Electricity metering cloud platform for electricity cost allocation of lifting devices

Abstract

The application relates to a kilowatt-hour metering cloud platform for electricity cost allocation of a lifting device, which comprises an intelligent data acquisition module, a secure transmission module, a big data storage and preprocessing module, a dynamic intelligent allocation algorithm module, a user interaction and service module and an intelligent control and early warning module; the dynamic intelligent allocation 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; the application has the effect that the accuracy of electricity cost allocation is remarkably improved through the cooperation of the dynamic intelligent allocation algorithm module and various functional modules. The data input integration sub-module performs standardized processing and time dimension alignment on multi-source data, aligns the scattered power and elevator records according to daily / weekly / monthly cycles, avoids calculation deviation caused by data format confusion or time misalignment, and makes the allocation result more accurately reflect the effect of actual power consumption behavior of users.

Description

Technical Field

[0001] This application relates to the technical field of elevator cost metering, and in particular to a cloud platform for electricity metering for the apportionment of electricity costs for lifting devices. Background Technology

[0002] An elevator installation electricity cost-sharing system for older residential communities is a management mechanism designed to reasonably distribute the electricity costs of elevator operation after elevators are installed. Its core purpose is to improve residents' convenience while achieving fair, just, and transparent electricity cost sharing. This system typically combines property management platform data with smart meter data. Based on factors such as the frequency of use by residents on each floor, elevator operating hours, floor height, and number of residents, a scientific algorithm calculates the proportion of electricity costs each household should bear. Bills are then pushed and payments are settled through online payment platforms or the property management system. Some systems also support residents customizing the cost-sharing rules, such as tiered sharing by floor or equal sharing per household, to meet the specific circumstances of different communities and residents' needs. Through this system, not only can neighborhood disputes arising from electricity cost sharing be effectively reduced, but the level of intelligent community management can also be improved, providing strong support for the sustainable renovation of older residential communities.

[0003] However, existing technologies generally suffer from insufficient data integration capabilities in electricity cost allocation systems. Multi-source data often leads to calculation deviations due to inconsistent formats, misaligned time dimensions, or improper handling of missing values. The parameter configuration mechanism is rigid, making it difficult to quickly adjust allocation rules according to different scenarios. Algorithms often need to be redeveloped, resulting in high adaptation costs. Summary of the Invention

[0004] To address the problem of insufficient data integration capabilities in existing electricity cost sharing systems, this application provides an electricity metering cloud platform for improving electricity cost sharing for devices.

[0005] This application provides an electricity metering cloud platform for allocating electricity costs for lifting devices, which adopts the following technical solution: including an intelligent data acquisition module, a secure transmission module, a big data storage and preprocessing module, a dynamic intelligent allocation algorithm module, a user interaction and service module, and an intelligent control and early warning module;

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

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

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

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

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

[0011] The input terminal of the access control submodule of the intelligent control and early warning module is connected to the user application output terminal of the user interaction and service module, and its output terminal is connected in reverse to the input terminal of the elevator behavior recognition terminal of the intelligent data acquisition module.

[0012] The input end of the abnormal early warning submodule 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 is connected to the management backend input end of the user interaction and service module.

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

[0014] In a preferred embodiment, the intelligent data acquisition module internally includes an IoT intelligent acquisition gateway, a power monitoring sensor, a passenger behavior recognition terminal, and an elevator status sensor. The IoT intelligent acquisition gateway integrates edge computing capabilities, supports communication protocols such as Modbus and MQTT, and serves as a connection hub between the sensors and the cloud platform. The power monitoring sensor is installed in the elevator power cabinet, collecting real-time data on the elevator's total power consumption, voltage, and current fluctuations via a current transformer, with an accuracy of up to 0.1 kWh. The passenger behavior recognition terminal is deployed in the elevator hall door, integrating an IC card reader, a 3D structured light camera, and a semiconductor fingerprint sensor, supporting contactless identification methods such as card swiping, facial recognition, and fingerprint scanning, and recording the user's elevator travel time and direction, including going up and down stairs. The elevator status sensor includes a door magnetic sensor to monitor the number of door openings and closings, a temperature and humidity sensor to monitor the motor compartment environment, and an acceleration sensor to monitor the elevator's operational stability, covering the operational status collection of the elevator's core components.

