A heat metering system

Abstract

The application provides a heat metering system, and relates to the technical field of intelligent heat supply. The system comprises a plurality of terminal equipment groups, a data concentrator and a cloud platform. Each terminal equipment group comprises a room temperature collector and an intelligent valve. The room temperature collector is used for collecting room temperature information in real time, the intelligent valve is used for recording activation information, the data concentrator is used for reporting the room temperature information and the activation information to the cloud platform, and the cloud platform is used for determining the effective heat use duration of a target terminal user to which a target terminal equipment group belongs within a preset time period according to the room temperature information reported by the room temperature collector in the target terminal equipment group and the activation information reported by the intelligent valve, and determining heat supply charging information of the target terminal user within the preset time period according to the effective heat use duration. The heat metering system of the application effectively solves the problem of unreasonable metering results caused by single metering parameters by introducing a multi-dimensional and correlation data collection and intelligent analysis mechanism.

Description

Technical Field

[0001] This application relates to the field of intelligent heating technology, and more specifically, to a heating metering system. Background Technology

[0002] With the increasing demand for building energy conservation and energy management, reasonable heat metering has become an important means to promote the energy-saving and equitable operation of heat metering systems. However, existing heat metering systems suffer from problems such as single metering parameters and potentially unreasonable metering results in practical applications. Therefore, improving the rationality of the metering results of heat metering systems is of great importance. Summary of the Invention

[0003] The purpose of this application is to provide a heating metering system to address the shortcomings of the existing technology, thereby solving the problems that existing heating metering systems have single metering parameters and may produce unreasonable metering results in practical applications.

[0004] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0005] In a first aspect, embodiments of this application provide a heating metering system, which includes: multiple terminal device groups, a data concentrator, and a cloud platform, wherein each terminal device group includes a room temperature collector and a smart valve;

[0006] The room temperature sensor is used to collect room temperature information in real time.

[0007] The smart valve is used to record activation information, which includes valve status and cumulative opening time. The valve status includes either an open state or a closed state.

[0008] The data concentrator is used to receive the room temperature information reported by each of the room temperature collectors and the activation information reported by each of the smart valves, and to report the room temperature information and the activation information to the cloud platform.

[0009] The cloud platform is used to determine the effective heating duration of the target terminal user within a preset time period based on the room temperature information reported by the room temperature collector in the target terminal device group and the activation information reported by the smart valve, and to determine the heating billing information of the target terminal user within the preset time period based on the effective heating duration.

[0010] As one possible implementation, receiving room temperature information reported by each of the room temperature collectors and activation information reported by each of the smart valves, and reporting the room temperature information and activation information to the cloud platform, includes:

[0011] The data concentrator receives the room temperature information reported by each of the room temperature collectors and the activation information reported by each of the smart valves in real time, and stores the room temperature information reported by each of the room temperature collectors and the activation information reported by each of the smart valves in the local memory of the data concentrator.

[0012] The data concentrator reports the room temperature information reported by each of the room temperature collectors and the activation information reported by each of the smart valves, stored in the local memory, to the cloud platform at preset time intervals.

[0013] As one possible implementation, determining the effective heating duration for the target terminal user within a preset time period based on the room temperature information reported by the room temperature collector in the target terminal device group and the activation information reported by the smart valve includes:

[0014] Determine the effective room temperature range based on the preset target temperature and the effective temperature difference range;

[0015] Based on the room temperature information reported by the room temperature collector and the activation information reported by the smart valve, the effective heating time of the target terminal user to which the target terminal device group belongs within the effective room temperature range during the preset time period is determined. The room temperature information reported by the room temperature collector includes multiple collection times and the room temperature corresponding to each collection time.

[0016] As one possible implementation, determining the effective heating duration for the target terminal user belonging to the target terminal device group within the effective room temperature range during the preset time period, based on the room temperature information reported by the room temperature collector and the activation information reported by the smart valve, includes:

[0017] Based on the room temperature information and the activation information, determine whether each of the collection times is an effective heat utilization time;

[0018] The effective heating duration is obtained by accumulating the collected data as effective heating time.

