Electricity metering cloud platform for electric charge allocation of lifting device

Abstract

The invention relates to an electricity metering cloud platform for electric charge apportionment of a lifting device. The electricity metering cloud platform comprises an intelligent data acquisition module, a secure transmission module, a big data storage and preprocessing module, a dynamic intelligent apportionment algorithm module, a user interaction and service module and an intelligent control and early warning module. A data input integration sub-module, a parameter configuration management sub-module, a multi-factor calculation engine sub-module and a result output verification sub-module are arranged in the dynamic intelligent allocation algorithm module; according to the invention, through cooperation of the dynamic intelligent allocation algorithm module and each function module, the accuracy of electric charge allocation is significantly improved. The data input integration sub-module carries out standardization processing and time dimension alignment on multi-source data, and dispersive electric quantity and elevator taking records are aligned according to a day / week / month period, so that calculation deviation caused by data format disorder or time dislocation is avoided, and an apportionment result can more accurately reflect the effect of the actual power consumption behavior of a user.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of elevator cost metering, and in particular to an electric energy metering cloud platform for electricity cost sharing of a lifting device. BACKGROUND

[0002] The old community elevator electricity cost sharing system is a management mechanism designed for reasonably sharing the electricity cost of the elevator after the elevator is installed in the old community. The core purpose is to improve the convenience of residents' life while realizing fair, just and transparent electricity cost sharing. The system usually combines the property management platform and the smart meter data, calculates the electricity cost proportion that each household should bear according to the use frequency of each floor household, the elevator operation period, the floor height and the number of households and other factors, and pushes the bill and settles the cost through the online payment platform or the property management system. Some systems also support residents to customize the sharing rules, such as floor ladder sharing or household sharing, to meet the actual situation and residents' needs of different communities. Through the system, not only can the neighborhood disputes caused by electricity cost sharing be effectively reduced, but also the intelligent level of community management can be improved, providing strong support for the sustainable transformation of old communities.

[0003] However, the existing technology has the problem of insufficient data integration capability in the electricity cost sharing system. Multi-source data often deviates from the calculation due to non-uniform format, time dimension misplacement or improper handling of missing values. The parameter configuration mechanism is rigid and difficult to quickly adjust the sharing rules according to different scenarios, often requiring re-development of algorithms with high adaptation cost. SUMMARY

[0004] In order to solve the problem of insufficient data integration capability in the electricity cost sharing system in the prior art, the application provides an electric energy metering cloud platform for electricity cost sharing of a lifting device.

[0005] The electric energy metering cloud platform for electricity cost sharing of a lifting device provided by the application adopts the following technical solution: an intelligent data acquisition module, a secure transmission module, a big data storage and preprocessing module, a dynamic intelligent sharing algorithm module, a user interaction and service module, and an intelligent control and early warning module are arranged.

[0006] The dynamic intelligent sharing algorithm module is internally provided with a data input integration sub-module, a parameter configuration management sub-module, a multi-factor calculation engine sub-module, and a result output verification sub-module.

[0007] The output end of the intelligent data acquisition module is connected to the input end of the secure transmission module.

[0008] The real-time data lake output end and the distributed database output end of the big data storage and preprocessing module are connected to the data input integration submodule input end of the dynamic intelligent allocation algorithm module, respectively;

[0009] The result output verification submodule output end of the dynamic intelligent allocation algorithm module is connected to the input end of the user interaction and service module;

[0010] The management end background output end of the user interaction and service module is connected to the parameter configuration management submodule input end of the dynamic intelligent allocation algorithm module;

[0011] The permission control submodule input end of the intelligent control and early warning module is connected to the user end application output end of the user interaction and service module, and the output end thereof is reversely connected to the elevator riding behavior recognition terminal input end of the intelligent data acquisition module;

[0012] The abnormal early warning submodule input end of the intelligent control and early warning module is connected to the real-time data lake output end of the big data storage and preprocessing module, and the output end thereof is connected to the management end background input end of the user interaction and service module;

[0013] The predictive maintenance submodule input end of the intelligent control and early warning module is connected to the distributed database output end of the big data storage and preprocessing module, and the output end of the intelligent control and early warning module is connected to the management end background input end of the user interaction and service module.

