Smart heating resource allocation system and method based on power data analysis

Abstract

The present invention belongs to the field of smart heating technology, and in particular relates to a smart heating resource allocation system and method based on electricity data analysis. The system includes an electricity collection system, an electricity big data cloud computing platform and a display; the electricity collection system includes an electricity data collector, an electricity meter and an electric carrier transmission line for collecting user electricity consumption information; the electricity big data cloud computing platform includes a system function of collecting user information, user electricity consumption information, etc., to realize the optimal allocation of heating resources, transmit the calculation results to the display, and provide heating resource allocation analysis results; the display is used to realize human-computer interaction, display and query functions. The present invention combines the division of heating areas, cleans the electricity industry expansion application information, conducts logical judgment, and pushes the information of new users predicted to be for heating to the heating enterprise, optimizes the allocation of heating resources, and promotes heating services such as the business handling of heat users by heating enterprises.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart heating, and in particular relates to a smart heating resource allocation system and method based on power data analysis. Background Art

[0002] Currently, the heating network has largely transitioned from large-scale, centralized, long-distance heating. However, it remains in the extensive management stage, striving to transition to lean management. The level of automated control across the entire process, from heat source to heat network to heat exchange station to user, is relatively low. Intelligent control of the heating system, data analysis, and systematic data analysis of energy consumption, including coal, heat, water, and electricity, are still lacking.

[0003] my country's heating industry currently operates on a fragmented and autonomous basis, lacking a unified regional system. Energy consumption and emissions vary widely. Heating system operations and control remain isolated, with no closed-loop control loop. This large amount of discrete heating data is impractical for energy consumption analysis, equipment operating status and efficiency analysis, and equipment management. This is particularly true for electricity consumption, which accounts for the largest portion of electricity consumption and lacks the support of big data collection. Summary of the Invention

[0004] To address the shortcomings of the aforementioned existing technologies, the present invention provides a smart heating resource allocation system and method based on power data analysis. Its purpose is to push information about the number of new heating users to heating companies, optimize heating resource allocation, and facilitate heating companies' service processing for heating users.

[0005] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0006] A smart heating resource allocation system based on power data analysis, including: a power collection system, a power big data cloud computing platform and a display;

[0007] The power collection system includes a power data collector, a power meter and a power carrier transmission line, which is used to collect user power consumption information;

[0008] The power big data cloud computing platform includes a system function for collecting user information, user electricity usage information, user heating payment information, user heating complaint information, user electricity expansion registration information, interaction, processing, analysis, and calculation of power data and heating information, realizing the optimal allocation of heating resources, and transmitting the calculation results to a display, providing the power supply company and the heating company with the analysis results of the heating resource allocation;

[0009] The display is used to realize human-computer interaction, display and query functions.

[0010] Furthermore, the user's electricity usage information includes: voltage value, current value and power value.

[0011] Furthermore, the voltage value, current value and power value are obtained by connecting the power meter to the power data collector, and the data is transmitted through the power line connection by the power carrier method. The power data collector is connected to the database, and the data is transmitted to the database through the power line connection by the power carrier method.

[0012] Furthermore, the overall system architecture includes:

[0013] (1) Data access layer: Establish a data interface to open up the data transmission channel between heating enterprises and the Northeast Energy Big Data Center, and realize the sharing, integration and aggregation of heating resources, electricity consumption information and external environmental data;

[0014] (2) Infrastructure layer: Based on the cloud platform and power data center, it realizes the storage, cleaning and conversion of multi-party data;

[0015] (3) Data analysis layer: Relying on the computing power of the big data center, it provides data integration, big data analysis and computing, artificial intelligence, microservices, micro-applications, and blockchain technology support services to achieve in-depth analysis and mining of data;

[0016] (4) Business application layer: Targeted at decision makers of heating companies, it provides heating resource GIS overview, operating power analysis, heating user forecast, and electric energy substitution management data application services, assisting heating companies in scientific and reasonable planning and lean management operations.

[0017] A smart heating resource allocation method based on power data analysis includes the following steps:

[0018] Step 1. Establish a heating user prediction model;

[0019] Step 2. Predict the addition or suspension of heating supply based on the prediction model and send the prediction results to the heating company;

[0020] Step 3. Conduct supply analysis based on the predicted results;

[0021] Step 4. Conduct vacant heating analysis based on the prediction results;

[0022] Step 5. Based on the results of the analysis of unsupplied space and vacant heating space, add an analysis for business expansion application.

