A heating system scheduling system based on cloud computing

Abstract

The present invention relates to a heating system scheduling system based on cloud computing, and in particular to the field of heating technology. The system comprises a heat load analysis module for acquiring pipeline information, environmental information and heating history data, for analyzing the heat load of each preset time period, a heat loss analysis module for analyzing the heat loss of a heating system pipeline, an adjustment module for adjusting the analysis process of the heat loss of the pipeline, a heat load distribution module for distributing the heat load of each preset time period, a correction module for correcting the heat load distribution process according to the acquired wind speed and sunshine time, a monitoring module for analyzing the return water temperature of the pipeline within the monitoring period after the heat load distribution is completed, and a feedback module for optimizing the analysis process of the heat load of the next monitoring period according to the temperature abnormality early warning result. The present invention improves the scheduling efficiency of the heating system and the utilization rate of energy.

Description

Technical Field

[0001] The present invention relates to the field of heating technology, and in particular to a heating system scheduling system based on cloud computing. Background Art

[0002] Traditional heating system scheduling mainly relies on manual experience and a single data source for decision-making, lacking intelligence and real-time performance. With the development of cloud computing technology, cloud computing-based intelligent heating system scheduling systems have gradually become an innovative technology that can make full use of technologies such as big data analysis, machine learning, and real-time monitoring to improve the operating efficiency of heating systems and user satisfaction.

[0003] Chinese Patent Publication No.: CN116663870A discloses a cloud computing-based heating system scheduling method and system, including uploading the thermal energy information obtained by the thermal energy sensor preset at the heating pipeline to the cloud computing service center through the front-end proxy server, and synchronously obtaining the electricity price information and thermal energy demand information of the target area; determining the thermal energy supply information based on the electricity price information and the power consumption information corresponding to the electricity price information, and uploading it to the cloud computing service center, if the thermal energy supply information cannot meet the thermal energy demand information, then obtaining the energy consumption cost, load transfer cost and load penalty cost corresponding to the target area to determine the comprehensive heating cost; setting the heating constraint conditions corresponding to the comprehensive heating cost, and determining the heating scheduling strategy corresponding to the target area through the preset heating scheduling optimization algorithm, taking the minimum comprehensive heating cost as the optimization goal; it can be seen that when analyzing the scheduling of the heating system, the scheme only considers the economic benefits and pipeline factors. When analyzing the scheduling of the heating system in mountainous areas at high altitudes, there are problems of low scheduling efficiency and low energy utilization efficiency of the heating system. Summary of the invention

[0004] To this end, the present invention provides a heating system scheduling system based on cloud computing to overcome the problems of low scheduling efficiency and low energy utilization efficiency of the heating system in the prior art.

[0005] To achieve the above object, the present invention provides a heating system scheduling system based on cloud computing, the system comprising:

[0006] Information acquisition module, used to obtain pipeline information, environmental information and heating history data;

[0007] A heat load analysis module is used to analyze the heat load of each preset time period based on the acquired heating history data;

[0008] A heat loss analysis module is used to analyze the heat loss of the heating system pipeline according to the obtained pipeline length and hot water flow rate in the pipeline;

[0009] An adjustment module, for adjusting the analysis process of the heat loss of the pipeline according to the obtained altitude and the thickness of the pipeline insulation layer, wherein the adjustment module is provided with an adjustment unit, for adjusting the analysis process of the heat loss of the heating pipeline according to the obtained altitude, and the adjustment module is further provided with a correction unit, for correcting the adjustment process of the analysis process of the heat loss of the heating pipeline according to the obtained thickness of the pipeline insulation layer;

[0010] A heat load distribution module is used to analyze the abnormality of the historical return water temperature of each area according to the historical return water temperature of each area and the number of historical return water temperature data of each area, and distribute the heat load of each preset time period according to the analysis results of the abnormality of the historical return water temperature of each area, the obtained ambient temperature, the analysis results of the heat load of each preset time period and the analysis results of the pipeline heat loss;

[0011] A correction module is used to correct the heat load distribution process according to the obtained wind speed and sunshine time;

[0012] The monitoring module is used to analyze the return water temperature of the pipeline within the monitoring period after the heat load distribution is completed, and to issue an abnormal temperature warning based on the analysis results;

[0013] The feedback module is used to optimize the analysis process of the heat load of the next monitoring cycle according to the temperature anomaly warning results.

[0014] Furthermore, the heat load analysis module calculates the mean of the heat supply history data of each preset period in each area, and sets the heat load prediction value of each preset period to Yim, setting Yim=(Yim1+Yim2+...+Yim z ) / Z, where Yim1 is the first historical heating data in the i-th preset period, Yim2 is the second historical heating data in the i-th preset period, and Yim z is the zth heating history data in the ith preset time period, 0<i≤I, I is the number of preset time periods, 0<z≤Z, Z is the number of heating history data in each preset time period, m=1,2,3, m=1 is the first area, m=2 is the second area, and m=3 is the third area.

[0015] Furthermore, the heat loss analysis module is provided with a pipeline length analysis unit, which compares the acquired pipeline length a0 with each preset length, and analyzes the first heat loss of the heating pipeline according to the comparison result, wherein:

[0016] When a0≤a1, the pipeline length analysis unit determines that the pipeline length is short, and sets the first heat loss of each preset period to Si1, setting Si1=b1×Yim×[1-(a1-a0) / (a1+a0)];

[0017] When a1<a0<a2, the pipeline length analysis unit determines that the pipeline length is normal, and sets the first heat loss of each preset time period to Si2, and sets Si2=b1×Yim;

[0018] When a0≥a2, the pipeline length analysis unit determines that the pipeline length is long, and sets the first heat loss of each preset time period to Si3, setting Si3=b1×Yim×[1+(a0-a2) / (a2+a0)].

[0019] Furthermore, the heat loss analysis module is provided with a flow rate analysis unit, which compares the hot water flow rate v0 in the pipeline in the current preset period with the preset flow rate v1, and analyzes the second heat loss of the heating pipeline in the next preset period according to the comparison result and the result of the first heat loss analysis of the heating pipeline, wherein:

[0020] When v0<v1, the flow rate analysis unit determines that the hot water flow rate in the pipeline is normal, and sets the second heat loss of the heating pipeline in the next preset period to Pi1, setting Pi1=Si s ;

[0021] When v0≥v1, the flow rate unit determines that the hot water flow rate in the pipeline is abnormal, and sets the second heat loss of the heating pipeline in the next preset period to Pi2, setting Pi2=Si s ×(1+e 4(v0-v1) / v0-4 ).

