CN113048548A

CN113048548A - Method and device for determining target set value by twin number of HTM (high-speed transmission) machine, HTM machine and computer-readable storage medium

Info

Publication number: CN113048548A
Application number: CN202010283611.7A
Authority: CN
Inventors: 周绮丽
Original assignee: Shenzhen Huihong Technology Co ltd
Current assignee: Shenzhen Huihong Technology Co ltd
Priority date: 2019-12-29
Filing date: 2020-04-13
Publication date: 2021-06-29

Abstract

The invention discloses a method and a device for determining a target set value by twin numbers of an HTM (hyper text transport memory) machine, the HTM machine and a computer readable storage medium, wherein a pre-stored data table is formed by obtaining local weather forecast information, screening historical energy consumption working condition parameter data matched with the weather forecast information in a database, screening historical energy consumption working condition data which are the same as and similar to the load energy consumption of the HTM according to the pre-stored data table, generating a twin data table and storing the twin data table locally, and the historical energy consumption working condition parameter data corresponding to the highest heat efficiency value of the HTM machine in the twin data table is confirmed to be used as prediction data corresponding to the weather forecast information; the energy consumption working condition parameter matched with the weather forecast information and the load energy consumption information in the database is automatically searched to serve as the working condition parameter of the local HTM machine at the future time based on the weather forecast information and the load energy consumption information of the position of the HTM machine, so that the working condition can be predicted in advance, heat supply is conveniently arranged in advance at a heat supply position according to the working condition parameter, and the effects of saving energy and reducing cost are achieved.

Description

Method and device for determining target set value by twin number of HTM (high-speed transmission) machine, HTM machine and computer-readable storage medium

Technical Field

The invention relates to the technical field of heat balance equipment, in particular to a method and a device for determining a target set value by twin numbers of an HTM (high-speed transmission) machine, the HTM machine and a computer-readable storage medium.

Background

It is known that in cold areas in winter, people often need to ensure indoor temperature through central heating so as to ensure that people have comfortable living and working environments.

The traditional heating equipment realizes energy transmission through a centralized heating end, and the intelligent distribution of heat supply can not be realized when the specific transmission is determined, so that the heat supply in some areas is excessive, and the heat supply in some places is too little, thereby greatly wasting heat, and wasting a large amount of funds and cost; but also brings great inconvenience to the life of the people.

Therefore, an intelligent technology is needed to confirm the heat supply amount of different areas, so as to realize optimal heat supply, reduce cost, save resources and facilitate the life of common people. Such an intelligent system needs to acquire a large amount of databases representing energy consumption of the region at different times in different regions to complete intelligent distribution and control. Particularly, when performing intelligent control, it is necessary to intelligently search corresponding data in the database to set the control of the mobile terminal. Such a technique has not yet appeared, and thus this problem is solved.

Disclosure of Invention

The invention mainly aims to provide a method and a device for determining a target set value by twin numbers of an HTM machine, the HTM machine and a computer readable storage medium, and aims to solve the technical problem that the conventional HTM machine cannot automatically search the set value which accords with the working condition parameters of the HTM machine on the basis of the data of a database.

In order to achieve the above object, an aspect of the present invention provides a method for determining a target setting value for an HTM machine based on a twin digital database, for predicting an operating condition parameter of the HTM machine, including the following steps:

acquiring local weather forecast information;

screening historical energy consumption working condition parameter data matched with the weather forecast information in a database to form a prestored data table;

screening historical energy consumption working condition data which are the same as or similar to the load energy consumption of the HTM in the pre-stored data table, generating a twin data table and storing the twin data table locally;

and screening historical energy consumption working condition parameter data corresponding to the highest heat efficiency value of the HTM machine in the twin data table as prediction data corresponding to the weather forecast information.

