A heat meter data collection and sharing system and method for interconnecting thousands of households

Abstract

The present invention discloses a heat meter data collection and sharing system and method for interconnecting thousands of households, belonging to the technical field of heat meter data processing, and the method comprises: collecting data from each heat meter, and transmitting each collected data to a platform end; the platform end shares the collected data to a user end in real time; the user end analyzes the collected data, obtains user feedback data, and transmits the desensitized user feedback data to the platform end; the platform end analyzes each user feedback data, and obtains each target analysis result; each reference classification is set, and each user feedback data is classified according to each reference classification to obtain shared classification data, and the shared classification data is shared and displayed to the corresponding user end; the user end sets a reference user according to the shared classification data, obtains a billing representative curve and a heat usage curve corresponding to the reference user, and displays the billing reference curve and the heat reference curve to the user.

Description

Technical Field

[0001] The present invention belongs to the technical field of heat meter data processing, and in particular is a heat meter data collection and sharing system and method for interconnecting thousands of households. Background Art

[0002] In the current field of heating and energy management, heat collection and data analysis have become important means to improve heating efficiency, optimize energy distribution, and achieve on-demand heating. However, most traditional heat collection systems adopt a centralized data processing mode, that is, all collected heat data is first transmitted to the platform side for unified processing and analysis. Although this mode can centrally manage data, it has significant disadvantages: the platform side needs to bear a huge burden of data processing and analysis, especially when the amount of data surges, which easily leads to tight computing resources and low analysis efficiency; at the same time, the centralized processing of all data also increases the risk of user privacy leakage.

[0003] In addition, traditional data analysis methods often ignore the computing resources on the user side and fail to fully utilize the powerful computing power of smart devices such as smartphones in the hands of users. These devices usually have high-performance processors and sufficient storage space, and can undertake certain data processing and analysis tasks. However, in existing heat collection systems, user-side devices are often only used as terminals for data display, and their computing resources are not fully utilized.

[0004] More importantly, since data is processed centrally on the platform, it is difficult for users to understand other users’ usage and assess their own efficiency and cost. This results in a lack of reference and comparison in the process of energy use, making it difficult for users to discover the potential for energy saving and consumption reduction, and unable to adjust their energy use habits according to their actual situation.

[0005] In view of the above problems, the present invention provides a heat meter data collection and sharing system and method for interconnecting thousands of households. Summary of the invention

[0006] In order to solve the problems existing in the above scheme, the present invention provides a heat meter data collection and sharing system and method for interconnecting thousands of households.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A heat meter data collection and sharing system for interconnecting thousands of households, comprising a collection module, a platform end and a user end;

[0009] The acquisition module is used to collect data from each heat meter, obtain the collected data from each heat meter, and transmit the collected data to the platform end.

[0010] The platform end includes a platform transmission module, a user analysis module and a user sharing module;

[0011] The platform transmission module is used for data transmission, receiving the collected data transmitted by the collection module, and transmitting the received collected data to the user end of the corresponding user in real time and sharing.

[0012] The user analysis module is used to analyze the feedback data of each user, preset the analysis target of each user and the target analysis plan corresponding to each user analysis target;

[0013] Identify each user's feedback data, analyze each user's feedback data based on each target analysis scheme, and obtain target analysis results corresponding to each user's analysis target.

[0014] The user sharing module is used for data sharing, setting various reference categories, classifying the feedback data of each user according to the reference categories, obtaining shared classification data, and sharing and displaying the shared classification data to the corresponding user terminal according to the reference categories to which each user belongs.

[0015] Furthermore, the reference classification setting method includes:

[0016] Identify each user's information, classify each user according to the user's information, and obtain each initial classification;

[0017] An equivalent judgment model is established, and the expression of the equivalent judgment model is: ;

[0018] Where: q is the input data, the input data is the initial classification for comparison, and the output data is the equivalent judgment value TB(q);

[0019] Each initial classification is analyzed through the equivalent judgment model to obtain the corresponding equivalent judgment value; each initial classification is merged according to the equivalent judgment value to obtain each reference classification.