[0023] Furthermore, the establishment of the heating user prediction model is to combine the heating area division, clean the power industry expansion registration information, carry out logical judgment, and push the number of users predicted to be possible new heating users to the heating enterprise, optimize the allocation of heating resources, and promote the heating enterprise to provide heating services to heat users;

[0024] The aforementioned method of predicting the addition / discontinuation of heating supply based on the pre-judgment model and pushing the prediction result to the heating enterprise is to combine the division of heating areas, clean the vacancy rate information of power houses and the business expansion registration information, and carry out logical judgment. According to the situation of unsupplied and vacant heating, the new addition / discontinuation of heating supply is predicted, and the number of users and user information are pushed to the heating enterprise, thereby optimizing the allocation of heating resources and promoting the heating enterprise to provide heating services to heat users.

[0025] The analysis of unavailable heating, i.e., if the electricity data shows that the house is not vacant but the heating fee has not been paid, it is in a state of unavailable heating. Based on the surrounding heating conditions, it can intelligently determine whether the unavailable heating is due to poor heating effect, or whether the heating fee does not need to be paid because the surrounding heating effect is good;

[0026] The vacant heating analysis, i.e., a house that is vacant but still pays heating fees, predicts that the user may stop heating in the next heating cycle, helping heating companies accurately predict the heating area;

[0027] The business expansion and new application analysis is to clean the power business expansion application information and conduct logical judgment based on the results of the supply but not supply analysis and the vacant heating analysis, and push the information on the number of users predicted to be possible new heating users to the heating company.

[0028] Furthermore, the analysis of supply failure is performed based on the prediction results in step 3, including: analysis of the community outage rate, prediction of user restoration of supply, and judgment of heating effect;

[0029] Among them, the analysis of the community outage rate is as follows:

[0030]

[0031] In the above formula, v 停 represents the power outage rate of the community; a represents the number of households with power outage in the community; A represents the number of households with network access in the community;

[0032] Among them, the user's reuse prediction is as follows:

[0033]

[0034] In the above formula, p 复 It indicates the predicted resupply rate of the community in the next heating season; a n Indicates the calculation weight for the nth year, ∑a n =1, generally calculate the data within four years, take a1 = 0.1; a2 = 0.2; a3 = 0.3; a4 = 0.4; n represents the number of sampling years;

[0035] Among them, the heating effect judgment is for users who have stopped supplying, and the temperature data of the upper and lower floors of the same unit can be selected:

[0036] If the indoor heating temperature is above 20℃, it is considered that the user can maintain the indoor temperature without paying heating fees due to the high temperature of the upper and lower floors, and the heating pipeline may be overloaded;

[0037] If the indoor temperature of the upper and lower floors of the same unit is lower than 16°C, it means that the unit has insufficient heating. We need to further investigate the user's electricity usage to see whether electric heating is used.

[0038] Furthermore, the vacant heating analysis according to the prediction result in step 4 includes: power house vacancy analysis, including the following steps:

[0039] Step (1) associating the basic archive information of the residential user with the user's daily (monthly) electricity consumption meter to obtain the residential user's daily (monthly) electricity consumption data;

[0040] When obtaining the user's monthly electricity meter in step (2), if the resident's monthly electricity consumption is greater than 5 kWh, it is included in the calculation range; otherwise, it is not included in the calculation range.

[0041] Furthermore, when obtaining the user's monthly electricity consumption meter, if the monthly electricity consumption of the resident is greater than 5 kWh, it is included in the calculation range; otherwise, it is not included in the calculation range, including:

[0042] The housing vacancy rate is as follows:

[0043]

[0044] In the above formula: v 空 represents the housing vacancy rate; a>5 represents the number of households with monthly electricity consumption greater than 5 kWh; A represents the total number of households in the community;

[0045] Predict the users who may be out of service, as shown in the following formula:

[0046] {a 停}={b 空}∩{c 开}

[0047] In the above formula: {a 停} indicates the users who are likely to be cut off from supply; {b 空} represents the vacancy rate user set; {c 开} represents the set of heating renewal users.

[0048] A computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the intelligent heating resource allocation method based on power data analysis.