In addition, to achieve the above object, the present invention further provides a twin digital database based HTM device for determining a target setting value, specifically including:

the acquisition module is configured to acquire local weather forecast information;

the first screening module is configured to screen historical energy consumption working condition parameter data matched with the weather forecast information in a database to form a prestored data table;

the second screening module is configured to screen historical energy consumption working condition data which are the same as or similar to the load energy consumption of the HTM in the pre-stored data table, generate a twin data table and store the twin data table locally;

and the determining module is configured to screen historical energy consumption working condition parameter data corresponding to the highest thermal efficiency value of the HTM machine in the twin data table as prediction data corresponding to the weather forecast information.

In addition, an HTM machine is proposed, which is configured with an execution unit, a memory, a processor, and a program stored on the memory and executable on the processor for determining a target setting value of a twin digital database based HTM machine, which when executed by the processor performs the steps of any of the above-described methods for determining a target setting value of a twin digital database based HTM machine.

Finally, in order to solve the above-mentioned problems, a computer-readable storage medium is also proposed, which has stored thereon a program for determining a target setting value by a twin digital database based HTM machine, which when executed by the processor performs the steps of any of the above-mentioned methods for determining a target setting value by a twin digital database based HTM machine.

The invention can realize the following beneficial effects:

according to the method, the device, the HTM machine and the computer-readable storage medium for determining the target set value by the twin number of the HTM machine, provided by the embodiment of the invention, through acquiring local weather forecast information, screening historical energy consumption working condition parameter data matched with the weather forecast information in a database to form a pre-stored data table, screening historical energy consumption working condition data which are the same as and similar to the load energy consumption of the HTM according to the pre-stored data table, generating the twin data table, storing the twin data table locally, and determining the historical energy consumption working condition parameter data corresponding to the highest heat efficiency value of the HTM machine in the twin data table as the prediction data corresponding to the weather forecast information; the energy consumption working condition parameter matched with the weather forecast information and the load energy consumption information in the database is automatically searched to serve as the working condition parameter of the local HTM machine at the future time based on the weather forecast information and the load energy consumption information of the position of the HTM machine, so that the working condition can be predicted in advance, heat supply is conveniently arranged in advance at a heat supply position according to the working condition parameter, and the effects of saving energy and reducing cost are achieved.

Drawings

FIG. 1 is a schematic diagram of an HTM machine in a hardware operating environment according to an embodiment of the present invention

FIG. 2 is a schematic structural diagram of a control device;

FIG. 3 is a schematic diagram of a first form of twin sub-table;

FIG. 4 is a schematic diagram of a second form of twin sub-table;

FIG. 5 is a schematic flow chart of a twin digital database based HTM machine determination target set point method of the present invention;

FIG. 6 is a flow chart of forming a table of pre-stored data;

FIG. 7 is a schematic diagram of an apparatus for a twin digital database based HTM machine to determine a target set point;

FIG. 8 is a schematic structural diagram of a first screening unit;

FIG. 9 is a schematic diagram of the structure of the HTM machine.

Detailed Description

The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to fig. 1 to 9, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

The HTM machine in the prior art cannot realize the function of predicting the future working state, so the technical scheme of the invention is provided.

The invention relates to a terminal which is an HTM (high temperature transient Talentmachine) high-temperature intelligent machine, which can intelligently extract heat energy of a lower-temperature heat medium into high-temperature heat energy, and output the low-temperature medium and the high-temperature medium in a balanced manner, thereby meeting the application requirements of low temperature and high temperature. The HTM is a heat balance unit that meets the requirements of both low and high temperature applications. The system is mainly used for a heat energy recycling system, and is commonly used for a water chiller and a heat energy storage/utilization system. It should be understood that other types of heat balancing devices are possible in addition to the HTM machine.