[0022] Furthermore, the adjustment unit compares the obtained altitude g0 with the preset altitude g1, and calculates an adjustment coefficient according to the comparison result to adjust the analysis process of the heat loss of the heating pipeline, wherein:

[0023] When g0≤g1, the adjustment unit determines that the altitude is normal and does not make any adjustment;

[0024] When g0>g1, the adjustment unit determines that the altitude is high, and sets the adjustment coefficient α to adjust the analysis process of the heat loss of the heating pipeline, setting α=1-arctan[(g0-g1) / (g0+g1)×(π / 4)], and sets the adjusted first heat loss as Si s ', set Si s '=Si s ×α;

[0025] The correction unit compares the obtained pipeline insulation layer thickness h0 with the preset thickness j0, and calculates a correction coefficient according to the comparison result to correct the adjustment process of the heat loss analysis process of the heating pipeline, wherein:

[0026] When h0≤j0, the correction unit determines that the insulation of the pipeline is poor, and sets a correction coefficient β to correct the adjustment process of the analysis process of the heat loss of the heating pipeline, setting β=1+sin{[(j0-h0) / j0]×(π / 2)};

[0027] When h0>j0, the correction unit determines that the insulation of the pipeline is normal and does not make corrections;

[0028] The correction unit corrects the adjustment process of the analysis process of the heat loss of the heating pipeline according to the correction coefficient β, and sets the corrected adjustment coefficient as α', setting α'=α×β.

[0029] Furthermore, the heat load distribution module is provided with an abnormality analysis unit, which calculates the mean of the historical return water temperature of each area, compares the calculation result with each preset standard temperature, and analyzes the abnormality of the historical return water temperature of each area according to the comparison result, wherein:

[0030] When X m ≤x1, the abnormal analysis unit determines that the historical return water temperature in the area is low;

[0031] When x1<X m <x2, the abnormal analysis unit determines that the historical return water temperature of the area is normal;

[0032] When X m ≥x2, the abnormal analysis unit determines that the historical return water temperature in the area is high;

[0033] Among them, x1 is the preset minimum standard temperature, x2 is the preset maximum standard temperature, and X m =(X1 m +X2 m +...+XR m ) / M, 0<R≤M, M is the number of historical return water temperature data in each area, X1 m is the first historical return water temperature data when the area category is m, X2 m is the second historical return water temperature data when the area category is m, XR m It is the Rth historical return water temperature data when the area category is m.

[0034] Furthermore, the heat load distribution module is provided with a heat load distribution unit, which compares the obtained ambient temperature t0 with each preset temperature, and distributes the heat load of each preset period according to the comparison result and the analysis result of the abnormality of the historical return water temperature of each area and the heat load analysis result of each preset period and the second heat loss and the ambient temperature, wherein:

[0035] When t0≤t1, the heat load distribution module determines that the ambient temperature is low. If the historical return water temperature of the area is low, the heat load distribution unit sets the distribution value of the heat load of each preset time period in the area to Qmi1, setting Qmi1=Yim+G1×Yim×[1+(t1-t0) / (t1+t0)]+b1×Yim-Pi r If the historical return water temperature of the area is normal, the heat load distribution unit sets the distribution value of the heat load of each preset time period in the area to Qmi2, and sets Qmi2=Yim+G2×Yim×[1+(t1-t0) / (t1+t0)]+b1×Yim-Pi r If the historical return water temperature of the area is high, the heat load distribution unit sets the distribution value of the heat load of each preset time period in the area to Qmi3, setting Qmi3=Yim+G3×Yim×[1+(t1-t0) / (t1+t0)]+b1×Yim-Pi r ;

[0036] When t1<t0<t2, the heat load distribution module determines that the ambient temperature is normal, and sets the distribution value of the heat load in each preset time period to Qmi4, setting Qmi4=Yim+b1×Yim-Pi r ;

[0037] When t0≥t2, the heat load distribution module determines that the ambient temperature is high. If the historical return water temperature of the area is low, the heat load distribution unit sets the distribution value of the heat load of each preset time period in the area to Qmi5, setting Qmi5=Yim-G3×Yim×(t0-t2) / (t2+t0)+b1×Yim-Pi r If the historical return water temperature of the area is normal, the heat load distribution unit sets the distribution value of the heat load of each preset period in the area to Qmi6, setting Qmi6=Yim-G2×Yim×(t0-t2) / (t2+t0)+b1×Yim-Pi r If the historical return water temperature of the area is high, the heat load distribution unit sets the distribution value of the heat load of each preset time period in the area to Qmi7, setting Qmi7=Yim-G1×Yim×(t0-t2) / (t2+t0)+b1×Yim-Pi r ;

[0038] Among them, t1 is the preset minimum temperature, t2 is the preset maximum temperature, G1 is the preset first thermal adjustment coefficient, G2 is the preset second thermal adjustment coefficient, G3 is the preset third thermal adjustment coefficient, G1>G2>G3, r=1,2.

[0039] Furthermore, the correction module is provided with a correction unit, which compares the acquired wind speed f0 with the preset wind speed f1, and calculates a correction coefficient according to the comparison result to correct the distribution process of the heat load in each preset time period, wherein:

[0040] When f0≤f1, the correction unit determines that the current wind speed is normal and does not perform correction;

[0041] When f0>f1, the correction unit determines that the current wind speed is large, and sets the correction coefficient L to correct the distribution process of the heat load in each preset period, setting L=1+0.6×(f0-f1) / (f0+f1), and sets the distribution value of the heat load in each preset period after correction as Qmi w ', set Qmi w =L×Qmi w , w=1,2...7;

[0042] The correction module is further provided with a compensation unit, which compares the obtained sunshine time k0 with each preset sunshine time, and calculates a compensation coefficient according to the comparison result to compensate for the correction process of the heat load distribution process in each preset time period, wherein:

[0043] When k0≤k1, the compensation unit determines that the sunshine time is short, and sets the compensation coefficient U1 to compensate for the correction process of the distribution process of the heat load in each preset time period, and sets U1=1+0.3×(k1-k0) / (k1+k0);

[0044] When k1<k0<k2, the compensation unit determines that the sunshine time is normal and does not perform compensation;

[0045] When k0≥k2, the compensation unit determines that the sunshine time is long, and sets the compensation coefficient U2 to compensate for the correction process of the distribution process of the heat load in each preset period, and sets U2=1-0.3×(k0-k2) / (k2+k0);

[0046] The compensation unit compensates the correction process of the heat load distribution process of each preset period according to the compensation coefficient Uu, and sets the compensated correction coefficient to L', setting L'=L×Uu, u=1,2;

[0047] Among them, k1 is the preset shortest sunshine time, and k2 is the preset longest sunshine time.

[0048] Furthermore, the monitoring module compares the return water temperature dm0 of each area in the monitoring period with each preset return water temperature, and issues a temperature abnormality warning according to the comparison result, wherein:

[0049] When dm0≤d1, the monitoring module determines that the current return water temperature in the area is low and issues a low temperature warning;

[0050] When d1<dm0<d2, the monitoring module determines that the current return water temperature in the area is normal and does not issue an early warning;

[0051] When dm0≥d2, the monitoring module determines that the current return water temperature in the area is high and issues a high temperature warning;

[0052] Among them, d1 is the preset minimum return water temperature, and d2 is the preset maximum return water temperature.

