Intelligent heat supply charging method and system based on multi-dimensional data

Abstract

The invention provides an intelligent heat supply charging method and system based on multi-dimensional data, and relates to the technical field of heat supply management, and the method comprises the steps: receiving a house building area and heat use state selection instruction sent by a user, so as to determine an area step coefficient and a heat use state coefficient, the dynamic basic cost is calculated by combining the house building area, the unit area reference heat supply cost and the number of heat supply days; the accumulated heat consumption amount and the indoor temperature setting interval of the user in each unit time period are obtained; the temperature coefficient of the indoor temperature is determined according to the indoor temperature setting interval in the target unit time period, and the dynamic heat consumption cost of the target unit time period is calculated in combination with the accumulated heat consumption amount in the target unit time period and the billing unit price of the heat supply time period; and calculating a heat supply expense bill of the user based on the dynamic basic expense and the dynamic heat consumption expense of all unit time periods in the heat supply period. The charging fairness and the cost matching degree can be improved, users are guided to use heat in an off-peak mode, temperature is controlled moderately, and the energy-saving excitation effect is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of heat supply management, and particularly relates to an intelligent heat supply charging method and system based on multi-dimensional data. BACKGROUND

[0002] In the field of central heating, the most widely used charging scheme in the industry is to use "heat meter + two-part heat price", that is, the heat supply cost is composed of "basic cost + metering cost". Among them, the basic cost is usually collected based on the fixed unit price of building area, which is used to allocate fixed costs such as pipeline maintenance and load reservation; the metering cost is calculated according to the cumulative heat consumption collected by the heat meter multiplied by the metering unit price, which reflects the actual heat consumption. However, the above charging method has the following outstanding problems: 1. Single measurement dimension: only cumulative heat consumption is used as the basis for charging, without considering the differences in individual heat consumption behaviors such as indoor temperature setting and heat consumption period, resulting in charging that cannot truly reflect heat demand and cost expenditure.

[0003] 2. Unfair basic cost: using a unified area unit price, without distinguishing the actual load and maintenance cost differences of large and small house types on the pipe network.

[0004] 3. Lack of energy-saving incentives: the fixed unit price mechanism is difficult to guide users to peak-shaving or actively save energy, which is not consistent with the goal of energy saving and consumption reduction.

[0005] Therefore, there is an urgent need for a new type of heat supply charging method that is more intelligent, fair and energy-saving oriented. SUMMARY

[0006] The present application aims to provide an intelligent heat supply charging method and system based on multi-dimensional data, to solve the problem of large house type dominance in the traditional fixed unit price mode, significantly improve the fairness of charging and the cost matching degree, and guide users to peak-shaving, moderate temperature control, and enhance the energy-saving incentive effect.

[0007] In a first aspect, the present invention provides an intelligent heating billing method based on multi-dimensional data, comprising: receiving a user's instruction to select the building area and heating status; wherein the heating status includes: normal heating, low-temperature supply, and complete heating shutdown; determining the area tier coefficient and heating status coefficient according to the building area and heating status selection instruction, and calculating the dynamic basic cost in combination with the building area, the benchmark heating cost per unit area, and the number of heating days; obtaining the user's cumulative heat consumption and indoor temperature setting range in each unit time period; determining the temperature coefficient according to the indoor temperature setting range in the target unit time period, and calculating the dynamic heating cost for the target unit time period in combination with the cumulative heat consumption in the target unit time period and the billing unit price of the heating period; wherein the target unit time period represents any unit time period within the heating cycle; the heating period includes: peak period and off-peak period; and calculating the user's heating bill based on the dynamic basic cost and the dynamic heating cost of all unit time periods within the heating cycle.

[0008] In an optional implementation, after determining the user's heating bill, the method further includes: receiving all historical data after the end of the heating season and calculating the cumulative heat consumption of each user during the heating cycle; calculating the average heat consumption of a user group of the same type based on the cumulative heat consumption of each user during the heating cycle; wherein, a user group of the same type represents a set of users with the same building area and the same heating status; determining whether the current user's cumulative heat consumption during the heating cycle is lower than a preset proportion of the average heat consumption of its user group of the same type; if so, generating a corresponding energy-saving subsidy for the current user and generating a heating settlement fee after deducting the corresponding amount from its heating bill.

[0009] In an optional implementation, generating a corresponding energy-saving subsidy for the current user includes: calculating the difference between the average heat consumption of the current user's group of users of the same type and its cumulative heat consumption during the heating cycle to obtain a heat consumption difference; and multiplying the heat consumption difference by the billing unit price during off-peak hours according to a preset incentive ratio to obtain the energy-saving subsidy for the current user.

[0010] In an optional implementation, after calculating the dynamic base fee, the method further includes: pushing a prepaid bill to the user; wherein the amount of the prepaid bill is a specified percentage of the dynamic base fee; after heating begins, monitoring the user's payment status and sending reminder notices to users who have not paid on time through various communication channels; if payment is not completed within the grace period, issuing control instructions to the intelligent thermostatic valve that controls the user's heating status to gradually reduce the opening of its heating pipes until it enters the limited supply mode.