[0014] In a preferred embodiment, the intelligent data acquisition module is internally provided with an Internet of Things intelligent acquisition gateway, an electric quantity monitoring sensor, an elevator riding behavior recognition terminal and an elevator state sensor. The Internet of Things intelligent acquisition gateway integrates an edge computing function, supports Modbus, MQTT and other communication protocols, and serves as a connecting hub of sensors and a cloud platform; the electric quantity monitoring sensor is installed on an elevator power cabinet, and collects elevator total power consumption, voltage and current fluctuation data in real time through a current transformer, with an accuracy of 0.1 degree; the elevator riding behavior recognition terminal is deployed at an elevator hall door, integrates an IC card reader, a 3D structured light camera and a semiconductor fingerprint sensor, supports non-contact identity recognition such as card swiping, face swiping and fingerprint swiping, records user elevator riding time and direction, including up and down; the elevator state sensor includes a door magnetic sensor for monitoring door opening and closing times, a temperature and humidity sensor for monitoring motor compartment environment, and an acceleration sensor for monitoring elevator running stability, covering the operation state acquisition of core components of the elevator.

[0015] In a preferred embodiment, the internal of the secure transmission module is provided with a hybrid communication unit, a data encryption unit and a breakpoint resume unit. The hybrid communication unit dynamically switches between 5G high-bandwidth communication mode, 4G wide-coverage communication mode, NB-IoT low-power strong penetration communication mode according to the signal difference between the elevator shaft and the ground area, to ensure the continuity of data transmission; the data encryption unit encrypts the data before leaving the terminal using AES-256 algorithm, and the cloud platform uses a private key to decrypt, to prevent data from being tampered with or leaked during transmission; the breakpoint resume unit caches the unuploaded data in the local gateway when the network is interrupted, and automatically supplements the transmission in time sequence after the network is restored, to avoid data loss.

[0016] In a preferred embodiment, the internal of the big data storage and preprocessing module is provided with a distributed database, a real-time data lake, a data preprocessing engine and a data mapping unit. The distributed database uses HBase architecture to store historical power data, including time stamp and power value, user elevator record, including user ID, time and direction, elevator state log, including temperature, acceleration and door opening times, to support long-term storage of massive data; the real-time data lake is built based on Apache Kafka, to cache 10-second-level real-time streaming data such as power fluctuation and elevator instantaneous record, for real-time calling by the algorithm module; the data preprocessing engine filters abnormal data such as negative power and invalid records with elevator time exceeding 30 minutes through preset rules, and fills in missing values with mean value; the data mapping unit maps different sources of user identification, such as ID number of card swiping and feature code of face recognition, to UUID, to ensure the uniqueness and traceability of user behavior data.

[0017] In a preferred embodiment, the internal of the data input integration sub-module is provided with a data source interface unit, a data standardization unit, an integrity verification unit and a time dimension alignment unit. The data source interface unit is responsible for interfacing the data channels inside and outside the system, including obtaining structured data such as cleaned elevator total power, user elevator record and elevator load rate from the big data storage and preprocessing module, and receiving configuration parameters such as administrator-defined time period division and coefficient weight from the user interaction and service module; the data standardization unit converts raw data of different formats into a unified data model, such as mapping user ID in card swiping record and biological feature code in face recognition to unique user identification, and unifying time stamp of power sensor and time label of elevator record to hourly time granularity; the integrity verification unit checks data missing or abnormal through preset rules, such as triggering a recording mechanism when there is only power data but no elevator record in a certain period, to fill in missing values with the mean value of historical data in the same period; the time dimension alignment unit re-aligns power, elevator and load data scattered in different databases according to time periods such as day, week and month, to ensure the consistency of data time dimension when calculating the algorithm.

[0018] In a preferred embodiment, the parameter configuration management submodule is internally provided with a parameter type classification unit, a visualization configuration unit, a template library management unit and a parameter storage unit. The parameter type classification unit divides the parameters involved in the algorithm into three categories: basic parameters, weight parameters and threshold parameters. The basic parameters include time-of-use electricity price (such as 1.2 yuan / degree during peak hours and 0.5 yuan / degree during off-peak hours), time period division (such as the default peak hours of 8:00-22:00) and other fixed values that are strongly related to external environment; the weight parameters include time period coefficients, load coefficients and other adjustment values that affect the allocation ratio; the threshold parameters include abnormal electricity fluctuation thresholds, minimum effective elevator ride number of users and other risk control values. The visualization configuration unit provides tools such as sliders and time selectors through the management end Web interface to support administrators to intuitively adjust parameters; the template library management unit stores parameter combinations of typical scenarios, and administrators can directly call or modify and save them; the parameter storage unit stores the configured parameters in the database in an encrypted manner to ensure that the latest version is called each time.