[0049] The present invention has the following beneficial effects and advantages:

[0050] The present invention combines the division of heating areas, cleans the electricity industry expansion registration information, conducts logical judgment, and pushes the information on the number of users predicted to be possible new heating users to the heating company, optimizes the allocation of heating resources, and promotes heating services such as heating company's business processing for heat users. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments with reference to the accompanying drawings, in which:

[0052] Figure 1 This is a system structure framework diagram of the present invention;

[0053] Figure 2 This is a working framework diagram of the data system of the present invention;

[0054] Figure 3 This is a data information interaction framework diagram of the present invention. DETAILED DESCRIPTION

[0055] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein may be combined with each other.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0057] Refer to the following Figure 1-Figure 3 Describe the technical solutions of some embodiments of the present invention.

[0058] Example 1

[0059] The present invention provides an embodiment, which is a smart heating resource allocation system based on power data analysis, such as Figure 1-Figure 3 As shown, the system of the present invention includes: an electricity collection system, an electric power big data cloud computing platform and a display.

[0060] The power collection system of the present invention includes a power data collector, a power meter, and a power carrier transmission line, and is used to collect user power usage information. This user power usage information includes voltage, current, and power values. Specifically, the power meter is connected to the power data collector, and data is transmitted via power carrier transmission over the power lines. The power data collector is then connected to a database, and data is also transmitted to the database via power carrier transmission over the power lines.

[0061] The power big data cloud computing platform of the present invention includes a system function for collecting user information, user electricity usage information, user heating payment information, user heating complaint information, user electricity expansion application information, interacting, processing, analyzing, and calculating power data and heating information, realizing the optimal allocation of heating resources, transmitting the calculation results to a display, and providing the heating resource allocation analysis results to the power supply company and the heating company;

[0062] The display of the present invention is used to realize human-computer interaction, display, query and other functions.

[0063] The system of the present invention belongs to the comprehensive analysis system. The system is deployed in the cloud platform environment of Northeast Energy Big Data Center. It follows the deployment requirements of Northeast Energy Big Data Center and selects the micro-service cloud model. The overall system architecture design includes:

[0064] (1) Data access layer: Establish a data interface to open up the data transmission channel between heating enterprises and the Northeast Energy Big Data Center, and realize the sharing, integration and aggregation of heating resources, electricity consumption information, external environment and other data;

[0065] (2) Infrastructure layer: Based on the cloud platform and power data center, it realizes the storage, cleaning and conversion of multi-party data;

[0066] (3) Data analysis layer: Relying on the powerful computing capabilities of the Northeast Energy Big Data Center, it provides technical support services such as data integration, big data analysis and computing, artificial intelligence, microservices, microapplications, and blockchain to achieve in-depth analysis and mining of data;

[0067] (4) Business application layer: Targeted at decision makers of heating companies, it provides data application services such as a GIS overview of heating resources, operating power analysis, heating user forecasts, and electric energy substitution management, assisting heating companies in scientific and rational planning and lean management operations.

[0068] Example 2

[0069] The present invention also provides an embodiment, which is a smart heating resource allocation method based on electricity data analysis. The present invention uses a smart heating resource allocation system based on electricity data analysis to push user number information of new heating users to heating companies, optimize heating resource allocation, and promote heating companies to provide heating services such as heat user business processing.

[0070] The present invention provides a smart heating resource allocation method based on power data analysis, which specifically includes the following steps:

[0071] Step 1. Establish a heating user prediction model;

[0072] Specifically, it is to combine the division of heating areas, clean the electricity industry expansion registration information, conduct logical judgment, and push the information on the number of users predicted to be possible new heating users to heating companies, optimize the allocation of heating resources, and promote heating companies to provide heating services such as business processing for heat users.

[0073] Step 2. Predict the addition or suspension of heating supply based on the prediction model and send the prediction results to the heating company;

[0074] Specifically, it is to combine the division of heating areas, clean the vacancy rate information of power houses and business expansion registration information, and conduct logical judgment. According to the situations of non-supply and vacant heating, it predicts the increase / suspension of heating supply, pushes the number of users and user information to heating companies, optimizes the allocation of heating resources, and promotes heating services such as heating companies' business processing for heat users.

[0075] Step 3. Conduct supply analysis based on the predicted results;

[0076] The analysis of non-supply when supply is due means that the electricity data shows that the house is not vacant, but the heating fee has not been paid, which means that the supply is out of service. Based on the surrounding heating conditions, it is intelligently determined whether the supply outage is caused by poor heating effect, or whether no heating fee is required because the surrounding heating effect is good.

