Intelligent electric power park data processing method and system based on big data

Abstract

The invention relates to the technical field of big data processing, and discloses an intelligent electric power park data processing method and system based on big data, and the method comprises the steps: obtaining park electric power data, taking the park real-time power consumption data and the preset power consumption demand as node data, and constructing a park power consumption graph; constructing a power flow calculation model according to the node connection relation of the park electricity utilization diagram; inputting each component data of the park power data into the power flow calculation model, and performing calculation operation to obtain a power correction parameter; constructing an electricity demand clustering model according to the electric power correction parameters, and performing data clustering analysis operation to obtain the minimum power of the park energy consumption; and determining a real-time power distribution scheme of each power device according to the minimum power of the park energy consumption, generating a power distribution instruction, and completing energy-saving control of the park power devices according to the power distribution instruction. According to the method, the energy consumption minimization of the park can be realized according to the electricity demand power of the users in the park.

Description

Technical Field

[0001] The present invention relates to the field of big data processing technology, and in particular to a smart power park data processing method and system based on big data. Background Art

[0002] With the development and progress of information intelligence, the requirements for the stability, reliability and efficiency of electricity consumption are increasing. On the one hand, there are many safety hazards in the current power system. On the other hand, traditional electricity management methods are difficult to meet the complexity and diversity of modern electricity demand. Therefore, the electricity industry urgently needs to explore new electricity strategies to ensure the stability and safety of electricity consumption. Based on the construction of smart grids and the construction of big data computing power, through the accurate collection of power information, efficient electricity services can be provided under the premise of ensuring the safe operation of power equipment.

[0003] In the prior art, a data processing method and system for a smart power park based on big data can analyze power consumption data, power consumption related types and non-power consumption related types of data by acquiring multi-type data from each monitoring node in the park and combining it with a pre-trained power load association model, and realize power load prediction based on the degree of data deviation and difference characteristics.

[0004] However, the existing technology only uses big data processing methods to process electricity consumption data, and does not fully consider the impact of the integration of multi-source data such as park environment data and power equipment data on the results. It often generates high energy consumption during long-term electricity use and cannot meet the actual needs of the park. Summary of the invention

[0005] The present invention provides a smart power park data processing method and system based on big data, aiming to solve the problem that the existing power park data processing technology lacks consideration of park fusion data, resulting in low utilization rate of electric energy and high energy consumption of users.

[0006] In the first aspect, in order to solve the above technical problems, the present invention provides a data processing method for a smart power park based on big data, which is executed by a computer and includes: Acquire park power data, the park power data including power equipment operation data, park power consumption data and park environment data; According to the park electricity consumption data, real-time park electricity consumption data is obtained, and the real-time park electricity consumption data and the preset electricity demand are used as node data to construct a park electricity consumption map; According to the node connection relationship of the park power consumption diagram, an analysis and calculation operation is performed to obtain a node association value, and the node association value is used as a branch connection weight to construct a power flow calculation model; Inputting each component data of the park power data into the power flow calculation model, performing power flow calculation operation, and obtaining power correction parameters; Constructing a power demand clustering model according to the power correction parameter and the park power data, and performing a data clustering analysis operation according to the power demand clustering model to obtain the minimum power consumption of the park; According to the minimum energy consumption power of the park, the real-time power distribution plan of each power equipment is determined, and the power distribution instruction is generated. According to the power distribution instruction, the energy-saving control of the power equipment in the park is completed.

[0007] As an optional implementation manner, the power equipment operation data includes at least one of the working efficiency of each power-consuming equipment, the power load voltage, the user load, the user power consumption, and the power load working time; The park electricity consumption data includes at least one of the electricity consumption equipment information corresponding to the user, the electricity consumption time period, the electricity consumption power, and the user's energy consumption data; The park environment data includes at least one of an average temperature and an average air pressure in an area where the electrical equipment is located.

[0008] As an optional implementation, according to the node connection relationship of the park power consumption diagram, an analysis and calculation operation is performed to obtain a node association value, and the node association value is used as a branch connection weight to construct a power flow calculation model, including: The calculation formula of the node association value is expressed as:

[0009] The node association value is used as the branch connection weight, and its relationship calculation expression is:

[0010] The power flow calculation model is represented by:

[0011] in, Represents a user node and user nodes The correlation value between Represents a user node and user nodes The active power between Represents a user node and user nodes The reactive power between and Represents user nodes and user nodes The voltage amplitude; Represents a user node and user nodes The branch connection weights Represents a user node and user nodes The modulus of the complex impedance between Indicates the corrected active power, Represents a user node The set of adjacent nodes, and represents the real and imaginary parts of the branch admittance, Represents a user node and user nodes The phase angle difference between them.