[0019] As one possible implementation, determining whether each of the data collection times is an effective heat consumption time based on the room temperature information and the activation information includes:

[0020] For each of the aforementioned collection times, if the room temperature corresponding to the collection time is within the effective room temperature range and the smart valve is in the open state, then the collection time is taken as the effective heat usage time.

[0021] As one possible implementation, the cloud platform is also used for:

[0022] The preset time period is divided into multiple sub-time periods, and a time weight is assigned to each sub-time period, the time weight including an effective heat consumption threshold;

[0023] Determine the rate of change of room temperature for each of the sub-time periods;

[0024] For each sub-time period, the room temperature within the sub-time period is determined based on the room temperature change rate corresponding to the sub-time period. If it is stable, the effective heating duration within the sub-time period is determined based on the time weight of the sub-time period.

[0025] The effective heating duration within the preset time period is determined based on the effective heating duration within each of the sub-time periods.

[0026] As one possible implementation, determining the heating billing information of the target end user within the preset time period based on the effective heating duration includes:

[0027] The room temperature coefficient is determined based on the average room temperature during the preset time period;

[0028] Based on the effective heating duration and the room temperature coefficient, the heating billing information for the target end user within the preset time period is determined.

[0029] As one possible implementation, determining the room temperature coefficient based on the average room temperature during the preset time period includes:

[0030] Based on the formula K=1+α(T) avg -T base The room temperature coefficient was calculated.

[0031] Where K represents the room temperature coefficient, T avg T represents the average room temperature during the preset time period. base This represents the regional reference temperature, and α represents the adjustment coefficient.

[0032] As one possible implementation, determining the heating billing information of the target end user within the preset time period based on the effective heating duration and the room temperature coefficient includes:

[0033] The heating billing information is calculated based on the formula M = N × t × K;

[0034] Where M represents heating billing information, N represents basic billing information, t represents effective heating duration, and K represents room temperature coefficient.

[0035] As one possible implementation, the cloud platform is also used for:

[0036] If the effective heating duration exceeds a preset threshold, the basic billing information is updated, and the heating billing information is determined based on the updated basic billing information, the effective heating duration, and the room temperature coefficient.

[0037] Secondly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps performed by each terminal device group, data concentrator, and cloud platform in the heating metering system described in any of the first aspects above.

[0038] A heating metering system according to an embodiment of this application includes multiple terminal device groups, a data concentrator, and a cloud platform. Each terminal device group includes a room temperature sensor and a smart valve. The room temperature sensor collects room temperature information in real time, and the smart valve records activation information, including valve status and cumulative opening time. The data concentrator receives room temperature information reported by each room temperature sensor and activation information reported by each smart valve, and reports this information to the cloud platform. The cloud platform determines the effective heating time of the target terminal user within a preset time period based on the room temperature information reported by the room temperature sensor and the activation information reported by the smart valve in the target terminal device group, and determines the heating billing information for the target terminal user within the preset time period based on the effective heating time. According to this embodiment, by integrating a room temperature sensor and a smart valve into each terminal device group, and simultaneously acquiring room temperature information and smart valve activation information, including valve status and cumulative opening time, dual monitoring of user heating behavior and actual heating effect is achieved. Based on this, when determining the heating billing information of end users within a preset time period, the cloud platform no longer relies solely on the valve opening time. Instead, it combines the room temperature change trend to determine whether the heating truly produces an effective heat effect and determines the effective heating duration of end users within the preset time period. This logic, based on the joint determination of room temperature response and valve status, avoids invalid billing caused by pipeline preheating, valve mis-opening, or system lag. It significantly improves the scientificity, fairness, and rationality of the metering results and effectively solves the problem of unreasonable metering results caused by the single metering parameter in the existing heating metering system. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1This paper shows a schematic diagram of the architecture of a heating metering system provided in an embodiment of this application;

[0041] Figure 2 This paper shows a schematic diagram of the architecture of a heating metering system provided in an embodiment of this application;

[0042] Figure 3 A flowchart illustrating a method for determining effective heating duration provided in an embodiment of this application is shown.

