An energy management platform based on wireless meter reading and energy-saving strategy

Abstract

The invention discloses an energy management platform based on wireless centralized metering and energy-saving strategies, comprising a management terminal, an energy-saving storage module, a central control module and a data analysis module. The invention analyzes the energy consumption data of residents of all buildings in a community through the data analysis module, divides the residents of each building in the community into heavy centralized metering residents, medium centralized metering residents, light centralized metering residents and other residents, and generates an evaluation value for each building based on the analysis. On the one hand, it avoids the occurrence of excessive useless data caused by high-frequency collection of energy consumption data of all users with low energy consumption in each building in the community, and on the other hand, it avoids the occurrence of waste of resources and non-energy saving in the collection process caused by invalid high-frequency collection. According to the judgment of the evaluation value of each building, the optimal energy consumption acquisition interval time of different types of residents in each building is generated, and the energy-saving strategy of each building is generated based on the analysis. The data collection is more targeted and the data transmission amount is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless energy collection and management, and in particular to an energy management platform based on wireless energy collection and energy-saving strategies. Background Art

[0002] At present, the communication methods of centralized meter reading solutions are mainly wired or wireless. Power line communication or dedicated line communication belongs to wired connection, while wireless centralized meter reading equipment solution does not require physical hardware in communication, so it is very attractive and is often used in residential areas, dormitory buildings, factories and other places. It is suitable for energy consumption monitoring and management systems within residential areas, especially for energy consumption management systems in some old buildings with difficult wiring.

[0003] In the process of monitoring the energy consumption within the community, an existing wireless energy management platform collects the energy consumption data of each household in the community and monitors it in the same specifications. This will lead to a large amount of mixed monitoring data on the one hand, and on the other hand, it does not focus on monitoring the residents with excessive energy consumption. In addition, monitoring the energy consumption data of each household in the community in the same specifications will cause the monitoring process to consume energy and not save energy.

[0004] In order to solve the above problems, the present invention proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide an energy management platform based on wireless meter reading and energy-saving strategies. The purpose is to solve the problem that in the prior art, the energy consumption data of each household in the community is monitored with the same specifications, which will lead to a large amount of mixed monitoring data on the one hand, and there is no targeted monitoring of residents with excessive energy consumption on the other hand. In addition, the energy consumption data of each household in the community is monitored with the same specifications, which will lead to energy consumption in the monitoring process and no energy saving.

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

[0007] An energy management platform based on wireless meter reading and energy-saving strategy, including:

[0008] A management terminal manages the energy consumption data of all residents in the community. The management terminal includes several collection modules, and the collection modules are used to collect energy consumption data of all users in a building in the community. The collection module includes several collection units, and one collection unit collects energy consumption data of one resident in the building. The energy consumption data includes electricity consumption data, water consumption data, and natural gas consumption data. The collection module monitors the energy consumption data of all residents in the building according to the energy-saving strategy of the building to generate energy consumption data of the residents of the building;

[0009] The data analysis module analyzes the energy consumption data of all households in the community to generate an energy-saving management calibration table for the community;

[0010] The energy-saving management calibration table stores the evaluation values of all buildings in the community, as well as the households with heavy metering, medium metering, and light metering in each building of the community;

[0011] The central control module conducts energy-saving monitoring on the energy consumption data of households in all buildings in the community. After obtaining the data in the energy-saving management calibration table, the central control module conducts energy-saving monitoring on the energy consumption data of all households in the community according to a certain energy-saving strategy. The specific energy-saving strategy is as follows:

[0012] S11: Taking a building in the community as an example, obtain the evaluation value of this building in the corresponding energy-saving management calibration table of the community, marked as R1, and obtain all the households with heavy metering, medium metering, and light metering in this building;

[0013] S12: If R1 < Rmin, set the optimal energy consumption data acquisition interval time for all households with heavy metering in this building to 12 hours, the optimal energy consumption data acquisition time for all households with medium metering to 24 hours, the optimal energy consumption data acquisition interval time for all households with light metering to 48 hours, and the optimal energy consumption acquisition interval time for the remaining households in this building to 72 hours;

[0014] S13: If Rmin ≤ R1 ≤ Rmax, set the optimal energy consumption data acquisition interval time for all households with heavy metering in this building to 6 hours, the optimal energy consumption data acquisition time for all households with medium metering to 12 hours, the optimal energy consumption data acquisition interval time for all households with light metering to 24 hours, and the optimal energy consumption acquisition interval time for the remaining households in this building to 48 hours;

[0015] S14: If Rmax < R1, set the optimal energy consumption data acquisition interval time for all households with heavy metering in this building to 1 hour, the optimal energy consumption data acquisition time for all households with medium metering to 3 hours, the optimal energy consumption data acquisition interval time for all households with light metering to 6 hours, and the optimal energy consumption acquisition interval time for the remaining households in this building to 12 hours;

[0016] The central control module generates an energy-saving strategy for this building based on the energy consumption acquisition interval times of the households with heavy metering, medium metering, light metering, and the remaining households in this building;

[0017] S15: Generate the energy-saving strategies for all buildings in the community in sequence according to steps S11 to S14. The Rmin is the minimum threshold of the preset evaluation value, and the Rmax is the maximum threshold of the preset evaluation value.