[0020] The user terminal includes a collection and analysis module and a user reference module;

[0021] The acquisition and analysis module is used to analyze the received acquisition data, generate a heat usage curve according to the acquisition data, wherein the horizontal axis of the heat usage curve is time and the vertical axis is the heat value; perform usage analysis according to the heat usage curve to obtain corresponding heat analysis results; the heat analysis results include equipment abnormality analysis results and billing costs;

[0022] Obtain a preset feedback time, determine a feedback period according to the feedback time, identify each heat analysis result within the feedback period, perform analysis according to each heat analysis result, and obtain user feedback data, wherein the user feedback data includes an equipment abnormality curve and a billing representative curve; desensitize the user feedback data, and transmit the desensitized user feedback data to the platform end.

[0023] Furthermore, the method for performing usage analysis based on the heat usage curve includes:

[0024] An abnormality recognition model is established, and the rated expression of the abnormality recognition model is: ;

[0025] Where: s is the input data, which is the heat usage curve and additional data. The additional data includes equipment monitoring data and user feedback data. The output data is the abnormal judgment value YB(s), which is 1 or 0.

[0026] Acquire additional data in real time, integrate the heat usage curve and the additional data as input data and input them into the abnormality recognition model for analysis to obtain equipment abnormality analysis results;

[0027] Obtain the heat billing rules, and calculate the corresponding billing fees based on the heat billing rules and heat usage curve;

[0028] Integrate equipment anomaly analysis results and billing charges into thermal analysis results.

[0029] Further, the method of analyzing according to each heat analysis result includes:

[0030] Identify the equipment abnormality analysis results and billing costs corresponding to each thermal analysis result;

[0031] Identify the abnormality judgment value corresponding to the abnormality analysis result of each device; generate a corresponding device abnormality curve according to each abnormality judgment value, where the horizontal axis of the device abnormality curve is time and the vertical axis is the abnormality judgment value;

[0032] Classify each billing fee according to a preset unit time period to obtain each unit classification; generate a unit billing curve according to each billing fee corresponding to the unit classification, wherein the horizontal axis of the unit billing curve is time and the vertical axis is the billing fee; merge and integrate each of the unit billing curves to obtain a billing representative curve;

[0033] Integrate the billing representative curve and the equipment abnormality curve into user feedback data.

[0034] Furthermore, the method for merging and integrating the unit billing curves includes:

[0035] Integrate the charging curves of each unit into a coordinate graph based on time, and label it as the unit comprehensive graph;

[0036] Identify the charging fees corresponding to each time on the unit comprehensive graph for each unit charging curve, and label it as FYit, where i represents the corresponding unit charging curve, i = 1, 2, ……, n, n is a positive integer; t represents time;

[0037] According to the formula Calculate the charging representative value for the corresponding time;

[0038] In the formula: DBt is the charging representative value; min() is the minimum value function;

[0039] Generate a charging representative curve based on the charging representative values corresponding to each time.

[0040] The user reference module is used to set a reference user for the user, obtain the charging representative curve and heat usage curve corresponding to the reference user, and label them as the charging reference curve and heat reference curve respectively; display the charging reference curve and heat reference curve to the user.

[0041] Further, the setting method of the reference user includes:

[0042] Identify the feedback data of each user in the shared classification data, and obtain the charging representative curve in the feedback data of each user; generate a heat usage curve based on the charging representative curve and the charging rule, and label each heat usage curve as a candidate usage curve;

[0043] Calculate the total usage amount corresponding to each candidate usage curve, and label it as the candidate usage value; identify the heat usage period in the candidate usage curve, set the segmentation value of the usage period to 1, and the segmentation value of the non-usage period to 0, and form a candidate segmentation graph of each candidate usage curve according to the segmentation values corresponding to each time;

[0044] Label the user's heat usage curve as the reference usage curve; calculate the total usage amount of the reference usage curve, and label it as the reference usage value; set a reference segmentation graph according to the reference usage curve;

[0045] Calculate the similarity between the reference segmentation graph and each candidate segmentation graph;

[0046] According to the formula Calculate the reference value between the reference usage curve and each candidate usage curve;

[0047] In the formula: KA is the reference value; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1; DP is the candidate usage amount; UP is the reference usage amount; AZ is the similarity;

[0048] Label each candidate usage curve with a reference value greater than the threshold X1 as a screening curve;

[0049] Identify the billing representative curves corresponding to each screening curve, calculate the corresponding billing total value, and select the user corresponding to the screening curve with the smallest billing total value as the reference user.