[0043] Figure 4 A flowchart illustrating another method for determining effective heating duration provided in an embodiment of this application is shown.

[0044] Figure 5 The illustration shows a flowchart of a method for determining heating billing information provided in an embodiment of this application. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0046] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0047] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0048] Traditional heating metering systems rely solely on the cumulative flow of the heat meter or the valve opening time for billing. Because the metering parameters used are relatively simple, they ignore the actual changes in room temperature and the user's real heat demand. This can easily lead to unreasonable phenomena such as billing upon valve opening even when the room temperature has not risen, resulting in unreasonable metering results.

[0049] To address the aforementioned problems, this application provides a heating metering system that effectively solves the problem of unreasonable metering results caused by the single metering parameter in existing technologies by introducing a multi-dimensional, correlated data acquisition and intelligent analysis mechanism. The heating metering system provided in this application is described in detail below.

[0050] Figure 1 A schematic diagram of the architecture of a heat metering system provided in an embodiment of this application is shown. (Refer to...) Figure 1 As shown, the heating metering system includes multiple terminal device groups, a data concentrator, and a cloud platform. The terminal device groups are typically deployed at the heating equipment inside the end-user's home, such as near the radiators or underfloor heating manifolds in each household. The data concentrator is typically deployed in the building's or unit's low-voltage electrical room, distribution box, or pipe shaft. The cloud platform is deployed in a remote data center or on a public / private cloud server. Thus, the heating metering system of this application, through layered deployment, achieves a complete system architecture from user-end perception and building-level aggregation to cloud-based intelligent decision-making, while also considering deployment feasibility, communication efficiency, and centralized management, making it suitable for various heating scenarios.

[0051] Optionally, each terminal device group is responsible for local data sensing, used to collect user heating behavior and obtain heating-related data of the terminal users to which each terminal device group belongs. The data concentrator, as an intermediate aggregation node, uniformly collects the heating-related data of each terminal user reported by each terminal device group and uploads the heating-related data of each terminal user reported by each terminal device group to the cloud platform. The cloud platform processes and analyzes the received heating-related data of each terminal user, and completes user-level heat consumption assessment and heating cost calculation by determining the effective heating duration of each terminal user, thereby constructing a complete and intelligent heating metering system from the client to the cloud.

[0052] Furthermore, referring to Figure 2As shown, each terminal device group includes a room temperature sensor and a smart valve. The room temperature sensor collects room temperature information in real time, providing dynamic data reflecting actual thermal comfort by continuously monitoring the indoor ambient temperature. The smart valve records its own activation information in real time, including valve status such as whether the valve is currently open or closed, and the cumulative opening time. Specifically, the room temperature sensor is usually installed on a wall in a well-ventilated area away from heat sources and direct sunlight to accurately reflect the actual room temperature, while the smart valve can be directly installed on the inlet branch of the heating pipeline or at the radiator inlet to control and record the heating on / off status and operating time of the household.

[0053] It is worth noting that the real-time room temperature information collected by the room temperature data acquisition device, the valve status recorded by the smart valve, and the cumulative opening time together characterize the actual heating behavior of the end users. Based on this, the target terminal device group can be any one of multiple terminal device groups. For each target terminal device group, the cloud platform combines the room temperature information reported by the room temperature data acquisition device and the activation information reported by the smart valve to determine the room temperature change trend and valve opening time of the target terminal device group. This allows the platform to determine whether there is effective heating demand within a preset time period, thereby obtaining the effective heating time of the target end users to which the target terminal device group belongs within the preset time period. This avoids the irrationality caused by using only the valve opening and closing as a single factor as the measurement basis, and thus achieves reasonable heating metering based on actual heating effects.