[0018] Further, it further includes an energy-saving storage module. The energy-saving storage module includes an energy consumption data table and an energy-saving management calibration table. The energy consumption data table stores the energy consumption data of all households in the community, and the set storage expiration time is 3 years.

[0019] Further, the specific steps for the data analysis module to analyze and generate the energy-saving management calibration table of the community are as follows: S31: First, select a building in the community as the building to be energy-saving, and obtain all households A1, A2,..., Aa in the building to be energy-saving, where a≥1;

[0020] S32: Create a abnormal variables marked as f1, f2,..., fa. The abnormal variables f1, f2,..., fa correspond to the households A1, A2,..., Aa in the building to be energy-saving one by one, and the initial values of the a abnormal variables are all 0;

[0021] S33: Conduct energy-saving segment division. Divide an energy-saving cycle into b equal-duration energy-saving segments, and mark the t energy-saving segments of an energy-saving cycle as B1, B2,..., Bb, where b≥1;

[0022] S34: Obtain the electricity consumption critical values E1, E2,..., Ea of households A1, A2,..., Aa in one energy-saving segment of t energy-saving cycles;

[0023] S341: Taking household A1 as an example, obtain the electricity consumption values C1, C2,..., Ct of household A1 in one energy-saving segment of t energy-saving cycles;

[0024] S342: Use the formula D = Calculate the dispersion degree of the electricity consumption values of household A1 in one energy-saving segment of t energy-saving cycles, compare the size of D with D1. If D≥D1, delete the corresponding Cd values in descending order of |Cd - C| and calculate the dispersion value D of the remaining Cd, and compare the size of D with D1 again until D < D1; C is the average value of the electricity consumption values participating in the remaining dispersion calculation in one energy-saving segment of t energy-saving cycles, and it is re-calibrated as the electricity consumption critical value E1; D1 is a preset threshold; in this embodiment, one energy-saving cycle is 1 day and one energy-saving segment is 60 minutes;

[0025] S35: According to S341 to S342, obtain the electricity consumption critical values E1, E2,..., Ea of households A1, A2,..., Aa in one energy-saving segment of t energy-saving cycles;

[0026] S36: Compare the power consumption values ​​C1, C2, ..., Ct of all households A1, A2, ..., Aa in an energy-saving period of t energy-saving cycles with the preset power consumption critical value Es, and add 1 to the value of the abnormal variable corresponding to the household whose power consumption value in an energy-saving period of t energy-saving cycles is greater than the preset power consumption critical value Es;

[0027] The electric energy consumption value of the household whose electric energy consumption value in an energy-saving period of t energy-saving cycles is greater than the preset electric energy consumption critical value Es is marked as the electric energy consumption exceeding the normal value;

[0028] S37: According to S33 to S36, calculate and obtain the abnormal variables corresponding to all residents A1, A2, ..., Aa in the energy-saving building in t energy-saving cycles and b energy-saving segments, and re-label them as h1, h2, ..., ha;

[0029] S38: Filter and generate a list of households I1 with excessive electricity consumption in the building to be saved according to certain filtering rules;

[0030] S39: According to S34 to S38, a list of households with excessive water energy consumption I2 and their corresponding water energy consumption threshold V2, a list of households with excessive natural gas consumption I3 and their corresponding natural gas energy consumption threshold V3 in the building to be saved are finally obtained;

[0031] S310: Filter and obtain heavy-collection households, medium-collection households, and light-collection households of the building to be energy-saving according to certain screening rules;

[0032] S311: Obtain the excessive average values ​​of electric energy consumption corresponding to all households in the re-collected electricity meter, and mark them as M1, M2, ..., Mj;

[0033] Obtain the energy consumption excess values ​​corresponding to all households in the collection, marked as N1, N2, ..., Nk;

[0034] Obtain the power consumption excess values ​​corresponding to all light-collection households, marked as P1, P2, ..., Pl;

[0035] S312: Use the formula Q1= Calculate and obtain the bid evaluation value Q1 of the building to be energy-saving, where α is the preset heavy-collection household ratio adjustment factor, β is the preset medium-collection household ratio adjustment factor, and θ is the preset light-collection household ratio adjustment factor;

[0036] S313: According to S31 to S312, all buildings in the community are selected as buildings to be energy-saving in sequence, and the evaluation values ​​Q1, Q2, ..., Qq of all buildings in the community are calculated, where q≥1, and the heavy-collection households, medium-collection households, and light-collection households of all buildings in the community are obtained;

[0037] The data analysis module generates an energy-saving management calibration table for the community based on the bid evaluation values of all buildings in the community, as well as the heavy-set meter reading households, medium-set meter reading households, and light-set meter reading households in each building of the community.