[0050] A method for sharing heat meter data collected by Wanjia Internet, the method comprising:

[0051] Collect data from each heat meter, obtain the collected data from each heat meter, and transmit the collected data to the platform end;

[0052] The received collected data is transmitted to the corresponding user's user terminal in real time for sharing;

[0053] The user end analyzes the received collected data, obtains user feedback data, desensitizes the user feedback data, and transmits the desensitized user feedback data to the platform end;

[0054] The platform analyzes the feedback data of each user to obtain the target analysis results corresponding to the preset analysis targets of each user;

[0055] Set reference categories, classify the feedback data of each user according to the reference categories, obtain shared classification data, and share and display the shared classification data to the corresponding user terminal according to the reference categories to which each user belongs;

[0056] The user end sets a reference user according to the shared classification data, obtains a billing representative curve and a heat usage curve corresponding to the reference user, and marks them as a billing reference curve and a heat reference curve respectively; and displays the billing reference curve and the heat reference curve to the user.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] The present invention adopts real-time data collection technology to transmit the collected heat data to the platform side in real time. However, unlike the traditional method, the present invention makes full use of the computing resources of the user-side smart phone and other devices to perform usage analysis of the collected data on the user side. This innovative design not only greatly reduces the data analysis burden on the platform side, saves computing resources, but also improves the efficiency and accuracy of data analysis.

[0059] During the analysis process on the user side, the user's personal information will be desensitized to ensure the security of the user's privacy; the processed analysis results will be uploaded to the platform side for summary and statistical analysis. Based on the analysis results of many users, the platform side can deeply understand the user's usage habits, energy consumption distribution and other related demand information in the jurisdiction, and provide decision-making support for heating companies;

[0060] At the same time, the present invention also determines a reference user for each user, and by desensitizing the analysis results of the reference user and then displaying them to the user, the user can intuitively understand the room for improvement in his or her own energy usage, thereby adjusting his or her energy usage habits to achieve the goal of energy saving and consumption reduction; this function solves the problem that current users find it difficult to understand the usage of other users and do not know whether their own usage is efficient, and provides users with scientific and reasonable energy usage guidance. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] 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.

[0062] Figure 1 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0063] 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.

[0064] like Figure 1 As shown, a heat meter data collection and sharing system for Wanjia Internet includes a collection module, a platform end, and a user end;

[0065] The acquisition module is used to collect data from the heat meters, obtain the collected data of each heat meter, and transmit each collected data to the platform end.

[0066] In one embodiment, the collection module includes a collector and a concentrator. The collector is installed in each building and is responsible for collecting data from multiple heat meters. The collector is usually a single-chip microcomputer system with data processing and transmission functions, and can transmit the collected data to the concentrator via wired or wireless means. The concentrator serves as a transit station for data transmission. The concentrator receives data from each collector and performs preliminary processing. The concentrator transmits the data to the platform end through a more powerful communication network (such as GPRS wireless network, ZigBee network, etc.).

[0067] The platform end is arranged at the platform side, and includes a platform transmission module, a user analysis module and a user sharing module;

[0068] The platform transmission module is used for data transmission, receiving the collected data transmitted by the collection module and the user feedback data transmitted by the user end, and transmitting the received collected data to the user end of the corresponding user in real time and sharing.