Specifically, referring to fig. 1, the HTM machine includes a data acquisition device 200 and a control device 100, wherein the data acquisition device 200 is used for acquiring information of various sensors at various positions of the HTM machine, and the control device 100 is used for receiving the information acquired by the data acquisition device 200 and controlling the operation of various mechanisms, such as a flow meter, an evaporator, an expansion valve, a pressure reducing valve, a condenser, and the like. In detail, the data collecting apparatus 200 has various sensors and various actuators, such as a temperature sensor, a pressure sensor, a controller, a memory, a communicator, a heat exchanger, a compressor, a condenser, a pressure reducing valve, an evaporator, an expansion valve, an evaporator, a circuit valve, and the like. The data acquisition device 200 is used for acquiring sensing information of each sensor, and the control device 100 is used for controlling the operation of each execution device. Specifically, the operating condition parameters of various execution devices during operation can be intelligently predicted according to the database pre-stored in the control device 200.

The control device 100 may be a PC, or may be a terminal such as a smart phone, a tablet computer, or an industrial personal computer, and is configured to control the operation of the HTM machine, and preferably, a PC is used. Referring to fig. 2, the control apparatus may include: a processor 1001, such as a CPU, communications bus 1002, data acquisition interface 1003, user interface 1004, network interface 1005, and memory 1006. Wherein a communication bus 1002 is used to enable connective communication between these components. The data collecting interface 1003 is connected to the data collecting apparatus 100 for receiving data transmitted by the data collecting apparatus, and the user interface 1004 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the like. The network interface 1005 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1006 may be a high-speed RAM memory or a non-volatile memory (e.g., a disk memory). The memory 1005 may also be a storage device independent of the processor 1001, such as a removable hard disk.

It will be appreciated by those skilled in the art that the configuration of the HTM machine and control device 100 according to the present invention is not limited thereto and may include more or less components than those described above, or some components in combination, or a different arrangement of components. For example, the HTM may be one of a number of similarly configured energy exchange machines such as air conditioners, boilers, chillers, etc.

The memory 1006, which is a kind of computer storage medium, may include therein an operating system and a program of a method of determining a target setting value by the HTM machine based on the twin digital database. And the processor 1001 may be used to call a program of a method for determining a target setting value of the twin digital database-based HTM machine stored in the memory 1006 and execute the steps of the program.

It should be understood that the database in the present technical solution is known, and the present invention implements automatic screening in the known database to satisfy the requirement of automatically searching and confirming the future operating condition parameters of the database, so that the technical solution which is not invented cannot be considered as unclear because the description of the database is unclear.

Preferably, the energy consumption condition parameter data stored in the database is preferably an index table, that is, the index table includes the load energy consumption parameter, the corresponding ambient temperature and the specific condition parameter, the geographic location information of the HTM machine corresponding to the data, and the thermal efficiency value of the HTM machine corresponding to the load energy consumption. In some embodiments, the list also includes the time of data generation. In the index table, the load energy consumption parameter is taken as a first characteristic value, and the corresponding environment temperature is taken as a second characteristic value. The working condition parameters may specifically include flow control values, heating temperatures, and working parameter values of various control devices. The various control devices described herein differ from device to device. Wherein, the temperature accuracy of the energy consumption working condition parameter data corresponding to each temperature in the database is 0.1 ℃, and certainly can be 1 ℃. When the database is built, massive information is obtained to meet the requirement that each temperature in a specific air temperature range corresponds to a determined working condition parameter.

The database may be a centralized data center. The data center stores historical energy consumption working condition parameter data uploaded by a plurality of HTM machines, and the historical energy consumption working condition parameter data are stored in the data center. The data center can be a large-scale storage server, is operated by a special mechanism, can manage stored energy consumption working condition parameters, and facilitates searching and screening of the HTM machine. It should be understood that the HTM machine is linked to the data center through the network interface when screening the historical energy consumption condition parameters in the database, and obtains the data of the data center by sending a screening request. Often, the temperature of the weather forecast information is a range of values, such as a temperature between-15 ℃ and-10 ℃. At this time, the energy consumption condition parameters corresponding to the air temperature interval are many. Generally, an energy consumption working condition parameter corresponding to a gas temperature value is taken as a prestored data table. Certainly, the present invention is not limited to this, and energy consumption operating condition parameters corresponding to a plurality of temperature values may also form a complete prestored data table, as shown in fig. 3. Of course, it should also be understood that, since the time period corresponding to the selected weather forecast information may be preset, the corresponding air temperature value may only include one temperature value, for example, the time period is one hour in the future, and the air temperature value in the one hour is predicted to be-10 degrees celsius; the energy consumption condition parameters at this time only include a pre-stored data table corresponding to one gas temperature value, specifically refer to fig. 4. It should be understood that a corresponding air temperature value in the pre-stored data table corresponds to a plurality of data of different load energy consumption.