[0019] In a preferred embodiment, the multi-factor calculation engine submodule realizes dynamic electricity cost allocation by integrating time-of-use electricity price, elevator ride time period and elevator load rate. The calculation process takes the total elevator electricity cost of a certain community in a certain month as input. First, the total electricity is divided into peak / off-peak periods and the electricity cost of each period is calculated; second, for each user, the user contribution value is calculated by weighting the time period coefficient and the load coefficient based on the number of elevator rides during peak / off-peak periods and the elevator load state (such as empty load or full load) during the rides; finally, the total electricity cost is allocated to each user in proportion to the proportion of all user contribution values. For example, the total electricity consumption of a certain community in July is 300 degrees, including 200 degrees during peak hours (8:00-22:00) and 100 degrees during off-peak hours (22:00-8:00). The local peak electricity price is 1.2 yuan / degree and the off-peak electricity price is 0.5 yuan / degree. User A rides the elevator 10 times during peak hours and 5 times during off-peak hours, with 3 full load rides (load coefficient 1.2) and the rest half load rides (load coefficient 1.0). User B rides the elevator 8 times during peak hours and 7 times during off-peak hours, with no full load ride record. The engine will complete the segmented calculation and final allocation based on these data

[0020] Let the total electricity cost be C total , the peak period electricity cost be C high , and the off-peak period electricity cost be C low :

[0021] C total =C high +C low ;

[0022] wherein C high =Ehigh × P high (E high peak time electricity E high × P low ), low C low = E low off-peak time electricity E low × P i ).

[0023] The contribution value Vi of user i is calculated by weighting the period coefficient and the load coefficient:

[0024] V high,i = n high × α low,i + n low × α full,i + n full × β high,i

[0025] In the formula, n high is the number of times of taking the elevator in the peak time of user i, α low,i is the peak time coefficient; n low is the number of times of taking the elevator in the off-peak time, α full,i is the off-peak time coefficient; n full is the number of times of taking the elevator when the elevator is full of load, and β i=1 is the full load coefficient.

[0026] The total contribution value Vtotal of all users is ∑ N Vi, N is the total number of users.

[0027] The electricity bill Ci that user i should share is:

[0028] ;

[0029] V total represents the sum of the contribution values of all users;

[0030] C i represents the electricity bill (yuan) that user i should share.

[0031] In a preferred embodiment, the result output verification submodule is internally provided with a numerical verification unit, a rationality verification unit, a detail generation unit, and an abnormality backtracking unit. The numerical verification unit ensures that the error is controlled within 0.1 yuan by calculating the difference between the sum of the electricity bills of all users and the total electricity bill; the rationality verification unit compares the electricity bill of a user this month with the historical data of the same period, and if the number of times a user takes the elevator increases by 50% but the electricity bill increases by only 10%, the abnormality is marked and manual review is triggered; the detail generation unit generates a user electricity bill allocation report based on the calculation process, including details such as elevator time period distribution, load participation times, coefficient weighting process, etc.; the abnormality backtracking unit records the full-process data of the algorithm calculation, and when the allocation result is complained by the user, the problem link can be quickly located.

[0032] In a preferred embodiment, the user interaction and service module is internally provided with a user end application, a management end background, a data display component, and an operation function component. The user end application takes APP or applet as the carrier, provides elevator ride times query, electricity bill detail viewing, including time period and load coefficient explanation, historical payment record tracing function, supports online recharge through WeChat and Alipay, electronic invoice download, and abnormal bill complaint; the management end background is developed based on Web, includes data dashboards such as community total electricity, unit electricity consumption ranking, and non-payment user proportion, supports time period division adjustment, such as holiday peak extension, load coefficient modification, such as increasing the full load coefficient of old elevators, and other strategy configurations, as well as remote recovery of user rights, batch export of electricity bill reports, such as Excel or PDF format, and other operations; the data display component visualizes user electricity consumption trends through line charts and bar charts, and the operation function component integrates message push interfaces to realize real-time notifications such as payment reminders and elevator ride abnormality prompts.

[0033] In a preferred embodiment, the intelligent control and early warning module is internally provided with a permission control submodule, an abnormality early warning submodule, and a predictive maintenance submodule. The permission control submodule is linked with the elevator recognition terminal to automatically disable the elevator ride permission of a user who has not paid for two consecutive times, such as no response when swiping the card, and please pay first when swiping the face, and the permission is restored in real time after payment; the abnormality early warning submodule sends electricity consumption abnormality and equipment failure warnings to the maintenance personnel by analyzing the electricity data, such as a sudden increase of more than 30% in daily electricity consumption, and the elevator state data, such as a vibration frequency of more than 5Hz and a duration of 10 seconds; the predictive maintenance submodule automatically generates a maintenance plan based on the historical operation data of the elevator, such as 100,000 times of door machine switching, and synchronizes it to the to-do list of the management end background, such as replacing the door machine bearing.

