An intelligent monitoring and management system for four household meters based on the Internet of Things

Abstract

The invention discloses an intelligent monitoring and management system for four household meters based on the Internet of Things, comprising a collection end, a platform end and a user end; the collection end is used for collecting data on four household meters, obtaining platform four-meter data and user four-meter data corresponding to the platform end and the user end respectively; the platform four-meter data and the user four-meter data are sent to the platform end and the user end respectively; the platform end is used for analyzing and processing the received platform four-meter data, obtaining platform processing results, and sending the platform processing results to the user end; and receiving user settlement data sent by the user end; performing deviation analysis according to the user settlement data, and obtaining corresponding deviation analysis results; the user end is used for analyzing the received user four-meter data, obtaining energy display data; performing planning management according to the energy display data; when the platform processing results are received, generating user settlement data, and sending the user settlement data to the platform end.

Description

Technical Field

[0001] The present invention belongs to the technical field of household four-meter monitoring and management, and in particular is a household four-meter intelligent monitoring and management system based on the Internet of Things. Background Art

[0002] In the current field of smart home and energy management, data collection and processing of four meters (water meter, electricity meter, gas meter, heat meter) is a key link in home energy monitoring and optimization. However, traditional data collection and transmission methods often have many limitations, especially in terms of data processing efficiency, system burden distribution and user experience.

[0003] At present, the collection end of the four meters mostly transmits data to the platform end at a fixed time (such as monthly settlement). Although this method simplifies the processing flow of the platform end and reduces the burden on the platform end, it cannot meet the user's demand for understanding the real-time energy usage. At the same time, the scheduled transmission also limits the system's ability to respond to abnormal energy usage in a timely manner. In addition, with the development of Internet of Things technology, the collection end already has the technical conditions for real-time data collection and transmission, but how to balance real-time performance and system burden has become a new problem.

[0004] Therefore, in order to solve the problem of monitoring and managing four household meters, the present invention provides an intelligent monitoring and management system for four household meters based on the Internet of Things. Summary of the invention

[0005] In order to solve the problems existing in the above scheme, the present invention provides a household four-meter intelligent monitoring and management system based on the Internet of Things.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] An intelligent monitoring and management system for four household meters based on the Internet of Things, comprising a collection end, a platform end and a user end;

[0008] The collection end is used to collect data from the four household meters, obtain the time collection requirements preset for the platform end and the user end respectively, collect data according to the time collection requirements corresponding to the platform end and the user end respectively, obtain the platform four meter data corresponding to the platform end and the user four meter data corresponding to the user end; and send the platform four meter data and the user four meter data to the platform end and the user end respectively.

[0009] The platform end is used to analyze and process the received platform four-table data, obtain corresponding platform processing results, and send the platform processing results to the user end; and receive user settlement data sent by the user end; perform deviation analysis based on the user settlement data to obtain corresponding deviation analysis results.

[0010] Furthermore, the method for deviation analysis based on user settlement data includes:

[0011] Identify the corresponding basic usage amount, benchmark unit price, and benchmark cost according to the platform processing result, and identify the corresponding settlement usage amount, settlement unit price, and settlement cost according to the user settlement data;

[0012] Calculate the corresponding deviation value according to the deviation formula; the deviation formula is:

[0013] In the formula: PC is the deviation value; b1, b2, and b3 are all proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1, 0 < b3 ≤ 1; exp() is the exponential function with the natural constant e as the base; SL is the settlement usage amount; CL is the benchmark usage amount; SD is the settlement unit price; CD is the benchmark unit price; SF is the calculated cost; CF is the benchmark cost;

[0014] When the deviation value is greater than the threshold X1, evaluate that the deviation exceeds the standard;

[0015] When the deviation value is not greater than the threshold X1, evaluate that the deviation is normal.