[0011] In an optional implementation, after collecting the user's cumulative heat consumption in each unit time period, the method further includes: if the difference between the cumulative heat consumption in the current unit time period and the cumulative heat consumption in the previous unit time period exceeds a preset threshold, then sending an early warning message to the corresponding user terminal.

[0012] In an optional implementation, after calculating the dynamic heating cost per target time period, the method further includes: updating the dynamic heating cost per unit time period, and pushing the accumulated dynamic heating cost to the user terminal according to a preset cycle.

[0013] In an optional implementation, after calculating the dynamic heating cost for the target unit time period, the method further includes: receiving a heating status change instruction sent by the user; and updating the dynamic basic cost based on the heating status change instruction and the remaining heating duration.

[0014] Secondly, this invention provides an intelligent heating billing system based on multi-dimensional data, comprising: a first receiving module for receiving a user's instruction on the building area and heating status selection; wherein the heating status includes: normal heating, low-temperature supply, and complete heating shutdown; a first calculation module for determining the area step coefficient and heating status coefficient according to the building area and heating status selection instruction, and calculating the dynamic basic cost in combination with the building area, the unit area benchmark heating cost, and the number of heating days; an acquisition module for acquiring the user's cumulative heat consumption and indoor temperature setting range in each unit time period; a second calculation module for determining the temperature coefficient according to the indoor temperature setting range in the target unit time period, and calculating the dynamic heating cost for the target unit time period in combination with the cumulative heat consumption in the target unit time period and the billing unit price of the heating period; wherein the target unit time period represents any unit time period within the heating cycle; the heating period includes: peak period and off-peak period; and a third calculation module for calculating the user's heating bill based on the dynamic basic cost and the dynamic heating cost of all unit time periods within the heating cycle.

[0015] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the intelligent heating billing method based on multi-dimensional data as described in any of the foregoing embodiments.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the intelligent heating billing method based on multi-dimensional data as described in any of the foregoing embodiments.

[0017] This invention provides an intelligent heating billing method based on multi-dimensional data. By introducing an area tiered coefficient and a heating status coefficient, the method enables the base fee to reasonably reflect the differences in resource usage of the heating system by different apartment types and the actual cost expenditure under different heating statuses. This solves the problem of larger apartments having an advantage under the traditional fixed unit price model, significantly improving the fairness and cost matching of the billing. Simultaneously, the dynamic heating cost calculation based on peak-valley time-of-use pricing and temperature coefficients effectively guides users to stagger their heating usage and appropriately control their temperature, enhancing the energy-saving incentive effect. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 A flowchart of an intelligent heating billing method based on multi-dimensional data is provided in an embodiment of the present invention; Figure 2 A flowchart of an optional intelligent heating billing method provided in an embodiment of the present invention; Figure 3 A functional module diagram of an intelligent heating billing system based on multi-dimensional data provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0021] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0022] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0023] Example 1 Figure 1 A flowchart of an intelligent heating billing method based on multi-dimensional data is provided for an embodiment of the present invention, as shown below. Figure 1 As shown, the method specifically includes the following steps: Step S102: Receive the building area and heating status selection instructions sent by the user.

[0024] The heating status includes: normal heating, low-temperature supply, and complete heating shutdown.

[0025] The method provided in this invention is applied to an intelligent heating billing system. Before the start of the heating season (e.g., 15 to 30 days prior), users complete account binding and basic information entry through the web or mobile app provided by the system platform. The building area is actively entered by the user and can be automatically verified by connecting with the property registration system to prevent false reporting or misfilling. The heating status selection command is a user's choice based on their own heating needs, specifically including three heating modes: 1. Normal heating: indicates that the user maintains regular heating during the heating season, and the indoor temperature can be automatically adjusted according to the settings; 2. Low-temperature supply: indicates that the user only needs to maintain pipe antifreeze and basic heating, for example, setting the indoor temperature range to 12℃–16℃; 3. Complete heating shutdown: indicates that the user does not need any heating service, and the heating valve will be remotely shut off. After receiving the above information sent by the user, the intelligent heating billing system uses it as input for subsequent fee model parameters and stores it in the user profile database for billing management.

[0026] Step S104: Based on the building area and heating status selection instructions, determine the area step coefficient and heating status coefficient respectively, and calculate the dynamic basic cost by combining the building area, the unit area benchmark heating cost and the number of heating days.

