A method for constructing an information model for a power internet of things terminal device

Abstract

The application discloses a power internet of things terminal device information model construction method, which comprises the following steps: dividing power internet of things terminal devices according to types in the breadth dimension; dividing the collection and measurement data of the power internet of things terminal devices according to time in the depth dimension; dividing the label information of the power internet of things terminal devices in the static information dimension; dividing the collection units of the power internet of things terminal devices according to sampling categories in the dynamic information dimension; the breadth dimension is associated with the static information dimension and the dynamic information dimension; and the dynamic information dimension is associated with the time depth dimension. The four-dimensional model construction method of breadth, depth, static and dynamic information is adopted, the data range, data time, device label and device collection unit of the power internet of things terminal device are uniformly managed, the tree structure and label structure information association are adopted, the huge resource consumption problem caused by the traditional relationship model is avoided, and support is provided for the energy digital transformation of the power internet of things terminal device.

Description

TECHNICAL FIELD

[0001] The application relates to a model construction method, in particular to an information model construction method for electric power Internet of Things terminal equipment. BACKGROUND

[0002] One of the main contents of the new power system is to promote the construction of the perception layer. The main value is to realize the data access of more terminal equipment and even elements. Not only power system equipment elements, but also terminal equipment of various energy subsystems such as electricity, cold, heat and gas. After the end information collection, the data layer carries out collection processing and management, realizes the fusion of energy flow and information flow, and breaks the energy barrier. Its deployment and construction will promote the upgrading of the power grid to the energy internet and promote the development of the integrated energy system.

[0003] At present, most of the electric power Internet of Things terminal system business data is stored in the form of traditional relational tables, so that the system must be quite familiar with the existing database structure when positioning data. Electric power Internet of Things terminal equipment is a component of digital power grid. The most critical step of power grid digitization is digital data modeling of electric power Internet of Things terminal equipment. It can be said that the integration and construction ability of the information model of electric power Internet of Things terminal equipment is related to the long-term development of the entire new power system and is the key to the successful transformation of traditional power grids to energy internet.

[0004] At present, there are three kinds of data modeling:

[0005] The first kind is CIM (Common Information Model) data modeling for the demand of power grid asset management. This modeling method considers factors such as equipment assets and different operation services of the equipment. At present, this model considers the overall architecture of equipment decomposition from space and time, but the granularity is not fine enough, especially without considering the establishment of the equipment geometric model. This modeling method is suitable for large power systems such as power master stations and marketing data centers, but not suitable for light and micro power equipment.

[0006] The second kind is BIM (Building Information Model) data modeling for the demand of infrastructure. This modeling method mainly considers the demand for 3D modeling. On the basis of the geometric model, a combination model is established, and on the basis of the combination model, a physical model is established, and on the basis of the physical model, an engineering model is established. The geometric model is composed of multiple basic primitives or parameterized definitions. The combination model includes the reference of the above files and the corresponding space transformation matrix. The physical model is composed of the combination model and the physical model attribute. The engineering model is composed of the physical model and the engineering attribute. This modeling method has insufficient granularity and does not consider various equipment operation service data after operation.

[0007] The third is PDM (Product Data Management) modeling. The PDM modeling is a technology for managing all product-related information and all product-related processes, including part information, configuration, documents, CAD files, structure, permission information and process definition and management. Through the implementation of the PDM modeling, the production efficiency can be improved, the product life cycle management can be facilitated, the efficient use of documents, drawings and data can be enhanced, and the work flow can be standardized. The modeling is mainly for data modeling of the equipment manufacturing link, without considering the modeling of the equipment entering the installation and operation link. Meanwhile, the modeling data is only file data, not digital data.

[0008] Therefore, the existing data modeling method has the problems of large size, insufficient granularity, relatively low performance, and generally lacks consideration of comprehensive energy, and cannot adapt to the low energy consumption, high efficiency, multi-dimension and other related requirements of the electric power IOT terminal device. Therefore, it is very crucial to design an effective information model construction method for the electric power IOT terminal device to solve this problem. SUMMARY

[0009] The purpose of the present application is to provide an information model construction method for electric power IOT terminal devices, which solves the problems of large size, insufficient granularity, relatively low performance, and inability to adapt to the low energy consumption, high efficiency, multi-dimension and other related requirements of the electric power IOT terminal device of the existing modeling method.