[0069] The user analysis module is used to analyze the feedback data of each user and set the analysis target of each user, such as the user's energy usage habits, energy usage at different times, statistical analysis of equipment abnormalities and other related analysis targets, which are specifically set according to the needs of the platform; and set a corresponding target analysis plan according to each user's analysis target. The target analysis plan is used to analyze the corresponding user's analysis target and is set by the platform;

[0070] Identify each user's feedback data, analyze it based on each target analysis plan, and obtain the target analysis results corresponding to each user's analysis target.

[0071] The user sharing module is used to share data and identify the information of each user in the jurisdiction, mainly including information that affects the use of heat, such as house area, heating equipment type, thermal insulation performance, etc., which can be obtained based on existing public data; classify each user according to the information of each user to obtain each initial classification, that is, classify the same information into one category and mark it as the initial classification; make an equivalence judgment on each initial classification, that is, judge whether the difference in the impact on heat use is within a preset range based on its information, and if so, it is deemed to meet the equivalence judgment; the impact on heat use under certain differences can be determined based on historical usage data, and then the corresponding equivalence judgment standard is set, which needs to be set according to the number of users, that is, to ensure that a certain number of users are used for the selection of subsequent reference users, and a certain range can be expanded without affecting the selection of subsequent reference users. In one embodiment, all can be merged into one category, but the efficiency of selecting reference users will be reduced; specifically, an equivalence judgment model is established, and the platform sets a corresponding training set for training, the training set includes input data and output data, the input data is different user information, and the output data is whether it meets the equivalence judgment; the expression of the equivalence judgment model is ; Where: q is the input data, the input data is the initial classification for comparison, and the output data is the equivalent judgment value TB(q);

[0072] Each initial classification is analyzed through the equivalent judgment model to obtain the corresponding equivalent judgment value; each initial classification is merged according to the equivalent judgment value to obtain each reference classification, that is, the merged initial classification is marked as a reference analysis, and the initial classifications with an equivalent judgment value of 1 are merged.

[0073] Classify each user's feedback data according to each reference classification to obtain shared classification data, and share and display the shared classification data to the corresponding user terminal according to the reference classification to which the user belongs; for example, establish a sharing platform based on existing sharing technologies to share each user's feedback data; specifically, the sharing can be based on a variety of existing technologies to ensure that only each user can access it.

[0074] The user terminal is used by the user and is generally installed in the user's mobile phone; it includes a collection and analysis module and a user reference module;

[0075] The acquisition and analysis module is used to analyze the received acquisition data, generate a corresponding heat usage curve according to the received acquisition data, the horizontal axis of the heat usage curve is time, and the vertical axis is the heat value; display the heat usage curve in real time; perform usage analysis according to the heat usage curve to obtain the corresponding heat analysis result; mainly analyze from two perspectives of heat equipment usage abnormality and billing, that is, the heat analysis result includes equipment abnormality analysis result and billing fee;

[0076] Get the preset feedback time, which is generally determined based on the monthly heat settlement collection time, that is, scheduled feedback; determine the feedback period based on the feedback time, that is, the period between the current feedback time and the previous feedback time, identify each heat analysis result within the feedback period, analyze each heat analysis result, obtain the corresponding equipment abnormality curve and billing representative curve, integrate them into user feedback data, desensitize the user feedback data, that is, remove private information such as user personal information; transmit the desensitized user feedback data to the platform.

[0077] Methods for using the heat usage curve include:

[0078] An abnormality recognition model is established. The abnormality recognition model is used to analyze the abnormality of the equipment. The platform makes judgments based on the existing abnormality judgment technology of thermal equipment, mainly from multiple directions such as abnormal changes in thermal usage, user feedback, and equipment data collection. That is, the platform combines the abnormality judgment technology of existing thermal equipment to establish an abnormality recognition model, and judges whether it meets the abnormality requirements based on the built-in judgment algorithm. The abnormality requirement is that the equipment has an abnormal condition; the rated expression of the abnormality recognition model is ; Where: s is the input data, the input data is the heat usage curve and additional data, the additional data is the device monitoring data, the user feedback data of the user's abnormal input, the device monitoring data can be monitored by the monitoring device of the heat device itself, or monitored by other methods, can be directly transmitted to the user end, or can be transmitted to the platform end first, and then shared with the user end by the platform end; there are multiple ways to obtain device monitoring data; the output data is the abnormal judgment value YB(s), YB(s) is 1 or 0;