[0015] In a preferred embodiment, the secure transmission module internally includes a hybrid communication unit, a data encryption unit, and a breakpoint resume unit. The hybrid communication unit dynamically switches between high-bandwidth communication methods such as 5G, wide-coverage communication methods such as 4G, and low-power, high-penetration communication methods such as NB-IoT, based on the signal differences between the elevator shaft and the ground area, ensuring continuous data transmission. The data encryption unit encrypts data using the AES-256 algorithm before it leaves the terminal, and decrypts it using a private key on the cloud platform, preventing data tampering or leakage during transmission. The breakpoint resume unit has a built-in storage chip in the local gateway that caches unuploaded data when the network is interrupted and automatically re-uploads it in chronological order after the network is restored, avoiding data loss.

[0016] In a preferred embodiment, the big data storage and preprocessing module internally includes a distributed database, a real-time data lake, a data preprocessing engine, and a data mapping unit. The distributed database uses an HBase architecture to store historical electricity consumption data (including timestamps and electricity values), user elevator ride records (including user ID, time, and direction), and elevator status logs (including temperature, acceleration, and door opening / closing counts), supporting long-term storage of massive amounts of data. The real-time data lake is built on Apache Kafka, caching 10-second-level real-time streaming data, such as electricity fluctuations and instantaneous elevator ride records, for real-time access by the algorithm module. The data preprocessing engine filters out abnormal data using preset rules, such as negative electricity values ​​or invalid records with ride times exceeding 30 minutes, and fills in missing values ​​with the mean. The data mapping unit maps user identifiers from different sources, such as card swipe IDs and facial recognition feature codes, to a unified UUID, ensuring the uniqueness and traceability of user behavior data.

[0017] In a preferred embodiment, the data input integration submodule internally includes 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 to internal and external data channels, including obtaining cleaned structured data such as total elevator power consumption, user elevator ride records, and elevator load rate from the big data storage and preprocessing module, and receiving administrator-defined configuration parameters such as time period divisions and coefficient weights from the user interaction and service module. The data standardization unit converts raw data of different formats into a unified data model, for example, mapping user IDs in card swipe records and biometric codes from facial recognition to unique user identifiers, and unifying the timestamps of power sensors and elevator ride records to hourly time granularity. The integrity verification unit checks for missing or abnormal data using preset rules; for example, if there is only power consumption data but no elevator ride records for a certain period, a supplementary recording mechanism is triggered, calling the average of historical data from the same period to fill in the missing values. The time dimension alignment unit realigns power consumption, elevator ride, and load data scattered across different databases according to daily, weekly, and monthly time periods to ensure consistency in the time dimension of the data during algorithm calculations.

[0018] In a preferred embodiment, the parameter configuration management submodule internally includes a parameter type classification unit, a visual configuration unit, a template library management unit, and a parameter storage unit. The parameter type classification unit categorizes the parameters involved in the algorithm into three types: basic parameters, weight parameters, and threshold parameters. Basic parameters include fixed values ​​strongly correlated with the external environment, such as time-of-use electricity pricing (e.g., peak 1.2 yuan / kWh, off-peak 0.5 yuan / kWh) and time period divisions (e.g., default peak 8:00-22:00). Weight parameters include adjustment values ​​affecting the sharing ratio, such as time period coefficients and load coefficients. Threshold parameters include risk control values ​​such as abnormal electricity fluctuation thresholds and minimum effective elevator rides for users. The visual configuration unit provides tools such as sliders and time selectors through the management web interface, allowing administrators to intuitively adjust parameters. The template library management unit stores parameter combinations for typical scenarios, which administrators can directly call or modify and save. The parameter storage unit encrypts and stores the configured parameters in the database, ensuring that the latest version is used for each calculation.

