CN106331037B - Computing node for distributed computing network - Google Patents
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Abstract
The invention discloses a computation node (CPN) for a distributed computation network, wherein the CPN is a computer with information receiving, processing and sending functions and is provided with a central processing unit, a memory and a communication interface; a plurality of CPNs form a distributed computing network; each CPN performs data interaction with the CPNs which are adjacent to the CPN in topology; the CPN has a spatial attribute which is embodied as an absolute spatial position where the CPN is located and/or a relative spatial position of the CPN in a topology network where the CPN is located; an operating system is built in the computing node, the operating system provides an API interface, and various management/control requirements and/or strategies are converted into standard computing sequences and converted into instruction sequences which can be identified by the operating system; the CPNs in the distributed computing network distributively and self-organizing together complete the computing sequence. The CPN can be applied to building automation systems to provide an open, flat, easily programmable control platform.
Description
Technical Field
The invention relates to the technical field of computing networks, in particular to a computing node for a distributed computing network.
Background
In the 80 s of the 20 th century, people began to utilize information technology to realize intelligent automatic control of buildings, such as broadcasting sound systems, IC card management systems, hotel room management systems, energy monitoring management systems, air conditioning control systems, security systems, fire protection systems, cold station control systems, electricity safety systems and the like which are oriented to specific electromechanical devices, however, the management control systems in the prior art are not so intelligent, and more than half of building automatic control systems can only realize remote monitoring of the building environment and the operation parameters of system devices in a central control room, and can manually start and stop or adjust the operation states of the electromechanical devices through a central control man-machine interface. Such systems are still substantially severely dependent on manual operation by the operator, and are not automated and intelligent. There are very few buildings, and automation control and management of building level can be realized, including optimization control inside each subsystem and integrated control among subsystems.
The root cause of this situation is that the automatic control system in the prior art adopts a centralized architecture, as shown in fig. 1, all terminal measurement and control points (sensors, actuators and field controllers) are communicated through a bus communication network, and the information measurement and control points at the tail ends of all subsystems (lighting system, air conditioning system, fire protection system and security protection system) are distributed in the same building subspace to a great extent, but are vertically integrated according to different subsystems, so that the centralized automatic control system has the following main defects:
1. the measurement and control points of the terminal need to carry out global communication naming, define physical attributes and define association relations among the measurement and control points, and when the number of measurement and control points is large, the work of site configuration and configuration becomes extremely large in workload and high in difficulty; the work can be carried out after the building construction is completed and the electromechanical equipment is in place, and the available construction period is very short, so that the time is rapid; when the layout or the function division of the building is changed in the later period, the automatic control system is difficult to flexibly change along with the change;
2. the real sharing of information among all subsystems is difficult to realize, and in order to realize cross-system information sharing, a new system is established at the upper layers of a plurality of existing systems, so that the overall situation needs to be reconfigured and defined, the difficulty and the cost are extremely high, and the requirements of building control intellectualization, informatization and front-end are not met;
3. The self-control platform is closed and has poor universality, and control software is often designed independently for individual buildings, so that a new control strategy is difficult to flexibly and simply realize on the existing self-control platform in the process of modifying and expanding the system.
4. The existing automatic control system and platform have poor universality and unfriendly software and hardware environments, so that a developer is required to have higher IT expertise, and various control strategies and control logics (such as a control strategy of a heating ventilation system, a control strategy of a fire protection system and a control strategy of a security protection system) operated in the automatic control system are often formulated by engineers (heating ventilation engineers, fire protection engineers and the like) in various fields, so that the engineers in various fields are difficult to convert the formulated control strategies and control logics into control software of the automatic control system, and the functions which should be realized by each subsystem are difficult to integrate in the existing automatic control system and automatic control platform.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art.
To this end, an object of the present invention is to propose a computing node (CPN) for a distributed computing network, which can be applied to building automation systems, providing an open, flat, easily programmable control platform. In addition, although the computing node (CPN) for a distributed computing network provided by the present invention is generated and can be applied to the building automation field, this does not limit the scope of protection of the present invention, and the distributed computing network formed by the computing node (CPN) for a distributed computing network of the present invention can be applied to other fields as a basic computing network.
To achieve the above object, an embodiment of the present invention discloses a computing node (CPN) for a distributed computing network, the computing node (CPN) being a computer having functions of information reception, processing, and transmission, the computing node (CPN) having a central processor, a memory, and a communication interface; a plurality of computing nodes (CPNs) form a distributed computing network; each computing node (CPN) performs data interactions with its topologically adjacent computing node (CPN); the computing node (CPN) has spatial properties, which are embodied as absolute spatial locations at which the computing node (CPN) is located and/or relative spatial locations of the computing node (CPN) in a topology network in which the computing node (CPN) is located; an operating system is built in the computing node, the operating system provides an API interface, and a user can convert various management/control requirements and/or strategies into a standard computing sequence through the API interface; computing nodes (CPNs) in the distributed computing network together complete the computing sequence in a distributed, self-organizing manner.
In addition, the computing node (CPN) for a distributed computing network according to the above embodiment of the present invention may further have the following additional technical features:
Further, the operating system supports parallel computing of multiple standard computing sequences.