[0012] As an optional implementation manner, the inputting of each component data of the park power data into the power flow calculation model, performing a power flow calculation operation, and obtaining a power correction parameter includes: According to each component data of the park power data, set classification is performed to construct a power set and an environment set; The user component voltage in the power set and the user load are input into the power flow calculation model, and the calculation formula is expressed as:

[0013] in, represents the corrected power, Represents a user node and user nodes The branch connection weights between Represents a user node The set of adjacent nodes, Represents a user node The voltage amplitude, Represents a user node The load current, and represents the real and imaginary parts of the branch admittance, Represents a user node and user nodes The phase angle difference between The power correction parameter is obtained by performing variance calculation based on the corrected power and the data of the environment set.

[0014] As an optional implementation manner, performing a data clustering analysis operation according to the power demand clustering model to obtain the minimum power consumption of the park includes: The calculation formula of the minimum power consumption of the park is expressed as:

[0015] in, represents the minimum total power demand of each user, represents the number of subcategories of the target component, Indicates The number of electricity users in the target component subcategory, No. The target component The weight of the sub-categories, Indicates The category centers of the cluster groups, Expressed as The target component The power demand corresponding to each subcategory.

[0016] As an optional implementation manner, the constructing of a power demand clustering model according to the power correction parameter and the park power data includes: The mathematical expression of the electricity demand clustering model is:

[0017] in, Expressed as the objective function of the electricity demand clustering model, Expressed as The weight of the feature parameters, It is represented as an input characteristic parameter, which is composed of the power correction parameter and the park power data. represents the number of feature parameters, Indicates characteristic parameters, The power correction parameter expressed as the corrected power, The power correction parameter is represented as the environmental set data.

[0018] As an optional implementation, the data clustering analysis operation includes: Acquire input characteristic parameters, wherein the input characteristic parameters include historical operation data of each power device, electric energy meter data, the park power consumption data, the park environment data, and the power correction parameter; The input characteristic parameters are used as calculation parameters of the power demand clustering model to analyze the time-series power consumption behavior and characteristics of the power-consuming equipment; According to the similarity of the power consumption time series and the similarity of the power consumption characteristics of the power consumption equipment, the power consumption equipment is clustered and analyzed to obtain the clustering grouping results; Calculate the category center of each cluster grouping. The calculation formula is expressed as

[0019] in, Indicates The category centers of the cluster groups, Indicates The number of devices grouped into clusters, Indicates that it belongs to The first cluster group Characteristic parameters of each device; The category center of the cluster grouping is combined with the power demand of each cluster grouping, and the minimum energy consumption power of the park is obtained by weighted summation.

[0020] In a second aspect, the present invention provides a smart power park data processing system based on big data, characterized in that it is configured in a computer and includes: A data acquisition module is used to acquire park power data, wherein the park power data includes power equipment operation data, park power consumption data and park environment data; A park electricity consumption map construction module is used to obtain the park's real-time electricity consumption data based on the park's electricity consumption data, and use the park's real-time electricity consumption data and preset electricity demand as node data to construct a park electricity consumption map; A power flow calculation model establishment module is used to perform analysis and calculation operations according to the node connection relationship of the park power consumption diagram, obtain node association values, use the node association values ​​as branch connection weights, and construct a power flow calculation model; A power correction parameter acquisition module, used to input each component data of the park power data into the power flow calculation model, perform power flow calculation operations, and obtain power correction parameters; A fusion module, used to construct a power demand clustering model according to the power correction parameter and the park power data, and perform data clustering analysis operations according to the power demand clustering model to obtain the minimum power consumption of the park; The output module is used to determine the real-time power distribution plan of each power device according to the minimum power consumption of the park, generate power distribution instructions, and complete energy-saving control of the park power equipment according to the power distribution instructions.

[0021] In a third aspect, the present invention also provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any one of the above-mentioned methods for processing data in a smart power park based on big data.

[0022] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for processing smart power park data based on big data.

[0023] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a data processing method for a smart electric power park based on big data, the method comprising: acquiring park power data, the park power data comprising power equipment operation data, park power consumption data and park environment data; acquiring real-time park power consumption data according to the park power consumption data, and using the real-time park power consumption data and preset power demand as node data to construct a park power consumption diagram; performing analysis and calculation operations according to the node connection relationship of the park power consumption diagram to obtain node association values, and using the node association values ​​as branch connection weights to construct a power flow calculation model; inputting each component data of the park power data into the power flow calculation model, performing power flow calculation operations, and obtaining power correction parameters; constructing a power demand clustering model according to the power correction parameters and the park power data, and performing data clustering analysis operations according to the power demand clustering model to obtain the minimum power consumption of the park; determining the real-time power distribution plan of each power equipment according to the minimum power consumption of the park, generating a power distribution instruction, and completing energy-saving control of the park power equipment according to the power distribution instruction.