[0019] In a preferred embodiment, the multi-factor calculation engine submodule achieves dynamic electricity cost allocation by integrating three core dimensions: time-of-use electricity pricing, elevator usage time, and elevator load rate. Its calculation process takes the total elevator electricity cost for a certain community in a certain month as input. First, the total electricity consumption is divided into peak / off-peak periods, and the electricity cost for each period is calculated. Second, for each user, based on the number of elevator rides during peak / off-peak periods and the elevator load status (e.g., empty, fully loaded) at the time of ride, a user contribution value is calculated using a preset time period coefficient and load coefficient weighting. Finally, based on the proportion of each user's contribution value, the total electricity cost is proportionally allocated to each user. For example, in July, the total electricity consumption of elevators in a certain residential community was 300 kWh, with 200 kWh consumed during peak hours (8:00-22:00) and 100 kWh during off-peak hours (22:00-8:00). The local peak electricity price was 1.2 yuan / kWh, and the off-peak price was 0.5 yuan / kWh. User A used the elevator 10 times during peak hours and 5 times during off-peak hours, with the elevator being fully loaded 3 times (load factor 1.2) and half-loaded the rest of the time (load factor 1.0). User B used the elevator 8 times during peak hours and 7 times during off-peak hours, with no records of fully loaded elevator use. The engine will use this data to perform segmented calculations and final allocation.

[0020] Let the total electricity cost be C. total Electricity charges during peak hours (C) high Electricity charges during off-peak hours C low composition:

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

[0022] Among them, C high =Ehigh ×P high (Peak period electricity consumption E) high Multiply by peak electricity price P high ), C low =E low ×P low (E of electricity consumption during off-peak hours) low Multiply by the off-peak electricity price P low ).

[0023] User i's contribution value Vi is calculated by weighting the time period coefficient and the load coefficient:

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

[0025] In the formula, n high,i α represents the number of times user i takes the elevator during peak hours. high n is the peak period coefficient; low,i α represents the number of elevator rides during off-peak hours. low This is the coefficient for the trough period; n full,i β represents the number of times the elevator is fully loaded when user i takes the elevator. full This represents the full load factor.

[0026] Total contribution value of all users Vtotal = ∑ i=1 N Vi, where N is the total number of users.

[0027] User i should share the electricity cost C i for:

[0028] ;

[0029] V total This represents the sum of all user contributions.

[0030] C i This indicates the electricity cost (in yuan) that user i should share.

[0031] In a preferred embodiment, a numerical verification unit, a rationality verification unit, a detail generation unit, and an exception tracing unit are provided inside the result output verification sub-module. The numerical verification unit calculates the difference between the sum of the electricity bills shared by all users and the total electricity bill, ensuring that the error is controlled within 0.1 yuan; the rationality verification unit compares the electricity bills shared by users this month with the historical data of the same period. If the number of elevator rides of a certain user increases by 50% but the shared electricity bill only increases by 10%, an exception is marked and manual review is triggered; the detail generation unit generates a user electricity bill sharing report based on the calculation process, including detailed information such as the distribution of elevator ride time periods, the number of load participation times, and the coefficient weighting process; the exception tracing unit records all process data calculated by the algorithm. When the sharing result is appealed by the user, the problem link can be quickly located.