Further, a plurality of functional subnets may be defined above the distributed computing network, each of the computing nodes (CPNs) may be affiliated with a different functional subnet, and the functional subnets do not affect each other.
Further, the computing node (CPN) is associated with a certain basic spatial unit or with a certain mechatronic device, and the relevant information of the basic spatial unit or the mechatronic device is described in the form of a standard data table, and the standard data table forms a set of standard information sets.
Further, the computing node (CPN) automatically recognizes a certain basic space unit or a certain electromechanical device which is associated with the computing node (CPN) based on the standard information set, so that plug and play of the computing node (CPN) is realized.
Further, the operating system provides algorithm libraries of all levels from simple mathematical computation to professional application algorithms, in particular an operator library and/or a basic algorithm library and/or a high-level algorithm library, and when writing the standard computing sequence, a user can call the algorithm in the algorithm library, and the operating system automatically forms bottom-layer program codes, so that agile programming is realized.
The standard calculation sequence is performed in a standardized manner, specifically, each calculation centerless algorithm, and the definition of the input/output interface parameters is determined and systematic.
Further, the operator library includes: adding, subtracting, multiplying, dividing, weighting, summing, integrating, logically computing, finding maximum value, minimum value, assembling, generating tree, jacobi/Gaussidell iteration and other common basic mathematical operations; the basic algorithm library comprises: matrix calculation algorithms, steepest descent methods, newton's methods, genetic algorithms, neuronal algorithms, other common basic mathematical algorithms; the advanced algorithm library comprises: sensor fault diagnosis algorithm, population distribution checking algorithm, fire inversion algorithm, CFD algorithm based on area, and other advanced algorithms for various professional fields.
Further, the computing node (CPN) is specifically associated with a certain basic space unit or a certain electromechanical device by a certain regional control system for collecting information about the basic space unit or the electromechanical device or for controlling actuators associated with the basic space unit or the electromechanical device.
Further, the computing node (CPN) has a number of the communication interfaces, which are classified into a class a and a class B; the computing node (CPN) performs data interaction with the topologically adjacent computing node (CPN) through the class A communication interface; the computing node (CPN) performs data interaction with the regional control system (DCS) through the B-class communication interface.
Further, the computing nodes (CPNs) may be locally named when accessing the network, and their names are different from their topologically adjacent computing nodes (CPNs), which may have the same names.
Further, the API interface is specifically an API interface based on a communication protocol or other forms of common interfaces.
Further, the standard computing sequence comprises a plurality of computing units, and the definition of the standard computing sequence comprises the following contents: logic flow diagrams among the plurality of computing units; the operators and/or algorithms involved by each computing unit, input variables, output variables, computing flows and/or steps.
The computing node (CPN) for the distributed computing network provided by the invention has the following characteristics:
1. Space-oriented: each computation node (CPN) is mutually associated with a basic space unit or an area control system of a certain electromechanical device, and when the computation node (CPN) is associated with the area control system, the position space information, the relative position relation or the topological relation of the basic space unit or the electromechanical device is naturally reflected on the computation node (CPN), so that the method has the advantage of quick deployment, can save the work of massive and repeated field wiring, adaptation, debugging and definition of the original control system, and saves a great deal of manpower;
2. standardization: the related information of the basic space unit or the electromechanical equipment is described in the form of a standard data table, and after the computing node is associated with the regional control system, the computing node can automatically identify that the computing node is associated with the basic space unit or the electromechanical equipment, so that the plug and play and the automatic identification of the computing node (CPN) can be realized;
3. centerless calculation: the whole computing network system formed by computing nodes (CPNs) for the distributed computing network is flattened and non-centralized, the positions of all nodes are completely equal, global computation is completed in a distributed manner through data interaction among all nodes, and various management control strategies running on the system are embodied and completed through the distributed computation;
4. Fast friendly programming environment: the system formed by the calculation nodes (CPNs) used for the distributed calculation network provides an open and humanized programming platform, and a user can easily finish the definition of events/tasks by utilizing an operator/algorithm library provided by the system, and the system automatically compiles bottom program codes, so that the control and management strategy is rapidly coded by software, and the system has the advantage of agile development; and massive application programs can be developed on the programming platform, so that the programming platform has great compatibility and flexibility.
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The foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
fig. 1 is a schematic diagram of the prior art general control system.
FIG. 2 is a schematic diagram of a distributed computing network system according to an embodiment of the invention.
FIG. 3 is a schematic diagram of a first implementation of a communication manner between a computing node and a DCS system according to an embodiment of the present invention.
FIG. 4 is a schematic diagram of another implementation of a communication manner between a computing node and a DCS system according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of a computing node (CPN) according to an embodiment of the present invention.
FIG. 6 is a schematic diagram of a library module of user modules of an operating system according to an embodiment of the invention.
FIG. 7 is a schematic diagram of a user interaction module of a user module of an operating system according to an embodiment of the present invention.
FIG. 8 is a schematic diagram of a kernel module of an operating system of an embodiment of the present invention.
FIG. 9 is a topology and naming convention diagram of a distributed computing network system in accordance with an embodiment of the present invention.
Fig. 10 is a schematic diagram of a communication frame between a user module and a kernel module according to an embodiment of the present invention.
Fig. 11 is a schematic diagram showing information from three dimensions.