[0024] This method uses a computer system to collect and process park data, and analyzes the power consumption of each power user in the park by constructing a park power consumption diagram. It integrates multiple data sources such as power equipment operation, park power consumption, and park environment, and uses a power flow calculation model and a power demand clustering model to calculate and analyze data. The power consumption of the park power data is classified based on a clustering algorithm. According to the classification results and the power demand characteristics of power equipment in each category, a minimum energy consumption strategy for the park is obtained. Based on the minimum energy consumption strategy for the park, a power allocation instruction is output to achieve energy-saving control of the park. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of a method for processing data in a smart power park based on big data provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of the structure of a park electricity consumption diagram provided by an embodiment of the present invention; Figure 3 It is a structural schematic diagram of a smart power park data processing system based on big data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] With the development and progress of information intelligence, the requirements for the stability, reliability and efficiency of electricity consumption are increasing. On the one hand, there are many safety hazards in the current power system. On the other hand, traditional electricity management methods are difficult to meet the complexity and diversity of modern electricity demand. Therefore, the electricity industry urgently needs to explore new electricity strategies to ensure the stability and safety of electricity consumption. Based on the construction of smart grids and the construction of big data computing power, through the accurate collection of power information, efficient electricity services can be provided under the premise of ensuring the safe operation of power equipment.

[0028] In the prior art, a data processing method and system for a smart power park based on big data can analyze power consumption data, power consumption related types and non-power consumption related types of data by acquiring multi-type data from each monitoring node in the park and combining it with a pre-trained power load association model, and realize power load prediction based on the degree of data deviation and difference characteristics.

[0029] However, the existing technology only uses big data processing methods to process electricity consumption data, and does not fully consider the impact of the integration of multi-source data such as park environment data and power equipment data on the results. It often generates high energy consumption during long-term electricity use and cannot meet the actual needs of the park.

[0030] To solve the above problems, refer to Figure 1 The first embodiment of the present invention provides a method for processing data in a smart power park based on big data, comprising the following steps: S1, obtaining park power data, wherein the park power data includes power equipment operation data, park power consumption data and park environment data; S2, acquiring real-time power consumption data of the park according to the power consumption data of the park, and using the real-time power consumption data of the park and the preset power demand as node data to construct a power consumption map of the park; S3, performing analysis and calculation operations according to the node connection relationship of the park power consumption diagram to obtain node association values, using the node association values ​​as branch connection weights to construct a power flow calculation model; S4, inputting each component data of the park power data into the power flow calculation model, performing a power flow calculation operation, and obtaining a power correction parameter; S5, constructing a power demand clustering model according to the power correction parameter and the park power data, and performing a data clustering analysis operation according to the power demand clustering model to obtain the minimum power consumption of the park; S6, determining a real-time power distribution plan for each power device according to the minimum power consumption of the park, generating a power distribution instruction, and completing energy-saving control of the power devices in the park according to the power distribution instruction; In step S1, park power data is acquired, where the park power data includes power equipment operation data, park power consumption data, and park environment data.

[0031] In one implementation, the power equipment operation data includes at least one of the working efficiency of each power-consuming device, the power load voltage, the user load, the user power consumption, and the power load working time; The park electricity consumption data includes at least one of the electricity consumption equipment information corresponding to the user, the electricity consumption time period, the electricity consumption power, and the user's energy consumption data; The park environment data includes at least one of the average temperature and the average air pressure of the area where the electrical equipment is located; Furthermore, the methods for obtaining the park power data include: The power equipment operation data is the real-time operation information of various power equipment in the park (such as transformers, generators, power distribution equipment, distribution cabinets, etc.), which is obtained through smart meters, real-time monitoring systems, monitoring and data acquisition systems and communication protocols in the park. The smart meters can collect the voltage, current, power, power factor and other information of the equipment in real time and transmit it to the central data management platform; The park electricity consumption data refers to the total electricity consumption of the entire park and the electricity consumption of each area, which is obtained through the total meter and sub-meter data and the intelligent metering software system; The park environment data includes environmental factors related to power consumption, which are obtained through environmental sensors, meteorological data integration and environmental monitoring software systems. The environmental sensors collect temperature, humidity, air quality and other data in real time, including but not limited to temperature and humidity sensors, gas sensors, light sensors, etc.; It should be noted that the process of obtaining park power data involves multiple links, including real-time monitoring of power equipment, collection of power consumption data, monitoring of environmental factors and data integration. Through technical means such as smart meters, sensors, SCADA systems and data management platforms, the park can obtain accurate power and environmental data in real time, and optimize power consumption behavior and save energy through big data analysis.

[0032] In step S2, the real-time power consumption data of the park is obtained according to the power consumption data of the park, and the real-time power consumption data of the park and the preset power demand are used as node data to construct a power consumption map of the park.