[0034] In summary, the present application includes at least one of the following beneficial technical effects:

[0035] 1. Through the cooperation of the dynamic intelligent allocation algorithm module and each functional module, the accuracy of electricity allocation is significantly improved. The data input integration sub-module standardizes and aligns the time dimension of multi-source data, such as unifying the card swiping ID and facial feature code into a unique user identifier, and aligning scattered power and elevator records by day / week / month cycles to avoid calculation deviations caused by data format confusion or time misalignment; the integrity checking unit fills in missing data through a supplementary recording mechanism to ensure the completeness and reliability of the basic data for algorithm invocation. These steps collectively ensure that the input data received by the multi-factor calculation engine is true and consistent, and the allocation result can more accurately reflect the user's actual electricity consumption behavior.

[0036] 2. The parameter configuration management sub-module subdivides the parameters involved in the algorithm into basic parameters (such as time-of-use electricity price), weight parameters (such as time period coefficient), and threshold parameters (such as abnormal fluctuation threshold), and supports administrators to intuitively adjust through a visual interface. For different elevator rules of residential and commercial office buildings, the "residential template" or "commercial office building template" in the template library can be called to quickly configure parameters without the need to redevelop algorithms; after template modification, it can be directly saved and reused, reducing the system adaptation cost in different scenarios, and enabling the allocation rules to be dynamically optimized according to user needs or external environment (such as electricity price adjustment).

[0037] 3. The result output verification sub-module ensures the credibility of the allocation result through a multi-dimensional verification mechanism: the numerical verification unit strictly controls the error of the sum of the total electricity fee and user allocation (allowing small deviations due to rounding), avoiding errors and omissions due to calculation flaws; the reasonableness verification unit compares historical allocation data of users, and if there is an abnormal situation of increased elevator trips but abnormal fee growth, it will trigger manual review to prevent result distortion due to data anomalies or parameter misconfiguration; the detail generation unit provides allocation reports containing key information such as elevator time period and load coefficient (e.g. "User A contributes 42% due to 10 peak elevator trips with weighted coefficient"), allowing users to clearly trace the cost composition; the abnormal backtracking unit records the data throughout the calculation process, allowing administrators to quickly locate problem areas (such as data input errors or improper parameter configuration) when users have doubts about the results, timely resolving disputes and enhancing user trust in the system. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 is the overall system block diagram of the present application;

[0039] Figure 2 is the dynamic intelligent allocation algorithm module system block diagram of the present application.

[0040] Explanation of reference signs: 1, intelligent data acquisition module; 2, secure transmission module; 3, big data storage and preprocessing module; 4, dynamic intelligent allocation algorithm module; 5, user interaction and service module; 6, intelligent control and early warning module; 7, data input integration sub-module; 8, parameter configuration management sub-module; 9, multi-factor calculation engine sub-module; 10, result output verification sub-module. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0042] The following will be described in detail below in combination with the drawings and examples. Figures 1-2 The present application will be further described in detail.

[0043] Reference will be made to Figure 1 An electric metering cloud platform for promoting electricity cost allocation of a lifting device, comprising an intelligent data acquisition module 1, a secure transmission module 2, a big data storage and preprocessing module 3, a dynamic intelligent allocation algorithm module 4, a user interaction and service module 5, and an intelligent control and early warning module 6;

[0044] The dynamic intelligent allocation algorithm module 4 is internally provided with a data input integration sub-module 7, a parameter configuration management sub-module 8, a multi-factor calculation engine sub-module 9, and a result output verification sub-module 10;

[0045] The output end of the intelligent data acquisition module 1 is connected to the input end of the secure transmission module 2;

[0046] The real-time data lake output end and the distributed database output end of the big data storage and preprocessing module 3 are respectively connected to the input end of the data input integration sub-module 7 of the dynamic intelligent allocation algorithm module 4;

[0047] The result output verification sub-module 10 output end of the dynamic intelligent allocation algorithm module 4 is connected to the input end of the user interaction and service module 5;

[0048] The management end background output end of the user interaction and service module 5 is connected to the input end of the parameter configuration management sub-module 8 of the dynamic intelligent allocation algorithm module 4;

[0049] The permission control sub-module input end of the intelligent control and early warning module 6 is connected to the user end application output end of the user interaction and service module 5, and the output end thereof is reversely connected to the elevator behavior recognition terminal input end of the intelligent data acquisition module 1;

[0050] The input end of the anomaly early warning submodule of the intelligent control and early warning module 6 is connected to the real-time data lake output end of the big data storage and preprocessing module 3, and the output end is connected to the management end background input end of the user interaction and service module 5.