[0053] Furthermore, the feedback module compares the low temperature warning times nm1 and the high temperature warning times nm2 of each preset period in each area within the monitoring cycle with the preset times n0, and optimizes the analysis process of the heat load of each preset period in the next monitoring cycle according to the comparison results, wherein:

[0054] When nm1≤n0, the feedback module determines that the number of low temperature warnings in the area is normal;

[0055] When nm1>n0, the feedback module determines that the number of low temperature warnings in the area is abnormal, and sets the optimization coefficient γ1 to optimize the analysis process of the heat load of each area and each preset time period in the next monitoring cycle, setting γ1=1+(π / 5)×arctan[(nm1-n0)×π / (nm1+n0)];

[0056] When nm2≤n0, the feedback module determines that the number of high temperature warnings in the area is normal;

[0057] When nm2>n0, the feedback module determines that the number of high temperature warnings in the area is abnormal, and sets the optimization coefficient γ2 to optimize the analysis process of the heat load of each area and each preset time period in the next monitoring cycle, setting γ2=1-(π / 4)×arctan[(nm2-n0)×π / (nm2+n0)];

[0058] The feedback module is based on the optimization coefficient γ E Optimize the analysis process of the heat load in each preset period of the next monitoring cycle, and set the optimized Yim as Yim', setting Yim'=Yim×γ E , E=1,2.

[0059] Compared with the prior art, the beneficial effect of the present invention lies in that the heat load analysis module calculates the mean of the historical heating data of each area respectively, and analyzes the heating data according to the preset time period to improve the accuracy of the heat load analysis, thereby improving the accuracy of the heat load distribution in each preset time period, and finally improving the scheduling efficiency and energy utilization of the heating system; the pipeline length analysis unit improves the accuracy of the analysis of the first heat loss by setting a preset length, thereby improving the accuracy of the analysis of the second heat loss, thereby improving the accuracy of the heat load distribution in each preset time period, and finally improving the scheduling efficiency and energy utilization of the heating system; the flow rate analysis unit sets a preset flow rate to improve the accuracy of the analysis of the first heat loss, thereby improving the accuracy of the analysis of the second heat loss, thereby improving the accuracy of the heat load distribution in each preset time period, and finally improving the scheduling efficiency and energy utilization of the heating system; In order to improve the accuracy of the second heat loss analysis, thereby improving the accuracy of the heat load distribution in each preset time period, and finally improving the scheduling efficiency and energy utilization rate of the heating system, the adjustment unit improves the accuracy of the adjustment coefficient by setting a preset height, thereby improving the accuracy of the analysis process of the heat loss of the heating pipeline, thereby improving the accuracy of the heat load distribution in each preset time period, and finally improving the scheduling efficiency and energy utilization rate of the heating system, the correction unit improves the accuracy of the correction coefficient by setting a preset thickness, thereby improving the accuracy of the analysis process of the heat loss of the heating pipeline, thereby improving the accuracy of the heat load distribution in each preset time period, and finally improving the scheduling efficiency and energy utilization rate of the heating system. The utilization rate of the energy source is improved. The abnormal analysis unit improves the accuracy of the abnormality analysis of the historical return water temperature of each area by setting a preset standard temperature, thereby improving the accuracy of the heat load distribution in each preset time period, and finally improving the scheduling efficiency and energy utilization rate of the heating system. The heat load distribution unit improves the efficiency and accuracy of the heat load distribution in each preset time period by setting a preset temperature and a preset heat adjustment coefficient, thereby improving the scheduling efficiency and energy utilization rate of the heating system. The correction unit improves the accuracy of the correction coefficient by setting a preset wind speed, thereby improving the efficiency and accuracy of the heat load distribution in each preset time period, thereby improving the scheduling efficiency and energy utilization rate of the heating system. The compensation unit improves the accuracy of the compensation coefficient by setting a preset sunshine time, thereby improving the efficiency and accuracy of the heat load distribution in each preset time period, thereby improving the scheduling efficiency and energy utilization of the heating system. The monitoring module improves the accuracy of the temperature abnormality warning by setting a preset return water temperature, thereby improving the accuracy of the analysis process of the heat load in each preset time period of the next monitoring cycle, thereby improving the scheduling efficiency and energy utilization of the heating system. The feedback unit improves the accuracy of the optimization coefficient by setting a preset number of times, thereby improving the accuracy of the analysis process of the heat load in each preset time period of the next monitoring cycle, thereby improving the scheduling efficiency and energy utilization of the heating system. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a schematic diagram of the structure of a heating system scheduling system based on cloud computing in this embodiment;

[0061] Figure 2 This is a schematic diagram of the structure of the heat loss analysis module of this embodiment;

[0062] Figure 3 This is a schematic diagram of the structure of the adjustment module in this embodiment;

[0063] Figure 4 This is a schematic diagram of the structure of the heat load distribution module of this embodiment;

[0064] Figure 5 Schematic diagram of the structure of the correction module in this embodiment. DETAILED DESCRIPTION

[0065] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0066] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0067] It should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0068] See also Figure 1 As shown, it is a schematic diagram of the structure of the heating system scheduling system based on cloud computing in this embodiment, and the system includes:

[0069] The information acquisition module is used to obtain pipeline information, environmental information and historical heating data, wherein the pipeline information includes pipeline length, hot water flow rate in the pipeline and thickness of the pipeline insulation layer, the environmental information includes ambient temperature, sunshine time, wind speed and altitude, and the historical heating data includes historical heating load of each preset period in each area, the number of historical heating data in each preset period, historical return water temperature of each area and the number of historical return water temperature data of each area; in this embodiment, the division of regions and the number of regions are not specifically limited, and those skilled in the art can freely set them as long as the requirements of the division of regions and the number of regions are met. In mountainous areas, they can be divided into three areas according to the concentration of residents: In this embodiment, there is no specific limitation on the setting of the preset time period, and those skilled in the art can set it freely, as long as the setting requirements of the preset time period are met. For example, in a mountainous area with a high altitude, every two hours in a day can be set as a preset time period; In this embodiment, there is no specific limitation on the acquisition method of pipeline information, environmental information and heating history data, and those skilled in the art can set it freely, as long as the acquisition requirements of pipeline information, environmental information and heating history data are met. The pipeline length can be obtained through interaction, the hot water flow rate can be obtained through a flow meter, the environmental information can be obtained through a meteorological website, and the pipeline insulation layer thickness and heating history data can be obtained through interaction;

[0070] A heat load analysis module, used to analyze the heat load of each preset time period according to the acquired heating history data, and the heat load analysis module is connected to the information acquisition module;

[0071] A heat loss analysis module, used to analyze the heat loss of the heating system pipeline according to the acquired pipeline length and the hot water flow rate in the pipeline, and the heat loss analysis module is connected to the heat load analysis module;

[0072] An adjustment module, used for adjusting the heat loss analysis process of the pipeline according to the acquired altitude and the thickness of the pipeline insulation layer, the adjustment module being connected to the heat loss analysis module;

[0073] A heat load distribution module, used to analyze the abnormality of the historical return water temperature of each area according to the historical return water temperature of each area and the number of historical return water temperature data of each area, and distribute the heat load of each preset time period according to the analysis results of the abnormality of the historical return water temperature of each area, the acquired ambient temperature, the analysis results of the heat load of each preset time period and the analysis results of the pipeline heat loss, and the heat load distribution module is connected to the adjustment module;

[0074] A correction module, used to correct the heat load distribution process according to the acquired wind speed and sunshine time, the correction module is connected to the heat load distribution module;

[0075] The monitoring module is used to analyze the return water temperature of the pipeline within the monitoring period after the heat load distribution is completed, and to issue an abnormal temperature warning according to the analysis result. The monitoring module is connected to the correction module. In this embodiment, the setting of the monitoring period is not specifically limited, and those skilled in the art can freely set it as long as the setting requirements of the monitoring period are met. Among them, the monitoring period can be set to 10 days, 15 days, 20 days, etc.;

[0076] The feedback module is used to optimize the analysis process of the heat load of the next monitoring cycle according to the temperature abnormality warning result, and the feedback module is connected to the monitoring module.

