Heat supply network metering data supervision method and system

Abstract

The invention relates to the technical field of data processing, in particular to a heat supply network metering data supervision method and system, and the method comprises the steps: obtaining heat supply network metering data of a heat supply system within a preset time period; determining a data normal range according to the average value and the standard deviation, and determining the heat data exceeding the data normal range as initial abnormal heat data; determining first predicted heat data at any moment in the preset time period according to the heat data at each moment in the preset time period and the outdoor temperature data; determining the assignment of the initial abnormal heat data according to the variable quantity of the heat data between a target moment corresponding to the initial abnormal heat data and a previous moment adjacent to the target moment in a preset time period and the variable quantity between the first predicted heat data; and under the condition that the assigned value is larger than the first threshold value, the initial abnormal heat data is determined as the actual abnormal heat data. Therefore, the monitoring accuracy is improved, and the fault detection rate in the heat supply system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to a method and system for supervising heat network measurement data. Background Art

[0002] Heat network measurement data refers to data of relevant parameters such as heat, temperature, pressure, and flow rate collected by various sensors, metering instruments, etc. installed at heat sources, heat exchange stations, pipe networks, and user ends in a central heating system. It is used to monitor and control the operation status of the heating system, evaluate energy usage, optimize heating strategies, and improve energy utilization efficiency.

[0003] In some scenarios, heat network measurement data is monitored to evaluate the operation status of the heating system. Among them, there are many factors causing abnormal heat data in heat network measurement data. For example, sensor failures, pipe leaks, and damages in the heating system can all cause abnormal heat data. In addition, sudden changes in environmental factors can also affect heating demand, resulting in instantaneous fluctuations in heat data. However, this kind of fluctuation caused by environmental factors is not necessarily a fault in the heating system. When monitoring heat data, the threshold method and time series analysis method are often used to detect abnormal data, and it is easy to misjudge normal heat data affected by environmental factors as abnormal data. Thus, when monitoring the heat in heat network measurement data, the monitoring accuracy is low, resulting in a low fault detection rate in the heating system. Summary of the Invention

[0004] In order to solve the technical problems of low accuracy in heat monitoring of heat network measurement data and low fault detection rate in the heating system, the purpose of the present invention is to provide a method and system for supervising heat network measurement data, and the specific technical solutions adopted are as follows:

[0005] In the first aspect, an embodiment of the present invention provides a method for supervising heat network measurement data, including: obtaining heat network measurement data of a heating system within a predetermined time period, where the heat network measurement data includes heat data and outdoor temperature data corresponding to the heat data; calculating the average value and standard deviation of the heat data within the predetermined time period, and determining the normal data range according to the average value and standard deviation, and determining the heat data exceeding the normal data range as initial abnormal heat data; determining the first predicted heat data at any moment within a predetermined time period according to the heat data and outdoor temperature data at each moment within the predetermined time period; determining the assignment of the initial abnormal heat data according to the change amount of the heat data between the target moment corresponding to the initial abnormal heat data within the predetermined time period and the previous moment adjacent to the target moment and the change amount between the first predicted heat data; and determining the initial abnormal heat data as actual abnormal heat data when the assignment is greater than a first threshold.

[0006] Optionally, obtaining the heat network measurement data of the heating system within a predetermined time period includes: obtaining the supply water temperature of the supply water pipe, the return water temperature of the return water pipe, the first flow rate in the supply water pipe, and the specific heat capacity of water in the heating system; calculating the absolute value of the first difference between the supply water temperature and the return water temperature; and determining the product of the first flow rate, the specific heat capacity, and the absolute value of the first difference as the heat data.

[0007] Optionally, determining the first predicted heat data at any moment within a predetermined time period according to the heat data and the outdoor temperature data at each moment within the predetermined time period includes: determining the second predicted heat data at any moment within the predetermined time period according to the heat data at the first moment within the predetermined time period and the change amount of the heat data at the first moment within the predetermined time period; determining the predicted outdoor temperature at any moment within the predetermined time period according to the outdoor temperature data at the first moment within the predetermined time period and the change amount of the outdoor temperature data at the first moment within the predetermined time period; using the first ratio of the second predicted heat data at the first moment within the predetermined time period to the predicted outdoor temperature and the change amount of the ratio between the change amount of the second predicted heat data at the adjacent moment and the change amount of the predicted outdoor temperature, determining the second ratio between the change amount of the heat data and the change amount of the outdoor temperature data at any moment within the predetermined time period; and determining the first predicted heat data at any moment within the predetermined time period according to the heat data at a moment before any moment within the predetermined time period, the second ratio at any moment, and the change amount of the predicted outdoor temperature between any moment and the moment before any moment.

[0008] Optionally, determining the second predicted heat data at any moment within a predetermined time period according to the heat data at the first moment within the predetermined time period and the change amount of the heat data at the first moment within the predetermined time period includes: calculating the reciprocal of the sorting value at any moment within the predetermined time period, and calculating the first product of the reciprocal and the change amount of the heat data at the first moment; and determining the first sum value between the first product and the heat data at the first moment as the second predicted heat data at any moment within the predetermined time period.