FIG. 12 is a schematic composition of an event according to an embodiment of the present invention.
Fig. 13 is a schematic diagram of example 1 of an embodiment of the present invention.
Fig. 14 is another schematic diagram of example 1 of an embodiment of the present invention.
Detailed Description
The basic structure is as follows:
with reference to fig. 2, fig. 2 shows the structure of the distributed computing network system formed by computing nodes (CPNs) for a distributed computing network according to the present invention, where the computing network system is formed by a plurality of computing nodes (CPNs), and all the computing nodes together form a flattened centerless computing network, and each computing node is equally located.
The computing nodes are computers (such as small computers) with information receiving, processing and transmitting functions, the structure and the composition of the computing nodes are shown in fig. 5, and each computing node is provided with a processor 1, a memory 2 and a communication interface 3.
Each computing node performs data interaction with the topologically adjacent computing nodes, the data interaction is one-hop communication, the data interaction is performed with the topologically adjacent computing nodes after the processing treatment of the information, the data interaction is also one-hop communication, and all the computing nodes finish the computing tasks in a distributed mode; the distributed computing network provided by the invention disassembles tasks into typical and reproducible basic computing, and the basic computing is realized by acquiring input information from the neighbor nodes through each computing node, completing local computing, transmitting computing results to the neighbor nodes, and completing the computing of the system through the cooperation of each node by a self-organizing cooperation mechanism without a concept of a center or a head.
The computing node (CPN) has spatial properties that are embodied as an absolute spatial location where the computing node (CPN) is located and/or as a relative spatial location in the topology network where the computing node (CPN) is located.
Referring again to fig. 5, the operating system is embedded in the distributed computing network system, where the operating system includes a kernel module and a user module, the kernel module of the operating system is distributed on each computing node, and the kernel modules in each computing node are identical.
It follows that the network formed by the interconnection of the computing nodes (CPNs) forms a large computer, the computing process being distributed among each computing node (CPN); the single computing node can be a computer with comprehensive information processing and operation computing capabilities, but the information processing and computing capabilities are weaker, and a system formed by interconnecting a plurality of computing nodes has stronger information storage, computing and communication capabilities.
The memory of the computing node (CPN) is provided with a plurality of cache modules for storing the states of tasks and/or events, the number of the cache modules can be 1024 or more, the system can process a plurality of tasks or events simultaneously and in parallel, and the system can adapt to the requirements of complex and changeable control tasks.
In combination with fig. 9, when each computing node (CPN) is connected to the network, a localized naming mode is adopted, that is, the name of each computing node (CPN) only needs to distinguish adjacent nodes, global naming or routing configuration is not needed, as shown in fig. 9, nodes named 1, 4 or 5 appear in the network for many times, normal operation of the computing network is not affected, each computing node (CPN) can directly and only perform data interaction with the topologically directly adjacent computing nodes, so that the names of the adjacent nodes are different, and therefore, the work of massive field naming and system configuration can be effectively avoided.
Each computing node (CPN) is provided with a plurality of communication interfaces, the communication interfaces are divided into A type and B type, the computing nodes are interconnected with the topologically adjacent computing nodes through the A type communication interfaces, and are interconnected with the regional control system (District Control System) through the B type communication interfaces. The regional control system is associated with a certain basic space unit (such as a subspace of a building: an office, a corridor and the like) or a certain electromechanical device (such as a chiller, a water pump and the like), and is used for collecting all measurement and control information of the subspace or the electromechanical device or controlling a controller or an actuator related to the subspace or the electromechanical device, and information interaction is realized between a computing node (CPN) and a corresponding DCS according to a standard information set, so that related information required by calculation can be acquired from the DCS, or a result obtained by calculation is sent to the corresponding DCS so as to execute related control.
The communication modes between the computing node (CPN) and the regional control system (DCS) include, but are not limited to, the following two modes:
the first communication scheme is shown in connection with fig. 3:
the computing node directly performs data interaction with a regional controller (DCU) in the DCS, the DCU performs data interaction with each controller, sensor or actuator, and the DCU and each controller, sensor or actuator form a master-slave relationship.
The second communication scheme is shown in connection with fig. 4:
the regional controller (DCU) in the DCS and each controller, sensor, actuator or lamp, shutter and FCU intelligent equipment are located on a local network system (can be a local area network), the computing nodes are interconnected with the local network system, information interaction can be carried out between the CPN and the DCU and each controller, sensor, actuator or lamp, shutter and FCU intelligent equipment, and no master-slave relationship exists among the components.
The communication mode between the CPN and the DCS supports various communication protocols, so that the CPN can be compatible with various DCU products in the prior art.
In addition, the function of the DCU can be integrated into the CPN, i.e. a module corresponding to the DCU is added to the CPN, and the module corresponds to a subspace or an electromechanical device of the building, collects related information, or performs related control.
Topological relation and functional subnetwork:
the network formed by interconnection of the CPNs belongs to a physical tangible network, and a plurality of virtual functional subnetworks can be defined on the network, such as an indoor traffic network, an air flow network, a heat transfer network, a power distribution system, an air-conditioning water system, a tap water system, a domestic hot water system, an air-conditioning air system, a smoke exhaust ventilation system, a fresh air system, a fuel gas supply system, a cold station, a heat station and the like, can be calculated in a single functional subnetwork, and can be associated with a plurality of functional subnetworks, so that information can be truly shared in a plurality of subnetworks.