[0077] Step 4. Conduct vacant heating analysis based on the prediction results;

[0078] The vacant heating analysis, that is, the house is vacant but the heating fee is still paid, predicts that the user may stop heating in the next heating cycle, and helps heating companies accurately predict the heating area.

[0079] Step 5. Based on the results of the analysis of unsupplied space and vacant heating space, add an analysis for business expansion application.

[0080] The business expansion and new application analysis is to clean the power business expansion application information and conduct logical judgment based on the results of the supply but not supply analysis and the vacant heating analysis, and push the information on the number of users predicted to be possible new heating users to the heating company.

[0081] Example 3

[0082] The present invention further provides an embodiment, which is a smart heating resource allocation method based on power data analysis. During specific implementation, the analysis of supply and non-supply as described in step 3 is performed based on the prediction results, including: community outage rate analysis, user re-supply prediction and heating effect judgment.

[0083] Among them, the analysis of the community outage rate is as follows:

[0084]

[0085] In the above formula, v 停 represents the power outage rate of the community; a represents the number of households with power outage in the community; A represents the number of households with network access in the community;

[0086] Among them, the user's reuse prediction is as follows:

[0087]

[0088] In the above formula, p 复 It indicates the predicted resupply rate of the community in the next heating season; a n Indicates the calculation weight for the nth year, ∑a n =1, generally calculate the data within four years, take a1=0.1; a2=0.2; a3=0.3; a4=0.4; n represents the number of sampling years.

[0089] Among them, the heating effect judgment is for users who have stopped supplying, and the temperature data of the upper and lower floors of the same unit can be selected:

[0090] If the indoor heating temperature is above 20℃, it is considered that the user can maintain the indoor temperature without paying heating fees because the temperature of the upper and lower floors is too high, and the heating pipeline may be overloaded.

[0091] If the indoor temperature of the upper and lower floors of the same unit is lower than 16°C, it means that the unit has insufficient heating. We need to further investigate the user's electricity usage to see whether electric heating is used.

[0092] The step 4 of the present invention performs vacant heating analysis based on the prediction result, including:

[0093] The analysis of power house vacancy includes the following steps:

[0094] Step (1) The basic profile information of the residential user is associated with the user's daily (monthly) electricity consumption meter to obtain the residential user's daily (monthly) electricity consumption data.

[0095] Step (2) When obtaining the monthly electricity consumption meter of the user, if the monthly electricity consumption of the resident is greater than 5 kWh, it is included in the calculation range; otherwise, it is not included in the calculation range;

[0096] The housing vacancy rate is as follows:

[0097]

[0098] In the above formula: v 空 represents the housing vacancy rate; a>5 represents the number of households with monthly electricity consumption greater than 5 kWh; A represents the total number of households in the community.

[0099] Predict the users who may be out of service, as shown in the following formula:

[0100] {a 停}={b空}∩{c 开}

[0101] In the above formula: {a 停} indicates the users who are likely to be cut off from supply; {b 空} represents the vacancy rate user set; {c 开} represents the set of heating renewal users.

[0102] Example 4

[0103] Based on the same inventive concept, an embodiment of the present invention also provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of a smart heating resource allocation method based on power data analysis described in Examples 2-3 are implemented.