[0051] The input end of the predictive maintenance submodule of the intelligent control and early warning module 6 is connected to the distributed database output end of the big data storage and preprocessing module 3, and the output end of the intelligent control and early warning module 6 is connected to the management end background input end of the user interaction and service module 5.

[0052] The internal part of the intelligent data acquisition module 1 is provided with an Internet of Things intelligent acquisition gateway, an electric quantity monitoring sensor, a lift behavior recognition terminal and an elevator state sensor. The Internet of Things intelligent acquisition gateway integrates edge computing function, supports Modbus, MQTT and other communication protocols, and serves as the connection hub of sensors and cloud platform; the electric quantity monitoring sensor is installed in the elevator power cabinet, and through the current transformer, it can acquire the total power consumption, voltage and current fluctuation data of the elevator in real time, with an accuracy of 0.1 degree; the lift behavior recognition terminal is deployed at the elevator hall door, and integrates an IC card reader, a 3D structured light camera and a semiconductor fingerprint sensor, supports non-contact identity recognition such as card swiping, face recognition and fingerprint recognition, records user lift time and direction, including up and down; the elevator state sensor includes a door magnetic sensor for monitoring the number of opening and closing doors, a temperature and humidity sensor for monitoring the motor compartment environment, and an acceleration sensor for monitoring the running stability of the elevator, covering the operation state acquisition of the core components of the elevator.

[0053] The internal part of the secure transmission module 2 is provided with a hybrid communication unit, a data encryption unit and a breakpoint resume unit. According to the signal difference between the elevator shaft and the ground area, the hybrid communication unit dynamically switches between high-bandwidth communication modes such as 5G, wide-coverage communication modes such as 4G, and low-power strong-penetration communication modes such as NB-IoT, to ensure the continuity of data transmission; the data encryption unit uses AES-256 algorithm to encrypt before data off-end, and the cloud platform end uses private key decryption to prevent data from being tampered with or leaked during transmission; the breakpoint resume unit caches the unuploaded data in the local gateway when the network is interrupted, and automatically supplements the transmission in time sequence after the network is restored, avoiding data loss.

[0054] The internal part of the big data storage and preprocessing module 3 is provided with a distributed database, a real-time data lake, a data preprocessing engine and a data mapping unit. The distributed database adopts HBase architecture, respectively stores historical power data containing time stamp and power value, user elevator record containing user ID, time and direction, elevator state log containing temperature, acceleration and door opening times, supports long-term storage of massive data; the real-time data lake is built based on Apache Kafka, caches 10-second-level real-time streaming data such as power fluctuation and elevator instantaneous record, for real-time calling by the algorithm module; the data preprocessing engine filters abnormal data such as power negative value and invalid record with elevator time exceeding 30 minutes through preset rules, and fills the missing values with mean value; the data mapping unit maps different sources of user identification such as ID number of card swiping and feature code of face recognition to UUID, ensuring the uniqueness and traceability of user behavior data.

[0055] The internal part of the data input integration sub-module 7 is provided with a data source interface unit, a data standardization unit, an integrity verification unit and a time dimension alignment unit. The data source interface unit is responsible for connecting the internal and external data channels of the system, including obtaining the cleaned elevator total power, user elevator record, elevator load rate and other structured data from the big data storage and preprocessing module, and receiving the administrator-defined time period division, coefficient weight and other configuration parameters from the user interaction and service module; the data standardization unit converts different formats of original data into a unified data model, such as mapping user ID in card swiping record and biological feature code in face recognition to unique user identification, and unifying the time stamp of power sensor and the time label of elevator record to hourly time granularity; the integrity verification unit checks data missing or abnormal through preset rules, such as triggering the recording mechanism when there is only power data but no elevator record in a certain period, calling the mean value of historical data to fill the missing values; the time dimension alignment unit re-aligns the power, elevator and load data scattered in different databases according to daily, weekly and monthly time periods, ensuring the consistency of data time dimension when calculating the algorithm.