[0027] This invention employs a composite model of "area ladder-state adaptation" to reconstruct traditional fixed foundation costs, specifically divided into the following sub-processes: 1. Determine the area step coefficient: The system maps the user's building area to a preset tiered range to obtain the corresponding area tier coefficient. For example: if the building area is ≤144m², the area tier coefficient is 1.0; if the building area is 144 < ≤200m², the area tier coefficient is 1.1; and if the building area is >200m², the area tier coefficient is 1.2. These values ​​are exemplary and can be configured by administrators according to local conditions. The tiered classification is based on research into the load distribution patterns of the heating network. Larger units occupy more pipeline capacity and reserved heat resources, therefore they should bear a higher proportion of fixed costs per unit area.

[0028] 2. Determine the thermal state coefficient: This invention assigns a corresponding heating status coefficient based on the user's selected heating status. For example, the heating status coefficient is 1.0 for normal heating (full coverage of fixed costs); 0.6 for low-temperature supply assurance (partial coverage, reflecting maintenance costs under low load); and 0.2 for complete heating shutdown (symbolic coverage, covering minimum costs such as valve control and inspection). This coefficient reflects the differences in actual fixed expenditures incurred by the enterprise under different states, avoiding the unfairness caused by a "one-size-fits-all" pricing approach.

[0029] After determining the area step factor and the heat utilization state factor, this embodiment of the invention uses the following formula to calculate the dynamic foundation cost: Dynamic basic cost = building area × benchmark heating cost per unit area × area tiered coefficient × heating status coefficient × number of heating days. The benchmark heating cost per unit area is uniformly published by the heating regulatory department, for example, 0.1 yuan / m². 2 The number of heating days is the total number of days in the local statutory or contractually agreed heating cycle, for example, 120 days.

[0030] As can be seen from the above formula, the embodiments of the present invention break through the traditional extensive model of "linear allocation by area", realize the dynamic matching of basic costs and actual cost structure, and take into account both fairness and economic rationality.

[0031] Step S106: Obtain the user's cumulative heat consumption and indoor temperature setting range for each unit time period.

[0032] During heating operation, the system collects key heat consumption data in real time through smart devices deployed at the user end. For example, smart heat meters installed on the heating circuit collect the cumulative heat consumption every 10 minutes, in GJ, with an accuracy of 0.01 GJ. Indoor temperature sensors sample the actual room temperature every 5 minutes, and record this data in conjunction with the target temperature range set by the user via the temperature control panel or APP, i.e., the indoor temperature setting range (e.g., 18–22℃). This embodiment of the invention does not specifically limit the duration of each time period; the system platform can be configured according to actual data collection needs, for example, it could be 30 minutes, 1 hour, etc.

[0033] All raw data is aggregated by the home gateway and then encrypted and uploaded to the platform's processing layer data receiving server via WiFi / Bluetooth or other communication methods. The platform performs integrity verification, anomaly detection (such as sudden increases or sharp drops), and noise reduction on the data to ensure that the data used for billing is authentic and reliable.

[0034] Step S108: Determine the temperature coefficient based on the indoor temperature setting range within the target unit time period, and calculate the dynamic heating cost for the target unit time period by combining the cumulative heat consumption within the target unit time period and the billing unit price of the heating period.

[0035] The target unit time period refers to any unit time period within the heating cycle; the heating time period includes: peak time period and off-peak time period.

[0036] This invention constructs a three-dimensional fusion-based elastic pricing mechanism, wherein the three dimensions include: 1. Time Dimension: Divide heating periods and set differentiated billing prices. Optionally, the day can be divided into two main periods: peak period (8:00-22:00), corresponding to a period of intensive social activities with high energy supply pressure and a higher billing price (e.g., 30 yuan / GJ); and off-peak period (22:00-8:00 the next day), when the power grid and heating network load are lower, encouraging users to use heat during this period and resulting in a lower billing price (e.g., 25 yuan / GJ). It can also be extended to support floating pricing strategies for special scenarios such as holidays and extreme weather.

[0037] 2. Temperature Dimension: A temperature coefficient is introduced to guide users in rational heat usage. Specifically, the temperature coefficient is dynamically adjusted based on the target temperature range (i.e., the indoor temperature setting range) set by the user within each time period. For example, if the set temperature is 18–22℃ (comfort range), the temperature coefficient is 1.0; if the set temperature is <18℃, the temperature coefficient is 0.9 to provide a slight discount and incentivize energy conservation; if the set temperature is >22℃, the temperature coefficient is 1.1, meaning a moderate price increase is applied to curb waste caused by excessive heating. The rules for determining the temperature coefficient can be adaptively configured in the system backend, supporting regional climate adaptation adjustments.

[0038] 3. Energy consumption dimension: Calculate heating costs based on actual heat consumption.

[0039] In this embodiment of the invention, the dynamic heating cost per target unit time period = cumulative heat consumption within the target unit time period × billing price of the heating period in which the target unit time period falls × temperature coefficient of the target unit time period. Clearly, the dynamic heating cost pricing method breaks away from the traditional single "heat consumption × unit price" model, deeply coupling price signals with user behavior to form a guiding market mechanism that promotes off-peak heating and energy efficiency optimization.