[0104] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0105] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0106] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A smart heating resource allocation method based on power data analysis is implemented using a smart heating resource allocation system based on power data analysis, which is characterized by: include: Electricity collection system, power big data cloud computing platform and display; The power collection system includes a power data collector, a power meter, and a power carrier transmission line for collecting user power consumption information; the power big data cloud computing platform includes a system function for collecting user information, user power consumption information, user heating payment information, user heating complaint information, user power expansion application information, and interacting, processing, analyzing, and calculating power data and heating information to achieve optimal allocation of heating resources, transmit the calculation results to a display, and provide heating resource allocation analysis results to the power supply company and the heating company; The display is used to realize human-computer interaction, display and query functions; The user's electricity consumption information includes: voltage value, current value and electricity value; the acquisition of the voltage value, current value and electricity value is achieved by connecting the electricity meter with the electricity data collector, and the data is transmitted through the power line by the power carrier method. The electricity data collector is connected to the database, and the data is transmitted to the database through the power line by the power carrier method; the overall architecture of the system includes: (1) Data access layer: Establishing a data interface to open up the data transmission channel between the heating enterprise and the energy big data center, and realizing the sharing, integration and aggregation of heating resources, electricity consumption information and external environment data; (2) Infrastructure layer: Based on the cloud platform and the power data middle platform, it realizes the storage and cleaning conversion of multi-party data; (3) Data analysis layer: Relying on the computing power of the big data center, it provides data integration, big data analysis and calculation, artificial intelligence, microservices, micro-applications, and blockchain technology support services to realize in-depth analysis and mining of data; (4) Business application layer: For decision makers of heating enterprises, it provides heating resource GIS overview, operating electricity analysis, heating user forecast, and electricity substitution management data application services to assist heating enterprises in scientific and reasonable planning and lean management operations; A smart heating resource allocation method based on power data analysis includes the following steps: Step 1. Establish a heating user prediction model; based on the heating area division, clean the power industry expansion registration information, conduct logical judgment, and push the number of users predicted to be potential new heating users to the heating company, optimize the allocation of heating resources, and promote the heating company to provide heating services to heat users; Step 2. Predict the addition or suspension of heating supply based on the heating user prediction model and send the prediction results to the heating company; Step 3. Analyze the power supply situation based on the predicted results. This means that if the power data shows that the house is not vacant but the heating fee has not been paid, it is considered a power outage. Based on the surrounding heating conditions, the system intelligently determines whether the power outage is due to poor heating performance, or whether the heating fee is not required due to good surrounding heating performance. Step 4. Conduct vacant heating analysis based on the prediction results. This means that if a house is vacant but heating fees are still paid, it is predicted that the user may not have heating service during the next heating cycle, helping heating companies accurately predict the heating area. Step 5. Based on the results of the analysis of supply but not supply and vacant heating, conduct new analysis on business expansion registration; that is, based on the results of the analysis of supply but not supply and vacant heating, clean the power business expansion registration information, conduct logical judgment, and push the information on the number of users who are predicted to be possible new heating users to the heating company.

2. The method for intelligent heating resource allocation based on power data analysis according to claim 1 is characterized by: The analysis of supply failure based on the prediction results in step 3 includes: analysis of the community outage rate, prediction of user restoration of supply, and judgment of heating effect; Among them, the analysis of the community outage rate is as follows: ； In the above formula, v 停 represents the power outage rate of the community; a represents the number of households with power outage in the community; A represents the number of households with network access in the community; Among them, the user's reuse prediction is as follows: ； In the above formula, p 复 It indicates the predicted resupply rate of the community in the next heating season; a n Indicates the calculation weight for the nth year, , n represents the number of sampling years; Among them, the heating effect judgment is for users who have stopped supplying, and the temperature data of the upper and lower floors of the same unit can be selected: If the indoor heating temperature is above 20℃, it is considered that the user can maintain the indoor temperature without paying heating fees due to the high temperature of the upper and lower floors, and the heating pipeline may be overloaded; If the indoor temperature of the upper and lower floors of the same unit is lower than 16°C, it means that the unit has insufficient heating. We need to further investigate the user's electricity usage to see whether electric heating is used.

3. The method for intelligent heating resource allocation based on power data analysis according to claim 1 is characterized by: Step 4, performing vacant heating analysis based on the prediction results, includes: power house vacancy analysis, including the following steps: Step (1) The basic archive information of the residential user is associated with the user's monthly electricity consumption meter to obtain the monthly electricity consumption data of the residential user; In step (2), when obtaining the monthly electricity consumption meter of the user, if the monthly electricity consumption of the resident is greater than 5 kWh, it is included in the calculation range; otherwise, it is not included in the calculation range.

4. The method for intelligent heating resource allocation based on power data analysis according to claim 3 is characterized by: When obtaining the monthly electricity consumption meter of a user, if the monthly electricity consumption of a resident is greater than 5 kWh, it is included in the calculation range; otherwise, it is not included in the calculation range, including: The housing vacancy rate is as follows: ； In the above formula: represents the housing vacancy rate; Indicates the number of households with monthly electricity consumption greater than 5 kWh; A indicates the total number of households in the community; Predict the users who may be out of service, as shown in the following formula: ； In the above formula: Indicates users who are predicted to have their supply cut off; represents the set of users with vacancy rate; Represents the set of heating renewal users.

5. A computer storage medium, characterized by: The computer storage medium stores a computer program, which, when executed by a processor, implements the steps of the smart heating resource allocation method based on power data analysis as described in any one of claims 1 to 4.

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