[0079] Acquire additional data in real time, integrate the heat usage curve and additional data as input data and input them into the anomaly recognition model for analysis to obtain the corresponding equipment anomaly analysis results;

[0080] Obtain the heat billing rules, and calculate the corresponding billing fees based on the heat billing rules and heat usage curve;

[0081] Integrate equipment anomaly analysis results and billing charges into thermal analysis results.

[0082] The methods for analyzing based on the results of each thermal analysis include:

[0083] Identify the equipment abnormality analysis results and billing costs corresponding to each thermal analysis result;

[0084] Identify the abnormality judgment value corresponding to the abnormality analysis result of each device; generate a corresponding device abnormality curve according to each abnormality judgment value, where the horizontal axis of the device abnormality curve is time and the vertical axis is the abnormality judgment value;

[0085] Classify each billing fee according to the preset unit time period to obtain each unit classification; the unit time period is generally one day, and can also be adjusted according to the needs of the platform; generate a corresponding unit billing curve according to each billing fee corresponding to the unit classification, and the horizontal axis of the unit billing curve is time, and the vertical axis is the billing fee;

[0086] The billing curves of each unit are combined and integrated to obtain a representative billing curve;

[0087] Integrate the billing representative curve and the equipment abnormality curve into user feedback data.

[0088] Methods for merging and integrating the billing curves of each unit include:

[0089] Integrate the billing curves of each unit into a coordinate diagram based on time, marked as the unit comprehensive diagram;

[0090] The billing fee corresponding to each unit billing curve at each time in the identification unit comprehensive diagram is marked as FYit, i represents the corresponding unit billing curve, i=1, 2, ..., n, n is a positive integer; t represents time;

[0091] According to the formula Calculate the billing representative value of the corresponding time;

[0092] Where: DBt is the representative value of the billing; min() is the minimum value function, that is, taking the minimum value;

[0093] Generate a billing representative curve according to the billing representative value corresponding to each time.

[0094] The user reference module is used to set a reference user for the user, obtain the billing representative curve and heat usage curve corresponding to the reference user, and mark them as the billing reference curve and heat reference curve respectively; and display the billing reference curve and heat reference curve to the user.

[0095] The method for setting the reference user includes:

[0096] Identify the user feedback data in the shared shared classification data, and identify the billing representative curve in each user feedback data; generate a heat usage curve according to the billing representative curve and the billing rule, and mark each heat usage curve as a candidate usage curve; calculate the total usage corresponding to each candidate usage curve, that is, the total heat usage, and mark it as the candidate usage value; identify the heat usage period in the candidate usage curve, set the segmentation value of the usage period to 1, and the segmentation value of the non-usage period to 0, to form a candidate segmentation diagram corresponding to each candidate usage curve;

[0097] Identify the heat usage curve of the user, and mark it as the reference usage curve; calculate the total usage corresponding to the reference usage curve, and mark it as the reference usage value; set the corresponding segmentation diagram according to the reference usage curve, and mark it as the reference segmentation diagram;

[0098] Calculate the similarity between the reference segmentation diagram and each candidate segmentation diagram;

[0099] According to the formula Calculate the reference value between the reference usage curve and each candidate usage curve;

[0100] In the formula: KA is the reference value; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1; DP is the candidate usage amount; UP is the reference usage amount; AZ is the similarity;

[0101] Mark each candidate usage curve with a reference value greater than the threshold X1 as a screening curve;

[0102] Identify the billing representative curve corresponding to each screening curve, calculate the corresponding total billing value, and select the user corresponding to the screening curve with the smallest total billing value as the reference user.

[0103] In one embodiment, according to the displayed billing reference curve and heat reference curve, it can assist the user in heat usage planning.