In addition, in addition to the centralized database described above, the database of the present embodiment may also be a block chain-based database. In this case, the index table of the database of the block chain is stored in the body, and the HTM machine is a node of the block chain, specifically, the control device of the HTM machine is a node of the block chain and stores the index table of the database of the block chain. The control device can decode the corresponding index table by only obtaining one key, screens out historical energy consumption working condition parameter data of the corresponding weather prediction information, and forms the pre-stored data table.

It should be understood that the database may be a collection of data that is manually imported and automatically received and stored locally, in addition to the centralized database and the blockchain database described above. For example, because the area where the HTM machine is located has no network for a period of time, the corresponding database can be imported into the memory of the HTM for storage, so that it can be ensured that the database stored by the HTM can be automatically used for data screening when the network connection fails.

Example 1

Referring to fig. 5, the present invention provides a twin digital database based HTM machine target setting determination method for predicting an operating condition parameter of an HTM machine, comprising the steps of:

acquiring local weather forecast information;

It should be understood that the local weather forecast information acquisition refers to receiving the weather forecast information transmitted by the external system. The local refers to an area where the HTM machine is located. The weather forecast information refers to weather information released by a weather department on the same day. The weather forecast information receiving information is the weather information in a future period, for example, the weather forecast information in a time period within 24 hours on the next day can be acquired; of course, particularly when applied, the weather forecast information in the predetermined time period may be used as a basis for predicting the working condition parameters of the HTM machine in the future operation according to the preset regulations. For example, weather forecast information for the next 1 o 'clock to 7 o' clock time period is used as the condition parameter for predicting 1 o 'clock to 7 o' clock in the future. The method can be specifically defined according to different types of equipment in different areas and different control requirements.

Specifically, the weather forecast information may be automatically acquired or manually input. When the networks are communicated, the HTM machine is connected to the Internet through the network interface 1005 and can acquire weather forecast information issued by a weather department; or an information receiving unit capable of automatically receiving weather forecast information issued by a weather department. The received weather forecast information is stored in memory 1006. In addition, in order to solve the problem that the weather forecast information can be obtained in case of network interruption, specific weather forecast information can be input through the user interface 1004, for example, a worker manually inputs the corresponding weather forecast information through a keyboard, or inputs the corresponding weather forecast information through a touch screen display.

And the weather forecast information comprises predicted air temperature values of all time intervals. For example, the weather forecast information of the next day is acquired, and the weather forecast information at least includes the air temperature at 0, the air temperature at 1, the air temperature at 2, the air temperature at 3 on the next day, and so on until the air temperature at 24. That is, the weather forecast information includes specific weather conditions for different time periods within the weather forecast time period. Specifically, in some embodiments, the weather forecast information may include information such as humidity, cloud cover, wind speed, and the like, in addition to the temperature forecast.

Referring to fig. 6, a flow chart for forming a pre-stored data table is shown; the step of forming a pre-stored data table by historical energy consumption working condition parameter data matched with the weather forecast information in the screening database specifically comprises the following steps:

s1: judging whether the temperature value in the database is the same as the temperature value of the weather forecast information or not;

s2: if the temperature values are the same, generating a pre-stored data table by using historical energy consumption working condition parameter data corresponding to the temperature values; if the temperature values are different from the air temperature values corresponding to the weather forecast information, historical energy consumption working condition parameter data in the error threshold value of the temperature values in the database and the air temperature values corresponding to the weather forecast information are searched, and a prestored data table is generated.