[0010] Technical scheme: The information model construction method for electric power IOT terminal devices, comprising:

[0011] The electric power IOT terminal devices are divided in the breadth dimension according to types; the collection and measurement data of the electric power IOT terminal devices are divided in the depth dimension according to time; the label information of the electric power IOT terminal devices is divided in the static information dimension; and the collection units of the electric power IOT terminal devices are divided in the dynamic information dimension according to sampling categories.

[0012] The breadth dimension is associated with the static information dimension and the dynamic information dimension; and the dynamic information dimension is associated with the depth dimension.

[0013] The types include electric power equipment, gas equipment, refrigeration equipment and heating equipment, and the subordinate relationship of the terminal devices is identified through multi-layer tree-shaped node names.

[0014] The depth dimension is divided according to the data triple format of data time, data value and data quality code to identify the collection and measurement data and the corresponding time tag information; and the data value includes floating point type, integer type, Boolean type and character type.

[0015] The static information dimension is divided horizontally according to the equipment collection measurement name, measurement type and measurement unit, and is used for identifying the characteristic information of the equipment collection index; the static information dimension is divided vertically according to the equipment collection measurement attribute, and is recorded as a characteristic quantity; and the data of the characteristic quantity is recorded in a digital manner, and is recorded as a characteristic value.

[0016] The sampling categories include three-phase voltage values, three-phase current values, active power values, reactive power values in power equipment, and pressure values and temperature values in heating equipment.

[0017] The breadth dimension is associated with the static information dimension and the dynamic information dimension through a tree structure information; the tree structure includes leaf nodes and non-leaf nodes, the non-leaf nodes are used as indexes of the tree and are used for recording the equipment breadth dimension information, and the leaf nodes record tag point names or IDs and are stored in a time sequence library.

[0018] The tree structure exists in the form of a two-dimensional table and includes a node number, a parent node number, a name, a type, and a belonging layer field.

[0019] The dynamic information dimension is associated with the time depth dimension through a tag structure; the identification information of the collected measurement data is recorded as a tag point, the leaf nodes of the tree structure are associated with the tag points through a hash algorithm, and the hash value selects the full attribute combination of the layer to which the leaf node belongs in the tree structure.

[0020] Advantages: Compared with the prior art, the present application has the following significant advantages:

[0021] (1) A four-dimensional model construction method mainly using breadth, depth, static information and dynamic information is adopted, and the data range, data time, equipment tag and equipment collection unit of the power Internet of Things terminal equipment are uniformly managed.

[0022] (2) The information is associated through a tree structure and a tag structure, and the resource consumption is reduced.

[0023] (3) Under a new power system, the power Internet of Things terminal equipment is low-energy-consumption, high-efficiency and multi-dimensional adaptive modeling, which provides basic support for the energy digital transformation of the power Internet of Things terminal equipment, has been applied to a lightweight real-time database, and good results have been achieved. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 The present application is an information model construction method for power Internet of Things terminal equipment;

[0025] Figure 2 The present application is a dynamic information dimension modeling schematic diagram;

[0026] Figure 3 The present application is a tree storage structure schematic diagram;

[0027] Figure 4 The figure shows the association between the breadth and dynamic information dimension of the device of the present application.

[0028] Figure 5 The figure shows the association between the dynamic information dimension and the depth dimension of the device of the present application. DETAILED DESCRIPTION

[0029] The technical solutions of the present application will be further described below in combination with the drawings.

[0030] Figure 1 The figure shows the information model construction method for the power Internet of Things terminal device of the present application. In the breadth dimension, the power Internet of Things terminal device is divided according to power equipment, gas equipment, refrigeration equipment, and heating equipment, meeting the comprehensive energy data range requirement of cold, heat, electricity, and gas in the new power system. The subordinate relationship of the terminal device is identified through a multi-layer tree node name.