[0032] In a preferred embodiment, a user-side application, a management-side background, a data display component, and an operation function component are provided inside the user interaction and service module. The user-side application uses an APP or a small program as a carrier, providing functions such as elevator ride count query, electricity bill detail viewing (including time period and load coefficient explanations), historical payment record tracing, supporting online recharge via WeChat and Alipay, electronic invoice downloading, and abnormal bill appeal; the management-side background is developed based on the Web, including data dashboards such as the total electricity consumption of the community, the electricity consumption ranking of each unit, and the proportion of unpaid users, supporting strategies configuration such as time period division adjustment (such as extending peak hours during holidays) and load coefficient modification (such as increasing the full load coefficient of old elevators), as well as operations such as remotely restoring user permissions and batch exporting electricity bill reports in formats such as Excel or PDF; the data display component visualizes the user's electricity consumption trend through line charts and bar charts, and the operation function component integrates a message push interface to achieve real-time notifications such as payment reminders and elevator ride exceptions.

[0033] In a preferred embodiment, a permission control sub-module, an exception warning sub-module, and a predictive maintenance sub-module are provided inside the intelligent control and warning module. The permission control sub-module is linked with the elevator ride identification terminal to automatically disable the elevator ride permission for users who have not paid the fee continuously twice (such as no response when swiping the card, or a face recognition prompt to pay the fee first), and the permission is restored in real time after payment; the exception warning sub-module analyzes the electricity consumption data and the elevator status data. When the single-day electricity consumption suddenly increases by more than 30% and the vibration frequency exceeds 5 Hz and lasts for 10 seconds, it respectively pushes electricity consumption exception and equipment failure warnings to the operation and maintenance personnel; the predictive maintenance sub-module generates a maintenance plan (such as replacing the door machine bearing) automatically based on the elevator's historical operation data (such as the number of door machine switch operations reaching 100,000 times) in combination with the equipment life model, and synchronizes it to the to-do list in the management-side background.

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

[0035] 1. Through the collaboration of the dynamic intelligent allocation algorithm module and various functional modules, the accuracy of electricity cost allocation is significantly improved. The data input integration submodule standardizes and aligns multi-source data across time dimensions. For example, it unifies card swipe IDs and facial recognition codes into unique user identifiers and aligns scattered electricity consumption and elevator usage records by daily / weekly / monthly cycles, avoiding calculation deviations caused by data format confusion or time misalignment. The integrity verification unit fills in missing data through a supplementary recording mechanism, ensuring the integrity and reliability of the basic data called by the algorithm. These steps together ensure that the input data received by the multi-factor calculation engine is accurate and consistent, and the allocation results can more accurately reflect users' actual electricity consumption behavior.

[0036] 2. The parameter configuration management submodule breaks down the parameters involved in the algorithm into basic parameters (such as time-of-use electricity pricing), weight parameters (such as time-of-use coefficients), and threshold parameters (such as abnormal fluctuation thresholds), and allows administrators to intuitively adjust them through a visual interface. For different elevator usage patterns in residential communities and commercial office buildings, parameters can be quickly configured by calling the "Residential Community Template" or "Commercial Office Building Template" from the template library, without the need to redevelop the algorithm. Modified templates can be directly saved and reused, reducing system adaptation costs for different scenarios and enabling the allocation rules to be dynamically optimized according to user needs or external environments (such as electricity price adjustments).

[0037] 3. The result output verification submodule ensures the credibility of the allocation results through a multi-dimensional verification mechanism: The numerical verification unit strictly controls the error between the total electricity cost and the sum of the user's allocation (allowing minor deviations due to rounding) to avoid cost errors caused by calculation loopholes; the reasonableness verification unit compares the user's historical allocation data, and if there is an abnormal increase in the number of elevator rides but an abnormal increase in cost, it will trigger manual review to prevent the results from being distorted due to data anomalies or parameter misconfigurations; the detail generation unit provides an allocation report containing key information such as elevator ride time and load factor (e.g., "User A took the elevator 10 times during peak hours, and the contribution value increased by 42% after weighting"), allowing users to clearly trace the cost composition; the anomaly backtracking unit records the data of the entire calculation process, so that when users have questions about the results, administrators can quickly locate the problem link (such as data input errors or improper parameter configuration), resolve disputes in a timely manner, and enhance users' trust in the system. Attached Figure Description

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

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

[0040] Explanation of reference numerals in the attached diagram: 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 submodule; 8. Parameter configuration management submodule; 9. Multi-factor calculation engine submodule; 10. Result output verification submodule. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0042] The following is in conjunction with the appendix Figures 1-2 This application will be described in further detail.

