Boiler room thermodynamic system energy-saving operation method and system based on big data and medium

Abstract

The invention relates to a boiler room thermodynamic system energy-saving operation method and system based on big data and a medium, and relates to the technical field of thermodynamic systems.The method comprises the steps that the heating coverage range of at least one heat dissipation device in a to-be-heated area is obtained; dividing the to-be-heated area into a plurality of sub-areas, and determining at least one to-be-selected heating sub-area in the to-be-heated area after the current time on the basis of a target sub-area in which people are easy to gather in the to-be-heated area and a corresponding target time period; according to all the heating coverage ranges and all the heating to-be-selected sub-areas, final conveying equipment is determined from all the heat dissipation equipment; and hot water in the target boiler equipment is conveyed to all the final conveying equipment through a preset heat distribution pipeline, and the target boiler equipment is boiler equipment matched with the to-be-heated area. The boiler room has the effect of improving the utilization efficiency of the heat source provided by the boiler room.

Description

Technical Field

[0001] The present application relates to the technical field of thermal systems, and in particular to a method, system and medium for energy-saving operation of a boiler room thermal system based on big data. Background Art

[0002] The boiler room thermal system refers to the equipment, thermal pipelines, control valves, metering instruments and monitoring instruments that transport and distribute heating media in the boiler room. Its main functions include connecting the boiler equipment and the heating network, sending the return water from the heating network to the boiler equipment and reasonably distributing it to each boiler equipment, heating the return water through the boiler equipment, and then sending the heated hot water to the network. Finally, the specific thermal pipelines transport the hot water to the heat dissipation equipment in the indoor area, so that it can transfer the heat of the hot water to the room, thereby achieving the effect of heating the room.

[0003] At present, the mode commonly adopted for the operation of the thermal system of the boiler room is: when heating the area that needs heating, in order to heat the heating area in a timely and effective manner, it is often necessary to transport the hot water heated by the boiler equipment to each heat dissipation device in the area that needs heating, so that the heat can be transferred to as many locations as possible. However, once the area that needs heating is large, not every location in the area that needs heating needs to be provided with heat. Under this universal heating method, heat will also be transferred to locations that do not need heating, resulting in low utilization efficiency of the heat source provided by the boiler room. Summary of the invention

[0004] In order to improve the utilization efficiency of the heat source provided by the boiler room, the present application provides an energy-saving operation method, system, equipment and medium for the thermal system of a boiler room based on big data.

[0005] In a first aspect of the present application, a method for energy-saving operation of a boiler room thermal system based on big data is provided, which specifically includes: Obtaining the heating coverage of at least one heat dissipation device in the area to be heated; Divide the area to be heated into multiple sub-areas, and determine at least one sub-area to be heated in the area to be heated after the current time based on the target sub-area where people are likely to gather and the corresponding target time period, wherein the target time period is the time period when people are likely to gather in the corresponding target sub-area; Determine a final delivery device from each of the heat dissipation devices according to each of the heating coverage areas and each of the heating to-be-selected sub-areas; The hot water in the target boiler equipment is transported to each of the final transport devices through a preset thermal pipeline, and the target boiler equipment is a boiler equipment matched with the area to be heated.

[0006] By adopting the above technical solution, after obtaining the heating coverage range of each heat dissipation device, based on the target sub-areas where people are likely to gather in each sub-area of ​​the heating area and the corresponding target time period, the possibility of people gathering in the sub-areas of the heating area after the current time is analyzed, and then the heating sub-areas with heating demand are determined. Then, the final delivery equipment is screened out from each heat dissipation device to determine the heat dissipation equipment that is likely to cover the sub-areas where people gather, and finally the hot water is delivered to this final delivery equipment in a targeted manner. During the operation of the boiler room thermal system, the heat source provided by the target boiler equipment can be used in a targeted manner for heating in places where people are likely to gather, through the reasonable distribution and delivery of hot water, so that the heat source provided by the target boiler equipment can be used in a targeted manner for heating in places where people are likely to gather, thereby improving the utilization efficiency of the heat source provided by the boiler room and avoiding energy waste.

