TWI533203B - Modeling method - Google Patents

Description

Model building method

The disclosure relates to a model building method, and more particularly to a model building method for building energy systems that can directly interpret data streams and correct errors.

The building energy system model is the key technology of green energy building. The model of this building can be built by sensing data measured by multiple sensors in a building. At present, the sensor data is usually manually input into the file to create an individual component model. However, since there may be dozens of hundreds of sensors in a building, the format of the sensing data output by each sensor is not completely the same. Therefore, when the data is manually input, it is very likely that the input data is incorrect and the resulting building model is in error.

In view of the above problems, the present disclosure proposes a model automatic establishment method for sensing the distribution relationship between data characters in the sensing data stream and the corresponding sensing data by sensing the header of the data stream. And each sensory data is judged whether it is correct or not, so as to selectively write it into a model file. This avoids the mistakes and omissions that occur in the artificial input of data when the model is built.

According to the disclosure, a method for automatically establishing a model includes: obtaining a stream of sensing data, the sensing data stream includes a header and M data bits, and M data sensing elements have N sensing data, wherein M And N is a positive integer. According to the header, the i-th sensed data in the N pieces of sensing data is obtained from the M data bits, where i is a positive integer less than or equal to N. Determine whether the i-th sense data is correct. When the i-th sense data is correct, the i-th sense data is written into the model file. When the i-th sense data is incorrect, the spare data about the i-th sense data is written into the model file.

According to the present disclosure, a building model establishing method is used for establishing a simulation model of a building, the method comprising: sensing respectively by a plurality of sensors in the building to obtain a corresponding plurality of sensing data streams, each sense The test stream includes a header and a plurality of data bits, and a plurality of sensed data are stored in the data bit. And for each of the sensing data streams, a sensing data is obtained from the data bits of the sensing data stream according to the header of the sensing data stream. Determining whether the obtained sensing data is correct, so as to selectively write the obtained sensing data into a model file corresponding to the sensing data stream.

The above description of the disclosure and the following embodiments are intended to illustrate and explain the spirit and principles of the disclosure, and to provide further explanation of the scope of the disclosure.

1000‧‧‧ system

1001~1009‧‧‧Sensor

1100‧‧‧Sensor database

1200‧‧‧Processing module

1300‧‧‧Component Model Database

1400‧‧‧Building Model Database

FIG. 1 is a flow chart of a method for establishing a model according to an embodiment of the present disclosure.

Figure 2 is a schematic diagram of a system in accordance with an embodiment of the present disclosure.

FIG. 3 is a flow chart of a method for constructing a building model according to an embodiment of the present disclosure.

The detailed features and advantages of the present disclosure are described in detail in the following detailed description of the embodiments of the present disclosure, which are The objects and advantages associated with the present disclosure can be readily understood by those skilled in the art. The following examples are intended to further illustrate the present disclosure, but are not intended to limit the scope of the disclosure.

A model building method according to the present disclosure can be operated on a device having a central processing unit and a storage medium. For the method disclosed, please refer to FIG. 1 , which is a flowchart of a method for establishing a model according to an embodiment of the present disclosure. As shown in Figure 1, this method includes the following steps. As described in step S100, a plurality of sensors in a building are respectively sensed, such as position, temperature, humidity, etc., to obtain a plurality of corresponding sensing data streams. Each of the sensing streams includes a header and a plurality of data bits, and the plurality of data bits are stored in the data bits, wherein the number of the data bits may be greater than or equal to the number of the sensing materials. . And for each of the sensing data streams, as described in step S200, a sensing data is obtained from the data bits of the sensing data stream according to the header of the sensing data stream. Then, as described in step S300, it is determined whether the obtained sensing data is correct, so as to selectively write the obtained sensing data into corresponding sensing resources. In a model file of the stream stream, the model file can be selected first.