[0056] The internal setting of the parameter configuration management submodule 8 is provided with a parameter type classification unit, a visual configuration unit, a template library management unit and a parameter storage unit. The parameter type classification unit divides the parameters involved in the algorithm into three categories of basic parameters, weight parameters and threshold parameters. The basic parameters include time-of-use electricity price (such as peak 1.2 yuan / degree, valley 0.5 yuan / degree), time period division (such as default peak 8:00-22:00) and other fixed values strongly related to external environment; the weight parameters include time period coefficient (such as peak elevator coefficient 1.5), load coefficient (such as full load elevator coefficient 1.2) and other adjustment values affecting the allocation ratio; the threshold parameters include abnormal electricity fluctuation threshold (such as single-day electricity surge exceeding 30%), user minimum effective elevator number (such as less than 2 times per month calculated as the basic value) and other risk control values. The visual configuration unit provides tools such as slide bar and time selector through the management end Web interface to support administrators to intuitively adjust parameters; the template library management unit stores parameter combinations of typical scenarios (such as residential area template, commercial office building template), and administrators can directly call or modify and save; the parameter storage unit stores the configured parameters in the database in encrypted form to ensure that the latest version is called each time.

[0057] The multi-factor calculation engine submodule 9 realizes dynamic electricity cost allocation by fusing three core dimensions of time-of-use electricity price, elevator use time period and elevator load rate. The calculation process takes the total elevator electricity cost of a certain community in a certain month as input, first splits the total electricity according to peak / valley time period and calculates the electricity cost of each time period; secondly, for each user, combines the elevator use times in peak / valley time period and the elevator load state during elevator use, and calculates the user contribution value by weighting the preset time period coefficient and load coefficient; finally, according to the proportion of all user contribution values, the total electricity cost is allocated to each user in proportion. For example, the total electricity consumption of a certain community in July is 300 degrees, of which 200 degrees is used in peak period (8:00-22:00) and 100 degrees is used in valley period (22:00-8:00), the local peak electricity price is 1.2 yuan / degree and the valley electricity price is 0.5 yuan / degree; user A uses the elevator 10 times in peak period and 5 times in valley period, and 3 times of elevator use is in full load state and the rest is in half load state; user B uses the elevator 8 times in peak period and 7 times in valley period, and has no full load elevator use record. The engine will complete segmented calculation and final allocation based on these data

[0058] Let the total electricity cost be C total , the peak period electricity cost C high and the valley period electricity cost C low :

[0059] C total =C high +C low ;

[0060] Wherein, C high=E high ×P high (peak period electricity E high times peak electricity price P high ), C low =E low ×P low (off-peak period electricity E low times off-peak electricity price P low ).

[0061] The contribution value Vi of user i is calculated by weighting the period coefficient and the load coefficient:

[0062] V i =n high,i ×α high +n low,i ×α low +n full,i ×β full

[0063] In the formula, n high,i is the number of times user i takes the elevator in the peak period, α high is the peak period coefficient; n low,i is the number of times user i takes the elevator in the off-peak period, α low is the off-peak period coefficient; n full,i is the number of times user i takes the elevator when it is full, β full is the full load coefficient.

[0064] The total contribution value Vtotal of all users is ∑ i=1 N Vi, N is the total number of users.

[0065] The electricity bill Ci that user i should share is:

[0066] ;

[0067] V total represents the sum of the contribution values of all users;

[0068] C i represents the electricity bill (yuan) that user i should share.

[0069] The internal setting of the result output verification submodule 10 is provided with a numerical verification unit, a rationality verification unit, a detail generation unit and an abnormal backtracking unit. The numerical verification unit ensures that the error is controlled within 0.1 yuan (a small deviation is allowed due to rounding) by calculating the difference between the sum of the electricity charges of all users and the total electricity charges; the rationality verification unit compares the electricity charges of a user this month with the historical data of the same period, and if the number of times a user takes the elevator increases by 50% but the electricity charges increase by only 10%, the abnormality is marked and manual review is triggered; the detail generation unit generates a user electricity charge allocation report based on the calculation process, including details such as elevator time period distribution, load participation times, coefficient weighting process, etc.; the abnormal backtracking unit records the full-process data of the algorithm calculation, and when the allocation result is complained by the user, the problem link can be quickly located.