[0077] See also Figure 2 As shown, it is a schematic diagram of the structure of the heat loss analysis module of this embodiment, and the heat loss analysis module includes:

[0078] A pipeline length analysis unit, used for analyzing the first heat loss of the heating pipeline according to the acquired pipeline length;

[0079] The flow rate analysis unit is used to analyze the second heat loss of the heating pipeline in the next preset period according to the obtained hot water flow rate in the pipeline and the result of the first heat loss analysis of the heating pipeline. The flow rate analysis unit is connected to the pipeline length analysis unit.

[0080] See also Figure 3 As shown, it is a schematic diagram of the structure of the adjustment module of this embodiment, and the adjustment module includes:

[0081] A regulating unit, used for regulating the heat loss analysis process of the heating pipeline according to the obtained altitude;

[0082] The correction unit is used to correct the adjustment process of the analysis process of the heat loss of the heating pipeline according to the obtained thickness of the pipeline insulation layer, and the correction unit is connected to the adjustment unit.

[0083] See also Figure 4 As shown, it is a schematic diagram of the structure of the heat load distribution module of this embodiment, and the heat load distribution module includes:

[0084] An abnormality analysis unit, used to analyze the abnormality of the historical return water temperature of each area according to the historical return water temperature of each area and the number of historical return water temperature data of each area;

[0085] The heat load distribution unit is used to distribute the heat load for each preset time period according to the analysis results of the abnormality of the historical return water temperature of each area, the obtained ambient temperature, the analysis results of the heat load for each preset time period and the analysis results of the pipeline heat loss. The heat load distribution unit is connected to the abnormality analysis unit.

[0086] See also Figure 5As shown, it is a schematic diagram of the structure of the correction module of this embodiment, and the correction module includes:

[0087] A correction unit, used to correct the distribution process of the heat load in each preset time period according to the acquired wind speed;

[0088] The compensation unit is used to compensate the correction process of the distribution process of the heat load in each preset time period according to the acquired sunshine time, and the compensation unit is connected to the correction unit.

[0089] Specifically, this embodiment is applied to the scheduling of heating systems for centralized heating in mountainous areas. It analyzes historical heating data and pipeline information, distributes heat loads for each time period based on the analysis results, adjusts the distribution process based on environmental factors such as the altitude and wind speed of the mountains, and monitors the distribution results, thereby improving the scheduling efficiency of the heating system and the utilization rate of energy.

[0090] Specifically, the heat load analysis module calculates the mean of the historical heating data of each area and analyzes the heating data according to the preset time period to improve the accuracy of the heat load analysis, thereby improving the accuracy of the heat load distribution in each preset time period, and finally improving the scheduling efficiency and energy utilization of the heating system. The pipeline length analysis unit sets a preset length to improve the accuracy of the first heat loss analysis, thereby improving the accuracy of the second heat loss analysis, thereby improving the accuracy of the heat load distribution in each preset time period, and finally improving the scheduling efficiency and energy utilization of the heating system. The flow rate analysis unit sets a preset flow rate to improve the accuracy of the second heat loss analysis. The accuracy of the heat load distribution in each preset time period is improved, thereby improving the accuracy of the heat load distribution in each preset time period, and finally improving the scheduling efficiency and energy utilization rate of the heating system. The adjustment unit improves the accuracy of the adjustment coefficient by setting a preset height, thereby improving the accuracy of the analysis process of the heat loss of the heating pipeline, thereby improving the accuracy of the heat load distribution in each preset time period, and finally improving the scheduling efficiency and energy utilization rate of the heating system. The correction unit improves the accuracy of the correction coefficient by setting a preset thickness, thereby improving the accuracy of the analysis process of the heat loss of the heating pipeline, thereby improving the accuracy of the heat load distribution in each preset time period, and finally improving the scheduling efficiency and energy utilization rate of the heating system. The abnormal analysis unit improves the accuracy of the abnormality analysis of the historical return water temperature of each area by setting a preset standard temperature, thereby improving the accuracy of the heat load distribution in each preset time period, and finally improving the scheduling efficiency and energy utilization of the heating system. The heat load distribution unit improves the efficiency and accuracy of the heat load distribution in each preset time period by setting a preset temperature and a preset heat adjustment coefficient, thereby improving the scheduling efficiency and energy utilization of the heating system. The correction unit improves the accuracy of the correction coefficient by setting a preset wind speed, thereby improving the efficiency and accuracy of the heat load distribution in each preset time period, thereby improving the scheduling efficiency and energy utilization of the heating system. The compensation unit The element improves the accuracy of the compensation coefficient by setting a preset sunshine time, thereby improving the efficiency and accuracy of the heat load distribution in each preset time period, thereby improving the scheduling efficiency and energy utilization of the heating system; the monitoring module improves the accuracy of the temperature abnormality warning by setting a preset return water temperature, thereby improving the accuracy of the heat load analysis process in each preset time period of the next monitoring cycle, thereby improving the scheduling efficiency and energy utilization of the heating system; the feedback unit improves the accuracy of the optimization coefficient by setting a preset number of times, thereby improving the accuracy of the heat load analysis process in each preset time period of the next monitoring cycle, thereby improving the scheduling efficiency and energy utilization of the heating system.

[0091] Specifically, the heat load analysis module calculates the mean of the heat supply history data of each preset period in each area, and sets the heat load prediction value of each preset period as Yim, setting Yim=(Yim1+Yim2+...+Yimz ) / Z, where Yim1 is the first historical heating data in the i-th preset period, Yim2 is the second historical heating data in the i-th preset period, and Yim z is the zth heating history data in the ith preset time period, 0<i≤I, I is the number of preset time periods, 0<z≤Z, Z is the number of heating history data in each preset time period, m=1,2,3, m=1 is the first area, m=2 is the second area, and m=3 is the third area.

[0092] Specifically, the heat load analysis module calculates the mean of the historical heating data of each area and analyzes the heating data according to a preset time period to improve the accuracy of the heat load analysis, thereby improving the accuracy of the heat load distribution in each preset time period, and ultimately improving the scheduling efficiency of the heating system and the utilization rate of energy.

