailed Description
The present application is described below based on examples, but the present application is not limited to only these examples. In the following detailed description of the present application, certain specific details are set forth in detail. It will be apparent to one skilled in the art that the present application may be practiced without these specific details. Well-known methods, procedures, components and circuits have not been described in detail so as not to obscure the present application.
Further, those of ordinary skill in the art will appreciate that the drawings provided herein are for illustrative purposes and are not necessarily drawn to scale.
Unless the context clearly requires otherwise, throughout the description and the claims, the words "comprise", "comprising", and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is, what is meant is "including, but not limited to".
In the description of the present application, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present application, "a plurality" means two or more unless otherwise specified.
According to the embodiment of the application, the artificial intelligence technology is used, personalized service customization and automatic operation decision of the building environment and the electromechanical equipment operation are achieved, a user can freely customize service standards, and people can directly select various human-living services facing to results.
Fig. 1 is a schematic architecture diagram of an embodiment of the present application, which respectively includes:
1. defining spaces and devices: standardized definitions are carried out on objects for operation management of the building electromechanical equipment, and the objects comprise standard codes and logic relation definitions, such as spaces where the equipment is located, equipment service spaces, space position relations, system topological relations and the like.
2. Defining a service standard: the system provides a quantifiable and verifiable standard Service Level Agreement (SLA), and a client can quantificationally set various targets of a control object according to a desired environment, wherein the targets comprise quantified targets in the aspects of environmental ecology, electromechanical safety, building sustainability, labor cost and the like.
3. Based on the data model, estimating the cost: on the basis of data accumulation, the cost generated by different service customization is estimated through an algorithm, and energy consumption cost, labor cost and consumable estimation results are provided for a user during customization, so that the user is helped to make a decision.
4. Real-time data, AI algorithm operation: the intelligent AI algorithm analyzes the current deviation situation, calculates an optimal adjustment scheme, immediately makes decision optimization, mobilizes measures such as management, control and human resources, and automatically operates the whole system, and finally ensures the stable realization of human settlements.
5. And (3) outputting an instruction: and the data is butted with an original automatic control system, a property information system, a work order system, a facility management system and the like, an adjusting instruction is sent out, actual work execution is driven to correct the deviation of a control object, and the customization requirement is met.
Based on the above framework, the embodiment of the application provides an operation management method for building electromechanical devices, which is applicable to server devices.
And S10, determining each item attribute of the customized service object according to the input customized target parameters.
The objects are mechatronic devices, systems, space environments, etc. in the building that need to be service customized to accommodate. The attribute is a description of the object, and each attribute has a corresponding parameter value or parameter value interval. The customized target parameters comprise parameter values or parameter value intervals of various attributes and effective moments or time periods of the parameter values or the parameter value intervals.
For object A, a set of attributes A may be used1，A2…, An. When service customization is carried out, a target interval is set for each attribute of A:
A1:[a11,a12]
A2:[a21,a22]
……
An:[an1,an2]；
at the same time, the time range [ t ] within which the set of customized target parameters is effective is set1,t2]。
In this way, the service customization results of any object (space, system, device, etc.) at any time can be described.
In one embodiment, the attributes of the items of the object include: user satisfaction, physical parameters of the object, and building electromechanical device parameters that control the object, such that service customization can be performed from multiple levels.
For example, the customized service standard definition for "spatial environment" is divided into three layers, the first layer defines the subjective feelings of the end user, such as satisfaction, complaint rate, etc.; the second layer defines physical attribute parameters of the space environment, such as temperature, humidity, PM2.5 concentration, CO2 concentration, illumination and the like; the third layer defines the electromechanical system parameters for regulating and controlling the space, such as fresh air volume, fan rotating speed, gear setting and the like. When setting the customization parameters, a set of target values can be input for each attribute in a period of time on the time axis, namely, the target values are used as service customization targets. The input may be made manually by a manager or may be generated automatically according to certain rules (e.g., based on historical satisfaction data, or based on industry regulations for a particular type of space). The service standard is defined as shown in the following table.