[0031] In the depth dimension, the collection and measurement data of the power Internet of Things terminal device is divided according to the time depth, meeting the identification requirement of the sampling data of various meters such as smart meters, water meters, and gas meters at different times, and providing necessary time tag information for sampling data statistical query and analysis calculation.

[0032] The collection and measurement data and the corresponding time tag information are identified according to the data triple format of data time, data value, and data quality code, and the data value is divided into various data types such as floating point type, integer type, Boolean type, and character type according to the objective data type.

[0033] A specific data structure example is as follows:

[0034]

[0035] In the static information dimension, the content to be collected by the power Internet of Things terminal device, i.e., the label information, is divided horizontally according to the equipment collection and measurement name, measurement type, and measurement unit, etc., to identify the characteristic information of the equipment collection index.

[0036] Meanwhile, the collection content of the equipment, i.e., the measurement attribute, is divided vertically, recorded as a characteristic quantity, and the data of the characteristic quantity is recorded in a digital way, recorded as a characteristic value. When the objective world changes, the equipment main body adjusts according to its own state and subordinate relationship, so that its working state meets the expectation. Taking the power distribution network remote terminal device (RTU) as an example, the static information dimension modeling is shown in Table 1.

[0037] Table 1

[0038]

[0039]

[0040] In the dynamic information dimension, each collection content of the power Internet of Things terminal device, i.e., the collection unit, is distinguished according to the sampling category, and is used to identify the data generated by the observable parameter of the terminal device. Unlike the static information, the observable parameter changes with the change of the device data range (time).

[0041] For example, the three-phase voltage value, the three-phase current value, the active power value, the reactive power value, etc. in the power equipment, the pressure value, the temperature value, etc. in the heating equipment, the dynamic information dimension modeling is as shown in FIG. 6, wherein the ordinate is a specific sampling value, such as total power / kilowatt, and the abscissa is time, such as hours. Figure 2

[0042] The breadth dimension of the power Internet of Things terminal device is associated with the static information dimension and the dynamic information dimension through a tree structure, ensuring the unique description of the device and its collection information. To solve the correlation problem of each collection and measurement unit in the device and the correlation problem between devices, a light tree structure model construction module is designed. The tree structure includes leaf nodes and non-leaf nodes, wherein the non-leaf nodes are used as indexes of the tree and are used to record the breadth dimension information of the device, and the leaf nodes mainly record the tag point name or ID, facilitating storage to the time sequence library. Physically, the tree model exists in the form of a two-dimensional table, including node number (ID), parent node number (PID), name (NAME), type (TYPE), belonging layer (LEVEL), etc. fields, used to represent the tree structure, and other fields are reserved, which are used to represent other description information of the corresponding node, and at the same time can be used as an association field to connect with other relationship tables, solving the relationship expansion problem under a complex structure, such as the scene of joint modeling with other relationship models, enhancing the horizontal expandability of the model, and the specific structure is as shown in FIG. 7. Figure 3

[0043] According to the above tree structure, the schematic diagram of associating the breadth dimension of the power Internet of Things terminal device with the static information dimension and the dynamic information dimension is as shown in FIG. 8. The left side is the measurement unit represented by the breadth dimension, which is identified by the serial number and the name; and the right side is the dynamic and static information dimension, which identifies the specific device information through the tree structure. Figure 4

[0044] ​​​The dynamic information dimension and the time depth dimension of the electric power IOT terminal device are associated through the label structure, ensuring the uniqueness and consistency of the sampling data of all devices at each moment. By associating and binding mode with the database label point, the identification information of the collected measurement data is recorded as a label point, and the leaf nodes of the tree structure are associated with the label point through a hash algorithm. The hash value selects the full attribute combination of the layer to which the leaf node belongs in the tree structure, so that the tree model node corresponds to the database measurement point one by one. When submitting and querying data through the model, the actual physical data can be accessed according to this structure, so that the tree model can play an actual value. The association diagram of the dynamic information dimension and the depth dimension of the device is shown in Figure 5 .