[0009] Optionally, determining the predicted outdoor temperature at any moment within a predetermined time period according to the outdoor temperature data at the first moment within the predetermined time period and the change amount of the outdoor temperature data at the first moment within the predetermined time period includes: calculating the reciprocal of the first difference between the sorting value at any moment within the predetermined time period and a predetermined value, and calculating the second product of the reciprocal of the first difference and the change amount of the outdoor temperature data at the first moment; and determining the second sum value between the second product and the outdoor temperature data at the first moment as the predicted outdoor temperature at any moment within the predetermined time period.

[0010] Optionally, determining a second ratio between the change in heat data and the change in outdoor temperature data at any moment within a predetermined time period by using the first ratio of the second predicted heat data at the first moment within the predetermined time period to the predicted outdoor temperature and the change in the ratio between the change in the second predicted heat data at adjacent moments and the change in the predicted outdoor temperature includes: calculating the reciprocal of the second difference between the ranking value at any moment within the predetermined time period and a predetermined value, and the third product between the reciprocal of the second difference and the change in the ratio between the change in the second predicted heat data at adjacent moments and the change in the predicted outdoor temperature; determining the third sum value between the second predicted heat data at the first moment within the predetermined time period and the third product as the second ratio.

[0011] Optionally, determining the first predicted heat data at any moment within a predetermined time period according to the heat data at a moment before any moment within the predetermined time period, the second ratio at any moment, and the change in the predicted outdoor temperature between any moment and the moment before any moment includes: calculating the fourth product between the second ratio at any moment and the change in the predicted outdoor temperature between any moment and the moment before any moment; determining the fourth sum value between the fourth product and the heat data at a moment before any moment within the predetermined time period as the first predicted heat data at any moment within the predetermined time period.

[0012] Optionally, determining the assignment of the initial abnormal heat data according to the change in the heat data and the change in the first predicted heat data between the target moment corresponding to the initial abnormal heat data within the predetermined time period and the moment immediately preceding the target moment includes: calculating the absolute value of the third difference between the change in the heat data and the change in the first predicted heat data between the target moment and the moment immediately preceding the target moment; performing normalization processing on the absolute value of the third difference to obtain the assignment of the initial abnormal heat data.

[0013] Optionally, after determining that the initial abnormal heat data is the actual abnormal heat data when the assignment is greater than the first threshold, the method further includes: clustering the actual abnormal heat data to obtain a plurality of first clusters; merging the first clusters according to the distances between the first cluster centers of the first clusters to obtain second clusters; using the second cluster centers of the second clusters as the second threshold; determining that the real-time heat data is abnormal and issuing an alarm when the real-time heat data of the heating system is greater than the second threshold.

[0014] Second aspect, an embodiment of the present invention provides a heat network metering data supervision system, including: a processor and a memory; wherein, the memory is used for storing a computer program that can run on the processor; the processor is used for executing the program stored on the memory to implement the steps of the heat network metering data supervision method as mentioned in the first aspect.

[0015] The present invention has the following beneficial effects: First, obtain the heat network metering data of the heating system within a predetermined time period, where the heat network metering data includes heat data and outdoor temperature data corresponding to the heat data; then calculate the average value and standard deviation of the heat data within the predetermined time period, and determine the normal data range according to the average value and standard deviation; secondly, determine the heat data that exceeds the normal data range as the initial abnormal heat data; then determine the first predicted heat data at any moment within the predetermined time period according to the heat data and outdoor temperature data at each moment within the predetermined time period; and determine the assignment of the initial abnormal heat data according to the change amount between the heat data corresponding to the target moment of the initial abnormal heat data within the predetermined time period and the heat data at the previous moment adjacent to the target moment and the change amount between the first predicted heat data; finally, when the assignment is greater than the first threshold, determine the initial abnormal heat data as the actual abnormal heat data.

[0016] In this way, the embodiment of the present invention can utilize the relationship characteristics between the heat data and the outdoor temperature data in the heating system to locate the abnormal heat data and identify the abnormal change of the heat data caused by the temperature drop due to abnormal weather. That is, first preliminarily determine the abnormal heat data that is not within the normal range, then predict the predicted heat data at each moment according to the corresponding relationship between the heat data and the outdoor temperature data, determine the assignment of the initially determined abnormal heat data according to the predicted heat data and the initially determined abnormal heat data, and if the assignment is greater than the first threshold, determine the initially determined abnormal heat data as the real heat abnormal data, rather than the abnormal heat data caused by the temperature change in the external environment. In this way, by adopting the technical solution provided by the embodiment of the present invention, when monitoring the heat in the heat network metering data, the monitoring accuracy is improved, and the fault detection rate in the heating system is also improved. Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to these drawings without creative efforts.