[0043] See Figure 1 A cloud platform for electricity metering for improving the allocation of electricity costs for equipment includes 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 has the following internal components: data input integration submodule 7, parameter configuration management submodule 8, multi-factor calculation engine submodule 9, and result output verification submodule 10.

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

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

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

[0048] The management backend output of the user interaction and service module 5 is connected to the input of the parameter configuration management submodule 8 of the dynamic intelligent allocation algorithm module 4.

[0049] The input end of the access control submodule of the intelligent control and early warning module 6 is connected to the user application output end of the user interaction and service module 5, and its output end is connected in reverse to the input end of the elevator behavior recognition terminal of the intelligent data acquisition module 1.

[0050] The input end of the abnormal 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 backend input end of the user interaction and service module 5.

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

[0052] The intelligent data acquisition module 1 internally includes an IoT intelligent acquisition gateway, a power monitoring sensor, a passenger behavior recognition terminal, and an elevator status sensor. The IoT intelligent acquisition gateway integrates edge computing capabilities, supports communication protocols such as Modbus and MQTT, and serves as a connection hub between the sensors and the cloud platform. The power monitoring sensor is installed in the elevator power cabinet, collecting real-time data on the elevator's total power consumption, voltage, and current fluctuations via a current transformer, with an accuracy of up to 0.1 kWh. The passenger behavior recognition terminal is deployed at the elevator hall door, integrating an IC card reader, a 3D structured light camera, and a semiconductor fingerprint sensor, supporting contactless identification methods such as card swiping, facial recognition, and fingerprint scanning, and recording user passenger time and direction, including going up and down stairs. The elevator status sensors include a door magnetic sensor to monitor the number of door openings and closings, a temperature and humidity sensor to monitor the motor compartment environment, and an acceleration sensor to monitor the elevator's operational stability, covering the operational status data of the elevator's core components.

[0053] The secure transmission module 2 internally includes a hybrid communication unit, a data encryption unit, and a breakpoint resume unit. The hybrid communication unit dynamically switches between high-bandwidth communication methods such as 5G, wide-coverage communication methods such as 4G, and low-power, high-penetration communication methods such as NB-IoT, based on the signal differences between the elevator shaft and the ground area, ensuring continuous data transmission. The data encryption unit encrypts data using the AES-256 algorithm before it leaves the terminal, and the cloud platform uses a private key for decryption, preventing data tampering or leakage during transmission. The breakpoint resume unit has a built-in storage chip in the local gateway that caches unuploaded data when the network is interrupted and automatically re-uploads it in chronological order after the network is restored, avoiding data loss.

[0054] The Big Data Storage and Preprocessing Module 3 internally comprises a distributed database, a real-time data lake, a data preprocessing engine, and a data mapping unit. The distributed database, built on an HBase architecture, stores historical electricity consumption data (including timestamps and electricity values), user elevator ride records (including user ID, time, and direction), and elevator status logs (including temperature, acceleration, and door opening / closing counts), supporting long-term storage of massive amounts of data. The real-time data lake, built on Apache Kafka, caches 10-second-level real-time streaming data, such as electricity fluctuations and instantaneous elevator ride records, for real-time use by the algorithm module. The data preprocessing engine filters out abnormal data using preset rules, such as negative electricity values ​​or invalid records with ride times exceeding 30 minutes, and fills in missing values ​​with the mean. The data mapping unit maps user identifiers from different sources, such as card swipe IDs and facial recognition feature codes, to UUIDs, ensuring the uniqueness and traceability of user behavior data.