[0093] Specifically, the pipeline length analysis unit compares the acquired pipeline length a0 with each preset length, and analyzes the first heat loss of the heating pipeline according to the comparison result, wherein:

[0094] When a0≤a1, the pipeline length analysis unit determines that the pipeline length is short, and sets the first heat loss of each preset period to Si1, setting Si1=b1×Yim×[1-(a1-a0) / (a1+a0)];

[0095] When a1<a0<a2, the pipeline length analysis unit determines that the pipeline length is normal, and sets the first heat loss of each preset time period to Si2, and sets Si2=b1×Yim;

[0096] When a0≥a2, the pipeline length analysis unit determines that the pipeline length is long, and sets the first heat loss of each preset period to Si3, setting Si3=b1×Yim×[1+(a0-a2) / (a2+a0)];

[0097] Among them, a1 is the preset shortest length, a2 is the preset longest length, b1 is the preset loss coefficient, and s=1,2,3.

[0098] Specifically, the pipeline length analysis unit improves the accuracy of the analysis of the first heat loss by setting a preset length, and then improves the accuracy of the second heat loss analysis, thereby improving the accuracy of the heat load distribution in each preset time period, and ultimately improving the scheduling efficiency and energy utilization of the heating system; in this embodiment, there is no specific limitation on the values ​​of the preset length and the preset loss coefficient, and technical personnel in this field can set them freely, as long as the requirements for the values ​​of the preset length and the preset loss coefficient are met, among which the optimal value of a1 is 12km, the optimal value of a2 is 24km, and the optimal value of b1 is 0.18.

[0099] Specifically, the flow rate analysis unit compares the hot water flow rate v0 in the pipeline in the current preset period with the preset flow rate v1, and analyzes the second heat loss of the heating pipeline in the next preset period according to the comparison result and the result of the first heat loss analysis of the heating pipeline, wherein:

[0100] When v0<v1, the flow rate analysis unit determines that the hot water flow rate in the pipeline is normal, and sets the second heat loss of the heating pipeline in the next preset period to Pi1, setting Pi1=Si s ;

[0101] When v0≥v1, the flow rate unit determines that the hot water flow rate in the pipeline is abnormal, and sets the second heat loss of the heating pipeline in the next preset period to Pi2, setting Pi2=Si s ×(1+e 4(v0-v1) / v0-4 ).

[0102] Specifically, the flow rate analysis unit improves the accuracy of the second heat loss analysis by setting a preset flow rate, thereby improving the accuracy of the heat load distribution in each preset time period, and ultimately improving the scheduling efficiency and energy utilization of the heating system; in this embodiment, there is no specific limitation on the value of the preset flow rate, and technical personnel in this field can set it freely as long as the value requirements of the preset flow rate are met. Among them, the optimal value of v1 is 1.12m / s.

[0103] Specifically, the adjustment unit compares the obtained altitude g0 with the preset altitude g1, and calculates the adjustment coefficient according to the comparison result to adjust the analysis process of the heat loss of the heating pipeline, wherein:

[0104] When g0≤g1, the adjustment unit determines that the altitude is normal and does not make any adjustment;

[0105] When g0>g1, the adjustment unit determines that the altitude is high, and sets the adjustment coefficient α to adjust the analysis process of the heat loss of the heating pipeline, setting α=1-arctan[(g0-g1) / (g0+g1)×(π / 4)], and sets the adjusted first heat loss as Si s ', set Si s '=Si s ×α.

[0106] Specifically, the adjustment unit improves the accuracy of the adjustment coefficient by setting a preset height, thereby improving the accuracy of the analysis process of the heat loss of the heating pipeline, thereby improving the accuracy of the heat load distribution in each preset time period, and ultimately improving the scheduling efficiency and energy utilization of the heating system; in this embodiment, there is no specific limitation on the value of the preset height, and technical personnel in this field can set it freely as long as the value requirement of the preset height is met. Among them, the optimal value of g1 is 2800m.

[0107] Specifically, the correction unit compares the obtained pipeline insulation layer thickness h0 with the preset thickness j0, and calculates a correction coefficient according to the comparison result to correct the adjustment process of the heat loss analysis process of the heating pipeline, wherein:

[0108] When h0≤j0, the correction unit determines that the insulation of the pipeline is poor, and sets a correction coefficient β to correct the adjustment process of the analysis process of the heat loss of the heating pipeline, setting β=1+sin{[(j0-h0) / j0]×(π / 2)};

[0109] When h0>j0, the correction unit determines that the insulation of the pipeline is normal and does not make corrections;

[0110] The correction unit corrects the adjustment process of the analysis process of the heat loss of the heating pipeline according to the correction coefficient β, and sets the corrected adjustment coefficient as α', setting α'=α×β.

[0111] Specifically, the correction unit improves the accuracy of the correction coefficient by setting a preset thickness, thereby improving the accuracy of the analysis process of the heat loss of the heating pipe, thereby improving the accuracy of the heat load distribution in each preset time period, and ultimately improving the scheduling efficiency and energy utilization of the heating system; in this embodiment, there is no specific limitation on the value of the preset thickness, and technical personnel in this field can set it freely as long as the value requirement of the preset thickness is met, among which the optimal value of j0 is 50mm.

[0112] Specifically, the abnormal analysis unit calculates the average of the historical return water temperatures of each area, and compares the calculated results with the preset standard temperatures, and compares the historical return water temperatures of each area according to the comparison results, wherein:

[0113] When X m ≤x1, the abnormal analysis unit determines that the historical return water temperature in the area is low;

[0114] When x1<X m <x2, the abnormal analysis unit determines that the historical return water temperature of the area is normal;

[0115] When X m≥x2, the abnormal analysis unit determines that the historical return water temperature in the area is high;

[0116] Among them, x1 is the preset minimum standard temperature, x2 is the preset maximum standard temperature, and X m =(X1 m +X2 m +...+XR m ) / M, 0<R≤M, M is the number of historical return water temperature data in each area, X1 m is the first historical return water temperature data when the area category is m, X2 m is the second historical return water temperature data when the area category is m, XR m It is the Rth historical return water temperature data when the area category is m.

[0117] Specifically, the abnormality analysis unit improves the accuracy of abnormality analysis of historical return water temperature in each area by setting a preset standard temperature, thereby improving the accuracy of heat load distribution in each preset time period, and ultimately improving the scheduling efficiency and energy utilization of the heating system; in this embodiment, there is no specific limitation on the value of the preset standard temperature, and technical personnel in this field can set it freely as long as the value requirements of the preset standard temperature are met, among which the best value of x1 is 40°C, and the best value of x2 is 50°C.