[0054] Based on this, the heating metering system provided in this application integrates a room temperature sensor and a smart valve in each terminal device group, simultaneously acquiring room temperature information and smart valve activation information, including valve status and cumulative opening time, thus achieving dual monitoring of user heating behavior and actual heating effect. Furthermore, when determining heating billing information for end users within a preset time period, the cloud platform no longer relies solely on the valve opening time. Instead, it combines room temperature change trends to determine whether the heating truly produces an effective heat effect, thus determining the effective heating duration for end users within the preset time period. This logic, based on the joint determination of room temperature response and valve status, avoids invalid billing caused by pipeline preheating, valve mis-opening, or system lag, significantly improving the scientific, fair, and reasonable nature of the metering results. It effectively solves the problem of unreasonable metering results caused by a single metering parameter in existing heating metering systems.

[0055] As one possible implementation, the data concentrator receives room temperature information reported by each room temperature sensor and activation information reported by each smart valve, and then reports the room temperature information and activation information to the cloud platform, including:

[0056] The data concentrator receives room temperature information reported by each room temperature sensor and activation information reported by each smart valve in real time, and stores the room temperature information reported by each room temperature sensor and the activation information reported by each smart valve in the local storage of the data concentrator. The data concentrator reports the room temperature information reported by each room temperature sensor and the activation information reported by each smart valve in the local storage to the cloud platform at preset time intervals.

[0057] For example, the data concentrator, acting as a data aggregation node in the heating metering system, possesses real-time receiving and local caching capabilities. It continuously receives room temperature information reported by room temperature collectors from various terminal device groups, as well as activation information reported by smart valves. Upon receiving the room temperature and activation information, the data concentrator first stores them in its built-in local storage, such as non-volatile memory (Flash Memory) or a Secure Digital Memory Card (SD). By using local storage as a temporary data buffer, it ensures that critical heating data, such as room temperature information reported by room temperature collectors and activation information reported by smart valves, is not lost in the event of a brief interruption of the communication link, network latency, or temporary unavailability of the cloud platform. This improves the data reliability and fault tolerance of the heating metering system.

[0058] Furthermore, the data concentrator reports the accumulated room temperature information and activation information from its local storage to the cloud platform in batches at preset time intervals, such as every 15 minutes, every hour, or every day. After receiving the batch data, the cloud platform can perform time-series analysis by combining the timestamps to accurately reconstruct the user's heating behavior process, providing complete and continuous data support for subsequent determination of effective heating duration and heating billing.

[0059] Based on this, the heating metering system of this application implements a periodic reporting mechanism based on the local memory built into the data concentrator. This not only reduces the network load and device power consumption caused by frequent communication, making it significantly suitable for resource-constrained communication scenarios such as low-power wide area networks, but also ensures the regularity and manageability of the data.

[0060] Figure 3 The diagram illustrates a flowchart of a method for determining effective heating duration according to an embodiment of this application. The method is executed by a cloud platform within a heating metering system. (Refer to...) Figure 3 As shown, the method specifically includes the following steps:

[0061] S301. Determine the effective room temperature range based on the preset target temperature and the effective temperature difference range.

[0062] Optionally, the preset target temperature is the desired indoor temperature set according to the heating comfort needs of the end user, reflecting the personalized heating needs of the end user. The preset target temperature can be denoted as T. set The effective temperature difference range is a preset temperature tolerance interval used to define a reasonable range of temperature fluctuations, avoiding frequent changes in billing status due to minor fluctuations. The effective temperature difference range can be denoted as ΔT.

[0063] Optionally, based on a preset target temperature T set Given the effective temperature difference range ΔT, the effective room temperature range can be determined as [T]. set -ΔT,T set +ΔT], that is, the effective room temperature range is a closed interval formed by extending ΔT above and below the preset target temperature. For example, if the preset target temperature T set If the temperature is 20℃ and the effective temperature difference range ΔT is 3℃, then the effective room temperature range is [17℃, 23℃].