[0055] The data input integration submodule 7 internally includes 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 to internal and external data channels, including obtaining cleaned structured data such as total elevator power consumption, user elevator ride records, and elevator load rate from the big data storage and preprocessing module, and receiving administrator-defined configuration parameters such as time period divisions and coefficient weights from the user interaction and service module. The data standardization unit converts raw data in different formats into a unified data model; for example, it maps user IDs from card swipe records and biometric codes from facial recognition to unique user identifiers, and unifies the timestamps of power sensors and elevator ride records to hourly time granularity. The integrity verification unit checks for missing or abnormal data using preset rules; for example, if there is only power consumption data but no elevator ride records for a certain period, it triggers a supplementary recording mechanism, calling the average of historical data from the same period to fill in the missing values. The time dimension alignment unit realigns power consumption, elevator ride, and load data scattered across different databases according to daily, weekly, and monthly time periods to ensure consistency in the time dimension of the data during algorithm calculations.

[0056] The parameter configuration management submodule 8 is internally configured 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: basic parameters, weight parameters, and threshold parameters. Basic parameters include fixed values ​​that are strongly correlated with the external environment, such as time-of-use electricity pricing (e.g., peak price 1.2 yuan / kWh, off-peak price 0.5 yuan / kWh) and time period division (e.g., default peak 8:00-22:00). Weight parameters include adjustment values ​​that affect the sharing ratio, such as time period coefficients (e.g., peak elevator coefficient 1.5) and load coefficients (e.g., full load elevator coefficient 1.2). Threshold parameters include risk control values ​​such as abnormal electricity fluctuation thresholds (e.g., a single day's electricity consumption suddenly increases by more than 30%) and the minimum number of effective elevator rides per user (e.g., less than 2 times per month, calculated based on the basic value). The visual configuration unit provides tools such as sliders and time selectors through the management web interface, allowing administrators to intuitively adjust parameters; the template library management unit stores parameter combinations for typical scenarios (such as residential templates and commercial office building templates), which administrators can directly call or modify and save; the parameter storage unit encrypts and stores the configured parameters in the database to ensure that the latest version is called every time a calculation is performed.

[0057] The multi-factor calculation engine submodule 9 achieves dynamic electricity cost allocation by integrating three core dimensions: time-of-use electricity pricing, elevator usage time, and elevator load rate. Its calculation process takes the total elevator electricity cost for a given month in a residential community as input. First, it breaks down the total electricity consumption into peak / off-peak periods and calculates the electricity cost for each period. Second, for each user, it calculates their contribution value by weighting the number of elevator rides during peak / off-peak periods and the elevator load status at those times using preset time-of-use and load coefficients. Finally, based on the proportion of each user's contribution value, the total electricity cost is proportionally allocated to each user. For example, in July, the total electricity consumption of elevators in a certain residential community was 300 kWh, with 200 kWh consumed during peak hours (8:00-22:00) and 100 kWh during off-peak hours (22:00-8:00). The local peak electricity price was 1.2 yuan / kWh, and the off-peak price was 0.5 yuan / kWh. User A used the elevator 10 times during peak hours and 5 times during off-peak hours, with the elevator being fully loaded 3 times and partially loaded the rest. User B used the elevator 8 times during peak hours and 7 times during off-peak hours, with no records of fully loaded elevator use. The engine will use this data to perform segmented calculations and final allocation.

[0058] Let the total electricity cost be C. total Electricity charges during peak hours (C) high Electricity charges during off-peak hours C low composition:

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

[0060] Among them, C high=E high ×P high (Peak period electricity consumption E) high Multiply by peak electricity price P high ), C low =E low ×P low (E of electricity consumption during off-peak hours) low Multiply by the off-peak electricity price P low ).

[0061] User i's contribution value Vi is calculated by weighting the time 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 α represents the number of times user i takes the elevator during peak hours. high n is the peak period coefficient; low,i α represents the number of elevator rides during off-peak hours. low This is the coefficient for the trough period; n full,i β represents the number of times the elevator is fully loaded when user i takes the elevator. full This represents the full load factor.