[0038] Further, the specific screening steps for screening and generating the list I1 of electricity consumption over-standard households in the building to be energy-saving in S38 are as follows:

[0039] S381: Create a list I1 of electricity consumption over-standard households, with the initial I1 = [];

[0040] S382: Taking household A1 in the building to be energy-saving as an example, obtain the value H1 of the abnormal variable h1 corresponding to household A1;

[0041] S383: Compare the size of H1 and H. If H1 ≥ H:

[0042] S3831: Obtain the electricity consumption over-standard values of household A1 within t energy-saving cycles and b energy-saving segments, marked as Z1, Z2,..., Zz, 1 ≤ z < t;

[0043] Obtain the electricity consumption critical values E1, E2,..., Ee corresponding to the electricity consumption values of all households A1, A2,..., Aa within t energy-saving cycles and b energy-saving segments;

[0044] S3832: Calculate and obtain the discrete value O of the electricity consumption over-standard value of the building to be energy-saving according to the formula O = Compare the size of O and O1. If O ≥ O1, delete the corresponding Zo values in descending order of |Zo - Z|, and calculate the discrete value of the remaining Zo values until O < O1; Z is the mean value of the electricity consumption over-standard values of the remaining buildings to be energy-saving, and O1 is a preset threshold;

[0045] Redefine the mean value Z of the electricity consumption over-standard values of the buildings to be energy-saving participating in the discrete value calculation of the remaining electricity consumption over-standard values as the electricity consumption over-standard value Y1;

[0046] Add household A1 and its corresponding electricity consumption over-standard value Y1 to the list I1 of electricity consumption over-standard households in the form of a dictionary. At this time, I1 = [{A1: Y1}, ];

[0047] S3833: Calculate and obtain the mean value of the electricity consumption critical values E1, E2,..., Ee corresponding to the electricity consumption values of all households A1, A2,..., Aa within t energy-saving cycles and b energy-saving segments participating in the calculation of the remaining discrete values, and redefine it as the electricity consumption critical value, marked as V1;

[0048] S384: According to S381 to S383, obtain the final list I1 of electricity consumption over-standard households in the building to be energy-saving, where H is a preset abnormal variable threshold.

[0049] Furthermore, the specific screening rules for screening and obtaining the heavy-collection households, medium-collection households, and light-collection households of the building to be energy-saving in S310 are as follows:

[0050] S3101: Filter and obtain duplicate households in the list of households with excessive electricity consumption I1, the list of households with excessive water consumption I2, and the list of households with excessive natural gas consumption I3, and re-mark them as re-collected households, marked as J1, J2, ..., Jj, 1≤j <a;

[0051] S3102: Filter and obtain the households in the list of households with excessive electricity consumption I1 and the list of households with excessive water consumption I2, and re-mark the households that are not the households with concentrated reading as the households with concentrated reading;

[0052] S3103: Filter and obtain the households in the list of households with excessive electricity consumption I1 and the list of households with excessive natural gas consumption I3, and re-mark them as households with concentrated energy consumption;

[0053] S3104: Filter and obtain the households in the water energy consumption exceeding the normal household list I2 and the natural gas energy consumption exceeding the normal household list I3, and re-mark them as the medium energy consumption exceeding the normal household list.

[0054] S3105: Get the final CIMC residents of the building to be energy-saving, marked as K1, K2, ..., Kk, 1≤k <a;

[0055] S3106: All households in the list of households with excessive electricity consumption I1, the list of households with excessive water consumption I2, and the list of households with excessive natural gas consumption I3 that are not heavy-metered households or medium-metered households are marked as light-metered households, and are marked as L1, L2, ..., Ll, 1≤l <a。

[0056] Beneficial effects of the present invention:

[0057] (1) The present invention analyzes the electric energy consumption, water energy consumption and natural gas energy consumption data of all residents of buildings in the community through a data analysis module, divides the residents of each building in the community into heavy-collection residents, medium-collection residents, light-collection residents and other residents, and generates an evaluation value for each building based on the data. On the one hand, it avoids the occurrence of excessive useless data caused by high-frequency collection of energy consumption data of all users with low energy consumption in each building of the community, and on the other hand, it avoids the waste of resources and energy loss during the collection process caused by invalid high-frequency collection;

[0058] (2) The present invention generates the optimal energy consumption acquisition interval for different types of residents in each building based on the determination of the evaluation value of each building, and generates an energy-saving strategy for each building based on the optimal energy consumption acquisition interval. On the one hand, it makes data collection more targeted, and on the other hand, it reduces the amount of data transmission and speeds up the data response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The present invention will be further described below in conjunction with the accompanying drawings.