[0018] Figure 1 It is a flowchart of a heat network metering data supervision method provided by an embodiment of the present invention;

[0019] Figure 2 A schematic diagram of the curves of outdoor temperature data and corresponding heat data changing with time provided by an embodiment of the present invention;

[0020] Figure 3 A schematic structural diagram of a heat network metering data supervision system provided by an embodiment of the present invention. Detailed implementation manners

[0021] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details a heat network metering data supervision method and system proposed according to the present invention, including its specific implementation manners, structures, features and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0023] The following specifically describes the specific solutions of a heat network metering data supervision method and system provided by the present invention with reference to the accompanying drawings.

[0024] Embodiment 1:

[0025] Please refer to Figure 1 , which shows a flowchart of a heat network metering data supervision method provided by an embodiment of the present invention, including:

[0026] S101, obtaining the heat network metering data of the heating system within a predetermined time period.

[0027] Among them, the heat network metering data includes heat data and outdoor temperature data corresponding to the heat data.

[0028] Specifically, the embodiment of the present invention includes obtaining historical heat data for multiple days at the same time period within a predetermined time period from a relevant database, historical outdoor temperature data corresponding to the historical heat data, obtaining real-time heat data within a predetermined time period, and outdoor temperature data corresponding to the real-time heat data. For the historical heat data and historical outdoor temperature data at the same time period of these days, calculate the average value of the historical heat data and historical outdoor temperature data for each time period as the historical data for each time period.

[0029] For real-time heat data and outdoor temperature data, the real-time heat data and outdoor temperature data within a predetermined time period can be obtained. Herein, the predetermined time period can be determined according to actual circumstances, and in the embodiments of the present invention, it is taken as 24 hours. Then, the 24 hours are evenly divided into multiple time points, where the 24 hours can be evenly divided into multiple time points at an interval of 1 second. In the embodiments of the present invention, a series of sensors and data acquisition devices are usually adopted in the heating system to collect various parameters in the heating system. For example, the supply water temperature in the supply water pipeline, the return water temperature in the return water pipeline, and the water flow rate in the supply water pipeline in the heating system are collected. In the embodiments of the present invention, the temperature data of the supply water pipeline in a certain period of time in the residential area and the return water temperature data corresponding to the temperature data of the supply water pipeline at the same moment are obtained. Since the pipelines in the residential area are short and the distance from the supply water pipeline to the return water pipeline of the large user is limited, the instantaneous difference between the supply water temperature and the return water temperature can basically reflect the heating condition, and the delay error can be ignored. Therefore, in the embodiments of the present invention, the Kalman filtering algorithm is used to perform dynamic smoothing processing on the return water temperature and the supply water temperature to obtain the denoised return water temperature and supply water temperature. For the obtained heat data, there are corresponding time points. In the embodiments of the present invention, the time points corresponding to these heat data are obtained, and based on this time point, the real-time outdoor temperature data is obtained from the network meteorological platform, and the obtained outdoor temperature data is fitted with a polynomial curve using the S-G filter to smooth the noise to obtain the denoised outdoor temperature data.

[0030] When obtaining the heat data of the heating system, as an optional embodiment of the present invention, obtaining the heat network measurement data of the heating system within a predetermined time period includes: first, obtaining the supply water temperature in the supply water pipeline of the heating system, the return water temperature in the return water pipeline, the first flow rate in the supply water pipeline, and the specific heat capacity of water; then calculating the absolute value of the first difference between the supply water temperature and the return water temperature; and finally determining the product of the first flow rate, the specific heat capacity, and the absolute value of the first difference as the heat data.

[0031] Specifically, the first flow rate in the supply water pipeline refers to the water flow rate in the supply water pipeline, and the supply water temperature and the return water temperature can be measured by temperature sensors. In the embodiments of the present invention, the following formula is used to calculate the heat data:

[0032] Q 实际 =C×M×|T in -T out |

[0033] In the above formula, T in is the supply water temperature at any moment in the supply water pipeline. T out is the return water temperature at any moment in the return water pipeline. M is the first flow rate in the supply water pipeline at any moment. Q 实际 is the heat data calculated in real time. C represents the specific heat capacity of water.

[0034] S102. Calculate the average value and standard deviation of the heat data within a predetermined time period, determine the normal data range based on the average value and standard deviation, and determine the heat data that exceeds the normal data range as the initial abnormal heat data.

[0035] Specifically, the abnormality of the heating system will directly or indirectly lead to heat data. That is, when abnormalities such as sensor failures, pipeline leaks, and damages occur in the heating system, it will directly cause the heat data obtained to suddenly show a peak higher than the normal level. When the equipment is shut down or the sensor fails, the data will suddenly drop significantly. These abnormal situations will cause abnormal peaks or underestimation of the heat data. Therefore, based on the obtained heat data, the present invention embodiment determines the normal data range of the heat data according to the characteristics that the abnormal heat data will show peaks or valleys.