[0016] The user terminal is used to analyze the received user four-meter data, mark the energy corresponding to the four meters as energy items, generate an energy usage curve corresponding to each energy item according to the user four-meter data, the horizontal axis of the energy usage curve is time, and the vertical axis is the usage amount of the corresponding energy; generate a pricing curve corresponding to the energy usage curve according to the billing rules of the corresponding energy item, the horizontal axis of the pricing curve is time, and the vertical axis is the unit price of the corresponding energy; generate a cost curve for each energy item according to the energy usage curve and the pricing curve; the horizontal axis of the cost curve is time, and the vertical axis is the cost; integrate the energy usage curve, the pricing curve, and the cost curve into energy display data; perform planning management according to the energy display data;

[0017] When receiving the platform processing result, generate user settlement data, where the user settlement data includes the settlement usage amount, settlement unit price, and settlement cost of each energy item; send the user settlement data to the platform terminal.

[0018] Furthermore, the method for generating user settlement data includes:

[0019] Identify the time collection requirements of the platform terminal, identify the usage amount, unit price, and cost of each energy item at the corresponding time according to the time collection requirements of the platform terminal, and mark them as the settlement usage amount, settlement unit price, and settlement cost respectively; integrate the settlement usage amount, settlement unit price, and settlement cost of each energy item to form user settlement data.

[0020] Furthermore, the method for performing planning management according to the energy display data includes:

[0021] Obtain preset benchmark application curves for each energy item;

[0022] Identify the historical energy usage curves of each energy item of the user, and determine the allocated energy consumption at each time based on each historical energy usage curve and the corresponding benchmark application curve;

[0023] According to the billing rules and allocated energy consumption of each energy item, a planning assessment is performed to obtain a planning assessment result of each energy item, wherein the planning assessment result includes no allocation, recommended allocation, and a corresponding target sequence; the user performs corresponding processing according to the planning assessment result.

[0024] Furthermore, the method for setting the benchmark application curve includes:

[0025] Obtain the historical energy consumption data of each user for each energy item, calculate the similarity of each historical energy consumption data, and determine the benchmark material data based on the similarity between each historical energy consumption data;

[0026] The energy consumption corresponding to each time in the benchmark material data is identified, and a corresponding benchmark application curve is generated according to the energy consumption corresponding to each time, wherein the horizontal axis of the benchmark application curve is time and the vertical axis is energy consumption.

[0027] Furthermore, the method for determining the allocated energy consumption includes:

[0028] Fitting each historical energy usage curve and the benchmark application curve to obtain a corresponding energy usage function and a benchmark application function;

[0029] integrating the benchmark application curve into each historical energy use curve to obtain each calibration curve;

[0030] Establish a transformation model, transform each calibration curve through the transformation model to obtain each transformation curve; fit each transformation curve to obtain a corresponding transformation function, and mark the transformation function as HAi(t);

[0031] According to the formula The conversion curves are superimposed to obtain a comprehensive function;

[0032] Where: HR(t) is the comprehensive function;

[0033] According to the formula Calculate the deployment energy consumption corresponding to each time;

[0034] Where: KPt is the allocation energy consumption.

[0035] Furthermore, the expression of the transformation model is: ;

[0036] Where: s is the input data, the input data is the calibration curve; the output data is the conversion value HD(s); HYi(t) is the corresponding energy use function, i=1, 2, ..., n, n is a positive integer; ZY(t) is the benchmark application function; t is time.

[0037] Furthermore, the method of planning and evaluating according to the billing rules of each energy item and the allocation of energy consumption includes:

[0038] Perform iterative simulation according to the energy consumption of each allocation, and calculate the corresponding simulation fee according to the billing rules during the simulation process;

[0039] Calculate the corresponding savings based on the simulated costs and the user's settlement costs;

[0040] Mark the allocation method with cost savings greater than the threshold value X2 as the allocation method to be selected;

[0041] When there is no allocation method to be selected, the planning analysis of the corresponding energy item is terminated;

[0042] When there is a deployment method to be selected, the energy usage curve corresponding to the deployment method to be selected is identified and marked as the deployment curve to be selected; the energy usage curve of the user is marked as the reference usage curve;