[0064] It's worth noting that when the indoor temperature collected in real-time by the room temperature sensor is within the effective room temperature range, the cloud platform can determine that the room is currently under effective heating. However, if the indoor temperature is below T... set -ΔT indicates insufficient indoor heating; if the indoor temperature is higher than T... set +ΔT indicates that there may be overheating or energy waste.

[0065] Based on this, compared with traditional heating metering systems that rely solely on valve opening time or heat meter flow rate as a single metering parameter, the heating metering system of this application uses the user's actual heating comfort experience as the metering basis. By setting an effective room temperature range, the cloud platform can identify invalid heating where the smart valve is open but the room temperature has not reached the set value, as well as excessive heating where the room temperature has reached the standard but the smart valve is still open, thereby improving the rationality of heating metering.

[0066] S302. Based on the room temperature information reported by the room temperature collector and the activation information reported by the smart valve, determine the effective heating time of the target terminal user to which the target terminal equipment group belongs within the effective room temperature range during the preset time period.

[0067] Optionally, the cloud platform integrates room temperature information reported by room temperature collectors with the on / off status and operating duration information reported by smart valves to comprehensively determine the cumulative time that end users actually achieve comfortable heating within a preset time period. Specifically, after determining the effective room temperature range, the cloud platform filters out the time periods when the smart valves are open and the indoor temperature is stably maintained within this effective room temperature range, and accumulates the duration of these time periods segment by segment to obtain the effective heating time. In this way, it not only avoids the irrationality of billing based solely on valve opening time, but also ensures that only the heating process that truly meets the user's heating needs is included in the billing time, thereby enabling the heating metering system to achieve more accurate, fair, and energy-efficient heating metering.

[0068] Optionally, the room temperature information reported by the room temperature data acquisition device includes multiple acquisition times and the corresponding room temperature for each acquisition time. Step S302 specifically includes: determining whether each acquisition time is an effective heating time based on the room temperature information and activation information; accumulating the acquisition times used as effective heating times to obtain the effective heating duration.

[0069] For example, based on room temperature information and activation information, determining whether each collection time is an effective heating time includes: for each collection time, if the room temperature corresponding to the collection time is within the effective room temperature range and the smart valve is in the open state, then the collection time is regarded as an effective heating time.

[0070] For example, the room temperature information reported by the room temperature data acquisition device includes multiple timestamped acquisition moments and their corresponding room temperature values. When determining the effective heating duration, the cloud platform makes a judgment for each acquisition moment separately. Specifically, if the smart valve is open at the current acquisition moment and the corresponding room temperature is within the effective room temperature range, then the current sampling moment is determined to be an effective heating moment. In this way, by accumulating all the sampling moments determined to be effective heating moments, the effective heating duration for the target end user within the preset time period can be obtained.

[0071] Based on this, the cloud platform comprehensively judges each timestamp-enabled data collection moment reported by the room temperature data collector. Only when the room temperature corresponding to the collection moment is within the effective room temperature range, meaning the heating effect meets comfort requirements, and the smart valve is open, is the collection moment determined as a valid heating usage moment. This dual-condition judgment mechanism ensures that the billing logic not only considers whether the heating equipment is operating, but also focuses on whether the actual indoor temperature reaches the expected level. This avoids unreasonable billing caused by pipe preheating, overheating, or ineffective operation, achieving accurate metering based on actual heating performance.

[0072] Figure 4This illustration shows a flowchart of another method for determining effective heating duration provided in an embodiment of this application. The execution entity of this method is a cloud platform in the heating metering system. (Refer to...) Figure 4 As shown, the cloud platform is also used to perform the following steps:

[0073] S401. Divide the preset time period into multiple sub-time periods and assign time weights to each sub-time period.

[0074] Optionally, the preset time period, for example, is one day. The entire preset time period can be divided into multiple consecutive sub-time periods according to the division interval, such as 1 hour, and a time weight can be assigned to each sub-time period. The time weight is used to reflect the difference in the importance of heating demand in different sub-time periods. For example, a higher time weight can be set for the morning and evening peak periods to indicate that end users are more dependent on heating in that sub-time period, while a lower time weight can be set for the night or daytime unoccupied sub-time periods to indicate that end users are less dependent on heating in that sub-time period.