There is a coupling relationship between each hierarchy attribute, and once a higher level service requirement is set, the lower level service requirement associated therewith is no longer in effect. For example, if "fan coil air supply gear" and "water valve switch" in the third stage are manually set, then "temperature" in the second stage is no longer valid because the space temperature is adjusted by the fan coil gear and the water valve switch in the third stage, and there is a strong coupling relationship. According to the embodiment of the application, various personalized requirements of the user are realized through multi-level attribute customization.
And S11, acquiring the current parameters of each attribute of the object.
And acquiring various current parameters of the building electromechanical equipment and the space environment in the modes of an internet of things sensor or a mobile internet and the like.
S12, obtaining an adjusting strategy according to the customized target parameter and the current parameter and a preset algorithm,
the adjustment strategy includes one or more adjustment instructions.
When the service is customized, any point t moment or any time period [ t ] on the time axis is set1,t2]Service customization requirements. For example, for object A, there is a set of attributes A1,A2，…,An. Target intervals are set respectively:
A1:[a11,a12]
A2:[a21,a22]
……
An:[an1,an2]
at time t, the attribute A is collected by an Internet of things system (or other modes such as mobile Internet)iIs bi(t) of (d). At this time, the attribute state parameter group describing the object is set to b (t).
Compile all actions that can be taken as c1～cnAs shown in FIG. 2, the action group c (t) to be taken at this time is determined by using a suitable algorithm such that b is within the time t +1i(t+1)∈[ai1,ai2]。
The algorithm may be Q-learning (Q-learning), neuron, deep learning, or fuzzy control based on comparison. The process is described herein with reference to the preferred Q-learning example.
And training the neural network based on historical data, and predicting the state b (t +1) at the next moment based on the current state b (t) and the action group c (t).
Based on historical data, a Q-table is trained with all possible actions c taken on the abscissa and all possible states b on the ordinate. Wherein d isijIn the b state, the score after the action c is taken. Predicting b '(t +1) from b (t) and c (t), if b' (t +1) ∈ [ a ]1,a2](namely, meeting the customization requirement), the corresponding d-score is adjusted upwards; otherwise, if the result is far away from the customized target, the d-score is adjusted downwards.
And (d) using the training result for actual operation, and taking the action group c corresponding to the d with the highest score as the command c (t) output at the moment for the object state group b (t) at the moment t.
And continuously and intensively training the neural network and the Q value table, and continuously correcting the neural network and the training Q value table by using the actual running result b (t +1) at the next moment so as to enable the prediction of the neural network and the Q value table to be more accurate.
Fig. 3 is a schematic view illustrating the adjustment of the environment of the internal space of the building. The adjustment object for service customization is sp235 (space 235) and its attributes dry-bulb temperature and CO2And (4) concentration. Then obtaining the current actual dry bulb temperature and CO2And the concentration is input into a space environment adjusting unit comprising a neural network and a Q value table. And outputting a corresponding adjusting strategy, wherein the adjusting strategy comprises a plurality of adjusting instructions, such as PAU2-1 (new fan 2-1) start/stop/fan frequency/water valve start/air supply temperature setting, FCU-05 (fan coil-05) start/stop/fan gear/set temperature, sp235 window opening and the like, and finally outputting the adjusting instructions to an external electromechanical control system. Wherein, the neural network and the Q value table of the space environment adjusting unit are trained by using historical data.
And S13, outputting a corresponding adjusting command to the building electromechanical equipment of the control object according to the adjusting strategy.
And driving the building electromechanical equipment to execute actual action according to the adjusting instruction so as to correct the deviation to meet the customized requirement of the user.
Further, the adjustment instructions may also be manual operation instructions for the non-networked devices. The manual operation instruction triggers the generation and dispatching of the work order, and the staff receiving the dispatched work order executes corresponding operation on the non-networked equipment.
The embodiment of the application provides quantifiable, customizable and verifiable integral electromechanical system services, so that the service value is transferred more clearly, the operation and maintenance management system of the electromechanical system of the building is differentiated, and the individual requirements are met.
In another embodiment, after determining the attributes of the customized service object according to the input customization target parameters at S10, the method further comprises the following steps.
And S13, estimating the running cost of the customized service according to the customized target parameter.
And energy consumption cost, labor cost and consumable estimation results are provided for the user during customization, so that the user is helped to make a decision.