[0045] Taking the intelligent fusion terminal device as an example, it is assumed that the data of the electric power measurement device A (collecting voltage and current) and the temperature measurement device B (collecting air temperature) need to be accessed at the same time. The complete modeling method steps are as follows:

[0046] Step 1: In terms of breadth, devices A and B are divided into electric power devices and heating devices, as shown in Table 2.

[0047] Table 2

[0048] Serial number Type Name 1 Power equipment Device A 2 Heating equipment Device B

[0049] Step 2: In terms of depth, the voltage and current collection measurement data of device A and the temperature measurement data of device B are respectively established in coordinate relationship with time, with voltage value, current value, temperature value and their respective quality codes as the vertical coordinates, and time as the horizontal coordinate, as shown in Figure 2 .

[0050] Table 3

[0051]

[0052] Step 3: The label information of the electric power IOT terminal device is divided horizontally according to the name, type, unit and whether to report, and is divided vertically according to the measurement attributes of the voltage value of device A, the current value of device A and the air temperature value of device B, and the static characteristic values are recorded as shown in Table 3. At the same time, in the dynamic information dimension, the collection units of the device are distinguished according to the sampling categories. As an optional solution, the measurement attribute name of the device can be used as the collection unit, which is divided according to PHv_phsA, A_PHSA and T_air, and the dynamic information dimension modeling is established.

[0053] Step 4: The association model between the device breadth dimension and the static information dimension and the dynamic information dimension is established in the tree structure mode of Figure 3 . The specific steps are as follows:

[0054] (41) is the 0th layer structure of the tree, the parent node (PID) is identified as -1, the node number (ID) is 0, the joint representation root node, the name (NAME) is " / ", the type (TYPE) is 0, indicating that the tree structure is a non-leaf node, the layer number (LEVEL) is 0, and the label name (PNAME) is "NULL" indicating a non-measured attribute, as shown in Table 4.

[0055] Table 4

[0056] ID PID NAME TYPE LEVEL PNAME 0 -1 / 0 0 NULL

[0057] (42) is the 1st layer structure of the tree, two child nodes are added on the basis of the 0th layer structure, LEVEL is recorded as 1 indicating the 1st layer, ID is recorded as 0, 1, PID is recorded as 0, indicating the 0th ID of the upper layer node, i.e. the root node, NAME is recorded as power equipment and heating equipment respectively, as shown in Table 5.

[0058] Table 5

[0059] ID PID NAME TYPE LEVEL PNAME 0 -1 / 0 0 NULL 0 0 Power equipment 0 1 NULL 1 0 Heating equipment 0 1 NULL

[0060] (43) is the 2nd layer structure of the tree, two child nodes are added on the basis of the 1st layer structure, LEVEL is recorded as 2 indicating the 2nd layer, ID is recorded as 0, 1, PID is recorded as 0, 1 respectively, indicating that the two devices belong to different nodes of the upper layer structure, NAME is recorded as device A and device B respectively, thus completing the breadth dimension modeling, as shown in Table 6.

[0061] Table 6

[0062] ID PID NAME TYPE LEVEL PNAME 0 -1 / 0 0 NULL 0 0 Power equipment 0 1 NULL 1 0 Heating equipment 0 1 NULL 0 0 Device A 0 2 NULL 1 1 Device B 0 2 NULL

[0063] (44) is the 3rd layer structure of the tree, three child nodes are added on the basis of the 2nd layer structure, LEVEL is recorded as 3, ID is recorded as 0, 1, 2, among them, the node PID of ID 0, 1 is recorded as 0, the node PID of ID 2 is recorded as 1, indicating different collection units of the two devices, NAME is recorded as PHv_phsA, A_PHSA and T_air indicating voltage, current and air temperature, TYPE is recorded as 1 indicating a leaf node, PNAME is calculated according to the combination value of layer number, ID, PID and NAME to obtain a unique label point name, thereby establishing the association between the device measurement attribute and the database label point structure. As shown in Table 7.