[0033] In one implementation, according to the park power consumption data, real-time park power consumption data is obtained, and the real-time park power consumption data and the preset power demand are used as node data to construct a park power consumption map, including: Using the node data as the basic point of the park electricity consumption map, construct edge data according to the correlation between the electricity users; Based on the node data and the edge data, a park electricity consumption graph is constructed, wherein the nodes of the graph represent the users in the park, the node attributes include the real-time electricity consumption, the estimated electricity demand and the historical electricity consumption characteristics, the edges of the graph represent the association between the users, and the edge weight reflects the strength of the association; Use graph drawing tools to visualize the campus electricity consumption map, showing the electricity consumption relationship and demand distribution of each user in the campus; It should be noted that the association is determined in the following ways: geographical proximity: the geographical relationship of the user's area; electricity demand similarity: the similarity is calculated based on the user's electricity consumption characteristics (such as real-time electricity consumption, historical electricity consumption characteristics) to determine the weight of the edge; power supply chain relationship: if some users share the same power supply line or substation, edges are added between these users.

[0034] In step S3, an analysis and calculation operation is performed according to the node connection relationship of the park power consumption diagram to obtain node association values, and the node association values ​​are used as branch connection weights to construct a power flow calculation model.

[0035] In one implementation, an analysis and calculation operation is performed according to the node connection relationship of the park power consumption diagram to obtain a node association value, and the node association value is used as a branch connection weight to construct a power flow calculation model, including: The calculation formula of the node association value is expressed as:

[0036] The node association value is used as the branch connection weight, and its relationship calculation expression is:

[0037] The power flow calculation model is represented by:

[0038] in, Represents a user node and user nodes The correlation value between Represents a user node and user nodes The active power between Represents a user node and user nodes The reactive power between and Represents user nodes and user nodes The voltage amplitude; Represents a user node and user nodes The branch connection weights Represents a user node and user nodes The modulus of the complex impedance between Indicates the corrected active power, Represents a user node The set of adjacent nodes, and represents the real and imaginary parts of the branch admittance, Represents a user node and user nodes The phase angle difference between them.

[0039] In step S4, each component data of the park power data is input into the power flow calculation model, and a power flow calculation operation is performed to obtain power correction parameters.

[0040] In one implementation, each component data of the park power data is input into the power flow calculation model, and a power flow calculation operation is performed to obtain power correction parameters, including: According to each component data of the park power data, set classification is performed to construct a power set and an environment set; The user component voltage in the power set and the user load are input into the power flow calculation model, and the calculation formula is expressed as:

[0041] in, represents the corrected power, Represents a user node and user nodes The branch connection weights between Represents a user node The set of adjacent nodes, Represents a user node The voltage amplitude, Represents a user node The load current, and represents the real and imaginary parts of the branch admittance, Represents a user node and user nodes The phase angle difference between Perform variance calculation based on the corrected power and the data of the environment set to obtain the power correction parameter; Further, the constructing of the power set and the environment set includes: Power Collection:

[0042] Environment Collection:

[0043] in, Indicates the real-time power consumption of each user. Indicates the historical peak power consumption of each user. Represents the historical electricity consumption of each user, It is expressed as the average power supply of each user. It is expressed as the estimated electricity demand of each user. Expressed as the load rate of the power equipment, It is the park environment temperature. It is expressed as the environmental humidity of the park. Expressed as the atmospheric pressure in the park, Represents the weather conditions of the park. It is expressed as the seasonal effect of the park; Further, according to the corrected power and the data of the environment set, variance calculation is performed to obtain the power correction parameter, including: The variance calculation formula of the corrected power is expressed as:

[0044] The variance calculation formula of the environmental set data is expressed as:

[0045] in, Expressed as Corrected power of each electrical device, Expressed as the average value of the corrected power, Expressed as Environmental data of power equipment, represents the average value of environmental data, Indicates the number of electrical equipment; It should be noted that the variance of the corrected power and the variance of the environmental aggregate data are used as power correction parameters for constructing a subsequent power demand clustering model.

[0046] In step S5, a power demand clustering model is constructed according to the power correction parameters and the park power data, and a data clustering analysis operation is performed according to the power demand clustering model to obtain the minimum power consumption of the park.

[0047] In one implementation, a power demand clustering model is constructed according to the power correction parameter and the park power data, and a data clustering analysis operation is performed according to the power demand clustering model to obtain the minimum power consumption of the park, including: The mathematical expression of the electricity demand clustering model is:

[0048] in, Expressed as the objective function of the electricity demand clustering model, Expressed as The weight of the feature parameters, It is represented as an input characteristic parameter, which is composed of the power correction parameter and the park power data. represents the number of feature parameters, Indicates characteristic parameters, The power correction parameter expressed as the corrected power, A power correction parameter represented by the environmental aggregate data; The calculation formula of the minimum power consumption of the park is expressed as:

[0049] in, represents the minimum total power demand of each user, represents the number of subcategories of the target component, Indicates The number of electricity users in the target component subcategory, No. The target component The weight of the sub-categories, Indicates The category centers of the cluster groups, Expressed as The target component The power demand corresponding to each subcategory; Furthermore, the data clustering analysis operation includes: Acquire input characteristic parameters, wherein the input characteristic parameters include historical operation data of each power device, electric energy meter data, the park power consumption data, the park environment data, and the power correction parameter; The input characteristic parameters are used as calculation parameters of the power demand clustering model to analyze the time-series power consumption behavior and characteristics of the power-consuming equipment; According to the similarity of the power consumption time series and the similarity of the power consumption characteristics of the power consumption equipment, the power consumption equipment is clustered and analyzed to obtain the clustering grouping results; Calculate the category center of each cluster grouping. The calculation formula is expressed as