[0040] Step S110: Calculate the user's heating bill based on the dynamic basic cost and the dynamic heating cost for all unit time periods within the heating cycle.

[0041] Within a designated period after the end of the heating season, the heating system initiates the final settlement process. First, it aggregates the dynamic heating costs for all time periods to obtain the total dynamic heating cost. Then, it adds this to the dynamic basic cost to form a preliminary total payable, resulting in the user's heating bill. If users prepaid fees during the early stages of the heating season, a refund or additional payment will be made accordingly.

[0042] This invention provides an intelligent heating billing method based on multi-dimensional data. By introducing an area tiered coefficient and a heating status coefficient, the method enables the base fee to reasonably reflect the differences in resource usage of the heating system by different apartment types and the actual cost expenditure under different heating statuses. This solves the problem of larger apartments having an advantage under the traditional fixed-price model, significantly improving the fairness and cost matching of the billing. Simultaneously, the dynamic heating cost calculation based on peak-valley time-of-use pricing and temperature coefficients effectively guides users to stagger their heating usage and appropriately control their temperature, enhancing the energy-saving incentive effect.

[0043] In one optional implementation, after calculating the dynamic base cost, the present invention further includes the following steps: Step S201: Push the prepaid bill to the user; wherein the amount of the prepaid bill is a specified percentage of the dynamic base fee.

[0044] After calculating the dynamic base fee, the system automatically generates a prepayment bill based on a specified percentage. This percentage is typically set at 50% of the dynamic base fee, but can be configured differently based on regional policies or user credit ratings (e.g., 60% for first-time users, 40% for existing users). This percentage is maintained centrally by the backend management module and can be flexibly adjusted according to regional policies.

[0045] Step S202: After heating begins, monitor the payment status of users and send payment reminders to users who have overdue payments through various communication channels.

[0046] From the official start of the heating season, the platform's processing layer activates a real-time payment status monitoring mechanism. The system periodically verifies whether users have actually received their prepayments. If a user fails to pay by the agreed payment deadline, their status is updated to overdue, triggering a multi-level reminder notification mechanism. For example, a progressive reminder strategy is adopted: Phase 1 (days overdue 1-3): Gentle reminders are sent via non-intrusive methods such as app pop-ups and WeChat messages; Phase 2 (days overdue 4-7): SMS notifications are increased in frequency, with billing links and quick payment options; Phase 3 (1-2 days before the end of the grace period): A formal reminder notice is sent, clearly informing users of the impending technical supply restriction measures. Furthermore, all notifications record the sending time, channel, and receipt status (e.g., whether the SMS was delivered, whether the app viewed the message), forming a complete reminder log as a basis for subsequent actions. The specific figures mentioned above are exemplary values; the system supports optional configurations.

[0047] In step S203, if payment is not completed within the grace period, a control command is sent to the intelligent thermostatic valve that controls the user's heating status to gradually reduce the opening of its heating pipes until it enters the limited supply mode.

[0048] If a user remains unpaid after the grace period, the system will trigger an automatic heating restriction mechanism. Specifically, the system platform generates a remote control command, which, after security authentication and encryption, is sent to the user's corresponding smart thermostatic valve via the IoT communication network. The control command is used to implement a tiered reduction in heating supply: Phase 1: The valve opening is reduced from 100% to 50%, achieving half-load heating (applicable to users with normal heating usage); Phase 2 (if payment is still not made after several hours): The opening is further reduced to 20%, maintaining only the anti-freeze cycle; Final Phase: The valve is closed to 0%, entering "heat restriction mode" and stopping heat supply. The specific figures mentioned above are exemplary values, and the system supports configuration options. For users who originally selected "low-temperature supply guarantee" or "complete heating shutdown," a corresponding locking mechanism can also be triggered, for example, preventing switching back to normal heating status. The system simultaneously records the entire heating restriction process log and pushes a final notification to the user: "Due to failure to pay on time, your heating supply has been restricted. Please pay as soon as possible to restore service."

[0049] To improve the safety, stability, and transparency of heating usage for users, in one optional embodiment, after collecting the cumulative heat consumption of users within each unit time period, the present invention further includes the following: If the difference between the cumulative heat consumption in the current unit time period and the cumulative heat consumption in the previous unit time period exceeds a preset threshold, an early warning message will be sent to the corresponding user terminal.

[0050] Specifically, the system continuously receives and records the cumulative heat consumption data uploaded by the user's smart heat meter at fixed time granularities (such as every 10 minutes or hour). It also maintains a sliding time-series database for each user, storing valid heat consumption records from multiple recent time periods. Upon entering a new time period, the system automatically extracts two key data points: the cumulative heat consumption within the current time period (the total heat consumption reported by the heat meter since the start of the current time period at the end of the current time period) and the cumulative heat consumption within the previous time period (the total heat consumption within the previous time period). The difference between these two values ​​is calculated to obtain the incremental heat consumption within the current time period.