That is, the data included in the pre-stored data table in this embodiment includes data with the same temperature value as the weather forecast information or data with the same temperature value as the weather forecast information and a data set with a temperature value close to the air temperature value of the weather forecast information. Therefore, the problem that data screening cannot be carried out when the corresponding temperature value cannot be found is solved. For example, if the temperature corresponding to the weather forecast information is-10 ℃, the matching means that the historical energy consumption working condition parameter data corresponds to data with an environmental temperature of-10 ℃, and the error threshold means a deviation value, the deviation value is self-defined according to specific operation, for example, the deviation value is 0.1-0.2 ℃, and the temperature value in the screening database corresponds to the historical energy consumption working condition parameter data with the temperature of-10.2 ℃ to-9.8 ℃. Specifically, during screening, historical energy consumption working condition parameter data with temperature values identical to the air temperature values of the weather forecast information in the database are screened firstly. And searching historical energy consumption working condition parameter data within the error threshold range when the historical energy consumption working condition parameter data with the same temperature does not exist. For example, if the index table of the database is based on the relational database relationship of MySQL, the screening is implemented by using the search statement based on MySQL. The screened data will form a new pre-stored data table and be stored in the flash memory of the HTM. The form of the pre-stored data table obtained in this embodiment is shown in fig. 5, that is, one air temperature value corresponds to a plurality of operating condition parameters and load energy consumption.

Importantly, the twin data table is screened and formed based on the pre-stored data table in the present embodiment. Specifically, historical energy consumption working condition data which are the same as or similar to the load energy consumption of the HTM in the pre-stored data table are screened, a twin data table is generated, and the twin data table is stored locally. It will be appreciated that the heat dissipation capacity per unit area per unit time (i.e. load energy consumption) of each given building is fixed at the corresponding ambient temperature, and therefore the load energy consumption is the same or similar (or has a deviation, but the deviation is small and can be ignored) at a given air temperature value, i.e. the load energy consumption is constant at a given air temperature value. Therefore, it is meaningful to search the historical energy consumption condition parameter data which is the same as or similar to the load energy consumption of the HTM machine in the prestored data table. For example at-10 c, a particular house is loaded with energy consumption values determined while maintaining a constant room temperature (e.g., 20 c), for example (a load energy consumption). Because different houses adopt different building materials, the load energy consumption of the houses can be different when heating, therefore, all data under specific environmental problems can be screened out through the pre-stored data table for searching the data with the same load energy consumption subsequently. Therefore, in this embodiment, after the pre-stored data table is formed, historical energy consumption condition parameter data, which is the same as or similar to the HTM load energy consumption, of the load energy consumption in the pre-stored data table is also screened. Specifically, historical energy consumption working condition parameter data with the same load energy consumption as the HTM load energy consumption in the pre-stored data table is searched first, and the selected historical energy consumption working condition parameter data is stored as a twin data table. It should be appreciated that since the database employed in the present embodiment is a large database, the data may be a database formed by HTMs throughout an industrial park, an entire city, an entire province, or even a whole country, there is always a greater amount of data forming the twin data table for each corresponding load energy consumption. And the gas temperature value and the load energy consumption value of the historical energy consumption working condition parameter data of the twin data table respectively correspond to the gas temperature of the weather forecast information and the load energy consumption of the HTM machine. Specifically, the screening is implemented by using a known software program, for example, based on MySQL data screening technology. It should be understood that the similar load energy consumption refers to data with a deviation value from the load energy consumption of the HTM machine within a certain range, for example, if the load energy consumption of the HTM machine corresponding to the temperature is 50, the deviation value is 1, and the similar load energy consumption is 49-51. The bias value can be customized. In addition, the twin is that a temperature value and a load energy consumption value in the weather forecast information necessarily correspond to a corresponding operating condition parameter, and the temperature value, the load energy consumption and the operating condition parameter value are called twin.