[0075] Optionally, the time weight includes an effective heat consumption threshold, which may be, for example, a minimum effective heat consumption duration or a room temperature stability standard, i.e., the minimum effective heat consumption duration or room temperature stability standard that the content of each sub-period must meet. In this application, the effective heat consumption information corresponding to each sub-period can be determined based on the actual heat consumption situation of each sub-period, and then the effective heat consumption information corresponding to each sub-period can be compared with the effective heat consumption threshold to allocate time weights to each sub-period.

[0076] S402. Determine the rate of change of room temperature for each sub-period.

[0077] Optionally, for each sub-period, the cloud platform calculates the rate of change of room temperature within that sub-period based on room temperature data reported by the room temperature data collector at multiple collection times. This rate of change represents the slope of temperature change per unit time. Specifically, the temperature change trend between adjacent collection points can be calculated through linear fitting or the difference method, thereby obtaining the overall heating, cooling, or stabilizing trend of that sub-period.

[0078] For example, if the temperature continues to rise during a certain sub-period and the rate of change is positive and large, it indicates that the temperature is in a warming phase. If the rate of change is close to zero, it indicates that the room temperature is stabilizing.

[0079] S403. For each sub-period, determine whether the room temperature is stable within the sub-period based on the room temperature change rate corresponding to the sub-period. If so, determine the effective heating duration within the sub-period based on the time weight of the sub-period.

[0080] Optionally, for each sub-period, the cloud platform determines whether the room temperature is stable within that sub-period based on its rate of change. For example, a rate of change threshold can typically be set, such as 0.2°C / min. If the rate of change in room temperature within a sub-period is lower than this threshold, the room temperature is considered to have stabilized, meaning the heating has reached the user-set target and maintained a balance, constituting a highly efficient and effective heating phase. In this case, the effective heating duration within the sub-period can be further determined by incorporating the time weight of the sub-period.

[0081] For example, for sub-periods that meet the stability conditions, the cloud platform further combines the time weight of the sub-period to determine the effective heating duration within that sub-period. For instance, if the weight of the sub-period is 1.2, the effective heating duration is actually calculated as 1.2 times the duration. In this way, by combining the room temperature change rate with the time weight to determine the effective heating duration within each sub-period, the selection of high-quality heating periods is achieved.

[0082] S404. Determine the effective heating duration within the preset time period based on the effective heating duration within each sub-time period.

[0083] Optionally, the cloud platform sums up the determined effective heating durations in all sub-periods to obtain the total effective heating duration for the entire preset period. It is worth noting that, since the contribution of each sub-period has been weighted or filtered according to its time weight and stability, the final determined effective heating duration is no longer a simple sum of the start times, but a comprehensive evaluation value reflecting the matching degree between heating quality and user demand in different periods.

[0084] Based on this, in determining the effective heating duration, the cloud platform not only considers whether the smart valve is open and whether the room temperature meets the standard, but also introduces two new dimensions: time importance weight and room temperature stability. This allows it to identify truly efficient and comfortable heating processes and assign them higher metering weights, while reasonably calculating heating usage during periods of frequent start-stop, drastic fluctuations, or non-critical periods. This significantly improves the rationality of heating metering in the heating metering system.

[0085] Figure 5 This illustration shows a flowchart of a method for determining heating billing information according to an embodiment of this application. The execution entity of this method is a cloud platform in the heating metering system. (Refer to...) Figure 5 As shown, the cloud platform determines the heating billing information for the target end user within a preset time period based on the effective heating duration, specifically including the following steps:

[0086] S501. Determine the room temperature coefficient based on the average room temperature within a preset time period.