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

[0065] User i should share the electricity cost C i for:

[0066] ;

[0067] V total This represents the sum of all user contributions.

[0068] C i This indicates the electricity cost (in yuan) that user i should share.

[0069] The output verification submodule 10 internally includes a numerical verification unit, a rationality verification unit, a detail generation unit, and an anomaly backtracking unit. The numerical verification unit calculates the difference between the sum of all users' allocated electricity costs and the total electricity cost, ensuring the error is controlled within 0.1 yuan (due to rounding, slight deviations are allowed). The rationality verification unit compares the user's allocated electricity cost for the current month with historical data from the same period. If a user's elevator usage increases by 50% but their allocated electricity cost only increases by 10%, an anomaly is flagged, triggering manual review. The detail generation unit generates a user electricity cost allocation report based on the calculation process, including detailed information such as elevator usage time distribution, load participation frequency, and coefficient weighting process. The anomaly backtracking unit records the entire algorithm calculation process data, allowing for rapid identification of the problem when users appeal the allocation results.

[0070] The User Interaction and Service Module 5 includes a user-side application, a management backend, a data display component, and operational function components. The user-side application, hosted as an app or mini-program, provides services such as elevator usage frequency queries, electricity bill details viewing (including time period and load factor explanations), historical payment record tracing, and supports online top-ups via WeChat and Alipay, electronic invoice downloads, and appeals for abnormal bills. The management backend, developed based on a web platform, includes data dashboards showing total electricity consumption for the community, electricity consumption rankings for each unit, and the percentage of users with unpaid bills. It supports adjustments to time period divisions (e.g., extending peak hours during holidays), modifications to load factors (e.g., increasing the full load factor for older elevators), and remote restoration of user permissions, as well as batch export of electricity bill reports (e.g., Excel or PDF formats). The data display component visualizes user electricity consumption trends using line charts and bar charts. The operational function components integrate a message push interface to provide real-time notifications such as payment reminders and elevator usage anomaly alerts.

[0071] The intelligent control and early warning module 6 internally includes an access control submodule, an anomaly early warning submodule, and a predictive maintenance submodule. The access control submodule works in conjunction with the elevator access terminal, automatically disabling access for users who fail to pay twice consecutively. If there is no response when swiping a card or a facial recognition prompt asks for payment, access is restored immediately after payment. The anomaly early warning submodule analyzes power consumption data; if a daily power consumption spike exceeds 30%, and elevator status data shows a vibration frequency exceeding 5Hz for 10 seconds, it pushes alerts to maintenance personnel for power anomalies and equipment malfunctions, respectively. The predictive maintenance submodule uses historical elevator operating data, such as door operator opening and closing times reaching 100,000 times, combined with an equipment lifespan model to automatically generate maintenance plans, such as replacing door operator bearings, and synchronizes these plans to the to-do list in the management backend.

[0072] From the above, we can conclude that:

[0073] In this invention, the accuracy of electricity cost allocation is significantly improved through the collaboration of the dynamic intelligent allocation algorithm module and various functional modules. The data input integration submodule standardizes and aligns multi-source data across time dimensions. For example, it unifies card swipe IDs and facial recognition codes into unique user identifiers and aligns scattered electricity consumption and elevator usage records by daily / weekly / monthly cycles, avoiding calculation deviations caused by data format confusion or time misalignment. The integrity verification unit fills in missing data through a supplementary recording mechanism, ensuring the integrity and reliability of the basic data used by the algorithm. These steps together guarantee the authenticity and consistency of the input data received by the multi-factor calculation engine, enabling the allocation results to more accurately reflect users' actual electricity consumption behavior.