[0064] Table 7

[0065]

[0066]

[0067] (45) is the 4th layer structure of the tree, 12 new child nodes are added on the basis of the 3rd layer structure, LEVEL is recorded as 4, and ID is recorded as 0-11, wherein the PIDs of the nodes with IDs 0-3, 4-7 and 8-11 are respectively recorded as 0, 1 and 2, which are used to respectively represent the static characteristic information of the upper 3 acquisition measurement units, the NAME is recorded according to the corresponding characteristic values in Table 2, and the TYPE is -1, indicating a static information node, thereby establishing the associated entity between the device measurement attribute and the breadth dimension. As shown in Table 8.

[0068] Table 8

[0069] ID PID NAME TYPE LEVEL PNAME 0 -1 / 0 0 NULL 0 0 Power equipment 0 1 NULL 1 0 Heating equipment 0 1 NULL 0 0 Device A 0 2 NULL 1 1 Device B 0 2 NULL 0 0 PHv_phsA 1 3 HASH("L3+ID1+PID1+PHv_phsA") 1 0 A_PHSA 1 3 HASH("L3+ID2+PID1+A_PHSA") 2 1 T_air 1 3 HASH("L3+ID1+PID2+T_air") 0 0 PHv_phsA -1 4 NULL 1 0 float -1 4 NULL 2 0 V -1 4 NULL 3 0 1 -1 4 NULL 4 1 A_PHSA -1 4 NULL 5 1 float -1 4 NULL 6 1 A -1 4 NULL 7 1 1 -1 4 NULL 8 2 T_air -1 4 NULL 9 2 float -1 4 NULL 10 2 ℃ -1 4 NULL 11 2 1 -1 4 NULL

Claims

1. A method for constructing an information model for power IoT terminal devices, characterized in that: This includes classifying power IoT terminal devices by type in the breadth dimension; classifying the collected measurement data of power IoT terminal devices by time in the depth dimension; classifying the tag information of power IoT terminal devices by static information dimension; and classifying the collection units of power IoT terminal devices by sampling category in the dynamic information dimension. The breadth dimension is associated with the static and dynamic information dimensions through a tree structure. The tree structure exists in the form of a two-dimensional table, including node number, parent node number, name, type, and level. The tree structure has four levels: the first and second levels are used to record breadth dimension information, the third level is used to record dynamic dimension information, and the fourth level is used to record static dimension information. In the first level of the tree structure, the name field of the two-dimensional table includes the device type of the breadth dimension information; in the second level of the tree structure, the name field of the two-dimensional table includes the device type name of the breadth dimension information; in the third level of the tree structure, the name field of the two-dimensional table includes the measurement attribute name of the dynamic dimension information; and in the fourth level of the tree structure, the name field of the two-dimensional table includes the static feature information of the static dimension information. In the third layer, the tag point name is obtained by hashing the combination of the layer number, node number, parent node number, and name. The dynamic information dimension and the depth dimension are associated through a label structure; the identification information of the collected measurement data is recorded as label points, and the third layer of the tree structure is associated with the label points through a hash algorithm; The depth dimension division identifies the collected measurement data and its corresponding time stamp information according to the data triplet format of data time, data value, and data quality code; the data value includes floating-point, integer, boolean, and character data types; The static information dimension division is divided horizontally according to the device's measurement name, measurement type, and measurement unit, which is used to identify the characteristic information of the device's collected indicators; the static information dimension division is divided vertically according to the device's measurement attributes, which are denoted as feature quantities; and the data of the feature quantities are recorded digitally, which are denoted as feature values.

2. The information model construction method for power IoT terminal devices according to claim 1, characterized in that: The types include electrical equipment, gas equipment, refrigeration equipment, and heating equipment.

3. The information model construction method for power IoT terminal equipment according to claim 1, characterized in that: The sampling categories include three-phase voltage values, three-phase current values, active power values, and reactive power values ​​in power equipment; and pressure values ​​and temperature values ​​in heating equipment.

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