[0050] in, Indicates The category centers of the cluster groups, Indicates The number of devices grouped into clusters, Indicates that it belongs to The first cluster group Characteristic parameters of each device; Combining the category center of the cluster grouping with the power demand of each cluster grouping, and performing weighted summation to obtain the minimum energy consumption power of the park; Furthermore, before using the power correction parameter as the input of the power demand clustering model, the park power consumption data is preprocessed, including: The components in the park electricity consumption data are screened to obtain the target component, and a normal cloud model is constructed, and the target component is constructed as the expectation of the normal cloud model; Constructing a super entropy value corresponding to each target component, wherein the super entropy value is the variance corresponding to each target component; A normal cloud inference structure is established according to the expectation and the super entropy value, and the data of the target component are fused using the normal cloud inference structure to obtain a standard input value; Furthermore, the components of the park electricity consumption data are screened, including: Classify each component data in the park's electricity consumption data to obtain basic data and advanced data; The calculation basic data includes: the power consumption equipment information corresponding to the user, the power consumption time period corresponding to the power consumption equipment information, the grey correlation between each component data in the user energy consumption data in each power consumption time period and the power consumption, retaining the elements with the first correlation greater than the first threshold, wherein the first threshold is (0.7~0.8), and obtaining the filtered data; Calculate the advanced data including: user load, user power consumption, working time of power load, working efficiency of each power load, voltage of each power load, average temperature of the user, average air pressure of the user, each component data and power equipment information, power time period corresponding to the power equipment information, and gray correlation coefficient between each component data in user energy consumption data in each power time period, and retain the elements with the second correlation greater than the second threshold, wherein the second threshold is (0.5~0.6); Further, the construction of the normal cloud model is: respectively inputting the super entropy value and variance corresponding to each component data into the normal cloud model to obtain the standard deviation, and calculating the normal distribution, super entropy value and expectation corresponding to the normal cloud model; The calculation formula of the super entropy value is expressed as:

[0051] in, Expressed as The super entropy value of the component data is Expressed as The number of values ​​in the component data, Expressed as The variance of the component data, Expressed as The average of the component data, Expressed as The first component data A value, exp is expressed as a natural constant An exponential function with base ; Furthermore, the standard input value is obtained according to the following process: The enhancement layer and comparison layer of the normal cloud inference structure are used to determine the fusion coefficient of each component data; The decision layer of the normal cloud inference structure is used to process the fusion coefficients to obtain the standard input values ​​corresponding to the user; The calculation formula of the standard input value corresponding to the user is as follows:

[0052] in, Indicates the standard input value corresponding to the user, Expressed as a fusion coefficient, it is used to adjust the weight or influence range of the standardized value. Indicates the actual value entered by the user. It is expressed as the expected value of the user. It represents the variance corresponding to the user and the degree of dispersion of the data.

[0053] In step S6, according to the minimum power consumption of the park, a real-time power distribution plan for each power device is determined, a power distribution instruction is generated, and energy-saving control of the park power devices is completed according to the power distribution instruction.

[0054] In one implementation, according to the minimum power consumption of the park, a real-time power distribution plan for each power device is determined, a power distribution instruction is generated, and energy-saving control of the park power devices is completed according to the power distribution instruction, including: According to the minimum energy consumption of the park combined with the real-time power consumption data of each power device and the equipment operation status, the power demand of each power device is calculated. The power demand calculation formula of the device is:

[0055] in, Expressed as The power demand of each electrical device, Expressed as The power allocation weight of each power device, It is expressed as the minimum power consumption of the park; Generate a real-time power distribution plan based on the calculated power requirements of each device, combined with the load characteristics, operation priorities and safety constraints of the power equipment; The total allocated power meets the minimum energy consumption of the park:

[0056] The power of a single device does not exceed the maximum rated power of the device :

[0057] Consider the minimum safe operating power of the equipment :

[0058] in, Expressed as The maximum power rating of the device, Expressed as The minimum safe operating power of each device; According to the real-time power distribution plan, a power distribution instruction is generated and sent to the control system of each power device. The power distribution instruction format includes: device number, allocated power value, and instruction timestamp. After each power device receives the power distribution instruction, it adjusts the operating status according to the allocated power value to complete the power distribution execution and realize low energy consumption control in the park.

[0059] In order to facilitate the understanding of the present invention, some preferred embodiments of the present invention are further described below.