[0051] The system has built-in dynamic or static preset thresholds to identify abnormal fluctuations in heat consumption. These thresholds can be determined based on the following methods: 1. Fixed threshold method: For example, set the maximum allowable increment for a single time period to 0.5GJ, and anything exceeding this limit is considered abnormal.

[0052] 2. Adaptive threshold method: Dynamically generate a reasonable fluctuation range based on factors such as the user's historical heating patterns (e.g., daily average increase, standard deviation), building thermal insulation performance, and outdoor temperature.

[0053] 3. Segmented control strategy: Different threshold standards are used for different heating states (such as normal heating vs. low temperature supply) to avoid misjudgment.

[0054] Once the preset threshold is determined, if the incremental heat consumption exceeds the preset threshold, an early warning message will be immediately sent to the corresponding user terminal. The warning message includes: the time of the anomaly, the sudden change in heat consumption, possible causes (such as "suspected equipment failure" or "potential water leakage risk"), suggested operation instructions (such as "please check if the indoor temperature is abnormal" or "contact property management for repair"), and a quick access link (jumping to the repair module or customer service channel in the APP). The warning is not only sent to users but also simultaneously notifies the heating company's back-end management system, triggering an update to the operation and maintenance alarm dashboard. Multi-dimensional cross-validation can also be performed using data from other sensors (such as indoor temperature, water supply temperature, and valve opening) to reduce the false alarm rate.

[0055] In one optional implementation, after calculating the dynamic heating cost per target unit time period, the embodiments of the present invention further include the following: Dynamic heating costs are updated according to unit time periods, and the cumulative dynamic heating costs are pushed to the user terminal according to a preset cycle.

[0056] After each unit period (e.g., 1 hour) of dynamic heating cost calculation is completed, the system immediately performs a cost status refresh operation, writing the newly generated cost value for that period into the user's real-time billing database and updating the total incurred heating costs under the user's account, which is the sum of costs for all completed unit periods. Users can also choose to mark the period as billed to prevent double counting or omissions.

[0057] The system supports a processing rhythm of minute-level data collection and hourly-level billing. Specifically, data collection frequency is high (e.g., raw data is acquired every 5-10 minutes) to ensure accuracy, but cost updates are made at the smallest granularity of unit time period to balance system load and response time. While continuously updating costs, the system does not send notifications to users for every unit time period (to avoid information overload). Instead, it uses a periodic summary push mechanism, that is, sending periodic cost reports centrally at preset intervals. Users can view their bills through the APP or by clicking on links in the periodic cost reports. Users can adjust subsequent heating status, temperature, or time periods according to the bill information.

[0058] In one optional implementation, after calculating the dynamic heating cost per target unit time period, the embodiments of the present invention further include the following: Receive heating status change instructions from users; update dynamic basic fees based on heating status change instructions and remaining heating duration.

[0059] This invention breaks through the rigid model of traditional heating charges where the basic fee is fixed and cannot be adjusted. It realizes the dynamic reconstruction and on-demand adaptation of the basic fee, which is an important innovation of this invention in improving user flexibility, billing fairness and operation refinement.

[0060] Specifically, during the heating season, users can proactively submit heating status change commands through interactive terminals provided by the platform (such as mobile apps, web portals, or local temperature control panels). Upon receiving the command, the system triggers a subsequent fee recalculation process. It is known that the original dynamic base fee is calculated once before the start of the heating season based on the initial heating status, covering the entire heating cycle. Therefore, when a user changes their status midway, the base fee for the portion not yet incurred needs to be recalculated in segments to ensure fair and reasonable billing. The fee recalculation process is as follows: First, the entire heating cycle is divided into two phases: Period already incurred: From the start of heating to the effective date of the status change, the basic cost shall be borne according to the status coefficient corresponding to the original heating status, and the already settled portion shall not be adjusted.

[0061] Periods without heating (i.e. remaining heating duration): From the effective date of the change to the end of the heating season, the basic cost allocation should be recalculated according to the new heating status.

[0062] Then, the basic cost for the remaining period is recalculated. The new basic cost = building area × unit area benchmark heating cost × area tiered coefficient × updated heating status coefficient × remaining heating duration (days). The updated dynamic basic cost = incurred basic cost + new basic cost. The incurred basic cost is calculated using the formula for dynamic basic cost in step S104, after modifying the number of heating days from the total number of days in the heating cycle to the number of days in the already occurred period.

[0063] In one alternative implementation, such as Figure 2 As shown, after determining the user's heating bill, this embodiment of the invention further includes the following steps: Step S301: Receive all historical data after the end of the heating season and calculate the cumulative heat consumption of each user during the heating cycle.

[0064] After the official end of the heating season, the platform's processing layer initiates the post-settlement processing flow, first retrieving the archived full historical data from the distributed database. This data includes not only each user's cumulative heat consumption during the heating cycle, but also: each user's real-time heat consumption recorded for each time period during the heating cycle, indoor temperature setpoints and measured values, and heating status change logs. If a user has multiple different heating statuses across different periods, only the actual heat consumption during the normal heating period and the low-temperature supply guarantee period is counted, avoiding the inclusion of invalid data from the off-season.