[0036] Further, after obtaining the heat data within a predetermined time period, calculate the average value and standard deviation of the heat data within the predetermined time period. In the present invention embodiment, the normal data range interval of the heat data is [a - 3b, a + 3b], where a - 3b represents the lower limit of the normal data range, a + 3b represents the upper limit of the normal data range, a represents the average value of the heat data within the predetermined time period, and b represents the standard deviation of the heat data within the predetermined time period. If the heat data at a certain moment is not within this normal data range, it indicates that the heat data may be abnormal heat data, and it is taken as the initial abnormal heat data. According to the method provided by the present invention embodiment, all the real-time heat data obtained within the predetermined time period is judged to obtain all the initial abnormal heat data suspected of being abnormal.

[0037] S103. Determine the first predicted heat data at any moment within a predetermined time period according to the heat data and outdoor temperature data at each moment within the predetermined time period.

[0038] Specifically, in the embodiments of the present invention, the abnormal heat data is presented in the form of peaks or troughs. The initial abnormal heat data is extracted and located. By analyzing the heat data of residents in a certain period of a day in this residential area, it is found that the use of the heating system by residents has obvious change characteristics during this period of a day. Therefore, based on this situation, the embodiments of the present invention will establish the change trend of heat data in a certain period of a day under normal circumstances to further screen the heat data. First, analyze the heat data in a certain period of a day. The heat data has certain change characteristics. By obtaining the heat data under normal circumstances in a certain period of a certain day, a curve graph is drawn. By analyzing the data change characteristics, it can be found that the data change characteristics are mainly affected by the usage habits of residents, that is, the air flow volume is larger and the heat dissipation is faster during the period when there is more activity of residents in the room, and the frequent opening and closing of doors and windows by residents will also cause heat loss. Therefore, the embodiments of the present invention determine the predicted heat data at any moment within a predetermined period based on the heat data and outdoor temperature data at each moment within the predetermined period.

[0039] Further, when determining the first predicted heat data at any moment within a predetermined period, as an optional embodiment of the present invention, first, according to the heat data at the first moment within the predetermined period and the change amount of the heat data at the first moment within the predetermined period, determine the second predicted heat data at any moment within the predetermined period; then, according to the outdoor temperature data at the first moment within the predetermined period and the change amount of the outdoor temperature data at the first moment within the predetermined period, determine the predicted outdoor temperature at any moment within the predetermined period; then, use the first ratio of the second predicted heat data at the first moment within the predetermined period to the predicted outdoor temperature and the change amount of the ratio between the change amount of the second predicted heat data at an adjacent moment and the change amount of the predicted outdoor temperature to determine the second ratio between the change amount of the heat data and the change amount of the outdoor temperature data at any moment within the predetermined period; finally, according to the heat data at a moment before any moment within the predetermined period, the second ratio at any moment, and the change amount of the predicted outdoor temperature between any moment and the moment before any moment, determine the first predicted heat data at any moment within the predetermined period.

[0040] Specifically, the change amount of the heat data at the first moment can be the difference between the heat data at a moment before the predetermined period and the heat data at the first moment within the predetermined period. In addition, each moment within the predetermined period is sorted in chronological order, that is, in terms of time from early to late, they are the first moment point, the second moment point to the nth moment point respectively.

[0041] Further, when determining the second predicted heat data at any moment within a predetermined time period, as an alternative embodiment of the present invention, first calculate the reciprocal of the sorting value at any moment within the predetermined time period, and calculate the first product of the reciprocal and the change in the heat data at the first moment; then determine the first sum value between the first product and the heat data at the first moment as the second predicted heat data at any moment within the predetermined time period.

[0042] Specifically, the embodiment of the present invention can calculate the second predicted heat data at any moment within the predetermined time period by using the following formula:

[0043]

[0044] In the calculation formula of the second predicted heat data, Q(t n ) 历史 represents the value of the predicted heat data at the nth moment within the extracted predetermined time period, that is, the second predicted heat data. Q(t1) represents the value of the historical heat data at the first moment within the extracted predetermined time period. ΔQ(t1) represents the change in the historical heat data at the first moment within the extracted predetermined time period. represents the correlation assignment between the change in the historical heat data at the nth moment and the change in the historical heat data at the first moment within the extracted predetermined time period, represents the change in the historical heat data at the nth moment within the historical heat data in the predetermined time period. The logic of this formula is that the value of the historical heat data at the nth moment in the predetermined time period is represented by the sum of the value of the historical heat data at the first moment and the change in the historical heat data at the nth moment.

[0045] In the calculation formula of the value of the predicted heat data at the nth moment, n represents the nth moment. The larger n is, the greater the time interval between the nth moment and the first moment, that is, the farther the distance between the data of the two moments. Since the heat data is time-series data that changes over time, it can be further explained that the numerical value of the change in the historical heat data at the nth moment in the predetermined time period is less affected by the change in the historical heat data at the first moment in the predetermined time period, and the correlation assignment between the two changes is smaller. Therefore, n and the correlation assignment are negatively correlated. Therefore, in order to ensure the rationality of the calculation relationship between the correlation assignment and the predicted heat, the reciprocal of n is used as the correlation assignment between the two. The smaller n is, the larger it is, and the greater the correlation assignment.