[0043] Calculating the similarity between the baseline usage curve and the candidate usage curve;

[0044] According to the formula Calculate the ranking value of each deployment method to be selected;

[0045] In the formula: GR is the ranking value; η is the similarity; FR is the cost saving;

[0046] Sort the candidate allocation methods in descending order according to the ranking value to obtain the target sequence.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] Through the mutual cooperation between the user end, the collection end and the platform end, intelligent monitoring and analysis of the user's four household meter data can be achieved, helping users to understand the usage of each energy in a timely manner. At the same time, data transfer analysis is carried out to reduce the analysis burden of many users on the platform end, and make full use of the equipment resources at the user end. By applying a large amount of real-time data at the user end, the platform end can only understand the overall situation of each month without causing the leakage of user's energy usage data at all times, thereby protecting user privacy.

[0049] Deviation analysis is performed on the platform to verify the received data, data processing, etc. on the user side. Because the platform has higher protection capabilities, identification capabilities, and professional personnel, it can avoid data distortion at the user side due to possible virus attacks on mobile phones and other devices, data tampering, etc., which affects the user experience and usage. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0052] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. 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.

[0053] like Figure 1 As shown, a household four-meter intelligent monitoring and management system based on the Internet of Things includes a collection end, a platform end and a user end;

[0054] The collection end is used to collect data from the four meters of the household and obtain the preset time collection requirements of the platform end and the user end. For example, the platform end generally collects data at a specified time every month, and the user end generally implements collection or collects data within a shorter interval, such as one day, 12 hours, etc.; the platform party sets the corresponding time collection requirements; data is collected according to the time collection requirements of the platform end and the user end, and the platform four meter data corresponding to the platform end and the user four meter data corresponding to the user end are obtained; the obtained platform four meter data and user four meter data are sent to the platform end and the user end respectively.

[0055] The platform side is used to analyze and process the received four-platform data of the platform to obtain the corresponding platform processing results. This step processes the collected data received monthly by the existing platform party, such as billing, etc.; sends the obtained platform processing results to the user side; and receives the user settlement data sent by the user side; performs deviation analysis based on the user settlement data to obtain the corresponding deviation analysis results. By performing deviation analysis on the platform side, it is possible to check the received data, data processing, etc. of the user side, because the platform side has higher protection capabilities, authentication capabilities, professional personnel, etc., to avoid data distortion at the user side caused by possible virus attacks, data tampering, etc. of devices such as mobile phones, which affects the user experience and usage.

[0056] The method for performing deviation analysis based on the user settlement data includes:

[0057] Identify the usage amount, unit price, and cost of each energy item at the corresponding time according to the platform processing results, and mark them as the basic usage amount, benchmark unit price, and benchmark cost respectively.

[0058] Calculate the corresponding deviation value according to the deviation formula; the deviation formula is:

[0059] In the formula: PC is the deviation value; b1, b2, and b3 are all proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1, 0 < b3 ≤ 1; exp() is the exponential function with the natural constant e as the base; SL is the settlement usage amount; CL is the benchmark usage amount; SD is the settlement unit price; CD is the benchmark unit price; SF is the calculated cost; CF is the benchmark cost.

[0060] When the deviation value is greater than the threshold X1, it is evaluated that the deviation exceeds the standard.

[0061] When the deviation value is not greater than the threshold X1, it is evaluated that the deviation is normal.