[0060] The following describes the working process of the present invention using a relatively common scenario as an example. The working process is as follows: Step 1: Obtaining park power data; Step 2: Drawing the park power consumption map; Step 3: Calculating power correction parameters; Step 4: Constructing a power demand clustering model; Step 5: Acquisition of real-time electricity consumption categories; Step 6: Low energy consumption control of electrical equipment; Preferably, in step 1, the park power data is acquired by acquiring the historical operation data of each power device from the historical database and acquiring the real-time operation data of the power device from the device monitoring device; Furthermore, abnormal data and noise data in the real-time operation data are screened for abnormalities. The abnormal screening method is as follows: Acquire a preset normal operating range of all monitored electric power equipment in the equipment monitoring device, wherein the preset normal operating range is obtained based on historical data of normal operating status of the monitored electric power equipment; If the real-time operating data of the monitored power equipment is not within the preset normal operating range, it is considered abnormal and removed; The real-time operation data of the monitored power equipment is de-noised by using a filtering algorithm to obtain the power equipment operation data; It should be noted that the filtering algorithm adopts the Kalman filtering algorithm, which is an optimal recursive filtering estimation in a linear system. It is a more practical and effective real-time estimation algorithm in current engineering practice.

[0061] Preferably, in step 2, a park electricity consumption diagram is drawn, historical park electricity consumption data is obtained from the power meter and the electricity consumption information collection system, and current park electricity consumption data is obtained from the electricity consumption information collection system to obtain the park electricity consumption record; The park's electricity consumption data includes: each user's real-time electricity consumption and estimated electricity demand; the park's historical electricity consumption data includes: each user's peak electricity consumption, minimum electricity demand and average power supply power, etc.; The real-time power consumption and estimated power demand of each user are used to form a park power consumption record, and a park power consumption map is drawn based on the park power consumption record; Preferably, in the step three, the calculation of the power correction parameter analyzes the connection relationship between the nodes on the park power consumption diagram to obtain the node association value; Then, the node association value is used as the connection branch weight to construct a power flow calculation model, and then the third-order fluctuation variation of voltage, current and power corresponding to each node is calculated based on the power flow calculation model. The third-order fluctuation variation is the power correction parameter; Preferably, in the step 4, the power demand clustering model is constructed, and the clustering principle of the power demand clustering model is: using the historical operation data of each power equipment, the park power consumption data and the park environment data in the power meter and the power consumption information collection system as input feature parameters, clustering the power equipment according to the periodic similarity of their power consumption time series and the similarity of their power consumption characteristics, and obtaining the clustering grouping results of each power equipment, calculating the category center of the grouping results through the power demand clustering model, and obtaining the historical demand distribution of each power equipment as the basis for clustering the power demand clustering model; Preferably, in step 5, the real-time electricity consumption category is obtained, the power correction parameters and the park environment data are input into the electricity demand clustering model, and the electricity consumption of each user is monitored in real time in combination with the smart meter billing module: the real-time electricity consumption category of each user is obtained; Preferably, in step six, the low energy consumption control of the electrical equipment generates the corresponding power demand power based on the real-time power consumption category of each user, and then determines the real-time power distribution plan for each electrical equipment in the preset terminal device of each user based on the power demand power; generates power distribution instructions according to the real-time power distribution plan, and then controls the power supply of each electrical equipment according to the power distribution instructions.

[0062] Reference Figure 2 The park electricity consumption graph is composed of user 1, user 2, user 3, user 4, user 5 and an external server. All the node users contain the real-time electricity consumption data and preset electricity demand of the park. The directed edges of the park electricity consumption graph are from E1 to E 10 , contains the correlation and weight data between each electricity user. Each user node performs data analysis according to the external server and uploads the analysis results to the computer system to process the big data of the smart power park, generate control instructions, and complete low-energy consumption control.

[0063] In summary, the present invention provides a data processing method for a smart power park based on big data, the method comprising: acquiring park power data, the park power data comprising power equipment operation data, park power consumption data and park environment data; acquiring real-time park power consumption data based on the park power consumption data, and using the real-time park power consumption data and preset power demand as node data to construct a park power consumption diagram; performing analysis and calculation operations based on the node connection relationship of the park power consumption diagram to obtain node association values, and using the node association values ​​as branch connection weights to construct a power flow calculation model; inputting each component data of the park power data into the power flow calculation model, performing power flow calculation operations, and obtaining power correction parameters; constructing a power demand clustering model based on the power correction parameters and the park power data, and performing data clustering analysis operations based on the power demand clustering model to obtain the minimum power consumption of the park; determining the real-time power distribution plan of each power equipment based on the minimum power consumption of the park, generating power distribution instructions, and completing energy-saving control of the park power equipment based on the power distribution instructions.

[0064] This method uses a computer system to collect and process park data, and analyzes the power consumption of each power user in the park by constructing a park power consumption diagram. It integrates multiple data sources such as power equipment operation, park power consumption, and park environment, and uses a power flow calculation model and a power demand clustering model to calculate and analyze data. The power consumption of the park power data is classified based on a clustering algorithm. According to the classification results and the power demand characteristics of power equipment in each category, a minimum energy consumption strategy for the park is obtained. Based on the minimum energy consumption strategy for the park, a power allocation instruction is output to achieve energy-saving control of the park.