The functional subnetwork is determined by the content of the specific application, is a virtual network on the distributed computing network provided by the invention, and has a topological structure consistent with that of the entity network.
Because multiple virtual subnets can be defined on the computing network of the invention, the same computing node (CPN) can be affiliated to multiple functional subnets; the multiple functional subnets do not affect each other, so that the computing nodes can process the computing tasks of the multiple functional subnets simultaneously and in parallel.
The role each computing node (CPN) plays in the functional subnetwork varies from application function to application function, e.g., the CPNs of all spatial units in the air flow subnetwork are equal to each other; in the air conditioning system, CPN of the air conditioning box and CPN of the subordinate space form a master-slave relationship.
Therefore, the invention naturally completes the work of system configuration by defining the functional subnetwork on the distributed computing network, and can complete the management and control tasks required by various functional subnetworks by further defining the computing program through the functional subnetwork and running the computing program on the subnetwork.
Term interpretation:
the terms related to the computing network system provided by the invention have the following meanings:
Standard calculation sequence: the user translates all control management requirements and tasks into a standard computing sequence through an API interface in the operating system provided by the distributed computing network.
The standard computing sequence comprises a plurality of computing units, which are embodied as a task or event, so that the definition of the standard computing sequence comprises the following:
logic flow diagrams among a plurality of computing units;
the operators and/or algorithms involved in each computing element, input variables, output variables, computational procedures and/or steps.
The definition of tasks and events is specifically described below.
Tasks: the control management strategy is a control management strategy which is cooperatively completed by a plurality of computing nodes (CPNs), and is formed by a plurality of events according to a certain sequence.
Events: is a centreless calculation completed by a calculation node (CPN), and is determined by defining operators/algorithms, input variables, output variables and intermediate variables.
The variables: each physical information related to the basic space unit and/or each physical information related to the electromechanical device and/or a user-defined temporary variable; for example: the area in the basic information of the area, the indoor temperature in the environment and system measurement parameters, the feedback of the switching state of the lighting equipment in the operation parameters of the intelligent equipment, the set value of the operation state of the fan coil in the area, the set value of the indoor environment temperature, the fault alarm of the fire-fighting equipment, the total average power consumption of the area in the past 10 minutes and the like.
Algorithm: operators (belonging to the lower concepts of algorithms), basic algorithms, advanced algorithms or user-defined algorithms provided by the system; the support system adopts a mechanism similar to transmission calculation to transfer the algorithm to each relevant calculation node (CPN); user-defined algorithms may be deleted by defining the effective range and/or effective time of the algorithm.
The concepts described above will be further explained and illustrated below in connection with specific examples.
Standard information set: why a compute node (CPN) can be automatically identified and plug and play.
Each information point involved in running the management control policy generally has three dimensions of information, as shown in fig. 11, where/what the first dimension solves, that is, where the information point is located (including information with spatial attributes such as space, location, approach to a main road or a branch road), what is (whether a room or a water pump or a chiller), what is the information point, the second dimension solves the specific meaning (specific content information indicating whether temperature or humidity or energy consumption is represented) represented by the data transmitted by the information point, and the third dimension is that the data changes with the lapse of time.
By the distributed computing network system provided by the invention, at the moment when a computing node (CPN) is combined or communicated with a DCS, the spatial attribute of the DCS (the DCS is either a basic spatial unit, namely a room or corridor, or an electromechanical device, the basic spatial unit or the electromechanical device naturally has the position or the spatial attribute) is naturally mapped in the CPN, and the network formed after the CPN is networked forms an Internet of things with the spatial attribute; thus solving the characterization problem of the first dimension.
Whereas the characterization of the second dimension is achieved by data normalization:
the standard information set is formed by a plurality of standard data tables, each basic space unit or electromechanical device corresponds to one standard data table (shown in table 1), the standard data table defines variables, variable names, standard formats of the variables and the like which need to be collected, all basic space units or electromechanical devices are described according to the unified standard information set, so that CPN can obtain information of corresponding areas or devices after data interaction with DCS, for example, the number 5 data in the standard information set is room air temperature, if the DCS is connected with the device with temperature measuring points, the number 5 data in the database of the DCS corresponds to the measured value of the sensor, and the number 5 data in the database of the CPN is mapped to the number 5 data in the CPN database; and when the bottom layer has no measuring point, the number 5 data in the DCS database is in a default state, and the number 5 data mapped to the CPN is also in a default state. Even if the measurement and control point is changed, such as equipment replacement and measurement and control point increase and decrease, the standard data table is unchanged, so that no influence is caused on a computing node (CPN), and by adopting the mode, the CPN can acquire information from a DCS no matter what equipment and what communication protocol are adopted in the bottom layer; in this way, after the CPN is connected with the DCS on site, the information interaction CPN can identify whether the DCS corresponds to a building space or a certain electromechanical device, such as a chiller, a water pump or the like, so that the plug and play and automatic identification of the CPN can be realized.
Table 1 is an example of the standard data table. The complete standard data is presented at the end of the examples of the specification.