And after the twin data table is obtained through screening, performing screening on historical energy consumption working condition parameters corresponding to the highest thermal efficiency value of the HTM machine in the twin data table to serve as prediction data corresponding to the weather forecast information.

It should be understood that there is a difference in the thermal efficiency values of the HTM machines among the data present in the twin data table because the thermal efficiency values of the HTM machines are different due to the problems of equipment aging and different structures in the case of the corresponding air temperature conditions and load power consumption. In the step, the historical energy consumption working condition parameter data with the highest thermal efficiency value in the twin data table is screened as prediction data. Of course, the screening can be carried out by using a known technique in the specific screening. And if the historical energy consumption working condition parameter data corresponding to the highest thermal efficiency value is at least two, judging the historical energy consumption working condition parameter data closest to the position of the HTM machine as the prediction data. Specifically, the determination is performed using the geographical position information described in the twin data table. For example, the distance in the geographical location information is determined, and the closest distance is used as the prediction data. In some embodiments, if the distances of the existing geographic location information are the same, the historical energy consumption condition parameter data with the latest update time is selected as the prediction data of the embodiment.

After the prediction data are obtained, the HTM can set the working condition parameters of the HTM to be the same as the working condition data of the historical energy consumption working condition parameter data of the corresponding prediction data at the corresponding gas temperature value. Through this technical scheme, realized that HTM machine has sought the historical energy consumption operating mode parameter that is most suitable, the thermal efficiency is the highest automatically and has set up as the operating mode of self, realized HTM machine intelligence and set up operating mode parameter, supply less or the problem of supplying more when avoiding traditional heat balance equipment heat supply, play intelligent control, energy saving, reduce cost's effect.

Example 2

In addition, referring to fig. 7, in order to solve the above technical problem, there is also provided an apparatus for determining a target setting value of an HTM machine based on a twin digital database, comprising:

an acquisition module 10 configured to acquire local weather forecast information;

the first screening module 20 is configured to screen historical energy consumption working condition parameter data matched with the weather forecast information in a database to form a prestored data table;

the second screening module 30 is configured to screen historical energy consumption working condition data, which are the same as or similar to the load energy consumption of the HTM, in the pre-stored data table, generate a twin data table and store the twin data table locally;

and the determining module 40 is configured to confirm the historical energy consumption working condition parameter data corresponding to the highest thermal efficiency value of the HTM machine in the twin data table as the prediction data corresponding to the weather forecast information.

Preferably, as shown in fig. 8, the first screening module 20 includes:

a determination unit 201 configured to determine whether the temperature value in the database is the same as the temperature value of the weather forecast information;

the generating unit 202 is configured to generate a pre-stored data table from the historical energy consumption working condition parameter data corresponding to the temperature value if the temperature values are the same, and search the historical energy consumption working condition parameter data in the error threshold of the temperature value in the database and the gas temperature value corresponding to the weather forecast information if the temperature values are different, and generate the pre-stored data table.

And the weather forecast information comprises predicted air temperature values of all time intervals.

In addition, the determining module 40 is further configured to determine, as the prediction data, historical energy consumption operating condition parameter data that is closest to the location of the HTM machine if there are at least two historical energy consumption operating condition parameter data corresponding to the highest thermal efficiency value.

The working principle of each module and unit in this embodiment is the same as that of the method corresponding to embodiment 1, and details are not described here, but it should not be considered that the technical solution of this embodiment is not fully disclosed.

The working condition parameters of the HTM machine are intelligently set through the embodiment, so that the functions of intelligent control, energy conservation and cost reduction are achieved.

Example 3

Referring to fig. 9, there is also proposed an HTM machine configured with an execution unit 1, a memory 2, a processor 3, and a twin digital database based HTM machine determination target setting program stored on the memory and operable on the processor, the twin digital database based HTM machine determination target setting program, when executed by the processor, performing the steps of the twin digital database based HTM machine determination target setting method of any one of the present invention.

The working condition parameters are intelligently set through the embodiment, so that the effects of intelligent control, energy conservation and cost reduction are achieved.