[0087] Optionally, the cloud platform can calculate the average room temperature of the preset time period based on the room temperature corresponding to multiple collection times reported by the room temperature collector within the preset time period, and determine the room temperature coefficient in combination with the regional benchmark temperature. This room temperature coefficient is used to reflect the degree of deviation of the user's actual heating level from the regional benchmark temperature.

[0088] Alternatively, the room temperature coefficient can be calculated based on the following formula (1):

[0089] K = 1 + α(T) avg -T base (1)

[0090] Where K represents the room temperature coefficient, T avg T represents the average room temperature during a preset time period. base This represents the regional reference temperature, and α represents the adjustment coefficient.

[0091] For example, the regional reference temperature T base For example, if the temperature is 18℃, the adjustment coefficient α is 0.02. The adjustment coefficient α is used to control the impact of room temperature deviation on billing. It can be dynamically adjusted according to the actual heating temperature in the region.

[0092] For example, when the average room temperature T avg Temperature above the regional benchmark temperature T base When the room temperature coefficient K is greater than 1, it indicates a higher demand for heat, while when the average room temperature T... avg Below the regional reference temperature T base When the room temperature coefficient K is less than 1, it indicates that the heating demand is lower.

[0093] S502. Based on the effective heating duration and room temperature coefficient, determine the heating billing information for the target end user within the preset time period.

[0094] Optionally, the heating billing information for the target end user within a preset time period can be calculated based on the following formula (2):

[0095] M=N×t×K(2)

[0096] Where M represents heating billing information, N represents basic billing information, t represents effective heating duration, and K represents room temperature coefficient.

[0097] For example, the basic billing information N is the basic cost per unit time. In the process of determining the heating billing information of the target terminal user in the preset time period, the cloud platform comprehensively considers the effective heating duration t and the room temperature coefficient K. The room temperature coefficient K, as a dynamic adjustment factor, reflects the difference in the user's heating intensity. Thus, the heating billing information M of the target terminal user in the preset time period is calculated through the above formula (2), realizing the accurate link between the metering results and the user's actual heat consumption and comfort needs, and ensuring the rationality of the metering results.

[0098] Furthermore, the cloud platform is also used to: update the basic billing information if the effective heating duration exceeds a preset threshold, and determine the heating billing information based on the updated basic billing information, the effective heating duration, and the room temperature coefficient.

[0099] For example, after calculating the effective heating duration, the cloud platform further introduces a dynamic billing adjustment mechanism. Specifically, when the effective heating duration of an end user within a preset time period exceeds a preset threshold, for example, if the preset time period is 24 hours and the end user's effective heating duration within the preset time period is 12 hours, while the preset threshold is 10 hours, then the condition of the effective heating duration exceeding the preset threshold is met, and the end user can be determined to be a user with high heating demand. The cloud platform can update the basic billing information and, based on the updated basic billing information, recalculate the final heating billing information in combination with the end user's actual effective heating duration and room temperature coefficient.

[0100] Based on this, the heating metering system of this application dynamically adjusts the heating billing by introducing a room temperature coefficient. The average room temperature is used as the core indicator of thermal comfort. The room temperature coefficient is generated by combining the regional benchmark temperature and the adjustment coefficient. The final heating billing information is determined based on the room temperature coefficient and the effective heating duration, so that the billing results can truly reflect the user's heat energy consumption level.

[0101] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps performed by each terminal device group, data concentrator, and cloud platform in the heating metering system described in any of the preceding claims.

[0102] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces; the indirect coupling or communication connection of devices or modules can be electrical, mechanical, or other forms.