[0060] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0061] 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.

[0062] like Figure 1 As shown, an energy management platform based on wireless meter reading and energy-saving strategy includes a management terminal, an energy-saving storage module, a central control module and a data analysis module;

[0063] The management terminal is used to manage the energy consumption data of all residents in the community. The management terminal includes several collection modules. In this embodiment, the energy consumption data of the residents include electricity consumption data, water consumption data and natural gas consumption data;

[0064] The collection module is used to collect energy consumption data of all users in a building in the community. The collection module includes a plurality of collection units. One collection unit collects energy consumption data of a household in the building. The energy consumption data includes electricity consumption data, water consumption data and natural gas consumption data. The collection module monitors the energy consumption data of all households in the building according to the energy-saving strategy of the building to generate energy consumption data of the residents of the building.

[0065] The energy-saving storage module is used to store the energy consumption data of all residents in the community. After receiving the energy consumption data of all residents in the community transmitted by the management terminal, the storage module permanently stores the data;

[0066] The energy-saving storage module includes an energy consumption data table and an energy-saving management calibration table. After receiving the energy consumption data of all households in the community transmitted by the management terminal, the storage module stores the data in the energy consumption data table, and sets the storage expiration time to 3 years;

[0067] The central control module is used to conduct energy-saving monitoring on the energy consumption data of households in all buildings in the community. After obtaining the data in the energy-saving management calibration table, the central control module conducts energy-saving monitoring on the energy consumption data of all households in the community according to a certain energy-saving strategy. The specific energy-saving strategy is as follows:

[0068] S11: Taking a building in the community as an example, obtain the evaluation value of this building in the energy-saving management calibration table corresponding to the community, marked as R1, and obtain all heavy-metering households, medium-metering households, and light-metering households in this building;

[0069] S12: If R1 < Rmin, set the energy consumption data acquisition interval time of all heavy-metering households in this building to W1, the energy consumption data acquisition time of all medium-metering households to W2, the energy consumption data acquisition interval time of all light-metering households to W3, and the energy consumption acquisition interval time of the remaining households in this building to W4. In this embodiment, the optimal value of W1 is 12 hours, the optimal value of W2 is 24 hours, the optimal value of W3 is 48 hours, and the optimal value of W4 is 72 hours;

[0070] S13: If Rmin ≤ R1 ≤ Rmax, set the energy consumption data acquisition interval time of all heavy-metering households in this building to W5, the energy consumption data acquisition time of all medium-metering households to W1, the energy consumption data acquisition interval time of all light-metering households to W2, and the energy consumption acquisition interval time of the remaining households in this building to W3. In this embodiment, the optimal value of W5 is 6 hours;

[0071] S14: If Rmax < R1, set the energy consumption data acquisition interval time of all heavy-metering households in this building to W6, the energy consumption data acquisition time of all medium-metering households to W7, the energy consumption data acquisition interval time of all light-metering households to W8, and the energy consumption acquisition interval time of the remaining households in this building to W1. In this embodiment, the optimal value of W6 is 1 hour, the optimal value of W7 is 3 hours, and the optimal value of W8 is 6 hours;

[0072] The central control module generates the energy-saving strategy of this building according to the energy consumption acquisition interval time of the heavy-metering households, medium-metering households, light-metering households, and the remaining households in this building;

[0073] S15: Generate the energy-saving strategies of all buildings in the community in sequence according to steps S11 to S14. Rmin is the minimum threshold of the preset evaluation value, and Rmax is the maximum threshold of the preset evaluation value;

[0074] The data analysis module is used to analyze the total energy consumption data of all households in the community stored in the storage module. The specific analysis steps are as follows:

[0075] S31: First, select a building in the community as the building to be energy-saving, and obtain all the households A1, A2, ..., Aa in the building to be energy-saving, where a≥1;

[0076] S32: Create a abnormal variables marked as f1, f2, ..., fa. The abnormal variables f1, f2, ..., fa correspond to the households A1, A2, ..., Aa in the building to be energy-saving one by one, and the initial values of the a abnormal variables are all 0;