[0104] A method for sharing heat meter collection data for ten thousand homes interconnected, the method includes:

[0105] Collect data from each heat meter, obtain the collection data of each heat meter, and transmit each collection data to the platform side;

[0106] Transmit the received collection data in real-time and share it to the user side of the corresponding user;

[0107] The user end analyzes the received collected data, obtains user feedback data, desensitizes the user feedback data, and transmits the desensitized user feedback data to the platform end;

[0108] The platform analyzes the feedback data of each user to obtain the target analysis results corresponding to the preset analysis targets of each user;

[0109] Set reference categories, classify the feedback data of each user according to the reference categories, obtain shared classification data, and share and display the shared classification data to the corresponding user terminal according to the reference categories to which each user belongs;

[0110] The user end sets a reference user according to the shared classification data, obtains a billing representative curve and a heat usage curve corresponding to the reference user, and marks them as a billing reference curve and a heat reference curve respectively; and displays the billing reference curve and the heat reference curve to the user.

[0111] 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.

[0112] 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 heat meter data collection and sharing system for Wanjia Internet, characterized in that: Including acquisition module, platform end and user end; The acquisition module is used to collect data from each heat meter, obtain the collected data from each heat meter, and transmit the collected data to the platform end; The platform end includes a platform transmission module, a user analysis module and a user sharing module; The platform transmission module is used to transmit data, receive the collected data transmitted by the collection module, and transmit the received collected data to the user end of the corresponding user in real time; The user analysis module is used to analyze the feedback data of each user, preset the analysis target of each user and the target analysis plan corresponding to each user analysis target; Identify each user's feedback data, analyze each user's feedback data based on each target analysis scheme, and obtain a target analysis result corresponding to each user's analysis target; The user sharing module is used to share data, set reference categories, classify the feedback data of each user according to the reference categories, obtain shared classification data, and share and display the shared classification data to the corresponding user terminal according to the reference categories to which each user belongs; The user terminal includes a collection and analysis module and a user reference module; The acquisition and analysis module is used to analyze the received acquisition data and generate a heat usage curve according to the acquisition data, wherein the horizontal axis of the heat usage curve is time and the vertical axis is heat value; Performing usage analysis according to the heat usage curve to obtain corresponding heat analysis results; The heat analysis results include equipment abnormality analysis results and billing costs; Obtaining a preset feedback time, determining a feedback period according to the feedback time, identifying each heat analysis result within the feedback period, performing analysis according to each heat analysis result, and obtaining user feedback data, wherein the user feedback data includes an equipment abnormality curve and a billing representative curve; desensitizing the user feedback data, and transmitting the desensitized user feedback data to the platform end; The user reference module is used to set a reference user for the user, obtain a billing representative curve and a heat usage curve corresponding to the reference user, mark them as a billing reference curve and a heat reference curve respectively; and display the billing reference curve and the heat reference curve to the user.

2. A heat meter data collection and sharing system for interconnecting thousands of households according to claim 1, characterized in that: The reference classification setting methods include: Identify each user's information, classify each user according to the user's information, and obtain each initial classification; An equivalent judgment model is established, and the expression of the equivalent judgment model is: ; Where: q is the input data, the input data is the initial classification for comparison, and the output data is the equivalent judgment value TB(q); Each initial classification is analyzed through the equivalent judgment model to obtain the corresponding equivalent judgment value; each initial classification is merged according to the equivalent judgment value to obtain each reference classification.

3. The heat meter data collection and sharing system for interconnecting thousands of households according to claim 1 is characterized in that: Methods for using the heat usage curve include: An abnormality recognition model is established, and the rated expression of the abnormality recognition model is: ; Where: s is the input data, which is the heat usage curve and additional data. The additional data includes equipment monitoring data and user feedback data. The output data is the abnormal judgment value YB(s), which is 1 or 0. Acquire additional data in real time, integrate the heat usage curve and the additional data as input data and input them into the abnormality recognition model for analysis to obtain equipment abnormality analysis results; Obtain the heat charging rule, and calculate the corresponding charging fee according to the heat charging rule and the heat usage curve; Integrate the device anomaly analysis result and the charging fee into the heat analysis result.