Example 4

Finally, the present invention proposes a computer-readable storage medium having stored thereon a program for a twin digital database based HTM machine to determine a target setting value, the program being executed by the processor to perform the steps of any of the methods of the present invention for a twin digital database based HTM machine to determine a target setting value.

The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device including one or more available media integrated servers, data centers, and the like. It should be noted that, those skilled in the art can understand that all or part of the steps in the methods of the above embodiments can be implemented by the relevant hardware instructed by the computer program, and the computer program can be stored in the computer readable storage medium, which can include but is not limited to: magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Versatile Disks (DVDs)), or semiconductor media (e.g., Solid State Disks (SSDs)), among others.

In conclusion, the technical scheme of the invention realizes the function of inquiring similar or same energy consumption working condition parameter data according to the existing database so as to control the setting of the working condition parameters of the HTM machine, realizes intelligent control, and plays the roles of saving energy and reducing cost.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.

The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.

The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims

1. A twin digital database based HTM machine target setpoint determination method for predicting an operating condition parameter of an HTM machine, comprising the steps of:

acquiring local weather forecast information;

and determining historical energy consumption working condition parameter data corresponding to the highest heat efficiency value of the HTM machine in the twin data table as prediction data corresponding to the weather forecast information.

2. The twin digital database based HTM machine target setting determination method of claim 1, wherein:

the step of forming a pre-stored data table by historical energy consumption working condition parameter data matched with the weather forecast information in the screening database specifically comprises the following steps:

3. The twin digital database based HTM machine target setting determination method of claim 1, wherein:

the weather forecast information comprises predicted air temperature values of all time intervals.

4. The twin digital database based HTM machine target setting determination method of claim 1, wherein:

the screening of the historical energy consumption working condition parameter corresponding to the highest thermal efficiency value of the HTM machine in the twin data table as the prediction data corresponding to the weather forecast information specifically includes:

and if the historical energy consumption working condition parameter data corresponding to the highest thermal efficiency value is at least two, judging the historical energy consumption working condition parameter data closest to the position of the HTM machine as the prediction data.

5. An apparatus for determining a target setting value of an HTM machine based on a twin digital database, comprising:

and the determining module is configured to confirm historical energy consumption working condition parameter data corresponding to the highest thermal efficiency value of the HTM machine in the twin data table as the prediction data corresponding to the weather forecast information.

6. The twin digital database based HTM machine target setting determination apparatus of claim 5, wherein:

the first screening module includes:

a judging unit configured to judge whether or not a temperature value in a database is the same as a temperature value of the weather forecast information;

and the generating unit is configured to generate a pre-stored data table from the historical energy consumption working condition parameter data corresponding to the temperature value if the temperature values are the same, and search the historical energy consumption working condition parameter data in the error threshold value of the temperature value in the database and the gas temperature value corresponding to the weather forecast information and generate the pre-stored data table if the temperature values are different.

7. The twin digital database based HTM machine target setting determination apparatus of claim 5, wherein:

8. The twin digital database based HTM machine target setting determination apparatus of claim 5, wherein:

the determination module is further configured to determine, as the prediction data, historical energy consumption operating condition parameter data that is closest to a location of the HTM machine if there are at least two historical energy consumption operating condition parameter data corresponding to a highest thermal efficiency value.

9. An HTM machine, characterized by:

the HTM machine is configured with an execution unit, a memory, a processor, and a twin digital database based HTM machine determination target setting program stored on the memory and operable on the processor, the twin digital database based HTM machine determination target setting program, when executed by the processor, performing the steps of the method of the twin digital database based HTM machine determination target setting of any of claims 1 to 4.

10. A computer-readable storage medium characterized by:

the computer readable storage medium has stored thereon a twin digital database based HTM machine determination target setting value program, which when executed by the processor, performs the steps of the method of determining a target setting value by a twin digital database based HTM machine as claimed in any one of claims 1 to 4.