[0046] Further, the method according to the embodiment of the present invention represents the change trend of the heat data within a day through the daily change characteristics of the heat data within a day, and locates the abnormal heat data by the gap between the change amount of the original heat data and the change amount of the predicted heat data. Although the interference of the change characteristics of the heat data itself on the detection of abnormal heat data is excluded, it is difficult to exclude the interference caused by the temperature factor in the weather on the change of the heat data to the abnormal heat data. Through analysis, it is found that when the external environmental temperature data changes violently in a short period of time, such as a cold snap or a sudden rise in temperature, it will directly affect the heat demand of the building. For every degree decrease in the outdoor temperature, the heat data will increase by a corresponding amount, and as time goes by, the correlation between the change amount of the outdoor temperature over time and the initial change amount becomes smaller and smaller. As Figure 2 shown, Figure 2 FIG. is a schematic diagram of a curve showing the change of outdoor temperature data and the corresponding heat data over time provided by an embodiment of the present invention. From Figure 2 it can be seen that whenever the outdoor temperature data decreases, the heat data increases, and when the outdoor temperature rises, the heat data decreases. Therefore, it can be concluded that there is heat exchange between the indoor temperature and the outdoor temperature. Since the rise and fall of the outdoor temperature will cause changes in the indoor temperature, which in turn affects the change of the heat data. That is, there is synchronization between the change of the heat data and the outdoor temperature data. Specifically, for every degree decrease in the outdoor temperature, the heat data will increase by a corresponding amount.

[0047] Further, when determining the predicted outdoor temperature at any moment within a predetermined time period, as an optional embodiment of the present invention, first calculate the reciprocal of the first difference between the ranking value at any moment within the predetermined time period and the predetermined value, and the second product between the reciprocal of the first difference and the change amount of the outdoor temperature data at the first moment; then determine the second sum of the second product and the outdoor temperature data at the first moment as the predicted outdoor temperature at any moment within the predetermined time period.

[0048] Specifically, the predetermined value in the embodiment of the present invention can be determined according to the actual situation, and the value in the embodiment of the present invention is 1.

[0049] The embodiment of the present invention specifically uses the following formula to calculate the predicted outdoor temperature at any moment within a predetermined time period.

[0050]

[0051] In the calculation formula of the predicted outdoor temperature, T 外，m represents the outdoor temperature data at the mth moment within the predicted predetermined time period, that is, the predicted outdoor temperature. T 外，1 represents the outdoor temperature data at the first moment within the predetermined time period. ΔT外，1 Indicates the change amount of the outdoor temperature data at the first moment in a predetermined time period. Indicates the correlation assignment between the change amount of the outdoor temperature data at the m-th moment and the change amount of the outdoor temperature data at the first moment. In the embodiment of the present invention, the logic of this formula is to express the outdoor temperature data at the m-th moment within a predetermined time period in the form of the sum of the outdoor temperature data at the first moment within the predetermined time period and the change amount of the outdoor temperature data at the m-th moment.

[0052] Furthermore, for every degree decrease in the outdoor temperature, the heat data will increase by a corresponding amount, and as time increases, the ratio of the change amount of the outdoor temperature at the same moment to the change amount of the heat data at the same moment also has the characteristic that the correlation with the initial ratio becomes smaller over time. Among them, the ratio of the change amount of the outdoor temperature at the same moment to the change amount of the heat data at the same moment is represented by Indicates. ΔQ m Indicates the change amount of the heat data at the m-th moment within a predetermined time period. ΔT m Indicates the change amount of the outdoor temperature data at the m-th moment within a predetermined time period, that is, ΔT m is based on the predicted outdoor temperature T at the m-th moment in the above embodiment of the present invention 外，m and the predicted outdoor temperature T at the (m - 1)-th moment 外，m-1 by taking the difference, that is, ΔT m = T 外，m - T 外，m-1 . B m Indicates the second ratio between the change amount of the heat data at the m-th moment and the change amount of the outdoor temperature data within a predetermined time period.

[0053] Furthermore, in the embodiment of the present invention, the following method is adopted to quantify the second ratio. First, calculate the reciprocal of the second difference between the ranking value at any moment within a predetermined time period and a predetermined value, and the third product between the reciprocal of the second difference and the change amount of the ratio between the second predicted change amount of the heat data and the change amount of the predicted outdoor temperature at adjacent moments; then determine the third sum value between the second predicted heat data at the first moment within the predetermined time period and the third product as the second ratio.

[0054] Specifically, the embodiment of the present invention represents the second ratio by the following formula:

[0055]

[0056] In the calculation formula of the second ratio, B mIt represents the ratio between the change in heat data and the change in outdoor temperature data at the m-th moment within a predetermined time period. B1 represents the ratio between the change in heat data and the change in outdoor temperature data at the 1st moment. ΔB2 represents the difference between the ratio of the change in heat data and the change in outdoor temperature data at the 2nd moment and the ratio of the change in heat data and the change in outdoor temperature data at the 1st moment. It represents the correlation assignment between the change in the ratio of the change in heat data and the change in outdoor temperature data at the m-th moment and the change in the ratio of the change in heat data and the change in outdoor temperature data at the 1st moment.