[0103] Furthermore, the functional units in the various embodiments of this application 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. If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or part 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: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0104] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A heating metering system, characterized in that, include: Multiple terminal device groups, data concentrators, and cloud platforms, wherein each terminal device group includes a room temperature sensor and a smart valve; The room temperature sensor is used to collect room temperature information in real time. The smart valve is used to record activation information, which includes valve status and cumulative opening time. The valve status includes either an open state or a closed state. The data concentrator is used to receive the room temperature information reported by each of the room temperature collectors and the activation information reported by each of the smart valves, and to report the room temperature information and the activation information to the cloud platform. The cloud platform is used to determine the effective heating duration of the target terminal user in the target terminal device group within a preset time period based on the room temperature information reported by the room temperature collector in the target terminal device group and the activation information reported by the smart valve, and to determine the heating billing information of the target terminal user within the preset time period based on the effective heating duration. The step of receiving room temperature information reported by each of the room temperature collectors and activation information reported by each of the smart valves, and reporting the room temperature information and activation information to the cloud platform, includes: the data concentrator receiving the room temperature information reported by each of the room temperature collectors and activation information reported by each of the smart valves in real time, and storing the room temperature information reported by each of the room temperature collectors and activation information reported by each of the smart valves in the local memory of the data concentrator; the data concentrator reporting the room temperature information reported by each of the room temperature collectors and activation information reported by each of the smart valves stored in the local memory to the cloud platform at preset time intervals; The step of determining the effective heating duration of the target terminal user belonging to the target terminal device group within a preset time period based on the room temperature information reported by the room temperature collector in the target terminal device group and the activation information reported by the smart valve includes: determining the effective room temperature range based on the preset target temperature and the effective temperature difference range; and determining the effective heating duration of the target terminal user belonging to the target terminal device group within the effective room temperature range within the preset time period based on the room temperature information reported by the room temperature collector and the activation information reported by the smart valve, wherein the room temperature information reported by the room temperature collector includes multiple collection times and the room temperature corresponding to each collection time.

2. The system according to claim 1, characterized in that, The step of determining the effective heating duration for the target terminal users belonging to the target terminal device group within the effective room temperature range during the preset time period, based on the room temperature information reported by the room temperature collector and the activation information reported by the smart valve, includes: Based on the room temperature information and the activation information, determine whether each of the collection times is an effective heat utilization time; The effective heating duration is obtained by accumulating the collected data as effective heating time.

3. The system according to claim 2, characterized in that, The step of determining whether each collection time is an effective heat consumption time based on the room temperature information and the activation information includes: For each of the aforementioned collection times, if the room temperature corresponding to the collection time is within the effective room temperature range and the smart valve is in the open state, then the collection time is taken as the effective heat usage time.

4. The system according to claim 1, characterized in that, The cloud platform is also used for: The preset time period is divided into multiple sub-time periods, and a time weight is assigned to each sub-time period, the time weight including an effective heat consumption threshold; Determine the rate of change of room temperature for each of the sub-time periods; For each sub-time period, the room temperature within the sub-time period is determined based on the room temperature change rate corresponding to the sub-time period. If it is stable, the effective heating duration within the sub-time period is determined based on the time weight of the sub-time period. The effective heating duration within the preset time period is determined based on the effective heating duration within each of the sub-time periods.

5. The system according to claim 1, characterized in that, The step of determining the heating billing information of the target end user within the preset time period based on the effective heating duration includes: The room temperature coefficient is determined based on the average room temperature during the preset time period; Based on the effective heating duration and the room temperature coefficient, the heating billing information for the target end user within the preset time period is determined.

6. The system according to claim 5, characterized in that, The step of determining the room temperature coefficient based on the average room temperature within the preset time period includes: Based on formula The room temperature coefficient was calculated. in, Indicates the room temperature coefficient. This represents the average room temperature during the preset time period. Indicates the regional reference temperature. This represents the adjustment coefficient.

7. The system according to claim 5, characterized in that, The step of determining the heating billing information for the target end user within the preset time period based on the effective heating duration and the room temperature coefficient includes: Based on formula The heating billing information is calculated. in, This indicates heating billing information. Indicates basic billing information, Indicates the effective heating duration. This represents the room temperature coefficient.

8. The system according to claim 7, characterized in that, The cloud platform is also used for: If the effective heating duration exceeds a preset threshold, the basic billing information is updated, and the heating billing information is determined based on the updated basic billing information, the effective heating duration, and the room temperature coefficient.

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