[0062] The user terminal is used to analyze the received data from the four meters of the user, mark the energy corresponding to the four meters as energy items, so as to facilitate the description; generate energy usage curves corresponding to each energy item according to the data from the four meters of the user, with the horizontal axis being time and the vertical axis being the usage of the corresponding energy; generate a pricing curve corresponding to the energy usage curve according to the billing rules of the corresponding energy items, with the horizontal axis of the pricing curve being time and the vertical axis being the unit price of the corresponding energy, and the unit price is determined according to the historical usage at the corresponding time and the billing rules; generate a cost curve for each energy item according to the energy usage curve and the pricing curve, with the horizontal axis being time and the vertical axis being cost; and support Dynamically identify increased costs in each time period. For example, if you click on 4 to 7 o'clock, the increased costs for that time period can be intelligently calculated; integrate the energy usage curve, pricing curve and fee curve into energy display data; perform planning and management based on the energy display data; when receiving the platform processing results, identify the time collection requirements of the platform end, and identify the usage, unit price and cost of each energy item at the corresponding time according to the time collection requirements of the platform end, and mark them as settlement usage, settlement unit price and settlement cost respectively; integrate the settlement usage, settlement unit price and settlement cost of each energy item into user settlement data, and send the user settlement data to the platform end.

[0063] Through the mutual cooperation between the user end, the collection end and the platform end, intelligent monitoring and analysis of the user's four household meter data can be achieved, helping users to understand the usage of each energy in a timely manner. At the same time, data transfer analysis is carried out to reduce the analysis burden of many users on the platform end, and make full use of the equipment resources at the user end. By applying a large amount of real-time data at the user end, the platform end can only understand the overall situation of each month without causing the leakage of user's energy usage data at all times, thereby protecting user privacy.

[0064] Methods for planning and management based on energy display data include:

[0065] Obtain the preset benchmark application curves for each energy item. The benchmark application curve is the necessary consumption curve for users' daily life needs at different times. The horizontal axis is time and the vertical axis is necessary consumption, such as necessary lighting, air conditioning, cooking gas, water, etc. in the evening. The specific settings are made by the platform.

[0066] Identify the user's historical energy usage curve for each energy item, and determine the allocated energy consumption at each time based on each historical energy usage curve and the corresponding benchmark application curve;

[0067] According to the billing rules and allocated energy consumption of each energy item, planning assessment is carried out to obtain the planning assessment results of each energy item. The planning assessment results include no allocation, recommended allocation and corresponding target sequence; users perform corresponding processing according to the planning assessment results; if no allocation is required, no corresponding processing is performed; if allocation is recommended, select one from the target sequence for application to change the usage habits.

[0068] In one embodiment, the method for setting the benchmark application curve is that the platform sends professionals to perform manual setting.

[0069] In one embodiment, the method for setting the benchmark application curve includes:

[0070] Obtain the historical energy consumption data of each user for each energy item, calculate the similarity of each historical energy consumption data, and determine the historical energy consumption data that the user prefers the most based on the similarity between each historical energy consumption data, mark it as the benchmark material data, or regard it as the data with the highest sum of similarities;

[0071] The energy consumption corresponding to each time in the benchmark material data is identified, and a corresponding benchmark application curve is generated according to the energy consumption corresponding to each time, wherein the horizontal axis of the benchmark application curve is time and the vertical axis is energy consumption.

[0072] Methods for determining the allocated energy consumption at each time based on each historical energy usage curve and the corresponding benchmark application curve include:

[0073] Fitting each historical energy usage curve and the benchmark application curve to obtain a corresponding energy usage function and a benchmark application function;

[0074] Integrate the benchmark application curve into each historical energy use curve to obtain each calibration curve, that is, move the benchmark application curve into the historical energy use curve according to the corresponding time;

[0075] A transformation model is established. The transformation model is used to transform the calibration curve. The expression of the transformation model is: ; Where: s is the input data, the input data is the calibration curve; the output data is the conversion value HD(s); HYi(t) is the corresponding energy use function, i=1, 2, ..., n, n is a positive integer; ZY(t) is the benchmark application function; t is the time, the value range is 0 to 24 hours, that is, one day;

[0076] Each calibration curve is transformed by a transformation model to obtain each transformation curve; each transformation curve is fitted to obtain a corresponding transformation function, and the transformation function is marked as HAi(t);

[0077] According to the formula The conversion curves are superimposed to obtain a comprehensive function; where HR(t) is the comprehensive function; if there are two conversion curves, and the conversion values ​​of the two conversion curves at time 1 are 2 and 3 respectively, then the conversion value at this time after superposition is 2+3=5;

[0078] According to the formula Calculate the deployment energy consumption corresponding to each time;

[0079] Where: KPt is the allocation energy consumption.