Specifically, please refer to FIG. 2, which is a schematic diagram of a system according to an embodiment of the present disclosure. As shown in FIG. 2, the system 1000 of the present disclosure includes, for example, a plurality of sensors 1001 to 1009, a sensing database 1100, a processing module 1200, a component model database 1300, and a building model database 1400. The processing module 1200 is electrically connected to other databases and sensors.

Each sensor outputs a stream of sensing data. However, each sensor is likely to be different depending on the brand and the sensed data. The sensing data stream has its own data format. Therefore, the processing module 1200 can determine, according to the header of each sensing data stream, which sensor type the sensing data stream is from, and determine a plurality of data bits in the sensing data stream. The distribution relationship between the element and the stored multiple sensed data. For example, if the 32-bit data bit of the sensing data stream sent by the sensor 1001 contains time information, temperature information, and humidity information. The first bit to the 18th bit record time information (seconds), while the 19th to 25th bits record temperature (degrees Celsius), and the 26th to 32nd records record relative humidity. (percentage) and so on. The processing module 1200 can learn the information such as the brand model of the sensor 1001 according to the header of the sensing data stream sent by the sensor 1001, so as to know that the 32-bit data bit is as described above. Allocation.

In one embodiment, when the format of the data stored in the data bit of the sensing data stream is 16-bit, the number of sensing data may be greater than the number of bits of the sensing data stream. For example, a sense data stream has a total of 10 bits. The data, the first to fourth digits are the card number A0FE, and the fifth digit of the stored value A is used to indicate that the sensing data stream has 10 sensing data. However, in another embodiment, when the data format stored in the data bit of the sensing data stream is 2-digit, the number of the sensing data (N) is less than or equal to the number of bits of the sensing data stream (M ).

In one embodiment, the processing module 1200 then determines the correctness of each of the sensing materials. First, the processing module 1200 determines whether the stored value of a sensed data is blank. The so-called blank is that when the sensor does not write the data into the corresponding data bit, the preset values of the data bits are, for example, all zeros or all ones. If the stored value of a sensed data is not blank, the processing module 1200 then determines whether the stored value is within a numerical interval corresponding to the sensed data. For example, it is known that the temperature sensed by the sensor 1001 in the past is between 10 degrees Celsius and 35 degrees Celsius, so if the stored value of the temperature recorded in the sensing data stream of the current sensor 1001 is 3 The processing module 1200 determines that the sensing data of the temperature is incorrect. On the other hand, if the stored value of the temperature is 25, the processing module 1200 determines that the sensing data of the temperature is correct. For incorrect sensing data, the processing module 1200 will be rounded off to automatically fill in the alternate data.

In an embodiment, the backup data may be preset data, for example, the preset data of the temperature is 25, and the preset data of the humidity is 60. In another embodiment, the backup data may be historical data, such as a temperature storage value 15 and a humidity storage value 70 sent by the previous sensor 1001. In still another embodiment, the backup The data is processed by the processing module 1200 from the sensing database 1100 according to the header to find the data measured by the sensor having a similar setting environment to the current sensor. For example, if the sensor 1001 is a sensor disposed on the left side of a building door, the processing module 1200 can find a sensor disposed in the sensing database 1100 near the gate. Since the positions of the two sensors are similar, it can be assumed that the sensed data may be close.

In another embodiment, the standby data is processed by the processing module 1200 from the sensing database 1100 according to the header to find the data measured by the sensor having the same or similar sensed object as the current sensor. For example, if the sensor 1001 senses the brightness and power consumption of the T5 tube located in a certain room of a building, the processing module 1200 can find another sense of the same room in the sensing database 1100. A sensor that measures the brightness and power consumption of a T5 lamp. Since the sensed objects of the two sensors are the same, it can be assumed that the sensed data may be similar.