[0070] The internal setting of the user interaction and service module 5 is provided with a user end application, a management end background, a data display component and an operation function component. The user end application takes APP or applet as the carrier, provides elevator ride times query and electricity charge detail viewing, including time period and load coefficient explanation, historical payment record tracing function, supports WeChat and Alipay online recharge, electronic invoice download and abnormal bill complaint; the management end background is developed based on Web, includes data dashboards such as total electricity of the community, electricity consumption ranking of each unit, proportion of non-payment users, supports time period division adjustment, for example, holiday peak extension, load coefficient modification, for example, increase of full load coefficient of old elevators, and other strategy configurations, as well as remote recovery of user rights, batch export of electricity charge report, such as Excel or PDF format, and other operations; the data display component visualizes user electricity consumption trend through line chart and column chart, and the operation function component integrates message push interface to realize real-time notification such as payment reminder and elevator abnormality prompt.

[0071] The internal setting of the intelligent control and early warning module 6 is provided with a permission control submodule, an abnormal early warning submodule and a predictive maintenance submodule. The permission control submodule is linked with the elevator recognition terminal to automatically disable the elevator ride permission of users who have not paid for two consecutive times, such as card swiping without response and face recognition prompting to pay first, and the permission is restored in real time after payment; the abnormal early warning submodule pushes electricity consumption abnormality and equipment failure warnings to maintenance personnel through analysis of electricity data, such as more than 30% of daily electricity consumption increase, and elevator state data, such as vibration frequency exceeding 5Hz and lasting for 10 seconds; the predictive maintenance submodule automatically generates maintenance plans based on historical elevator operation data, such as 100,000 times of door machine switching, combined with device life model, for example, replacing door machine bearings, and synchronizes to the to-do list of the management end background.

[0072] From the above, it can be seen that:

[0073] In the present application, through the cooperation of the dynamic intelligent allocation algorithm module and each functional module, the accuracy of electricity allocation is significantly improved. The data input integration sub-module standardizes and aligns the time dimension of multi-source data, such as unifying the card swiping ID and face feature code into a unique user identification, and aligning the scattered power and elevator records according to the daily / weekly / monthly cycle, to avoid calculation deviation caused by data format confusion or time misalignment; the integrity checking unit fills in the missing data through the supplementary recording mechanism to ensure the completeness and reliability of the basic data for algorithm calling. These links jointly ensure that the input data received by the multi-factor calculation engine is true and consistent, and the allocation result can more accurately reflect the actual electricity consumption behavior of the user.

[0074] In the present application, the parameter configuration management sub-module subdivides the parameters involved in the algorithm into basic parameters, weight parameters and threshold parameters, and supports intuitive adjustment by administrators through a visual interface. For different elevator rules of residential buildings and commercial office buildings, the "residential building template" or "commercial office building template" in the template library can be called to quickly configure parameters without the need to redevelop the algorithm; the template can be directly saved and reused after modification, reducing the system adaptation cost in different scenarios, and enabling the allocation rules to be dynamically optimized according to user needs or external environment (such as electricity price adjustment).

[0075] In the present application, the result output verification sub-module ensures the credibility of the allocation result through a multi-dimensional verification mechanism: the numerical checking unit strictly controls the error of the sum of the total electricity fee and user allocation (allowing a small deviation due to rounding), to avoid mistakes and omissions caused by calculation loopholes; the reasonableness verification unit compares historical allocation data of the user, and if there is an abnormal situation of increased elevator trips but abnormal fee growth, it will trigger manual review to prevent result distortion caused by data abnormalities or parameter misconfiguration; the detail generation unit provides an allocation report containing key information such as elevator time period and load coefficient (e.g. "User A contributes 42% due to 10 peak elevator trips with weighted coefficient"), which enables users to clearly trace the cost composition; the abnormal backtracking unit records the data of the entire calculation process, so that when the user has doubts about the result, the administrator can quickly locate the problem link (such as data input error or improper parameter configuration), solve the dispute in a timely manner, and enhance the user's trust in the system.

[0076] The above are preferred embodiments of the present application, which do not limit the protection scope of the present application, therefore: any equivalent changes made according to the structure, shape, principle of the present application shall be covered within the protection scope of the present application.