[0077] S33: Conduct energy-saving section division. Divide an energy-saving cycle into b equal-duration energy-saving sections, and mark the t energy-saving sections of an energy-saving cycle as B1, B2, ..., Bb, where b≥1;

[0078] S34: Obtain the power consumption critical values E1, E2, ..., Ea of the households A1, A2, ..., Aa in one energy-saving section of t energy-saving cycles;

[0079] S341: Take household A1 as an example, and obtain the power consumption values C1, C2, ..., Ct of household A1 in one energy-saving section of t energy-saving cycles;

[0080] S342: Use the formula D= Calculate the dispersion degree of the power consumption values of household A1 in one energy-saving section of t energy-saving cycles, compare D with D1. If D≥D1, delete the corresponding Cd values in descending order of |Cd - C| and calculate the dispersion value D of the remaining Cd values, and then compare D with D1 again until D < D1; C is the average value of the power consumption values participating in the remaining dispersion calculation in one energy-saving section of t energy-saving cycles, and it is re-designated as the power consumption critical value E1; D1 is a preset threshold; in this embodiment, one energy-saving cycle is 1 day and one energy-saving section is 60 minutes;

[0081] S35: According to S341 to S342, obtain the power consumption critical values E1, E2, ..., Ea of the households A1, A2, ..., Aa in one energy-saving section of t energy-saving cycles;

[0082] S36: Compare the power consumption values C1, C2, ..., Ct of all the households A1, A2, ..., Aa in one energy-saving section of t energy-saving cycles with the preset power consumption critical value Es. Increase the value of the abnormal variable corresponding to the household whose power consumption value in one energy-saving section of t energy-saving cycles is greater than the preset power consumption critical value Es by 1;

[0083] Mark the power consumption values of the households whose power consumption values in one energy-saving section of t energy-saving cycles are greater than the preset power consumption critical value Es as power consumption over-standard values;

[0084] S37: According to S33 to S36, calculate and obtain the abnormal variables corresponding to all households A1, A2, ..., Aa in the energy-saving building with t energy-saving cycles and b energy-saving segments, and re-label them as h1, h2, ..., ha;

[0085] S38: Generate a list I1 of households with excessive power consumption in the building to be energy-saving according to certain screening rules. The specific screening rules are as follows:

[0086] S381: Create a list I1 of households with excessive power consumption, and initially I1 = [];

[0087] S382: Take household A1 in the building to be energy-saving as an example, and obtain the value H1 of the abnormal variable h1 corresponding to household A1;

[0088] S383: Compare the size of H1 and H. If H1 ≥ H:

[0089] S3831: Obtain the excessive power consumption values of household A1 within t energy-saving cycles and b energy-saving segments, and label them as Z1, Z2, ..., Zz, 1 ≤ z < t;

[0090] Obtain the power consumption critical values E1, E2, ..., Ee corresponding to the power consumption values of all households A1, A2, ..., Aa within t energy-saving cycles and b energy-saving segments;

[0091] S3832: Calculate and obtain the discrete value O of the excessive power consumption value of the building to be energy-saving according to the formula O = Compare the size of O and O1. If O ≥ O1, delete the corresponding Zo values in descending order of |Zo - Z|, and calculate the discrete value of the remaining Zo values until O < O1; Z is the mean value of the remaining excessive power consumption values of the building to be energy-saving, and O1 is a preset threshold;

[0092] Re-calibrate the mean value Z of the excessive power consumption values of the building to be energy-saving participating in the calculation of the discrete value as the power consumption exceeding standard value Y1;

[0093] Add household A1 and its corresponding power consumption exceeding standard value Y1 to the list I1 of households with excessive power consumption in the form of a dictionary. At this time, I1 = [{A1: Y1}, ];

[0094] S3833: Calculate and obtain the mean value of the power consumption critical values E1, E2, ..., Ee corresponding to the power consumption values of all households A1, A2, ..., Aa within t energy-saving cycles and b energy-saving segments participating in the calculation of the remaining discrete values, and re-calibrate it as the power consumption boundary value, labeled as V1;

[0095] S384: According to S381 to S382, a final list of households I1 with excessive electricity consumption in the building to be saved is obtained, where H is a preset abnormal variable threshold;

[0096] S39: According to S34 to S38, a list of households with excessive water energy consumption I2 and their corresponding water energy consumption threshold V2, a list of households with excessive natural gas consumption I3 and their corresponding natural gas energy consumption threshold V3 in the building to be saved are finally obtained;

[0097] S310: Filter and obtain heavy-collection households, medium-collection households, and light-collection households of the building to be energy-saving according to certain screening rules;

[0098] S3101: Filter and obtain duplicate households in the list of households with excessive electricity consumption I1, the list of households with excessive water consumption I2, and the list of households with excessive natural gas consumption I3, and re-mark them as re-collected households, marked as J1, J2, ..., Jj, 1≤j <a;