In another embodiment, it is assumed that the temperature storage value in the past several sensing data streams of the sensor 1001 is substantially the same as the temperature storage value of the sensor 1003 in the past several sensing data streams. That is, the difference in value is less than a similar value threshold. Therefore, since the processing module 1200 determines that the temperature sensed by the sensor 1001 and the sensor 1003 is substantially the same according to the past sensing data stream, the processing module 1200 determines the sensing of the sensor 1001. When the temperature storage value of the data stream is blank or incorrect, the processing module 1200 selects the temperature storage of the current sensing data stream of the sensor 1003. The value is used as the temperature storage value of the sense data stream of the sensor 1001.

Immediately after the processing module 1200 creates a new model file, the way to create the model file can directly create a blank generic model file, that is, a model file having all the data fields. Alternatively, the processing module 1200 can determine a suitable model file based on the header of the sensing data stream. In the foregoing example, the so-called suitable model file is a model file with only three fields of time, temperature and humidity. After the new model file is created, the processing module 1200 writes the plurality of sensing data in the corrected sensing data stream into the corresponding data field in the model file. The post-processing module 1200 determines whether there is a blank field in the model file. If there is a blank field in the model file, the processing module 1200 writes the blank bar with the preset data according to the data type corresponding to the blank field. Bit. As such, the processing module 1200 can automatically create a model file in the sense data stream and store it in the component model database 1300.

In another embodiment of the present disclosure, the model file of the components in the component model repository 1300 can be used to build a building model. Please refer to FIG. 2 and FIG. 3 together, wherein FIG. 3 is a flow chart of a building model construction method according to an embodiment of the present disclosure. As shown in FIG. 3, the building model construction method of the present disclosure may further include the following steps: as described in step S400, selecting a building substrate from the building model database according to the modeling instruction. As described in step S500, some or all of the model files are selected according to the modeling instructions. And as described in step S600, the building model is established with the selected building substrate and the selected model file.

Specifically, the modeling instruction describes the user who wants to select the mode. Proposed information. For example, the user can choose to simulate the power consumption of each floor of Taipei 101 at a specific set illumination brightness. For example, if you want to simulate the illumination brightness of each office in Taipei 101 between 100 lumens and 150 lumens, set up different factories. The power consumption of a brand or model of lighting. The processing module 1200 selects a model file having luminance information and power information from the component model database 1300 according to the modeling instruction. For example, firstly, to simulate the situation in which all offices use T5 lamps, the processing module 1200 selects the corresponding lighting device from the component model database 1300 as a T5 lamp, and the brightness is between 100 lumens and 150 lumens. At the same time, there is a component model that records power consumption. And the processing module 1200 selects the building substrate of the Taipei 101 from the building model database 1400. The post-processing module 1200 writes the selected model files one by one into the corresponding component templates in the building substrate, thereby completing the building model in which all the offices in Taipei 101 use the T5 tube as the lighting device.

If it is to be simulated that all offices use the white light emitting diode, the processing module 1200 selects the corresponding lighting device from the component model database 1300 as a white light emitting diode, and the brightness is between 100 lumens and 150 lumens. At the same time, there is a component model that records power consumption. And the processing module 1200 selects the building substrate of the Taipei 101 from the building model database 1400. The post-processing module 1200 writes the selected model files one by one into the corresponding component templates in the building substrate, thereby completing the architectural model in which all the offices in Taipei 101 use white light emitting diodes as lighting devices.

The completed building model of Taipei 101 can be reused. The simulation analyzes the power consumed by the T5 tubes in all the offices on each floor of Taipei 101, and the power consumed by the white light-emitting diodes in all the offices on each floor of Taipei 101.

In addition, in an embodiment, the constructed building model can also be used to update the building substrate of Taipei 101 in the building model database 1400. In another embodiment, the constructed building model can be stored back into the building repository 1400 as another building substrate for Taipei 101. For example, when the power requirement of the air conditioning system in the case where the T5 tube is used in all offices is simulated, the processing module 1200 can select the building model used to simulate the use of the T5 tube as the lighting device in all offices. For the building substrate, continue to add cold air, air conditioning related component model files for the corresponding simulation analysis.