[0057] It should be noted that for m in the calculation formulas for predicting outdoor temperature and the second ratio, its calculation principle is the same as that of n in the calculation formula for the second predicted heat data. Therefore, the calculation principle of m will not be repeatedly analyzed here.

[0058] Furthermore, when performing anomaly analysis on heat data, if only the heat data is analyzed alone, the situation of the change in heat data caused by the change in outdoor temperature will be included. However, the above method in the embodiment of the present invention represents the relationship between outdoor temperature data and heat data in the form of a ratio through the change characteristics between outdoor temperature data and heat data. Therefore, based on the above ratio, the predicted heat data at a certain moment within a predetermined time period for heat data is updated. Among them, as an optional embodiment of the present invention, first, according to the heat data at a moment before any moment within a predetermined time period, the second ratio at any moment, and the change in the predicted outdoor temperature between any moment and the moment before any moment, determining the first predicted heat data at any moment within a predetermined time period includes: then calculating the fourth product between the second ratio at any moment and the change in the predicted outdoor temperature between any moment and the moment before any moment; finally, determining the fourth sum value between the fourth product and the heat data at a moment before any moment within a predetermined time period as the first predicted heat data at any moment within a predetermined time period.

[0059] Specifically, the embodiment of the present invention calculates the first predicted heat data at any moment within a predetermined time period using the following formula:

[0060] Q 历史，m =Q m-1 +B m ×ΔT m

[0061] In the above formula, Q 历史 , m is the first predicted heat data at the m-th moment within a predetermined time period. Q m-1is the value of the heat data at the (m - 1)-th moment within a predetermined time period. B m represents the ratio between the change amount of the heat data at the m-th moment and the change amount of the outdoor temperature data within a predetermined time period. ΔT m is based on the predicted outdoor temperature T at the m-th moment in the above embodiments of the present invention 外，m and the predicted outdoor temperature T at the (m - 1)-th moment 外，m-1 by taking the difference, that is, ΔT m = T 外，m - T 外，m-1 . The logic of this formula is that the historical value of the heat data at the m-th moment within a predetermined time period considering the influence of the change in outdoor temperature is expressed as the sum of the historical value of the heat data at the (m - 1)-th moment within the predetermined time period and the change amount of the heat data at the m-th moment

[0062] S104. Determine the assignment of the initial abnormal heat data according to the change amount of the heat data between the target moment corresponding to the initial abnormal heat data within a predetermined time period and the previous moment adjacent to the target moment, and the change amount between the first predicted heat data

[0063] Specifically, in the embodiments of the present invention, the actual real-time heat data is subtracted from the first predicted heat data predicted in the above embodiments to represent the difference between the actual heat data and the original heat data, and further reflect the abnormal degree of the actual heat data. When determining the assignment of the initial abnormal heat data, as an optional embodiment of the present invention, first calculate the absolute value of the third difference between the change amount of the heat data between the target moment and the previous moment adjacent to the target moment and the change amount between the first predicted heat data; then perform normalization processing on the absolute value of the third difference to obtain the assignment of the initial abnormal heat data

[0064] Specifically, taking the target moment as the t-th moment as an example in the embodiments of the present invention, the embodiments of the present invention use the following formula to calculate the assignment of the initial abnormal heat data

[0065] f t = norm|ΔQ 实际 , t' -ΔQ 历史，t' |

[0066] In the above formula, f t represents the assignment size of the initial abnormal heat data at the t-th moment as the abnormal heat data. ΔQ 实际，t' represents the change amount between the heat data at the t-th moment and the heat data at the previous moment of the t-th moment. ΔQ 历史 , tRepresents the change amount between the predicted first predicted heat data based on the passing relationship characteristics between the heat data at t moments and the heat data at the previous moment. |ΔQ 实际，t' -ΔQ 历史，t' | Represents the gap between the change amounts of these two heat data, and norm indicates normalizing |ΔQ 实际，t' -ΔQ 历史，t' |. If |ΔQ 实际，t' -ΔQ 历史，t' | is larger, f t is larger, indicating that the assignment value for this heat data as an abnormal heat data is larger.

[0067] S105. When the assignment value is greater than the first threshold, determine the initial abnormal heat data as the actual abnormal heat data.

[0068] Specifically, the first threshold can be determined according to the actual situation, and in the embodiments of the present invention, it takes a value of 0.8. When the assignment value f of the initial abnormal heat data t is greater than 0.8, it can be considered that this heat data is an actual abnormal heat data.