[0080] Methods for planning and evaluating energy consumption based on the billing rules of each energy item and allocation include:

[0081] Perform iterative simulation according to each allocation energy consumption, that is, gradually adjust a frequency modulation energy consumption to other times, and perform cyclic adjustment simulation until all possible allocations are iterated. In practical applications, the minimum allocation amount can be preset, and the simulation can be performed with the minimum allocation amount in the future to reduce the number of iterations; and calculate the corresponding simulation fee according to the billing rules during the simulation process, that is, after each allocation simulation, the fee is calculated according to the simulated usage, and the total fee to be spent is determined and marked as the simulation fee;

[0082] The corresponding savings are calculated based on the simulated cost and the user's settlement cost. If the savings exceed the settlement cost, the savings are negative.

[0083] Mark the allocation method with a cost saving greater than the threshold value X2 as a candidate allocation method, that is, eliminate the allocation method with a lower cost saving. In practical applications, it is generally difficult to change user habits for a lower cost saving;

[0084] When there is no allocation method to be selected, the planning analysis of the corresponding energy item is terminated;

[0085] When there is a deployment method to be selected, the energy usage curve corresponding to the deployment method to be selected is identified and marked as the deployment curve to be selected; the energy usage curve of the user is marked as the reference usage curve;

[0086] Calculating the similarity between the baseline usage curve and the candidate usage curve;

[0087] According to the formula Calculate the ranking value of each deployment method to be selected;

[0088] In the formula: GR is the ranking value; η is the similarity; FR is the cost saving;

[0089] Sort the candidate allocation methods in descending order according to the ranking value to obtain the target sequence.

[0090] The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data.

[0091] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A household four-meter intelligent monitoring and management system based on the Internet of Things, characterized in that: Including the collection end, platform end and user end; The collection end is used to collect data from the four household meters, obtain the time collection requirements preset for the platform end and the user end respectively, collect data according to the time collection requirements corresponding to the platform end and the user end respectively, and obtain the platform four meter data corresponding to the platform end and the user four meter data corresponding to the user end; Send the platform four-table data and the user four-table data to the platform end and the user end respectively; The platform end is used to analyze and process the received platform four-table data, obtain corresponding platform processing results, and send the platform processing results to the user end; and receiving user settlement data sent by the user terminal; performing deviation analysis according to the user settlement data to obtain corresponding deviation analysis results; The user terminal is used to analyze the received data from the four meters of the user, mark the energy corresponding to the four meters as energy items, generate energy usage curves corresponding to each energy item according to the data from the four meters of the user, the horizontal axis of the energy usage curve is time, and the vertical axis is the usage of the corresponding energy; generate a pricing curve of the energy usage curve according to the billing rules of the corresponding energy item, the horizontal axis of the pricing curve is time, and the vertical axis is the unit price of the corresponding energy; generate a cost curve for each energy item according to the energy usage curve and the pricing curve; the horizontal axis of the cost curve is time, and the vertical axis is cost; integrate the energy usage curve, the pricing curve and the cost curve into energy display data; Carry out planning and management based on energy display data; When the platform processing results are received, user settlement data is generated, which includes the settlement usage, settlement unit price and settlement fee of each energy item; Send user settlement data to the platform; Methods for planning and management based on energy display data include: Obtain preset benchmark application curves for each energy item; Identify the historical energy usage curves of each energy item of the user, and determine the allocated energy consumption at each time based on each historical energy usage curve and the corresponding benchmark application curve; According to the billing rules and allocated energy consumption of each energy item, a planning evaluation is performed to obtain a planning evaluation result of each energy item, wherein the planning evaluation result includes no allocation, recommended allocation, and a corresponding target sequence; the user performs corresponding processing according to the planning evaluation result; Methods for determining allocated energy consumption include: Fitting each historical energy usage curve and the benchmark application curve to obtain a corresponding energy usage function and a benchmark application function; integrating the benchmark application curve into each historical energy use curve to obtain each calibration curve; Establish a transformation model, transform each calibration curve through the transformation model to obtain each transformation curve; fit each transformation curve to obtain a corresponding transformation function, and mark the transformation function as HAi(t); According to the formula The conversion curves are superimposed to obtain a comprehensive function; Where: HR(t) is the comprehensive function; According to the formula Calculate the deployment energy consumption corresponding to each time; Where: KPt is the deployment energy consumption; The expression of the transformation model is: ; Where: s is the input data, the input data is the calibration curve; the output data is the conversion value HD(s); HYi(t) is the corresponding energy use function, i=1, 2, ..., n, n is a positive integer; ZY(t) is the benchmark application function; t is time.