A data model is first generated. The model is a Boosted tree regression (Booster Trees regression) model, and other models, such as a multi-layer neural network, may also be used. Generating a model based on historical data for an existing project, and generating a model based on simulation data for a newly-built project; if there is no simulation data, a model of a similar building is used.
And then, importing a service customization standard, and calculating the operation cost according to the model.
For example, the date, time, historical operating data of space environment (hourly temperature and humidity, carbon dioxide and pollutant targets), historical outdoor weather data of a place, historical work and rest of personnel and equipment, and the like are input, and the annual total energy consumption actual value is output.
When estimating the operation cost, as shown in fig. 4, the total operation cost can be estimated by integrating a plurality of models. Estimating the work and rest of the personnel (socket energy consumption) by using the model 0 according to the input date type and time; work and rest personnel (socket energy consumption), CO2The demand input model 3 obtains the fresh air volume and the fresh air energy consumption; inputting fresh air volume, work and rest (socket energy consumption), outdoor temperature and indoor average temperature into the model 1 to obtain total cold and heat load, and inputting the total cold and heat load into the model2, obtaining the energy consumption of cold and heat sources; inputting the date type and the time into the model 4 to obtain other energy consumption; and (4) estimating the total energy consumption customized at this time by integrating the fresh air energy consumption, the cold and heat source energy consumption and other energy consumption.
S14, the running cost is output to determine whether to execute the customized service.
And displaying the estimated operation cost, and making a decision by the user whether to execute the customized service. And executing or canceling the customized service in response to the selection operation of the user.
In this embodiment, the cost estimation can help the user select a more efficient and energy-saving personalized customized service scheme.
In addition, in this embodiment of the application, the server running the operation management method for the building electromechanical device may implement the functional steps through a hardware processor (hardware processor). The server includes: a processor, a memory for storing processor-executable instructions; wherein the processor is configured to: determining each attribute of the customized service object according to the input customized target parameter; acquiring current parameters of each attribute of an object; obtaining an adjusting strategy according to the customized target parameter and the current parameter and a preset algorithm, wherein the adjusting strategy comprises one or more adjusting instructions; and outputting a corresponding adjusting instruction to the building electromechanical equipment of the control object according to the adjusting strategy.
In one embodiment, after determining the attributes of the customized service object based on the input customization target parameters, the processor is further configured to: estimating the operation cost of the customized service according to the customized target parameter; the operating cost is output to determine whether to execute the customized service.
In one embodiment, estimating the operational cost of the customized service based on the customization objective parameter comprises: generating a data model according to historical parameters or simulation parameters of various attributes of the object; and inputting the customized target parameters into the data model to estimate the running cost.
In one embodiment, determining attributes of the customized service object according to the input customized target parameters comprises: and determining target values or target value intervals of all the attributes and the effective time or time period of the customized target parameters according to the input customized target parameters.
In one embodiment, the attributes of the items of the object include: user satisfaction, physical parameters of the object, and building electromechanical device parameters that control the object.
In one embodiment, obtaining the adjustment policy according to the customized target parameter and the current parameter by a preset algorithm includes: and inputting the current time parameter group into a preset algorithm to obtain an adjusting strategy, and estimating according to the adjusting strategy to obtain a next time parameter group meeting the customized target parameter.
In one embodiment, inputting the current time parameter group into a preset algorithm to obtain an adjustment strategy, and estimating according to the adjustment strategy to obtain the next time parameter group satisfying the customized target parameter includes: and inputting the state corresponding to the current time parameter group into the Q value table to obtain the action group with the highest Q value corresponding to the state in the Q value table.
In one embodiment, the state corresponding to the current time parameter group is input into the Q value table, and before the action group with the highest Q value corresponding to the state in the Q value table is obtained, the processor is further configured to:
and training a neural network and a Q value table according to the accumulated historical data, wherein the neural network is used for predicting the parameter set at the next moment according to the parameter set at the current moment and the executed action set, and the Q value table is used for maintaining the value relationship between the state and the executed action.