[0069] The embodiments of the present invention can utilize the relationship characteristics between the heat data and the outdoor temperature data in the heating system to locate abnormal heat data and identify abnormal changes in heat data caused by temperature drops due to abnormal weather. That is, first, initially determine abnormal heat data that is not within the normal range, then predict the predicted heat data at each moment according to the corresponding relationship between the heat data and the outdoor temperature data, determine the assignment value of the initially determined abnormal heat data according to the predicted heat data and the initially determined abnormal heat data. If this assignment value is greater than the first threshold, then determine that the initial abnormal heat data is the real heat abnormal data, rather than the abnormal heat data caused by temperature changes in the external environment. Thus, by adopting the technical solution provided by the embodiments of the present invention, when monitoring the heat in the heat network measurement data, the monitoring accuracy is improved, and the fault detection rate in the heating system is also improved.

[0070] Furthermore, the heat data threshold can also be determined according to the actual abnormal heat data to judge whether the real-time heat data is abnormal. As an optional embodiment of the present invention, cluster each actual abnormal heat data to obtain a plurality of first clusters; merge the first clusters according to the distances between the first cluster centers of each first cluster to obtain a second cluster; use the second cluster center of the second cluster as the second threshold; when the real-time heat data in the heating system is greater than the second threshold, determine that the real-time heat data is abnormal and issue an alarm.

[0071] Specifically, when merging the first clusters according to the distances between the first cluster centers of each first cluster, the two first clusters where the two closest first cluster centers are located can be merged to obtain a second cluster center, and the second cluster center can be the average value of these two first cluster centers. If the real-time heat data in the heating system is greater than the second threshold, it is determined that the real-time heat data is abnormal data, and an alarm is issued to remind the maintenance personnel to conduct inspections and repairs.

[0072] Embodiment 2:

[0073] Corresponding to the heat network metering data supervision method provided in the above embodiment, based on the same technical concept, an embodiment of the present invention further provides a heat network metering data supervision system, which is used to execute the above heat network metering data supervision method. Figure 3 As shown in the structural schematic diagram of a heat network metering data supervision system provided by an embodiment of the present invention. Figure 3 As shown. The heat network metering data supervision system may vary greatly due to configuration or performance differences, and may include one or more processors 301 and a memory 302. The memory 302 is used to store computer programs that can run on the processor 301. The processor 301 is used to execute the programs stored on the memory 302 to implement each step in the above Figure 1 method embodiment. Among them, the memory 302 can be short-term storage or persistent storage. The application programs stored in the memory 302 may include one or more modules (not shown in the figure), and each module may include a series of computer-executable instructions for the heat network metering data supervision system.

[0074] Furthermore, the processor 301 can be set to communicate with the memory 302 and execute a series of computer-executable instructions in the memory 302 on the heat network metering data supervision system. The heat network metering data supervision system may further include one or more power supplies 303, one or more wired or wireless network interfaces 304, one or more input / output interfaces 305, and one or more keyboards 306.

[0075] Specifically in this embodiment, the heat network metering data supervision system includes a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the bus. The memory is used to store computer programs. The processor is used to execute the programs stored on the memory to implement each step in the above Figure 1 method embodiment, and has the beneficial effects of the above method embodiment. To avoid repetition, the embodiments of the present invention will not be described in detail here.

[0076] It should be noted that the heat network metering data supervision system provided by the embodiments of the present invention and the heat network metering data supervision method provided by the embodiments of the present invention are based on the same application concept. Therefore, for the specific implementation of this embodiment, reference may be made to the implementation of the aforementioned heat network metering data supervision method, and they have the same or similar beneficial effects. The repeated parts will not be elaborated here.

[0077] It should be noted that the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0078] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for supervising heat network metering data, characterized in that, The above heat network measurement data supervision method includes: Obtaining heat network measurement data of a heat supply system within a predetermined time period, where the heat network measurement data includes heat quantity data and outdoor temperature data corresponding to the heat quantity data; Calculating the average value and standard deviation of the heat quantity data within the predetermined time period, determining the normal data range based on the average value and the standard deviation, and determining the heat quantity data exceeding the normal data range as initial abnormal heat quantity data; Determining the first predicted heat quantity data at any moment within the predetermined time period according to the heat quantity data and outdoor temperature data at each moment within the predetermined time period; Determining the assignment of the initial abnormal heat quantity data according to the change amount of the heat quantity data between the target moment corresponding to the initial abnormal heat quantity data within the predetermined time period and the previous moment adjacent to the target moment, and the change amount between the first predicted heat quantity data; When the assignment is greater than the first threshold, determining the initial abnormal heat quantity data as actual abnormal heat quantity data.

2. The heat network metering data supervision method according to claim 1, characterized in that, The obtaining of the heat network measurement data of the heat supply system within the predetermined time period includes: Obtaining the supply water temperature of the supply water pipeline, the return water temperature of the return water pipeline, the first flow rate in the supply water pipeline, and the specific heat capacity of water in the heat supply system; Calculating the absolute value of the first difference between the supply water temperature and the return water temperature; Determining the product of the first flow rate, the specific heat capacity, and the absolute value of the first difference as the heat quantity data.