2. According to the Internet of Things-based household four-meter intelligent monitoring and management system according to claim 1, it is characterized in that: Methods for deviation analysis based on user settlement data include: Identify the corresponding basic usage, benchmark unit price, and benchmark cost based on the platform processing results, and identify the corresponding settlement usage, settlement unit price, and settlement cost based on the user settlement data; Calculate the corresponding deviation value according to the deviation formula; the deviation formula is: ； In the formula: PC is the deviation value; b1, b2, and b3 are all proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1, 0 < b3 ≤ 1; exp() is the exponential function with the natural constant e as the base; SL is the settlement usage; CL is the benchmark usage; SD is the settlement unit price; CD is the benchmark unit price; SF is the calculated cost; CF is the benchmark cost; When the deviation value is greater than the threshold X1, evaluate that the deviation exceeds the standard; When the deviation value is not greater than the threshold X1, evaluate that the deviation is normal.

3. According to the Internet of Things-based household four-meter intelligent monitoring and management system according to claim 1, it is characterized in that: The method for generating user settlement data includes: Identify the time acquisition requirements on the platform side, identify the usage, unit price, and cost of each energy item at the corresponding time according to the time acquisition requirements on the platform side, and mark them as settlement usage, settlement unit price, and settlement cost respectively; integrate the settlement usage, settlement unit price, and settlement cost of each energy item to form user settlement data.

4. According to the Internet of Things-based household four-meter intelligent monitoring and management system according to claim 1, it is characterized in that: The method for setting the benchmark application curve includes: Obtain the historical energy consumption data of each user for each energy item, calculate the similarity of each historical energy consumption data, and determine the benchmark material data according to the similarity between each historical energy consumption data; Identify the energy consumption corresponding to each time in the benchmark material data, and generate the corresponding benchmark application curve according to the energy consumption corresponding to each time. The horizontal axis of the benchmark application curve is time, and the vertical axis is energy consumption.

5. According to the Internet of Things-based household four-meter intelligent monitoring and management system according to claim 1, it is characterized in that: The method for planning and evaluating according to the billing rules and allocated energy consumption of each energy item includes: Conduct allocation iteration simulation according to each allocated energy consumption, and calculate the corresponding simulation cost according to the billing rules during the simulation process; Calculate the corresponding cost savings according to the simulation cost and the user's settlement cost; Mark the allocation method with a cost savings greater than the threshold X2 as the candidate allocation method; When there is no candidate allocation method, end the planning analysis of the corresponding energy item; When there is a candidate allocation method, identify the energy usage curve corresponding to the candidate adjustment method and mark it as the candidate usage curve; mark the user's energy usage curve as the benchmark usage curve; Calculate the similarity between the benchmark usage curve and the candidate usage curve; According to the formula Calculate the ranking value of each deployment method to be selected; In the formula: GR is the sorting value; η is the similarity; FR is the cost savings; Sort each candidate allocation method in descending order according to the sorting value to obtain the target sequence.

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