[0065] Reference Figure 3 The second embodiment of the present invention provides a smart power park data processing system based on big data, which is configured on a computer and includes: 101 data acquisition module, used to acquire park power data, the park power data includes power equipment operation data, park power consumption data and park environment data; 102 a park electricity consumption diagram construction module, used to obtain the park's real-time electricity consumption data according to the park's electricity consumption data, and use the park's real-time electricity consumption data and preset electricity demand as node data to construct a park electricity consumption diagram; 103 a power flow calculation model establishment module, for performing analysis and calculation operations according to the node connection relationship of the park power consumption diagram, obtaining node association values, using the node association values ​​as branch connection weights, and constructing a power flow calculation model; 104 a power correction parameter acquisition module, used for inputting each component data of the park power data into the power flow calculation model, performing a power flow calculation operation, and obtaining a power correction parameter; 105 fusion module, used to build a power demand clustering model according to the power correction parameter and the park power data, and perform data clustering analysis operation according to the power demand clustering model to obtain the minimum power consumption of the park; The output module 106 is used to determine the real-time power distribution plan of each power device according to the minimum power consumption of the park, generate a power distribution instruction, and complete the energy-saving control of the power equipment in the park according to the power distribution instruction.

[0066] It should be noted that the smart power park data processing system based on big data provided in an embodiment of the present invention is used to execute all the process steps of the smart power park data processing method based on big data in the above-mentioned embodiment. The working principles and beneficial effects of the two correspond one to one, and therefore will not be repeated here.

[0067] In summary, the present invention provides a data processing method for a smart power park based on big data, the method comprising: acquiring park power data, the park power data comprising power equipment operation data, park power consumption data and park environment data; acquiring real-time park power consumption data based on the park power consumption data, and using the real-time park power consumption data and preset power demand as node data to construct a park power consumption diagram; performing analysis and calculation operations based on the node connection relationship of the park power consumption diagram to obtain node association values, and using the node association values ​​as branch connection weights to construct a power flow calculation model; inputting each component data of the park power data into the power flow calculation model, performing power flow calculation operations, and obtaining power correction parameters; constructing a power demand clustering model based on the power correction parameters and the park power data, and performing data clustering analysis operations based on the power demand clustering model to obtain the minimum power consumption of the park; determining the real-time power distribution plan of each power equipment based on the minimum power consumption of the park, generating power distribution instructions, and completing energy-saving control of the park power equipment based on the power distribution instructions.

[0068] This method uses a computer system to collect and process park data, and analyzes the power consumption of each power user in the park by constructing a park power consumption diagram. It integrates multiple data sources such as power equipment operation, park power consumption, and park environment, and uses a power flow calculation model and a power demand clustering model to calculate and analyze data. The power consumption of the park power data is classified based on a clustering algorithm. According to the classification results and the power demand characteristics of power equipment in each category, a minimum energy consumption strategy for the park is obtained. Based on the minimum energy consumption strategy for the park, a power allocation instruction is output to achieve energy-saving control of the park.

[0069] The embodiment of the present invention also provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a program for a smart power park data processing method based on big data. When the processor executes the computer program, the steps in the above-mentioned embodiments of the smart power park data processing method based on big data are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, for example, the data acquisition module 101.

[0070] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0071] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0072] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0073] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0074] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0075] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0076] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A data processing method for a smart power park based on big data, characterized in that: Executed by a computer, including: Acquire park power data, the park power data including power equipment operation data, park power consumption data and park environment data; According to the park electricity consumption data, real-time park electricity consumption data is obtained, and the real-time park electricity consumption data and the preset electricity demand are used as node data to construct a park electricity consumption map; According to the node connection relationship of the park power consumption diagram, an analysis and calculation operation is performed to obtain a node association value, and the node association value is used as a branch connection weight to construct a power flow calculation model; Inputting each component data of the park power data into the power flow calculation model, performing power flow calculation operation, and obtaining power correction parameters; Constructing a power demand clustering model according to the power correction parameter and the park power data, and performing a data clustering analysis operation according to the power demand clustering model to obtain the minimum power consumption of the park; According to the minimum energy consumption power of the park, the real-time power distribution plan of each power device is determined, and a power distribution instruction is generated. According to the power distribution instruction, energy-saving control of the power equipment in the park is completed.

2. According to the big data-based smart power park data processing method of claim 1, it is characterized in that: The power equipment operation data includes at least one of the working efficiency of each power-consuming equipment, the power load voltage, the user load, the user power consumption, and the power load working time; The park electricity consumption data includes at least one of the electricity consumption equipment information corresponding to the user, the electricity consumption time period, the electricity consumption power, and the user's energy consumption data; The park environment data includes at least one of an average temperature and an average air pressure in an area where the electrical equipment is located.