Claims

1. A watt-hour metering cloud platform for electricity cost allocation for hoisting devices, characterized by: Intelligent data acquisition module (1), safe transmission module (2), big data storage and preprocessing module (3), dynamic intelligent allocation algorithm module (4), user interaction and service module (5) and intelligent control and early warning module (6) are included; The dynamic intelligent allocation algorithm module (4) is internally provided with a data input integration sub-module (7), a parameter configuration management sub-module (8), a multi-factor calculation engine sub-module (9), and a result output verification sub-module (10); The output end of the intelligent data acquisition module (1) is connected to the input end of the safe transmission module (2); The real-time data lake output end and the distributed database output end of the big data storage and preprocessing module (3) are respectively connected to the data input integration sub-module (7) input end of the dynamic intelligent allocation algorithm module (4); The result output verification sub-module (10) output end of the dynamic intelligent allocation algorithm module (4) is connected to the input end of the user interaction and service module (5); The management end background output end of the user interaction and service module (5) is connected to the parameter configuration management sub-module (8) input end of the dynamic intelligent allocation algorithm module (4); The permission control sub-module input end of the intelligent control and early warning module (6) is connected to the user end application output end of the user interaction and service module (5), and the output end thereof is reversely connected to the elevator riding behavior recognition terminal input end of the intelligent data acquisition module (1); The abnormal early warning sub-module input end of the intelligent control and early warning module (6) is connected to the real-time data lake output end of the big data storage and preprocessing module (3), and the output end thereof is connected to the management end background input end of the user interaction and service module (5); The predictive maintenance sub-module input end of the intelligent control and early warning module (6) is connected to the distributed database output end of the big data storage and preprocessing module (3), and the output end of the intelligent control and early warning module (6) is connected to the management end background input end of the user interaction and service module (5).

2. The electricity metering cloud platform for electricity cost allocation for lift devices according to claim 1, wherein: The intelligent data acquisition module (1) is internally provided with an Internet of Things intelligent acquisition gateway, a power monitoring sensor, an elevator riding behavior recognition terminal and an elevator state sensor.

3. The electric energy metering cloud platform for electricity cost allocation for lifting devices according to claim 1, characterized in that: The safe transmission module (2) is internally provided with a hybrid communication unit, a data encryption unit and a breakpoint continuation unit.

4. The electric energy metering cloud platform for electricity cost allocation for lifting devices of claim 1, wherein: The big data storage and preprocessing module (3) is internally provided with a distributed database, a real-time data lake, a data preprocessing engine and a data mapping unit.

5. The electric energy metering cloud platform for electricity cost allocation for lift devices according to claim 1, characterized in that: The data input integration sub-module (7) is internally provided with a data source interface unit, a data standardization unit, an integrity verification unit and a time dimension alignment unit.

6. The electric energy metering cloud platform for electricity cost allocation for lift devices according to claim 1, wherein: The parameter configuration management sub-module (8) is internally provided with a parameter type classification unit, a visual configuration unit, a template library management unit and a parameter storage unit.

7. The electric energy metering cloud platform for electricity cost allocation for lift devices according to claim 1, characterized in that: The multi-factor calculation engine sub-module (9) is internally provided with a fusion time-of-use electricity price module, an elevator riding period module and an elevator load rate module. The total electricity fee calculation of the multi-factor calculation engine sub-module (9) includes: Let the total electricity cost be C total , which is composed of the peak period electricity cost C high and the off-peak period electricity cost C low . C total =C high +C low ; Where C high = E high × P high， peak period electricity E high times peak electricity price P high , C low = E low × P low， valley period electricity E low times valley electricity price P low ; The contribution value Vi of user i is calculated by weighting the time period coefficient and the load coefficient: V i =n high,i ×α high +n low,i ×α low +n full,i ×β full where n high,i is the number of trips by user i during peak hours, a high is the peak hours coefficient; n low,i is the number of trips by user i during off-peak hours, a low is the off-peak hours coefficient; n full,i is the number of full loads of the elevator during trips by user i, b full is the full load coefficient; Total contribution value of all users Vtotal =∑ i=1 N Vi, N is the total number of users; The electricity fee Ci that user i should allocate is: ； V total denotes the sum of all user contribution values; C i represents that the user i should share the electricity bill.

8. The electric energy metering cloud platform for electricity cost allocation for lift devices according to claim 1, characterized in that: The result output verification submodule (10) is internally provided with a numerical verification unit, a rationality verification unit, a detail generation unit and an exception backtracking unit.

9. The electric energy metering cloud platform for electricity cost allocation for lift devices according to claim 1, characterized in that: The user interaction and service module (5) is internally provided with a user end application, a management end background, a data display component and an operation function component.

10. The electric energy metering cloud platform for electricity cost allocation for lift devices according to claim 1, wherein: The intelligent control and early warning module (6) is internally provided with a permission control submodule, an exception early warning submodule and a predictive maintenance submodule, and the permission control submodule is connected with the elevator recognition terminal.

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