In summary, the automatic model establishing method proposed by the present disclosure distinguishes the distribution relationship between the data characters in the sensing data stream and the corresponding sensing data by sensing the header of the data stream. And each sensory data is judged whether it is correct or not, so as to selectively write it into a model file. This avoids the mistakes and omissions that occur when a large amount of data is entered artificially when the model is built.

Although the disclosure is disclosed above in the foregoing embodiments, it is not intended to limit the disclosure. All changes and refinements are beyond the scope of this disclosure. Please refer to the attached patent application for the scope of protection defined by this disclosure.

Claims (10)

A model establishing method is applicable to a building energy system, comprising: obtaining a sensing data stream, the sensing data stream comprising a header and M data bits, wherein the M data bits have a N sense Measuring data, wherein M and N are positive integers; obtaining, according to the header, the i-th sensing data in the N pieces of sensing data, wherein i is a positive integer less than or equal to N; Whether the i-th sense data is correct includes: determining whether a stored value of the i-th sensed data is blank; and when the stored value of the i-th sensed data is blank, determining the i-th sense The data is incorrect; when the stored value of the i-th sensed data is not blank, determining whether the stored value of the i-th sensed data belongs to a numerical interval of the i-th sensed data; When the stored value of the i-sense sensing data belongs to the numerical interval of the i-th sensing data, determining that the i-th sensing data is correct; and the stored value of the i-th sensing data does not belong to the When the value range of the i-th sense data is used, it is determined that the i-th sense data is not correct ; When the i-th pen sensing data correctly, the i-th data writing pen sensing a profile model; and When the ith pencil sensing data is incorrect, a spare data about the ith pencil sensing data is written into the model file. The method of claim 1, wherein the sensing data stream is obtained by a sensor or by a database. The method of claim 1, wherein the header is a source sensor for defining the stream of sensed data. The method of claim 1, wherein the standby data is selected from the group consisting of a historical data of the i-th sensing data and a predetermined data of the i-th sensing data. The method of claim 4, wherein the preset data is obtained from a plurality of sensing materials in a sensing database according to the header. A model establishing method for establishing a simulation model of a building, the method comprising: obtaining a plurality of sensing data streams in the building, each of the sensing data streams comprising a header and a plurality of data bits, A plurality of sensing data are stored in the data bits; and for each of the sensing data streams, the header of the sensing data stream is obtained from the data bits of the sensing data stream according to the header of the sensing data stream One of the sensing materials; and determining whether the obtained sensing material is correct, to selectively write the obtained sensing data to correspond to the sensing resource In a model file of the material stream, in the step of determining whether the obtained sensing data is correct, the method includes: determining whether a stored value of the obtained sensing data is blank; and when the stored value is When the blank is blank, it is determined that the obtained sensing data is incorrect; when the stored value is not blank, it is determined whether the stored value belongs to a numerical interval of the obtained sensing data; when the stored value belongs to the value In the interval, it is determined that the obtained sensing data is correct; and when the stored value does not belong to the numerical interval, it is determined that the obtained sensing data is incorrect. The method of claim 6, wherein determining whether the obtained sensing material is correct is to selectively write the obtained sensing data to the model file corresponding to the sensing data stream. The step of: when determining that the obtained sensing data is correct, writing the obtained sensing data to the model file; and when determining that the obtained sensing data is incorrect, The spare file associated with the obtained sensing data is written into the model file. The method of claim 7, wherein the standby data is selected from a historical data of the obtained sensing data and the obtained sensing resource. One of the groups consisting of a predetermined material. The method of claim 8, wherein the preset data is obtained from a plurality of sensing materials in a sensing database according to the header. The method of claim 6, further comprising: selecting a building substrate from a building model database according to a modeling instruction; selecting the model files according to the modeling instruction; and selecting the building substrate with The model files are selected to create a building model.