[0099] S3102: Filter and obtain the households in the list of households with excessive electricity consumption I1 and the list of households with excessive water consumption I2, and re-mark the households that are not the households with concentrated reading as the households with concentrated reading;

[0100] S3103: Filter and obtain the households in the list of households with excessive electricity consumption I1 and the list of households with excessive natural gas consumption I3, and re-mark them as households with concentrated energy consumption;

[0101] S3104: Filter and obtain the households in the water energy consumption exceeding the normal household list I2 and the natural gas energy consumption exceeding the normal household list I3, and re-mark them as the medium energy consumption exceeding the normal household list.

[0102] S3105: Get the final CIMC residents of the building to be energy-saving, marked as K1, K2, ..., Kk, 1≤k <a;

[0103] S3106: All households in the list of households with excessive electricity consumption I1, the list of households with excessive water consumption I2, and the list of households with excessive natural gas consumption I3 that are not heavy-metered households or medium-metered households are marked as light-metered households, and are marked as L1, L2, ..., Ll, 1≤l <a;

[0104] S311: Obtain the excessive average values ​​of electric energy consumption corresponding to all households in the re-collected electricity meter, and mark them as M1, M2, ..., Mj;

[0105] Obtain the energy consumption excess values ​​corresponding to all households in the collection, marked as N1, N2, ..., Nk;

[0106] Obtain the power consumption excess values ​​corresponding to all light-collection households, marked as P1, P2, ..., Pl;

[0107] S312: Use the formula Q1= Calculate and obtain the bid evaluation value Q1 of the building to be energy-saving, where α is the preset heavy-collection household ratio adjustment factor, β is the preset medium-collection household ratio adjustment factor, and θ is the preset light-collection household ratio adjustment factor;

[0108] S313: According to S31 to S312, all buildings in the community are selected as buildings to be energy-saving in sequence, and the evaluation values ​​Q1, Q2, ..., Qq of all buildings in the community are calculated, where q≥1, and the heavy-collection households, medium-collection households, and light-collection households of all buildings in the community are obtained;

[0109] The data analysis module generates an energy-saving management calibration table for the community based on the evaluation values ​​of all buildings in the community, as well as the heavy-collection households, medium-collection households and light-collection households of each building in the community;

[0110] The energy-saving management calibration table of the community is provided with evaluation value, household type and building number fields, and the data analysis module sequentially fills the type of the household in the community, the building number in the community and the corresponding evaluation value into the corresponding fields of the energy-saving management calibration table of the community; in this embodiment, the household type includes heavy collective metering household, medium collective metering household, light collective metering household and other users;

[0111] The data analysis module transmits the energy-saving management calibration table of the cell to the energy-saving storage module, and the energy-saving storage module permanently stores the energy-saving management calibration table after receiving it.

[0112] In the description of the specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0113] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

[0114] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An energy management platform based on wireless meter reading and energy-saving strategy, characterized in that: Including: A management terminal that manages the energy consumption data of all households in the community. The management terminal includes several data collection modules. One data collection module corresponds to collecting the energy consumption data of all users in a building in the community. The data collection module includes several data collection units. One data collection unit corresponds to collecting the energy consumption data of one household in the building. The energy consumption data includes electricity energy consumption data, water energy consumption data, and natural gas energy consumption data. The data collection module monitors the energy consumption data of all households in the building according to the energy-saving strategy of the building to generate the energy consumption data of the households in the building; A data analysis module that analyzes the energy consumption data of all households in the community to generate an energy-saving management calibration table for the community; The energy-saving management calibration table stores the evaluation values of all buildings in the community, as well as the households with intensive data collection, medium data collection, and light data collection in each building of the community; A central control module that conducts energy-saving monitoring on the energy consumption data of all households in the community. After obtaining the data in the energy-saving management calibration table, the central control module generates the energy-saving strategy for each building in the community according to a certain generation rule. The specific generation rule is as follows: S11: Taking a building in the community as an example, obtain the evaluation value of this building in the energy-saving management calibration table corresponding to the community, marked as R1, and obtain all the households with intensive data collection, medium data collection, and light data collection in this building; S12: If R1 < Rmin, set the optimal energy consumption data acquisition interval time for all households with intensive data collection in this building to 12 hours, the optimal energy consumption data acquisition time for all households with medium data collection to 24 hours, the optimal energy consumption data acquisition interval time for all households with light data collection to 48 hours, and the optimal energy consumption acquisition interval time for the remaining households in this building to 72 hours; S13: If Rmin ≤ R1 ≤ Rmax, set the optimal energy consumption data acquisition interval time for all households with intensive data collection in this building to 6 hours, the optimal energy consumption data acquisition time for all households with medium data collection to 12 hours, the optimal energy consumption data acquisition interval time for all households with light data collection to 24 hours, and the optimal energy consumption acquisition interval time for the remaining households in this building to 48 hours; S14: If Rmax < R1, set the optimal energy consumption data acquisition interval time for all households with intensive data collection in this building to 1 hour, the optimal energy consumption data acquisition time for all households with medium data collection to 3 hours, the optimal energy consumption data acquisition interval time for all households with light data collection to 6 hours, and the optimal energy consumption acquisition interval time for the remaining households in this building to 12 hours; The central control module generates the energy-saving strategy for this building based on the energy consumption acquisition interval times of the households with intensive data collection, medium data collection, light data collection, and the remaining households in this building; S15: Generate the energy-saving strategies for all buildings in the community in sequence according to steps S11 to S14. The Rmin is the minimum threshold of the preset evaluation value, and the Rmax is the maximum threshold of the preset evaluation value.