[0065] Step S302: Calculate the average heat consumption of the same type of user group based on the cumulative heat consumption of each user during the heating cycle; where the same type of user group refers to a set of users with the same building area and the same heating status.

[0066] The platform uses building area and heating status as two-dimensional classification keys to construct dynamic user clusters. For each group (i.e., a user group of the same type), the system first extracts the cumulative heat consumption of all members in the group, and then calculates the average heat consumption of the group. This average heat consumption serves as a reference standard for measuring individual energy-saving performance, and has regional adaptability and group representativeness.

[0067] Step S303: Determine whether the cumulative heat consumption of the current user during the heating cycle is lower than the preset ratio of the average heat consumption of its similar user group.

[0068] If yes, proceed to step S304 below; if no, proceed to step S305 below.

[0069] Step S304: Generate the corresponding energy-saving subsidy for the current user, and generate the heating settlement fee after deducting the corresponding amount from their heating bill.

[0070] Step S305: Use the fees in the heating bill as the heating settlement fee.

[0071] After calculating the average heat consumption of the current user's group of users of the same type, the cumulative heat consumption of the current user is compared with the average heat consumption of the current user's group of users of the same type to determine the energy-saving compliance: First, a preset ratio (e.g., 80%) is set as the trigger condition for energy-saving incentives. If the current user's cumulative heat consumption during the heating cycle is less than the average heat consumption of the current user group of the same type × the preset ratio, then the user is deemed to have significant energy-saving behavior and enters the subsidy generation process; otherwise, the user is considered to have regular heating behavior and will not enjoy additional rewards.

[0072] For users who pass the energy-saving assessment, the system automatically generates an energy-saving subsidy amount, which is directly deducted from the total original heating cost bill in the form of a fee reduction item, forming the final heating settlement fee.

[0073] In this embodiment of the invention, generating a corresponding energy-saving subsidy for the current user specifically includes the following steps: Step S3041: Calculate the difference between the average heat consumption of the current user's group of users of the same type and its cumulative heat consumption during the heating cycle to obtain the heat consumption difference.

[0074] Step S3042: The product of the heat consumption difference and the billing unit price during off-peak hours is converted according to a preset incentive ratio to obtain the current user's energy-saving subsidy.

[0075] Specifically, the current user's energy-saving subsidy = (average heat consumption of the same type of user group - current user's cumulative heat consumption) × off-peak billing price × preset incentive ratio. Optionally, the preset incentive ratio is 10%.

[0076] Example of calculation for a normal heating user: a 120m² 2 The user selects normal heating, sets the temperature to 20℃, and the heating season lasts for 120 days. The cumulative heat consumption is 15GJ (8GJ at peak times and 7GJ at low times). The average heat consumption for the same area under the same conditions is 16GJ.

[0077] Dynamic base cost = 120 × 0.1 × 1.0 × 1.0 × 120 = 1440 yuan.

[0078] Dynamic heating cost = (8 × 30 × 1.0) + (7 × 25 × 1.0) = 240 + 175 = 415 yuan.

[0079] Energy saving subsidy = (16-15) × 25 × 10% = 2.5 yuan.

[0080] Total cost = 1440 + 415 - 2.5 = 1852.5 yuan.

[0081] Special calculation example for users who have stopped heating: a certain 160m 2 The user chooses to completely stop heating, with 120 days of heating provided.

[0082] Dynamic basic cost = 160 × 0.1 × 1.1 × 0.2 × 120 = 422.4 yuan (no dynamic heating cost or subsidy).

[0083] Total cost = 422.4 yuan (only the basic fee needs to be paid).

[0084] In summary, compared with the existing "heat meter + two-part heating pricing" scheme, the embodiments of this invention have significant advantages. Regarding cost fairness, the dynamic basic cost model solves the problem of "larger apartments benefiting while smaller apartments suffer" in traditional fixed basic costs, and the off-heating fees are more accurately matched to enterprise costs. In terms of energy saving and user experience, the embodiments of this invention guide users to proactively adjust their heating behavior through a triple incentive of "peak-valley pricing + temperature coefficient + energy-saving subsidies," combined with real-time billing feedback. Simultaneously, users can flexibly control heating costs through the APP, significantly improving satisfaction and truly achieving a triple benefit of cost reduction for enterprises, savings for users, and energy conservation for society.

[0085] Example 2 This invention also provides an intelligent heating billing system based on multi-dimensional data. This system is mainly used to execute the intelligent heating billing method based on multi-dimensional data provided in Embodiment 1 above. The system provided in this invention will be described in detail below.