[0118] Specifically, the heat load distribution unit compares the obtained ambient temperature t0 with each preset temperature, and distributes the heat load for each preset period according to the comparison result and the analysis result of the abnormality of the historical return water temperature of each area and the heat load analysis result of each preset period and the second heat loss and the ambient temperature, wherein:

[0119] When t0≤t1, the heat load distribution module determines that the ambient temperature is low. If the historical return water temperature of the area is low, the heat load distribution unit sets the distribution value of the heat load of each preset time period in the area to Qmi1, setting Qmi1=Yim+G1×Yim×[1+(t1-t0) / (t1+t0)]+b1×Yim-Pi r If the historical return water temperature of the area is normal, the heat load distribution unit sets the distribution value of the heat load of each preset time period in the area to Qmi2, and sets Qmi2=Yim+G2×Yim×[1+(t1-t0) / (t1+t0)]+b1×Yim-Pi r If the historical return water temperature of the area is high, the heat load distribution unit sets the distribution value of the heat load of each preset time period in the area to Qmi3, setting Qmi3=Yim+G3×Yim×[1+(t1-t0) / (t1+t0)]+b1×Yim-Pi r ;

[0120] When t1<t0<t2, the heat load distribution module determines that the ambient temperature is normal, and sets the distribution value of the heat load in each preset time period to Qmi4, setting Qmi4=Yim+b1×Yim-Pi r ;

[0121] When t0≥t2, the heat load distribution module determines that the ambient temperature is high. If the historical return water temperature of the area is low, the heat load distribution unit sets the distribution value of the heat load of each preset time period in the area to Qmi5, setting Qmi5=Yim-G3×Yim×(t0-t2) / (t2+t0)+b1×Yim-Pi r If the historical return water temperature of the area is normal, the heat load distribution unit sets the distribution value of the heat load of each preset period in the area to Qmi6, setting Qmi6=Yim-G2×Yim×(t0-t2) / (t2+t0)+b1×Yim-Pi r If the historical return water temperature of the area is high, the heat load distribution unit sets the distribution value of the heat load of each preset time period in the area to Qmi7, setting Qmi7=Yim-G1×Yim×(t0-t2) / (t2+t0)+b1×Yim-Pi r ;

[0122] Among them, t1 is the preset minimum temperature, t2 is the preset maximum temperature, G1 is the preset first thermal adjustment coefficient, G2 is the preset second thermal adjustment coefficient, G3 is the preset third thermal adjustment coefficient, G1>G2>G3, r=1,2.

[0123] Specifically, the heat load distribution unit sets a preset temperature and a preset heat regulation coefficient to improve the efficiency and accuracy of heat load distribution in each preset time period, thereby improving the scheduling efficiency and energy utilization of the heating system; in this embodiment, there is no specific limitation on the values ​​of the preset temperature and the preset heat regulation coefficient, and technical personnel in this field can set them freely as long as the value requirements of the preset temperature and the preset heat regulation coefficient are met. Among them, the best value of t1 is -15°C, the best value of t2 is 0°C, the best value of G1 is 0.21, the best value of G2 is 0.18, and the best value of G3 is 0.16.

[0124] Specifically, the correction unit compares the acquired wind speed f0 with the preset wind speed f1, and calculates a correction coefficient according to the comparison result to correct the distribution process of the heat load in each preset time period, wherein:

[0125] When f0≤f1, the correction unit determines that the current wind speed is normal and does not perform correction;

[0126] When f0>f1, the correction unit determines that the current wind speed is large, and sets the correction coefficient L to correct the distribution process of the heat load in each preset period, setting L=1+0.6×(f0-f1) / (f0+f1), and sets the distribution value of the heat load in each preset period after correction as Qmi w ', set Qmi w =L×Qmi w , w=1,2...7.

[0127] Specifically, the correction unit improves the accuracy of the correction coefficient by setting a preset wind speed, thereby improving the efficiency and accuracy of the heat load distribution in each preset time period, thereby improving the scheduling efficiency of the heating system and the energy utilization rate; in this embodiment, there is no specific limitation on the value of the preset wind speed, and technical personnel in this field can set it freely as long as the value requirement of the preset wind speed is met. Among them, the optimal value of f1 is 13m / s.

[0128] Specifically, the compensation unit compares the obtained sunshine time k0 with each preset sunshine time, and calculates the compensation coefficient according to the comparison result to compensate for the correction process of the distribution process of the heat load in each preset time period, wherein:

[0129] When k0≤k1, the compensation unit determines that the sunshine time is short, and sets the compensation coefficient U1 to compensate for the correction process of the distribution process of the heat load in each preset time period, and sets U1=1+0.3×(k1-k0) / (k1+k0);

[0130] When k1<k0<k2, the compensation unit determines that the sunshine time is normal and does not perform compensation;

[0131] When k0≥k2, the compensation unit determines that the sunshine time is long, and sets the compensation coefficient U2 to compensate for the correction process of the distribution process of the heat load in each preset period, and sets U2=1-0.3×(k0-k2) / (k2+k0);

[0132] The compensation unit compensates the correction process of the heat load distribution process of each preset period according to the compensation coefficient Uu, and sets the compensated correction coefficient to L', setting L'=L×Uu, u=1,2;

[0133] Among them, k1 is the preset shortest sunshine time, and k2 is the preset longest sunshine time.

[0134] Specifically, the compensation unit improves the accuracy of the compensation coefficient by setting the preset sunshine time, thereby improving the efficiency and accuracy of the heat load distribution in each preset time period, thereby improving the scheduling efficiency of the heating system and the energy utilization rate; in this embodiment, there is no specific limitation on the value of the preset sunshine time, and technical personnel in this field can set it freely, as long as the value requirements of the preset sunshine time are met, among which the optimal value of k1 is 7h, and the optimal value of k2 is 8h.

[0135] Specifically, the monitoring module compares the return water temperature dm0 of each area within the monitoring period with each preset return water temperature, and issues a temperature abnormality warning according to the comparison result, wherein:

[0136] When dm0≤d1, the monitoring module determines that the current return water temperature in the area is low and issues a low temperature warning;

[0137] When d1<dm0<d2, the monitoring module determines that the current return water temperature in the area is normal and does not issue an early warning;

[0138] When dm0≥d2, the monitoring module determines that the current return water temperature in the area is high and issues a high temperature warning;

[0139] Among them, d1 is the preset minimum return water temperature, and d2 is the preset maximum return water temperature.

[0140] Specifically, the monitoring module improves the accuracy of temperature anomaly warning by setting a preset return water temperature, thereby improving the accuracy of the analysis process of the heat load in each preset time period of the next monitoring cycle, thereby improving the scheduling efficiency and energy utilization of the heating system; in this embodiment, there is no specific limitation on the value of the preset return water temperature, and technical personnel in this field can set it freely, as long as the value requirements of the preset return water temperature are met, among which the optimal value of d1 is 40°C, and the optimal value of d2 is 55°C.