TABLE 1
The third dimension is characterized by being realized through a recording and storing function of the DCS or the CPN, the information points can be recorded according to the change development condition of the time lapse, and the description is omitted.
Therefore, the computing nodes in the distributed computing network system provided by the invention can be used in plug and play, can be identified intelligently, and have extremely strong robustness and flexibility.
Description of the user module:
the user module is specifically an API based on a communication protocol, the API comprises a library module and a user interaction module, namely an operating system provides algorithm libraries of all levels from simple mathematical calculation to professional application algorithm, a user can call the algorithm in the algorithm libraries when writing a standard calculation sequence, and the operating system automatically forms a bottom program code, so that agile programming is realized; in connection with fig. 6, the library module is built with three-level operator/algorithm libraries, namely an operator library, a basic algorithm library and a high-level algorithm library.
The operator library comprises: addition, subtraction, multiplication, division, weighted summation, product, logical operations (AND, OR, NOT, etc.), maximum, minimum, collective operations (intersection, union, etc.), spanning tree, jacobi/Gauss seidel iterations, and other common basic mathematical operations.
The basic algorithm library comprises: matrix computing algorithms, steepest descent methods, newton's methods, genetic algorithms, neuronal algorithms, and other common basic mathematical algorithms.
The advanced algorithm library includes: sensor fault diagnosis algorithm, population distribution checking algorithm, fire inversion algorithm, CFD algorithm based on area, and other advanced algorithms applied to various professional fields.
In connection with fig. 7, the user interaction module provides the following interfaces to the user: task definition interface, event definition interface, algorithm definition interface, variable definition interface;
under the task definition interface, the user may define one or more of the following: task name, task execution condition, total number of task steps, event and execution step of event included in task, and calculation structure included in task return result.
Under the event definition interface, a user may define one or more of the following: event name, which task the event belongs to, event execution conditions, input variables of the event, output variables, names of intermediate variables, operators/algorithms of event invocation.
Under the algorithm definition interface, the user may define one or more of the following: which task/event the algorithm acts on, the name of the algorithm, the specific content of the algorithm;
Under the variable definition interface, a user may define one or more of the following: variable name, which task and/or event the variable belongs to, variable byte length, variable initial value.
It is to be understood that the above list is illustrative only and is not exhaustive or possible.
The user module can adopt the computing node as a carrier, can also adopt the regional control system as a carrier, can also adopt other independent software and hardware as a carrier, and the setting mode is flexible and changeable.
Description of the kernel module:
in connection with fig. 8: the kernel module further comprises:
the calculation processing module: for performing a basic calculation;
parallel computing collaboration service module: for supporting and servicing parallel computing; enabling the system to perform parallel computations for multiple standard computation sequences.
And a communication module: is used for completing the communication task.
The parallel computing collaboration service module further includes the following sub-modules: the system comprises a task management module, an event management module, an operator/algorithm management module and a variable management module.
The communication module further comprises the following sub-modules: the device comprises a bottom layer communication interface driving module, a communication protocol analysis module and a communication frame editing module.
The modules have the following cooperative relationship:
the bottom layer communication interface driving module receives information from the communication interface and stores the received information in a receiving buffer; the receive cache is located in a memory of a compute node (CPN).
The communication protocol analysis module retrieves information from the receiving cache, processes the information into four subclasses of task related information, event related information, operator/algorithm related information and variable related information after analysis processing, and stores the processed information in a task management space, an event management space, an operator/algorithm management space and a variable management space respectively according to the subclasses, wherein the task management space, the event management space, the operator/algorithm management space and the variable management space are all located in a memory of a computing node (CPN).
And the task management module is used for managing and maintaining the task related information in the task management space.
The event management module manages and maintains event related information in the event management space.
The operator/algorithm management module manages and maintains operator/algorithm related information in an operator/algorithm management space.
And the variable management module is used for managing and maintaining the variable related information in the variable management space.
The calculation processing module calls related information from the task management space, the event management space, the operator/algorithm management space and the variable management space and calculates, and then stores the calculated result in the task management space, the event management space, the operator/algorithm management space and the variable management space according to categories respectively.
The communication frame editing module calls out related information from the task management space, the event management space, the operator/algorithm management space and the variable management space, edits the related information into a communication frame, stores the communication frame into a sending cache, and the sending cache is positioned in a memory of the computing node.
And the bottom communication interface driving module sends the communication frames in the sending buffer through the communication interface.
Communication between the kernel module and the user module:
the information interaction is performed between the kernel module and the user module under a certain communication protocol, the certain communication protocol can be various communication protocols existing in the prior art, or can be a custom communication protocol, for example, as shown in fig. 10, a communication frame sent by the user module to the kernel module can be in a three-layer structure, the bottom layer is a physical link layer protocol, which comprises a synchronization code, a length, a verification part and a data part, and the physical link layer can be in various mature communication technologies such as Ethernet, wifi, zigbee; the second layer is an application bottom layer and comprises a frame header and a data part, and is used for processing communication problems such as communication response, verification, fragmented transmission and the like; the third layer is an application upper layer and comprises a message type and a message content part, wherein the message content part is used for solving the problems of task definition, event definition, algorithm downloading, variable assignment, system upgrading and the like. The structure of the application layer may also take other forms. The user module sends one or more of tasks, events, algorithms and variables defined by the user to the kernel module in the form of communication frame shown in fig. 10.