[0086] Figure 3 A functional module diagram of an intelligent heating billing system based on multi-dimensional data provided in an embodiment of the present invention is shown below. Figure 3 As shown, the device mainly includes: a first receiving module 10, a first calculation module 20, an acquisition module 30, a second calculation module 40, and a third calculation module 50, wherein: The first receiving module 10 is used to receive the building area and heating status selection instructions sent by the user; wherein the heating status includes: normal heating, low temperature supply and complete heating stop.

[0087] The first calculation module 20 is used to determine the area step coefficient and the heat use status coefficient respectively based on the building area and the heat use status selection instruction, and to calculate the dynamic basic cost in combination with the building area, the unit area benchmark heating cost and the number of heating days.

[0088] The acquisition module 30 is used to acquire the user's cumulative heat consumption and indoor temperature setting range for each unit time period.

[0089] The second calculation module 40 is used to determine the temperature coefficient based on the indoor temperature set range within the target unit time period, and to calculate the dynamic heating cost of the target unit time period by combining the cumulative heat consumption within the target unit time period and the billing unit price of the heating period. The target unit time period refers to any unit time period within the heating cycle. The heating period includes peak time and off-peak time.

[0090] The third calculation module 50 is used to calculate the user's heating bill based on the dynamic basic cost and the dynamic heating cost for all unit time periods within the heating cycle.

[0091] This invention provides an intelligent heating billing system based on multi-dimensional data. By introducing area tiered coefficients and heating status coefficients, the system ensures that the base fee reasonably reflects the differences in resource usage by different apartment types and the actual cost expenditure under different heating statuses. This solves the problem of larger apartments dominating the traditional fixed-price model, significantly improving the fairness and cost-effectiveness of the billing. Furthermore, the dynamic heating cost calculation based on peak-valley time-of-use pricing and temperature coefficients effectively guides users to stagger their heating usage and control their temperature appropriately, enhancing the incentive effect for energy conservation.

[0092] Optionally, the system also includes: The second receiving module is used to receive all historical data after the end of the heating season and to calculate the cumulative heat consumption of each user during the heating cycle.

[0093] The fourth calculation module is used to calculate the average heat consumption of a user group of the same type based on the cumulative heat consumption of each user during the heating cycle; where a user group of the same type refers to a set of users with the same building area and the same heating status.

[0094] The judgment module is used to determine whether the cumulative heat consumption of the current user during the heating cycle is lower than the preset ratio of the average heat consumption of the same type of user group.

[0095] The generation module is used to generate the corresponding energy-saving subsidy for the current user when the judgment result is yes, and to generate the heating settlement fee after deducting the corresponding amount from the user's heating bill.

[0096] Optionally, the generation module is specifically used for: The difference between the average heat consumption of the current user's group of similar users and their cumulative heat consumption during the heating cycle is calculated to obtain the heat consumption difference.

[0097] The energy-saving subsidy for the current user is calculated by multiplying the difference in heat consumption by the billing price during off-peak hours, according to a preset incentive ratio.

[0098] Optionally, the system is also used for: Send a prepaid bill to the user; the amount of the prepaid bill is a specified percentage of the dynamic base fee.

[0099] After heating begins, the system monitors users' payment status and sends overdue payment reminders to users through various communication channels.

[0100] If payment is not completed within the grace period, a control command will be sent to the smart thermostatic valve that controls the user's heating status to gradually reduce the opening of its heating pipes until it enters the limited supply mode.

[0101] Optionally, the system is also used for: If the difference between the cumulative heat consumption in the current unit time period and the cumulative heat consumption in the previous unit time period exceeds a preset threshold, an early warning message will be sent to the corresponding user terminal.

[0102] Optionally, the system is also used for: Dynamic heating costs are updated according to unit time periods, and the cumulative dynamic heating costs are pushed to the user terminal according to a preset cycle.

[0103] Optionally, the system is also used for: Receive user-sent instructions to change heating status.

[0104] Update the dynamic base cost based on the heating status change command and the remaining heating duration.

[0105] Example 3 See Figure 4 This invention provides an electronic device, which includes a processor 60, a memory 61, a bus 62, and a communication interface 63. The processor 60, the communication interface 63, and the memory 61 are connected via the bus 62. The processor 60 is used to execute executable modules, such as computer programs, stored in the memory 61.

[0106] The memory 61 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 63 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0107] Bus 62 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0108] The memory 61 is used to store programs. After receiving an execution instruction, the processor 60 executes the program. The method executed by the apparatus defined by the process disclosed in any of the foregoing embodiments of the present invention can be applied to the processor 60 or implemented by the processor 60.

[0109] Processor 60 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 60 or by instructions in software form. Processor 60 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 61. Processor 60 reads the information in memory 61 and, in conjunction with its hardware, completes the steps of the above method.