In one embodiment, the processor is further configured to: and updating the neural network and the Q value table according to the actual parameter group at the next moment.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, apparatus (device), or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (6)
1. A method for managing the operation of building electromechanical devices, characterized in that it comprises:
determining each attribute of the customized service object according to the input customized target parameter;
acquiring current parameters of each attribute of the object;
obtaining an adjusting strategy according to the customized target parameter and the current parameter and a preset algorithm, wherein the adjusting strategy comprises a plurality of adjusting instructions; outputting a corresponding adjusting instruction to the building electromechanical equipment for controlling the object according to the adjusting strategy, wherein the building electromechanical equipment at least comprises a fan coil and a water valve;
wherein, determining each attribute of the customized service object according to the input customized target parameter comprises:
determining target values or target value intervals of various attributes and the effective time or time period of the customized target parameters according to the input customized target parameters;
the object's various attributes include: user satisfaction, physical parameters of the object, and building electromechanical device parameters that control the object;
obtaining an adjusting strategy according to the customized target parameter and the current parameter and a preset algorithm comprises the following steps:
inputting the parameter set at the current moment into the preset algorithm to obtain the adjusting strategy, and estimating according to the adjusting strategy to obtain a parameter set at the next moment to meet the customized target parameter;
wherein, inputting the parameter set at the current moment into the preset algorithm to obtain the adjusting strategy, and estimating according to the adjusting strategy to obtain the parameter set at the next moment to meet the customized target parameter comprises:
inputting the state corresponding to the current time parameter group into a Q value table to obtain an action group with the highest Q value corresponding to the state in the Q value table;
performing multi-layer service customization on the user satisfaction degree, physical parameters of an object and parameters of building electromechanical equipment for controlling the object; the first layer comprises user satisfaction, the second layer comprises physical parameters of an object, the third layer comprises parameters of building electromechanical equipment for controlling the object, coupling relations exist among service customizations of each layer, and once a higher-level service requirement is set, a lower-level service requirement related to the service customizations is not effective.
2. The method of claim 1, wherein after determining the attributes of the customized service object according to the input customized target parameters, the method further comprises:
estimating the operation cost of the customized service according to the customized target parameter;
outputting the operating cost to determine whether to execute the customized service.
3. The method of claim 2, wherein estimating the operational cost of the customized service based on the customization objective parameter comprises:
generating a data model according to historical parameters or simulation parameters of each attribute of the object;
and inputting the customized target parameters into the data model to estimate the operation cost.
4. The method of claim 1, wherein the state corresponding to the current time parameter set is input into a Q-value table, and the state is obtained before the action group with the highest Q-value in the Q-value table, the method further comprising:
and training a neural network and a Q value table according to the accumulated historical data, wherein the neural network is used for predicting the parameter group at the next moment according to the parameter group at the current moment and the executed action group, and the Q value table is used for maintaining the value relationship between the state and the executed action.
5. The method of claim 4, further comprising:
and updating the neural network and the Q value table according to the actual parameter group at the next moment.
6. A server, characterized in that,
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to: the method of managing the operation of the building electromechanical device according to any one of claims 1 to 5 is performed.
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| CN103807988A (en) * | 2014-03-12 | 2014-05-21 | 侯春海 | Air conditioner control method for reserving comfortable environmental temperature |
| CN105843037A (en) * | 2016-04-11 | 2016-08-10 | 中国科学院自动化研究所 | Q-learning based control method for temperatures of smart buildings |
| CN106225172A (en) * | 2016-08-17 | 2016-12-14 | 珠海格力电器股份有限公司 | Air conditioner control device, method and system |
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| CN102052739B (en) * | 2010-12-27 | 2012-10-17 | 重庆大学 | Central air conditioner intelligent control system based on wireless sensor network and method |
| US10460254B2 (en) * | 2015-03-17 | 2019-10-29 | Vmware, Inc. | System and method for reducing state space in reinforced learning by using decision tree classification |
| CN105223876B (en) * | 2015-10-23 | 2018-04-06 | 山东大卫国际建筑设计有限公司 | One kind building electromechanical equipment TT＆C system |
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| CN103807988A (en) * | 2014-03-12 | 2014-05-21 | 侯春海 | Air conditioner control method for reserving comfortable environmental temperature |
| CN105843037A (en) * | 2016-04-11 | 2016-08-10 | 中国科学院自动化研究所 | Q-learning based control method for temperatures of smart buildings |
| CN106225172A (en) * | 2016-08-17 | 2016-12-14 | 珠海格力电器股份有限公司 | Air conditioner control device, method and system |
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