4. The heat meter data collection and sharing system for interconnecting thousands of households according to claim 1 is characterized in that: The method of analyzing according to each heat analysis result includes: Identify the device anomaly analysis result and the charging fee corresponding to each heat analysis result; Identify the anomaly judgment value corresponding to each device anomaly analysis result; generate a corresponding device anomaly curve according to each anomaly judgment value, where the horizontal axis of the device anomaly curve is time and the vertical axis is the anomaly judgment value; Classify each charging fee according to a preset unit time period to obtain each unit classification; generate a unit charging curve according to the charging fees corresponding to each unit classification, where the horizontal axis of the unit charging curve is time and the vertical axis is the charging fee; merge and integrate each of the unit charging curves to obtain a charging representative curve; Integrate the charging representative curve and the device anomaly curve into user feedback data.

5. A heat meter data collection and sharing system for interconnecting thousands of households according to claim 4, characterized in that: The method of merging and integrating each unit charging curve includes: Integrate each unit charging curve into a coordinate graph based on time and mark it as a unit comprehensive graph; Identify the charging fees corresponding to each unit charging curve at each time in the unit comprehensive graph and mark them as FYit, where i represents the corresponding unit charging curve, i = 1, 2,..., n, n is a positive integer; t represents time; According to the formula Calculate the billing representative value of the corresponding time; In the formula: DBt is the charging representative value; min() is the minimum value function; Generate a charging representative curve according to the charging representative values corresponding to each time.

6. The heat meter data collection and sharing system for interconnecting thousands of households according to claim 1 is characterized in that: The method of referring to user settings includes: Identify each user feedback data in the shared classification data, and obtain the charging representative curve in each user feedback data; generate a heat usage curve according to the charging representative curve and the charging rule, and mark each heat usage curve as a candidate usage curve; Calculate the total usage amount corresponding to each candidate usage curve and mark it as a candidate usage value; identify the heat usage period in the candidate usage curve, set the segmentation value of the usage period to 1, and the segmentation value of the non-usage period to 0, and form a candidate segmentation graph of each candidate usage curve according to the segmentation values corresponding to each time; Mark the user's heat usage curve as the reference usage curve; calculate the total usage amount of the reference usage curve and mark it as the reference usage value; set the reference segmentation graph according to the reference usage curve; Calculate the similarity between the reference segmentation graph and each candidate segmentation graph; According to the formula Calculate the reference value between the benchmark usage curve and each candidate usage curve; In the formula: KA is the reference value; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1; DP is the candidate usage amount; UP is the reference usage amount; AZ is the similarity; Mark each candidate usage curve with a reference value greater than the threshold X1 as a screening curve; Identify the charging representative curve corresponding to each screening curve, calculate the corresponding total charging value, and select the user corresponding to the screening curve with the smallest total charging value as the reference user.

7. A method for sharing heat meter data collected by Wanjia Internet, characterized in that: Applied to a heat meter data acquisition and sharing system for ten thousand home interconnection as described in any one of claims 1 to 6, the method includes: Collect data from each heat meter, obtain the collected data of each heat meter, and transmit each collected data to the platform side; The platform side transmits the received collected data in real time and shares it with the user side of the corresponding user; The user end analyzes the received collected data, obtains user feedback data, desensitizes the user feedback data, and transmits the desensitized user feedback data to the platform end; The platform analyzes the feedback data of each user to obtain the target analysis results corresponding to the preset analysis targets of each user; Set reference categories, classify the feedback data of each user according to the reference categories, obtain shared classification data, and share and display the shared classification data to the corresponding user terminal according to the reference categories to which each user belongs; The user end sets a reference user according to the shared classification data, obtains a billing representative curve and a heat usage curve corresponding to the reference user, and marks them as a billing reference curve and a heat reference curve respectively; and displays the billing reference curve and the heat reference curve to the user.

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