[0110] The computer program product of the intelligent heating billing method and system based on multi-dimensional data provided in this invention includes a computer-readable storage medium storing non-volatile program code executable by a processor. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0111] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0112] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0113] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0114] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of this invention is in use. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention. In addition, the terms "first," "second," "third," etc., are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0115] Furthermore, terms such as "horizontal," "vertical," and "sag" do not imply that components must be absolutely horizontal or suspended, but rather that they can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal relative to "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0116] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A smart heating billing method based on multi-dimensional data, characterized in that, include: Receive instructions from users regarding the building area and heating status selection; wherein, the heating status includes: normal heating, low-temperature supply, and complete heating shutdown; Based on the building area and the heating status selection instruction, the area step coefficient and heating status coefficient are determined respectively, and the dynamic basic cost is calculated by combining the building area, the unit area benchmark heating cost and the number of heating days. Obtain the user's cumulative heat consumption and indoor temperature setting range for each unit of time period; The temperature coefficient is determined based on the indoor temperature setting range within the target unit time period, and the dynamic heating cost for the target unit time period is calculated by combining the cumulative heat consumption within the target unit time period and the billing unit price of the heating period. Here, the target unit time period represents any unit time period within the heating cycle; the heating period includes: peak period and off-peak period. The user's heating bill is calculated based on the dynamic basic cost and the dynamic heating cost for all time periods within the heating cycle.

2. The intelligent heating billing method based on multi-dimensional data according to claim 1, characterized in that, After determining the user's heating bill, the process also includes: Receive all historical data after the end of the heating season and calculate the cumulative heat consumption of each user during the heating cycle; Based on the cumulative heat consumption of each user during the heating cycle, the average heat consumption of the same type of user group is calculated; wherein, the same type of user group refers to a set of users with the same building area and the same heating status. Determine whether the cumulative heat consumption of the current user during the heating cycle is lower than the preset ratio of the average heat consumption of its similar user group; If so, a corresponding energy-saving subsidy will be generated for the current user, and the corresponding amount will be deducted from their heating bill to generate a heating settlement fee.

3. The intelligent heating billing method based on multi-dimensional data according to claim 2, characterized in that, Generate corresponding energy-saving subsidies for the current user, including: Calculate the difference between the average heat consumption of the current user's group of similar users and their cumulative heat consumption during the heating cycle to obtain the heat consumption difference; The energy-saving subsidy for the current user is calculated by multiplying the difference in heat consumption by the billing price during off-peak hours according to a preset incentive ratio.

4. The intelligent heating billing method based on multi-dimensional data according to claim 1, characterized in that, After calculating the dynamic base cost, it also includes: A prepaid bill is pushed to the user; wherein the amount of the prepaid bill is a specified percentage of the dynamic base fee; After heating begins, the payment status of users is monitored, and reminder notices are sent to users who have overdue payments through various communication channels. If payment is not completed within the grace period, a control command will be sent to the intelligent thermostatic valve that controls the user's heating status to gradually reduce the opening of its heating pipes until it enters the limited supply mode.

5. The intelligent heating billing method based on multi-dimensional data according to claim 1, characterized in that, After collecting the user's cumulative heat consumption within each unit time period, it also includes: If the difference between the cumulative heat consumption in the current unit time period and the cumulative heat consumption in the previous unit time period exceeds a preset threshold, an early warning message will be sent to the corresponding user terminal.

6. The intelligent heating billing method based on multi-dimensional data according to claim 1, characterized in that, After calculating the dynamic heating cost per target unit time period, the method further includes: The dynamic heating cost is updated according to the unit time period, and the cumulative dynamic heating cost is pushed to the user terminal according to the preset cycle.

7. The intelligent heating billing method based on multi-dimensional data according to claim 1, characterized in that, After calculating the dynamic heating cost per target unit time period, the method further includes: Receive user-sent instructions to change heating status; The dynamic basic cost is updated based on the heating status change command and the remaining heating duration.

8. A smart heating billing system based on multi-dimensional data, characterized in that, include: The first receiving module is used to receive the building area and heating status selection instructions sent by the user; wherein, the heating status includes: normal heating, low temperature supply, and complete heating shutdown; The first calculation module is used to determine the area step coefficient and the heat use state coefficient according to the building area and the heat use state selection instruction, and to calculate the dynamic basic cost in combination with the building area, the unit area benchmark heating cost and the number of heating days. The acquisition module is used to acquire the user's cumulative heat consumption and indoor temperature setting range for each unit of time. The second calculation module is used to determine the temperature coefficient based on the indoor temperature set range within the target unit time period, and to calculate the dynamic heating cost of the target unit time period by combining the cumulative heat consumption within the target unit time period and the billing unit price of the heating period; wherein, the target unit time period represents any unit time period within the heating cycle; the heating period includes: peak period and off-peak period; The third calculation module is used to calculate the user's heating bill based on the dynamic basic cost and the dynamic heating cost for all unit time periods within the heating cycle.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the intelligent heating billing method based on multi-dimensional data as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which, when executed by a processor, implement the intelligent heating billing method based on multi-dimensional data as described in any one of claims 1 to 7.

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