Calculation example explanation:
the invention provides a distributed computing network system, wherein various operation management tasks and strategies operated on the distributed computing network system are embodied and completed through a standard computing sequence, and the operation management tasks and strategies are basically completed after the network completes the standard computing sequence, and the distributed computing is completed by combining a calculation node of the system with a calculation example.
A standard computing sequence is composed of several computing units, a task or an event, which is composed of several events in a certain sequence, an event is a basic computation performed by one computing node, and is defined by input variables, output variables, intermediate variables (which may be absent in some cases), and operators/algorithms, as shown in fig. 12.
Calculation example 1:
automatic optimizing of the pressure difference set value of the water pump:
description of the problem: the chilled water pump in the air-conditioning chilled water system in the building is operated and regulated according to the terminal cooling condition, and the control requirement reduces the pressure difference set value of the chilled water pump as much as possible on the premise of meeting the cooling capacity requirement of each terminal so as to reduce the energy consumption of the water pump.
Network structure: as shown in fig. 13, the left half part of the figure shows a chilled water system control network, the tail end of the chilled water system is a network of a plurality of branches, in the figure, a riser branch is used for simplifying the representation, the corresponding space node network is also a multidimensional network structure, in the figure, the chilled water system adopts a one-time pump setting mode, 2 chillers correspond to 3 chilled pumps, and chilled water flows to coils in rooms at the tail end through a water separator. The cooling machine and the water pump respectively correspond to a calculation node of a device type, the tail coil pipe belongs to a calculation node of a space type, the cooling machine room where the cooling machine is located is provided with the calculation node of the space type, the calculation nodes are interconnected to form a functional sub-network, and the topological structure of the functional sub-network is shown in the right half part of fig. 13.
Control logic: if the water quantity of the terminal equipment is insufficient, the pressure difference set value is increased according to the terminal quantity which does not meet the requirement, and the rotating speed of the water pump is increased or the water pump is started; in the case that all the ends meet the requirement, if the amount of water at the ends is too large, the pressure difference set value is reduced, so that the rotation speed of the water pump is reduced or the water pump is turned off.
The whole problem can be decomposed into two relatively independent control loops, one is to adjust the pressure difference set value at two ends of the chilled water pump according to the terminal supply condition, and the other is to adjust the number and frequency of the running water pumps of the water pump group according to the pressure difference set value so as to minimize the energy consumption of the water pumps. The calculation process of the calculation network system provided by the present invention is described herein with respect to the first control loop, i.e., the problem of how to determine the differential pressure set point.
The calculation process comprises the following steps:
this task of automatically adjusting the differential pressure set point of the water pump is accomplished by a combination of three events, as shown in table 2, where events 1 and 2 can be calculated in parallel, the results of events 1 and 2 being input as event 3, the entire process proceeds unidirectionally, without cycling, and is performed once every time period (e.g., 5 minutes) has elapsed.
TABLE 2
The specific process comprises the following steps:
1. the space node to which each terminal coil belongs judges whether the cold energy requirement of the area is enough according to the temperature change condition in the area, and the basic logic is as follows: the current temperature measured value of the area is within the control precision range of the temperature set value, and the valve opening (or duty ratio) of the coil pipe in the area is not completely closed, so that the current cooling capacity can be considered to meet the requirement; if the temperature measured value in the current area is higher than the control precision range of the temperature set value and the valve opening or the duty ratio of the coil pipe in the area is fully opened or is in a very high threshold value, the cold in the current area is considered to be unable to meet the requirement, and a 'too hot' signal is sent; if the current temperature measured value is lower than the control precision range of the temperature set value and the valve opening or the duty ratio of the coil pipe in the area is totally closed or is in a very low threshold value, the cold quantity of the current area is considered to exceed the actual cold quantity requirement, and a 'too cold' signal is sent; two variables can be defined, variable 1 being "whether overheated", if so, then 1, otherwise 0, variable 2 being "whether overcooled", then 1, otherwise 0.
2. The space nodes at the tail end form a spanning tree, the value of the variable 1 or 2 is transmitted to the neighbor node from the first calculation node, the neighbor node carries out local summation on the number transmitted by the neighbor and the local data, and then transmits the calculation result to the next node, so that the summation of all nodes is completed when the calculation result is transmitted to the tail end node; in the whole process, each node participates in summation calculation, but none of the nodes knows how many nodes are in the whole system. Event 1, which counts whether it is overheated, and event 2, which counts whether it is too cold, can be calculated in parallel.
3. After the calculation of the event 1 and the event 2 is completed, the calculation results of the two events are transmitted to any one calculation node (may be the calculation node where the cryopump is located), and the calculation node calculates a new differential pressure set value according to the following algorithm, namely, the event 3.
In conjunction with fig. 14. If the "too hot" ratio of the end region is higher than 5%, then the new differential pressure set point = original differential pressure set point +1; if the "too hot" proportion of the end region is less than or equal to 5% but the "too cold" proportion is greater than 5%, then the new differential pressure set point = the original differential pressure set point-1; if the "too cold" proportion and the "too hot" proportion of the end region are both less than or equal to 5%, the differential pressure set point is unchanged.