2. According to claim 1, an energy management platform based on wireless meter reading and energy-saving strategy is characterized in that: It also includes an energy-saving storage module. The energy-saving storage module includes an energy consumption data table and an energy-saving management calibration table. The energy consumption data table stores the energy consumption data of all households in the community, and the set storage expiration time is 3 years.

3. According to claim 1, an energy management platform based on wireless meter reading and energy-saving strategy is characterized in that: The specific steps of the data analysis module analyzing and generating the cell energy-saving management calibration table are as follows: S31: First, a building in the community is selected as the building to be energy-saving, and all residents A1, A2, ..., Aa in the building to be energy-saving are obtained, where a≥1; S32: create a number of abnormal variables labeled as f1, f2, ..., fa, the abnormal variables f1, f2, ..., fa correspond to the residents A1, A2, ..., Aa in the building to be saved, and the initial values ​​of the a number of abnormal variables are all 0; S33: Divide an energy-saving period into b energy-saving periods of equal duration, and mark the t energy-saving periods in an energy-saving period as B1, B2, ..., Bb, where b≥1; S34: Obtaining critical values ​​of electric energy consumption E1, E2, ..., Ea of households A1, A2, ..., Aa in an energy-saving period of t energy-saving cycles; S341: Taking household A1 as an example, obtaining the electric energy consumption values ​​C1, C2, ..., Ct of household A1 in an energy-saving period of t energy-saving cycles; S342: Use the formula D = Calculate and obtain the dispersion of the power consumption value of household A1 in an energy-saving period within t energy-saving cycles. Compare the magnitudes of D and D1. If D ≥ D1, delete the corresponding Cd values in descending order of |Cd - C| and calculate the dispersion value D of the remaining Cd values. Then compare the magnitudes of D and D1 again until D < D1. Here, C is the average value of the power consumption values participating in the remaining dispersion calculation in an energy-saving period within t energy-saving cycles, which is re-designated as the power consumption critical value E1. D1 is a preset threshold. One energy-saving cycle is 1 day, and one energy-saving period is 60 minutes. S35: According to S341 to S342, the critical values ​​of electric energy consumption E1, E2, ..., Ea of households A1, A2, ..., Aa in an energy-saving period of t energy-saving cycles are obtained; S36: Compare the power consumption values ​​C1, C2, ..., Ct of all households A1, A2, ..., Aa in an energy-saving period of t energy-saving cycles with the preset power consumption critical value Es, and add 1 to the value of the abnormal variable corresponding to the household whose power consumption value in an energy-saving period of t energy-saving cycles is greater than the preset power consumption critical value Es; The electric energy consumption value of the household whose electric energy consumption value in an energy-saving period of t energy-saving cycles is greater than the preset electric energy consumption critical value Es is marked as the electric energy consumption exceeding the normal value; S37: According to S33 to S36, calculate and obtain the abnormal variables corresponding to all residents A1, A2, ..., Aa in the energy-saving building in t energy-saving cycles and b energy-saving segments, and re-label them as h1, h2, ..., ha; S38: Filter and generate a list of households I1 with excessive electricity consumption in the building to be saved according to certain filtering rules; S39: According to S34 to S38, a list of households with excessive water energy consumption I2 and their corresponding water energy consumption threshold V2, a list of households with excessive natural gas consumption I3 and their corresponding natural gas energy consumption threshold V3 in the building to be saved are finally obtained; S310: Filter and obtain heavy-collection households, medium-collection households, and light-collection households of the building to be energy-saving according to certain screening rules; S311: Obtain the excessive average values ​​of electric energy consumption corresponding to all households in the re-collected electricity meter, and mark them as M1, M2, ..., Mj; Obtain the energy consumption excess values ​​corresponding to all households in the collection, marked as N1, N2, ..., Nk; Obtain the power consumption excess values ​​corresponding to all light-collection households, marked as P1, P2, ..., Pl; S312: Use the formula Q1= Calculate and obtain the bid evaluation value Q1 of the building to be energy-saving, where α is the preset heavy-collection household ratio adjustment factor, β is the preset medium-collection household ratio adjustment factor, and θ is the preset light-collection household ratio adjustment factor; S313: According to S31 to S312, all buildings in the community are selected as buildings to be energy-saving in sequence, and the evaluation values ​​Q1, Q2, ..., Qq of all buildings in the community are calculated, where q≥1, and the heavy-collection households, medium-collection households, and light-collection households of all buildings in the community are obtained; The data analysis module generates an energy-saving management calibration table for the community based on the evaluation values ​​of all buildings in the community and the heavy-collection households, medium-collection households and light-collection households in each building of the community.