[0141] Specifically, the feedback module compares the low temperature warning times nm1 and the high temperature warning times nm2 of each preset period in each area within the monitoring cycle with the preset times n0, and optimizes the analysis process of the heat load of each preset period in the next monitoring cycle according to the comparison results, wherein:

[0142] When nm1≤n0, the feedback module determines that the number of low temperature warnings in the area is normal;

[0143] When nm1>n0, the feedback module determines that the number of low temperature warnings in the area is abnormal, and sets the optimization coefficient γ1 to optimize the analysis process of the heat load of each area and each preset time period in the next monitoring cycle, setting γ1=1+(π / 5)×arctan[(nm1-n0)×π / (nm1+n0)];

[0144] When nm2≤n0, the feedback module determines that the number of high temperature warnings in the area is normal;

[0145] When nm2>n0, the feedback module determines that the number of high temperature warnings in the area is abnormal, and sets the optimization coefficient γ2 to optimize the analysis process of the heat load of each area and each preset time period in the next monitoring cycle, setting γ2=1-(π / 4)×arctan[(nm2-n0)×π / (nm2+n0)];

[0146] The feedback module is based on the optimization coefficient γ E Optimize the analysis process of the heat load in each preset period of the next monitoring cycle, and set the optimized Yim as Yim', setting Yim'=Yim×γ E , E=1,2.

[0147] Specifically, the feedback unit improves the accuracy of the optimization coefficient by setting a preset number of times, thereby improving the accuracy of the analysis process of the heat load in each preset time period of the next monitoring cycle, thereby improving the scheduling efficiency of the heating system and the energy utilization rate; in this embodiment, there is no specific limitation on the value of the preset number of times, and technical personnel in this field can set it freely, as long as the value requirements of the preset number of times are met. Among them, when the monitoring period is 15 days, the best value of n0 is 5.

[0148] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A heating system scheduling system based on cloud computing, characterized in that: include, Information acquisition module, used to obtain pipeline information, environmental information and heating history data; A heat load analysis module is used to analyze the heat load of each preset time period based on the acquired heating history data; A heat loss analysis module is used to analyze the heat loss of the heating system pipeline according to the obtained pipeline length and hot water flow rate in the pipeline; An adjustment module, for adjusting the analysis process of the heat loss of the pipeline according to the obtained altitude and the thickness of the pipeline insulation layer, wherein the adjustment module is provided with an adjustment unit, for adjusting the analysis process of the heat loss of the heating pipeline according to the obtained altitude, and the adjustment module is further provided with a correction unit, for correcting the adjustment process of the analysis process of the heat loss of the heating pipeline according to the obtained thickness of the pipeline insulation layer; A heat load distribution module is used to analyze the abnormality of the historical return water temperature of each area according to the historical return water temperature of each area and the number of historical return water temperature data of each area, and distribute the heat load of each preset time period according to the analysis results of the abnormality of the historical return water temperature of each area, the obtained ambient temperature, the analysis results of the heat load of each preset time period and the analysis results of the pipeline heat loss; A correction module is used to correct the heat load distribution process according to the obtained wind speed and sunshine time; The monitoring module is used to analyze the return water temperature of the pipeline within the monitoring period after the heat load distribution is completed, and to issue an abnormal temperature warning based on the analysis results; The feedback module is used to optimize the analysis process of the heat load of the next monitoring cycle according to the temperature anomaly warning results.

2. The cloud computing-based heating system scheduling system according to claim 1, characterized in that: The heat load analysis module calculates the mean of the heat supply history data of each preset period in each area, and sets the heat load forecast value of each preset period as Yim, setting Yim=(Yim1+Yim2+...+Yim z ) / Z, where Yim1 is the first historical heating data in the i-th preset period, Yim2 is the second historical heating data in the i-th preset period, and Yim z is the zth heating history data in the ith preset time period, 0<i≤I, I is the number of preset time periods, 0<z≤Z, Z is the number of heating history data in each preset time period, m=1,2,3, m=1 is the first area, m=2 is the second area, and m=3 is the third area.

3. The cloud computing-based heating system scheduling system according to claim 1, characterized in that: The heat loss analysis module is provided with a pipeline length analysis unit, which compares the acquired pipeline length a0 with each preset length, and analyzes the first heat loss of the heating pipeline according to the comparison result, wherein: When a0≤a1, the pipeline length analysis unit determines that the pipeline length is short, and sets the first heat loss of each preset period to Si1, setting Si1=b1×Yim×[1-(a1-a0) / (a1+a0)]; When a1<a0<a2, the pipeline length analysis unit determines that the pipeline length is normal, and sets the first heat loss of each preset time period to Si2, and sets Si2=b1×Yim; When a0≥a2, the pipeline length analysis unit determines that the pipeline length is long, and sets the first heat loss of each preset period to Si3, setting Si3=b1×Yim×[1+(a0-a2) / (a2+a0)]; Among them, a1 is the preset shortest length, a2 is the preset longest length, and b1 is the preset loss coefficient.

4. The cloud computing-based heating system scheduling system according to claim 3 is characterized in that: The heat loss analysis module is provided with a flow rate analysis unit, which compares the hot water flow rate v0 in the pipeline in the current preset period with the preset flow rate v1, and analyzes the second heat loss of the heating pipeline in the next preset period according to the comparison result and the result of the first heat loss analysis of the heating pipeline, wherein: When v0<v1, the flow rate analysis unit determines that the hot water flow rate in the pipeline is normal, and sets the second heat loss of the heating pipeline in the next preset period to Pi1, setting Pi1=Si s ; When v0≥v1, the flow rate unit determines that the hot water flow rate in the pipeline is abnormal, and sets the second heat loss of the heating pipeline in the next preset period to Pi2, setting Pi2=Si s ×(1+e 4(v0-v1) / v0-4 ), s=1,2,3.

5. The cloud computing-based heating system scheduling system according to claim 3, characterized in that: The adjustment unit compares the obtained altitude g0 with the preset altitude g1, and calculates the adjustment coefficient according to the comparison result to adjust the analysis process of the heat loss of the heating pipeline, wherein: When g0≤g1, the adjustment unit determines that the altitude is normal and does not make any adjustment; When g0>g1, the adjustment unit determines that the altitude is high, and sets the adjustment coefficient α to adjust the analysis process of the heat loss of the heating pipeline, setting α=1-arctan[(g0-g1) / (g0+g1)×(π / 4)], and sets the adjusted first heat loss as Si s ', set Si s '=Si s ×α; The correction unit compares the obtained pipeline insulation layer thickness h0 with the preset thickness j0, and calculates a correction coefficient according to the comparison result to correct the adjustment process of the heat loss analysis process of the heating pipeline, wherein: When h0≤j0, the correction unit determines that the insulation of the pipeline is poor, and sets a correction coefficient β to correct the adjustment process of the analysis process of the heat loss of the heating pipeline, setting β=1+sin{[(j0-h0) / j0]×(π / 2)}; When h0>j0, the correction unit determines that the insulation of the pipeline is normal and does not make corrections; The correction unit corrects the adjustment process of the analysis process of the heat loss of the heating pipeline according to the correction coefficient β, and sets the corrected adjustment coefficient as α', setting α'=α×β.