In this example, the "whether too cold" or "too hot" variables may be system variables provided by the computing network system, or custom variables at compile time for the user; the local summation is a basic operator provided by a library module in the user module, can be built in each computing node, and can be directly called by a user without specific compiling implemented codes; the algorithm of the 'spanning tree' is also provided by a computing network and can be directly invoked; the specific input variables, output variables, sequencing, etc. of the events 1, 2, 3 can be compiled by the user.
Calculation example 2:
statistical personnel distribution:
on the basis of the computing network system provided by the invention, the personnel distribution in each area can be counted. An infrared detector is arranged at a gate of a region connected with a space, when a person leaves from the region A and enters the region B, the infrared detector can send a signal of the number-1 of the person to a computing node of the region A, the computing node of the region A can initiate a calculation to a neighbor computing node B after receiving the signal of the person-1 of the region A, and the number +1 of the person in the region B is enabled, so that a physical process of the person entering the region B from the region A in a corresponding actual process is simulated on the system.
All infrared detectors arranged at the joints of the communicated areas can detect personnel transfer conditions in the building in real time and send signals to calculation nodes of the corresponding areas, and the real-time calculation of the whole system is triggered, so that personnel number distribution of all the areas can be obtained. This information can be used as basic information to implement optimal control of other functional subsystems (e.g., air conditioning system, lighting system).
Calculation example 3:
collecting the energy consumption values of all rooms:
each electromechanical device of the system can record own energy consumption condition through the DCS or the CPN, or the DCS or the CPN calculates the power consumption of the device according to the running time of the electromechanical device, so that the energy consumption values of all the devices in each basic space unit can be counted by the system. At the point of view of property management personnel, there is a need to collect the energy consumption values of all areas for comparison analysis.
From a mathematical operation perspective, collecting the energy consumption values of all rooms is essentially a global union process. In the actual calculation process, a calculation node accessed by a property manager initiates the energy consumption statistical task, the energy consumption statistical task is globally combined, and the variable of CPN operation is the total energy consumption value of the related equipment of each basic space unit. After the task is initiated, the computing node initiating the task is diffused to the whole world, the whole network system firstly forms a spanning tree, and then the end node of each branch transmits the energy consumption value of the node back to the neighbor node; after each computing node receives the energy consumption value transmitted by the neighbor, the energy consumption value of the corresponding area is added, and the corresponding area is repackaged and sent to other neighbors (namely other neighbor nodes except the neighbor node for inputting the energy consumption information); all the computing nodes in the computing network system follow the principle, and finally the energy consumption information of all the nodes is packaged and sent to the computing node of the task initiator, namely the task of global union collection and collecting the energy consumption values of all the basic space units is completed.
The computing node (CPN) for the distributed computing network provided by the invention has the following characteristics:
1. space-oriented: each computation node (CPN) is mutually associated with a basic space unit or an area control system of a certain electromechanical device, and when the computation node (CPN) is associated with the area control system, the position space information, the relative position relation or the topological relation of the basic space unit or the electromechanical device is naturally reflected on the computation node (CPN), so that the method has the advantage of quick deployment, can save the work of massive and repeated field wiring, adaptation, debugging and definition of the original control system, and saves a great deal of manpower;
2. standardization: the related information of the basic space unit or the electromechanical equipment is described in the form of a standard data table, and after the computing node is associated with the regional control system, the computing node can automatically identify that the computing node is associated with the basic space unit or the electromechanical equipment, so that the plug and play and the automatic identification of the computing node (CPN) can be realized;
3. centerless calculation: the whole computing network system formed by computing nodes (CPNs) for the distributed computing network is flattened and non-centralized, the positions of all nodes are completely equal, global computation is completed in a distributed manner through data interaction among all nodes, and various management control strategies running on the system are embodied and completed through the distributed computation;
4. Fast friendly programming environment: the system formed by the calculation nodes (CPNs) used for the distributed calculation network provides an open and humanized programming platform, and a user can easily finish the definition of events/tasks by utilizing an operator/algorithm library provided by the system, and the system automatically compiles bottom program codes, so that the control and management strategy is rapidly coded by software, and the system has the advantage of agile development; and massive application programs can be developed on the programming platform, so that the programming platform has great compatibility and flexibility.