3. The heat network metering data supervision method according to claim 1, wherein, The determining of the first predicted heat quantity data at any moment within the predetermined time period according to the heat quantity data and outdoor temperature data at each moment within the predetermined time period includes: Determining the second predicted heat quantity data at any moment within the predetermined time period according to the heat quantity data at the first moment within the predetermined time period and the change amount of the heat quantity data at the first moment within the predetermined time period; Determining the predicted outdoor temperature at any moment within the predetermined time period according to the outdoor temperature data at the first moment within the predetermined time period and the change amount of the outdoor temperature data at the first moment within the predetermined time period; Using the first ratio of the second predicted heat quantity data at the first moment within the predetermined time period to the predicted outdoor temperature and the change amount of the ratio between the change amount of the second predicted heat quantity data at an adjacent moment and the change amount of the predicted outdoor temperature, determining the second ratio between the change amount of the heat quantity data and the change amount of the outdoor temperature data at any moment within the predetermined time period; Determining the first predicted heat quantity data at any moment within the predetermined time period according to the heat quantity data at a moment before any moment within the predetermined time period, the second ratio at any moment, and the change amount of the predicted outdoor temperature between any moment and the moment before any moment.

4. The heat network metering data supervision method according to claim 3, characterized in that The determining of the second predicted heat quantity data at any moment within the predetermined time period according to the heat quantity data at the first moment within the predetermined time period and the change amount of the heat quantity data at the first moment within the predetermined time period includes: Calculate the reciprocal of the sorting value at any moment within the predetermined time period, and calculate the first product of the reciprocal and the change amount of the heat data at the first moment; Determine the first sum value between the first product and the heat data at the first moment as the second predicted heat data at any moment within the predetermined time period.

5. The heat network metering data supervision method according to claim 3, characterized in that, The determining of the predicted outdoor temperature at any moment within the predetermined time period according to the outdoor temperature data at the first moment within the predetermined time period and the change amount of the outdoor temperature data at the first moment within the predetermined time period includes: Calculate the reciprocal of the first difference between the sorting value at any moment within the predetermined time period and the predetermined value, and the second product of the reciprocal of the first difference and the change amount of the outdoor temperature data at the first moment; Determine the second sum value between the second product and the outdoor temperature data at the first moment as the predicted outdoor temperature at any moment within the predetermined time period.

6. The heat network metering data supervision method according to claim 3, wherein, The determining of the second ratio between the change amount of the heat data and the change amount of the outdoor temperature data at any moment within the predetermined time period by using the first ratio of the second predicted heat data and the predicted outdoor temperature at the first moment within the predetermined time period and the change amount of the ratio between the change amount of the second predicted heat data at an adjacent moment and the change amount of the predicted outdoor temperature includes: Calculate the reciprocal of the second difference between the sorting value at any moment within the predetermined time period and the predetermined value, and the third product of the reciprocal of the second difference and the change amount of the ratio between the change amount of the second predicted heat data at an adjacent moment and the change amount of the predicted outdoor temperature; Determine the third sum value between the second predicted heat data at the first moment within the predetermined time period and the third product as the second ratio.

7. The heat network metering data supervision method according to claim 3, wherein, The determining of the first predicted heat data at any moment within the predetermined time period according to the heat data at a moment before any moment within the predetermined time period, the second ratio at any moment, and the change amount of the predicted outdoor temperature between any moment and the moment before any moment includes: Calculate the fourth product of the second ratio at any moment and the change amount of the predicted outdoor temperature between any moment and the moment before any moment; Determine the fourth sum value between the fourth product and the heat data at a moment before any moment within the predetermined time period as the first predicted heat data at any moment within the predetermined time period.

8. The heat network metering data supervision method according to claim 1, wherein The determining of the assignment of the initial abnormal heat data according to the change amount of the heat data between the target moment corresponding to the initial abnormal heat data within the predetermined time period and the previous moment adjacent to the target moment and the change amount between the first predicted heat data includes: Calculate the absolute value of the third difference between the change amount of the heat data between the target moment and the previous moment adjacent to the target moment and the change amount between the first predicted heat data; Normalize the absolute value of the third difference to obtain the assignment of the initial abnormal heat data.

9. The heat network metering data supervision method according to claim 1, characterized in that, After determining that the initial abnormal heat data is the actual abnormal heat data when the assignment is greater than the first threshold, the method further includes: Cluster each of the actual abnormal heat data to obtain a plurality of first clusters; Merge the first clusters according to the distances between the first cluster centers of the first clusters to obtain second clusters; Use the second cluster center of the second cluster as the second threshold; When the real-time heat data of the heating system is greater than the second threshold, determine that the real-time heat data is abnormal and issue an alarm.

10. A heat network metering data supervision system, characterized in that, The heat network metering data supervision system includes: a processor and a memory; wherein, the memory is used to store a computer program that can run on the processor; the processor is used to execute the program stored on the memory to implement the steps of the heat network metering data supervision method according to any one of claims 1-9.

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