4. According to claim 3, an energy management platform based on wireless meter reading and energy-saving strategy is characterized in that: The specific screening steps for screening and generating the list of households I1 with excessive electricity consumption in the building to be saved in S38 are as follows: S381: Create a list of households with excessive electricity consumption I1, where the initial I1=[]; S382: Taking household A1 of the building to be energy-saving as an example, obtaining the value H1 of the abnormal variable h1 corresponding to household A1; S383: Compare H1 with H. If H1≥H: S3831: Obtain the excess power consumption of household A1 in t energy-saving cycles and b energy-saving segments, marked as Z1, Z2, ..., Zz, 1≤z <t; Obtain the critical values ​​of electric energy consumption E1, E2, ..., Ee corresponding to the electric energy consumption values ​​of all households A1, A2, ..., Aa in t energy-saving cycles and b energy-saving segments; S3832: Calculate and obtain the discrete value O of the electricity consumption exceeding the average value of the building to be energy-saving according to the formula O = Compare the size of O with O1. If O ≥ O1, delete the corresponding Zo values in descending order of |Zo - Z|, and calculate the discrete value of the remaining Zo values until O < O1; Z is the average value of the electricity consumption exceeding the average value of the remaining buildings to be energy-saving, and O1 is a preset threshold value. The average value Z of the excessive average value of the electricity consumption of the buildings to be saved that participate in the calculation of the excessive average value discrete value of the electricity consumption of the remaining buildings to be saved is recalibrated as the excessive value of electricity consumption Y1; Add household A1 and its corresponding excessive energy consumption value Y1 to the excessive energy consumption household list I1 in the form of a dictionary, and I1=[{A1:Y1},]; S3833: Calculate and obtain the average of the critical values ​​of electric energy consumption E1, E2, ..., Ee corresponding to the electric energy consumption values ​​of all households A1, A2, ..., Aa in b energy-saving periods of t energy-saving cycles and b energy-saving periods according to S3832, and recalibrate it as the electric energy consumption boundary value, marked as V1; S384: According to S381 to S383, the final list of residents whose electricity consumption exceeds the normal level I1 in the building to be saved is obtained, where H is a preset abnormal variable threshold.

5. According to claim 3, an energy management platform based on wireless meter reading and energy-saving strategy is characterized in that: The specific screening rules for screening and obtaining the heavy-collection households, medium-collection households, and light-collection households of the building to be energy-saving in S310 are as follows: S3101: Filter and obtain duplicate households in the list of households with excessive electricity consumption I1, the list of households with excessive water consumption I2, and the list of households with excessive natural gas consumption I3, and re-mark them as re-collected households, marked as J1, J2, ..., Jj, 1≤j <a; S3102: Filter and obtain the households in the list of households with excessive electricity consumption I1 and the list of households with excessive water consumption I2, and re-mark the households that are not the households with concentrated reading as the households with concentrated reading; S3103: Filter and obtain the households in the list of households with excessive electricity consumption I1 and the list of households with excessive natural gas consumption I3, and re-mark them as households with concentrated energy consumption; S3104: Filter and obtain the households in the water energy consumption exceeding the normal household list I2 and the natural gas energy consumption exceeding the normal household list I3, and re-mark them as the medium energy consumption exceeding the normal household list. S3105: Get the final CIMC residents of the building to be energy-saving, marked as K1, K2, ..., Kk, 1≤k <a; S3106: All households in the list of households with excessive electricity consumption I1, the list of households with excessive water consumption I2, and the list of households with excessive natural gas consumption I3 that are not heavy-metered households or medium-metered households are marked as light-metered households, and are marked as L1, L2, ..., Ll, 1≤l <a。

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