The complete standard data table is shown below, it being understood that the standard data table is only an exemplary description and does not constitute a limitation on the scope of the invention:
a.1 Space unit
Appendix A.1 building space information set description
A.2 Distributor
Additional Table A.2 distributor information Point description
A.3 Power generation equipment
Appendix A.3 Power plant information Point description
A.4 Cooling machine
Table A.4 description of Cold and Heat Source information Point
A.5 Boiler
Appendix A.5 boiler information Point description
A.6 Water pump
Additional Table A.6 Water Pump information Point description
A.7 Cooling tower
Appendix A.7 Cooling tower information Point description
A.8 Air conditioner box
Additional Table A.8 description of air Conditioning Point of information
A.9 Fresh air processor
Additional Table A.9 fresh air processor information Point description
A.10 Fresh air heat recovery equipment
Additional Table A.10 fresh air heat recovery plant information Point description
A.11 Energy storage pool
Additional Table A.11 description of energy storage pool information points
A.12 Bypass valve
Additional Table A.12 bypass valve information Point description
A.13 Ventilator
Table a.13 ventilator information point description
A.14 Heat exchanger
Appendix A.14 Heat exchanger information Point description
A.15 Elevator with a motor
Table a.15 description of elevator information points
Claims (10)
1. A computing node (CPN) for a distributed computing network, wherein the computing node (CPN) is a computer having information receiving, processing and transmitting functions, all the computing nodes (CPN) together form a flattened centerless computing network, and the computing node (CPN) has a central processor, a memory and a communication interface;
a plurality of computing nodes (CPNs) form a distributed computing network; each computing node (CPN) performs data interaction with the topologically adjacent computing node (CPN) thereof, and performs data interaction with the topologically adjacent computing node (CPN) thereof after processing information, wherein the data interaction is one-hop communication;
the computing node (CPN) has spatial properties, which are embodied as absolute spatial locations at which the computing node (CPN) is located and/or relative spatial locations of the computing node (CPN) in a topology network in which the computing node (CPN) is located;
The computing nodes (CPNs) are internally provided with an operating system, the operating system comprises kernel modules, the kernel modules of the operating system are distributed in each computing node (CPN), and the kernel modules in each computing node (CPN) are identical;
the operating system provides an API interface through which a user can translate various management/control requirements and/or policies into standard computing sequences;
the distributed computing network disassembles tasks into typical and reproducible basic computation, the basic computation is achieved by acquiring input information from neighbor nodes through each computing node (CPN), then transmitting a computing result to the neighbor nodes, and the whole network has no concept of a center or a head, and the computing nodes (CPNs) in the distributed computing network complete the computing sequence together in a distributed and self-organizing manner;
the network formed by each computing node (CPN) belongs to a physical tangible network, a plurality of virtual functional sub-networks can be defined on the network, the computation can be performed in a single functional sub-network, and/or the association computation can be performed on the functional sub-networks;
The computing node (CPN) is associated with a certain basic space unit or with a certain electromechanical device, and the related information of the basic space unit or the electromechanical device is described in the form of a standard data table, and the standard data table forms a set of standard information sets;
the computing node (CPN) automatically recognizes a certain basic space unit or a certain electromechanical device which is associated with the computing node (CPN) based on the standard information set, so that plug and play of the computing node (CPN) is realized.
2. The compute node (CPN) of claim 1, wherein the operating system supports parallel computation of multiple standard compute sequences.
3. A computing node (CPN) according to claim 1 or 2, characterized in that a plurality of functional subnetworks can be defined over said distributed computing network, each of said computing nodes (CPN) being subordinate to a different functional subnetwork, said functional subnetworks being mutually independent.
4. A compute node (CPN) according to claim 1 or 2, characterized in that said operating system provides a library of algorithms at various levels ranging from simple mathematical computation to professional application algorithms, the algorithms in said library being called by the user when writing said standard computation sequence, said operating system automatically forming the underlying program code, thereby realizing agile programming.
5. The computing node (CPN) according to claim 4, wherein,
the operator library comprises: adding, subtracting, multiplying, dividing, weighting, summing, integrating, calculating logic, calculating maximum value and minimum value, integrating, generating tree, and Jacobi/Gaussidell iteration;
the basic algorithm library comprises: matrix calculation algorithm, steepest descent method, newton method, genetic algorithm, and neuron algorithm;
the advanced algorithm library includes: sensor fault diagnosis algorithm, population distribution checking algorithm, fire inversion algorithm and CFD algorithm based on areas.
6. The computing node (CPN) according to claim 1, characterized in that said computing node (CPN) is associated with a certain basic space unit or a certain electromechanical device, in particular by means of a zone control system for collecting information about said basic space unit or electromechanical device or for controlling actuators associated with said basic space unit or electromechanical device.
7. The computing node (CPN) according to claim 6, wherein said computing node (CPN) has a number of said communication interfaces, said communication interfaces being classified into class a and class B;
the computing node (CPN) performs data interaction with the topologically adjacent computing node (CPN) through the class A communication interface;
The computing node (CPN) performs data interaction with the regional control system (DCS) through the B-class communication interface.
8. A computing node (CPN) according to claim 1, characterized in that said computing node (CPN) is locally named when accessing the network, its name being different from its topologically adjacent computing node (CPN), which can have the same name.
9. The computing node (CPN) according to claim 1, wherein said API interface is in particular a communication protocol based API interface.
10. The computing node (CPN) according to claim 1, wherein,
the standard calculation sequence comprises a plurality of calculation units, and the definition of the standard calculation sequence comprises the following contents:
logic flow diagrams among the plurality of computing units;
the operators and/or algorithms involved by each computing unit, input variables, output variables, computing flows and/or steps.
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| EP16817102.3A EP3318938A4 (en) | 2015-06-30 | 2016-06-01 | DISTRIBUTED COMPUTER NETWORK SYSTEM AND COMPUTER NODE USED THEREFOR |
| PCT/CN2016/084332 WO2017000738A1 (en) | 2015-06-30 | 2016-06-01 | Distributed computing network system and computing node used therefor |
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| JP7167687B2 (en) * | 2018-12-18 | 2022-11-09 | 富士通株式会社 | Information processing device, information processing method and information processing program |
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