6. The cloud computing-based heating system scheduling system according to claim 1, characterized in that: The heat load distribution module is provided with an abnormality analysis unit, which calculates the mean of the historical return water temperature of each area, compares the calculation result with each preset standard temperature, and analyzes the abnormality of the historical return water temperature of each area according to the comparison result, wherein: When X m ≤x1, the abnormal analysis unit determines that the historical return water temperature in the area is low; When x1<X m <x2, the abnormal analysis unit determines that the historical return water temperature of the area is normal; When X m ≥x2, the abnormal analysis unit determines that the historical return water temperature in the area is high; Among them, x1 is the preset minimum standard temperature, x2 is the preset maximum standard temperature, and X m =(X1 m +X2 m +...+XR m ) / M, 0<R≤M, M is the number of historical return water temperature data in each area, X1 m is the first historical return water temperature data when the area category is m, X2 m is the second historical return water temperature data when the area category is m, XR m It is the Rth historical return water temperature data when the area category is m.

7. The cloud computing-based heating system scheduling system according to claim 6, characterized in that: The heat load distribution module is provided with a heat load distribution unit, which compares the obtained ambient temperature t0 with each preset temperature, and distributes the heat load of each preset period according to the comparison result and the analysis result of the abnormality of the historical return water temperature of each area and the heat load analysis result of each preset period and the second heat loss and the ambient temperature, wherein: When t0≤t1, the heat load distribution module determines that the ambient temperature is low. If the historical return water temperature of the area is low, the heat load distribution unit sets the distribution value of the heat load of each preset time period in the area to Qmi1, setting Qmi1=Yim+G1×Yim×[1+(t1-t0) / (t1+t0)]+b1×Yim-Pi r If the historical return water temperature of the area is normal, the heat load distribution unit sets the distribution value of the heat load of each preset time period in the area to Qmi2, and sets Qmi2=Yim+G2×Yim×[1+(t1-t0) / (t1+t0)]+b1×Yim-Pi r If the historical return water temperature of the area is high, the heat load distribution unit sets the distribution value of the heat load of each preset time period in the area to Qmi3, setting Qmi3=Yim+G3×Yim×[1+(t1-t0) / (t1+t0)]+b1×Yim-Pi r ; When t1<t0<t2, the heat load distribution module determines that the ambient temperature is normal, and sets the distribution value of the heat load in each preset time period to Qmi4, setting Qmi4=Yim+b1×Yim-Pi r ; When t0≥t2, the heat load distribution module determines that the ambient temperature is high. If the historical return water temperature of the area is low, the heat load distribution unit sets the distribution value of the heat load of each preset time period in the area to Qmi5, setting Qmi5=Yim-G3×Yim×(t0-t2) / (t2+t0)+b1×Yim-Pi r If the historical return water temperature of the area is normal, the heat load distribution unit sets the distribution value of the heat load of each preset period in the area to Qmi6, setting Qmi6=Yim-G2×Yim×(t0-t2) / (t2+t0)+b1×Yim-Pi r If the historical return water temperature of the area is high, the heat load distribution unit sets the distribution value of the heat load of each preset time period in the area to Qmi7, setting Qmi7=Yim-G1×Yim×(t0-t2) / (t2+t0)+b1×Yim-Pi r ; Among them, t1 is the preset minimum temperature, t2 is the preset maximum temperature, G1 is the preset first thermal adjustment coefficient, G2 is the preset second thermal adjustment coefficient, G3 is the preset third thermal adjustment coefficient, G1>G2>G3, r=1,2.

8. The cloud computing-based heating system scheduling system according to claim 7, characterized in that: The correction module is provided with a correction unit, which compares the acquired wind speed f0 with the preset wind speed f1, and calculates a correction coefficient according to the comparison result to correct the distribution process of the heat load in each preset time period, wherein: When f0≤f1, the correction unit determines that the current wind speed is normal and does not perform correction; When f0>f1, the correction unit determines that the current wind speed is large, and sets the correction coefficient L to correct the distribution process of the heat load in each preset period, setting L=1+0.6×(f0-f1) / (f0+f1), and sets the distribution value of the heat load in each preset period after correction as Qmi w ', set Qmi w =L×Qmi w , w=1,2...7; The correction module is further provided with a compensation unit, which compares the obtained sunshine time k0 with each preset sunshine time, and calculates a compensation coefficient according to the comparison result to compensate for the correction process of the heat load distribution process in each preset time period, wherein: When k0≤k1, the compensation unit determines that the sunshine time is short, and sets the compensation coefficient U1 to compensate for the correction process of the distribution process of the heat load in each preset time period, and sets U1=1+0.3×(k1-k0) / (k1+k0); When k1<k0<k2, the compensation unit determines that the sunshine time is normal and does not perform compensation; When k0≥k2, the compensation unit determines that the sunshine time is long, and sets the compensation coefficient U2 to compensate for the correction process of the distribution process of the heat load in each preset period, and sets U2=1-0.3×(k0-k2) / (k2+k0); The compensation unit compensates the correction process of the heat load distribution process of each preset period according to the compensation coefficient Uu, and sets the compensated correction coefficient to L', setting L'=L×Uu, u=1,2; Among them, k1 is the preset shortest sunshine time, and k2 is the preset longest sunshine time.

9. The cloud computing-based heating system scheduling system according to claim 7, characterized in that: The monitoring module compares the return water temperature dm0 of each area within the monitoring period with each preset return water temperature, and issues a temperature abnormality warning according to the comparison result, wherein: When dm0≤d1, the monitoring module determines that the current return water temperature in the area is low and issues a low temperature warning; When d1<dm0<d2, the monitoring module determines that the current return water temperature in the area is normal and does not issue an early warning; When dm0≥d2, the monitoring module determines that the current return water temperature in the area is high and issues a high temperature warning; Among them, d1 is the preset minimum return water temperature, and d2 is the preset maximum return water temperature.

10. The cloud computing-based heating system scheduling system according to claim 2, characterized in that: The feedback module compares the low temperature warning times nm1 and the high temperature warning times nm2 of each preset time period in each area within the monitoring cycle with the preset times n0, and optimizes the analysis process of the heat load of each preset time period in the next monitoring cycle according to the comparison results, wherein: When nm1≤n0, the feedback module determines that the number of low temperature warnings in the area is normal; When nm1>n0, the feedback module determines that the number of low temperature warnings in the area is abnormal, and sets the optimization coefficient γ1 to optimize the analysis process of the heat load of each area and each preset time period in the next monitoring cycle, setting γ1=1+(π / 5)×arctan[(nm1-n0)×π / (nm1+n0)]; When nm2≤n0, the feedback module determines that the number of high temperature warnings in the area is normal; When nm2>n0, the feedback module determines that the number of high temperature warnings in the area is abnormal, and sets the optimization coefficient γ2 to optimize the analysis process of the heat load of each area and each preset time period in the next monitoring cycle, setting γ2=1-(π / 4)×arctan[(nm2-n0)×π / (nm2+n0)]; The feedback module is based on the optimization coefficient γ E Optimize the analysis process of the heat load in each preset period of the next monitoring cycle, and set the optimized Yim as Yim', setting Yim'=Yim×γ E , E=1,2.

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