3. The method for processing data in a smart power park based on big data according to claim 1 is characterized in that: According to the node connection relationship of the park power consumption diagram, an analysis and calculation operation is performed to obtain a node association value, and the node association value is used as a branch connection weight to construct a power flow calculation model, including: The calculation formula of the node association value is expressed as: ; The node association value is used as the branch connection weight, and its relationship calculation expression is: ； The power flow calculation model is represented by: ； in, Represents a user node and user nodes The correlation value between Represents a user node and user nodes The active power between Represents a user node and user nodes The reactive power between and Represents user nodes and user nodes The voltage amplitude of Represents a user node and user nodes The branch connection weights Represents a user node and user nodes The modulus of the complex impedance between Indicates the corrected active power, Represents a user node The set of adjacent nodes, and represents the real and imaginary parts of the branch admittance, Represents a user node and user nodes The phase angle difference between them.

4. The method for processing data in a smart power park based on big data according to claim 2 is characterized in that: The step of inputting each component data of the park power data into the power flow calculation model, performing a power flow calculation operation, and obtaining power correction parameters includes: According to each component data of the park power data, set classification is performed to construct a power set and an environment set; The user component voltage in the power set and the user load are input into the power flow calculation model, and the calculation formula is expressed as: ； in, represents the corrected power, Represents a user node and user nodes The branch connection weights between Represents a user node The set of adjacent nodes, Represents a user node The voltage amplitude, Represents a user node The load current, and represents the real and imaginary parts of the branch admittance, Represents a user node and user nodes The phase angle difference between The power correction parameter is obtained by performing variance calculation based on the corrected power and the data of the environment set.

5. The method for processing data in a smart power park based on big data according to claim 1 is characterized in that: The data clustering analysis operation is performed according to the power demand clustering model to obtain the minimum power consumption of the park, including: The calculation formula of the minimum power consumption of the park is expressed as: ； in, represents the minimum total power demand of each user, represents the number of subcategories of the target component, Indicates The number of electricity users in the target component subcategory, No. The target component The weight of the sub-categories, Indicates The category centers of the cluster groups, Expressed as The target component The power demand corresponding to each subcategory.

6. The method for processing data in a smart power park based on big data according to claim 4 is characterized in that: The constructing of a power demand clustering model according to the power correction parameter and the park power data includes: The mathematical expression of the electricity demand clustering model is: ； in, Expressed as the objective function of the electricity demand clustering model, Expressed as The weight of the feature parameters, It is represented as an input characteristic parameter, which is composed of the power correction parameter and the park power data. represents the number of feature parameters, Indicates characteristic parameters, The power correction parameter expressed as the corrected power, The power correction parameter is represented as the environmental set data.

7. The method for processing data in a smart power park based on big data according to claim 4 is characterized in that: The data clustering analysis operation includes: Acquire input characteristic parameters, wherein the input characteristic parameters include historical operation data of each power device, electric energy meter data, the park power consumption data, the park environment data, and the power correction parameter; The input characteristic parameters are used as calculation parameters of the power demand clustering model to analyze the time-series power consumption behavior and characteristics of the power-consuming equipment; According to the similarity of the power consumption time series and the similarity of the power consumption characteristics of the power consumption equipment, the power consumption equipment is clustered and analyzed to obtain the clustering grouping results; Calculate the category center of each cluster grouping. The calculation formula is expressed as ； in, Indicates The category centers of the cluster groups, Indicates The number of devices grouped into clusters, Indicates that it belongs to The first cluster group Characteristic parameters of each device; The category center of the cluster grouping is combined with the power demand of each cluster grouping, and the minimum energy consumption power of the park is obtained by weighted summation.

8. A smart power park data processing system based on big data, characterized in that: Configured in the computer, including: A data acquisition module is used to acquire park power data, wherein the park power data includes power equipment operation data, park power consumption data and park environment data; A park electricity consumption map construction module is used to obtain the park's real-time electricity consumption data based on the park's electricity consumption data, and use the park's real-time electricity consumption data and preset electricity demand as node data to construct a park electricity consumption map; A power flow calculation model establishment module is used to perform analysis and calculation operations according to the node connection relationship of the park power consumption diagram, obtain node association values, use the node association values ​​as branch connection weights, and construct a power flow calculation model; A power correction parameter acquisition module, used to input each component data of the park power data into the power flow calculation model, perform power flow calculation operations, and obtain power correction parameters; A fusion module, used to construct a power demand clustering model according to the power correction parameter and the park power data, and perform data clustering analysis operations according to the power demand clustering model to obtain the minimum power consumption of the park; The output module is used to determine the real-time power distribution plan of each power device according to the minimum power consumption of the park, generate power distribution instructions, and complete energy-saving control of the park power equipment according to the power distribution instructions.

9. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the big data-based smart power park data processing as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to perform the big data-based smart power park data processing as described in any one of claims 1 to 7.

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