[0074] In this invention, the parameter configuration management submodule subdivides the parameters involved in the algorithm into basic parameters, weight parameters, and threshold parameters, and allows administrators to intuitively adjust them through a visual interface. For different elevator usage patterns in residential communities and commercial office buildings, parameters can be quickly configured by calling the "Residential Community Template" or "Commercial Office Building Template" from the template library, without the need to redevelop the algorithm. Modified templates can be directly saved and reused, reducing system adaptation costs for different scenarios and enabling the allocation rules to be dynamically optimized according to user needs or external environments (such as electricity price adjustments).

[0075] In this invention, the result output verification submodule ensures the credibility of the allocation results through a multi-dimensional verification mechanism: the numerical verification unit strictly controls the error between the total electricity cost and the sum of the user's allocation (allowing minor deviations due to rounding) to avoid cost errors caused by calculation loopholes; the rationality verification unit compares the user's historical allocation data, and if there is an abnormal increase in the number of elevator rides but an abnormal increase in cost, it will trigger manual review to prevent result distortion caused by data anomalies or parameter misconfiguration; the detail generation unit provides an allocation report containing key information such as elevator ride time and load factor (e.g., "User A took the elevator 10 times during peak hours, and the contribution value increased by 42% after weighting"), allowing users to clearly trace the cost composition; the anomaly backtracking unit records the data of the entire calculation process, so that when users have questions about the results, administrators can quickly locate the problem link (such as data input errors or improper parameter configuration), resolve disputes in a timely manner, and enhance users' trust in the system.

[0076] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A watt-hour metering cloud platform system 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); The multi-factor calculation engine sub-module (9) realizes dynamic electricity cost allocation by fusing three core dimensions of time-of-use electricity price, elevator riding period and elevator load rate. The calculation process takes the total elevator electricity cost of a certain community in a certain month as input. Firstly, the total electricity quantity is split according to peak / valley periods and the electricity cost of each period is calculated. Secondly, for each user, the user contribution value is calculated by weighting the preset period coefficient and load coefficient according to the elevator riding times in peak / valley periods and the elevator load state during the elevator riding. Finally, the total electricity cost is allocated to each user according to the proportion of all user contribution values.

2. The electric charge metering cloud platform system for electricity cost allocation for lifting devices according to claim 1, characterized in that: The intelligent data acquisition module (1) is internally provided with an Internet of Things intelligent acquisition gateway, an electricity quantity monitoring sensor, an elevator riding behavior recognition terminal and an elevator state sensor.

3. The electric energy metering cloud platform system 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 resume transmission unit.

4. The electric charge metering cloud platform system for electricity cost allocation for lifting devices according to claim 1, characterized in that: 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 charge metering cloud platform system for electricity cost allocation for lifting devices according to claim 1, characterized in that: The data input integration submodule (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 charge metering cloud platform system for electricity cost allocation for lifting devices according to claim 1, characterized in that: The parameter configuration management submodule (8) is internally provided with a parameter type classification unit, a visualization configuration unit, a template library management unit and a parameter storage unit.

7. The electric charge metering cloud platform system for electricity cost allocation for lifting devices according to claim 1, characterized in that: The multi-factor calculation engine submodule (9) is internally provided with a fusion time-of-use electricity price module, a lift boarding period module and an elevator load rate module. The total electricity fee calculation of the multi-factor calculation engine submodule (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 the user i is calculated by weighting the time period coefficient and the load coefficient: V i = n high,i x a high + n low,i x a low + n full,i x b 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; User i should share the electricity bill C i 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 charge metering cloud platform system for electricity cost allocation for lifting devices according to claim 1, characterized in that: The result output verification submodule (10) is internally provided with a numerical verification unit, a reasonableness verification unit, a detail generation unit and an abnormality backtracking unit.

9. The electric charge metering cloud platform system for electricity cost allocation for lifting 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 charge metering cloud platform system for electricity cost allocation for lifting devices according to claim 1, characterized in that: The intelligent control and early warning module (6) is internally provided with a permission control submodule, an abnormality early warning submodule and a predictive maintenance submodule, and the permission control submodule is connected with the lift boarding recognition terminal.

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