[0007] Optionally, determining at least one sub-region to be heated in the area to be heated after the current time based on the target sub-region in the area to be heated where people are likely to gather and the corresponding target time period specifically includes: Obtain the first historical sub-regions in which the number of people distributed in the area to be heated exceeds a preset number threshold during the same period, count the first occurrence times of each of the first historical sub-regions, and select the first historical sub-region with the first number from each of the first historical sub-regions in descending order of the first occurrence times to determine it as the target sub-region; Obtaining the first historical period in each of the target sub-areas when the number of personnel distribution exceeds the number threshold, counting the second occurrence times of each of the first historical period, and selecting the first historical period with the second number from each of the first historical periods in descending order of the second occurrence times to determine as the target period of the corresponding target sub-area; Calculating a first weight of each target sub-region and a second weight of each corresponding target time period, wherein the first weight is a ratio of a first occurrence number of each target sub-region to a sum of first occurrence numbers of all target sub-regions, and the second weight is a ratio of a second occurrence number of a single target time period corresponding to the target sub-region to a sum of second occurrence numbers of all corresponding target time periods; At least one sub-region to be heated in the region to be heated after the current time is determined based on the first weight and the corresponding second weights.

[0008] By adopting the above technical solution, the larger the first occurrence number, the more likely it is that the corresponding first historical sub-region will have a gathering of people, and thus the target sub-region is determined; the larger the second occurrence number, the more likely it is that the target sub-region will have a gathering of people in the corresponding first historical period, and thus the target period is determined. Finally, the first weight and the corresponding second weight are combined to analyze the possibility that the target sub-region in the heating area is likely to have a gathering of people after the current time, and then analyze the corresponding heating demand, and then accurately determine the heating sub-region with heating demand.

[0009] Optionally, the determining, based on the first weight and the corresponding second weights, at least one sub-area to be heated in the area to be heated after the current time specifically includes: Determine the target sub-regions including the current time in the corresponding target time periods as key sub-regions, determine the target time period including the current time as a key time period, and calculate a first product of a first weight of each key sub-region and a second weight of the corresponding key time period; summing up the first products to obtain a sum of first products, and if the sum of the first products is greater than a preset first product sum threshold, comparing the first products with a preset product threshold; If the first product exceeds the product threshold, the corresponding key sub-area is determined as a sub-area to be selected for heating.

[0010] By adopting the above technical solution, the larger the first product is, the more likely it is that the corresponding key sub-area will have gatherings of people after the current time. The sum of the first products is obtained by summing up the first products. The larger the sum of the first products is, the greater the overall possibility of gatherings of people after the current time is. If the sum of the first products is greater than the preset threshold of the sum of the first products, the overall possibility of gatherings of people in the area to be heated after the current time is greater, and then it is necessary to specifically determine the sub-area with a high probability of gatherings of people after the current time. If the first product is greater than the product threshold, it means that the corresponding key sub-area is more likely to have gatherings of people after the current time, and targeted heating may be required, so it is determined as the sub-area to be selected for heating. This facilitates the subsequent determination of the final conveying equipment.

[0011] Optionally, determining the final delivery device from each of the heat dissipation devices according to each of the heating coverage areas and each of the heating selected sub-areas specifically includes: Determine a heating coverage area including the heating candidate sub-area as a key coverage area, and sum the first products of each heating candidate sub-area within each key coverage area to obtain a sum of corresponding second products; The sum of the second products is compared with a preset second product sum threshold. If the sum of the second products is greater than the second product sum threshold, the heat dissipation device belonging to the corresponding key coverage range is determined as the final delivery device.

[0012] By adopting the above technical solution, the larger the sum of the second products is, the greater the overall possibility of gathering of people in the selected sub-area for heating within the corresponding key coverage area. If the sum of the second products is greater than the preset second product sum threshold, it means that the overall possibility of gathering of people within the corresponding key coverage area is relatively large, and the probability of heat waste when the corresponding heat dissipation equipment is started for heating is relatively low. In this case, the heat dissipation equipment belonging to the corresponding key coverage area is determined as the final delivery equipment, so as to carry out more reasonable targeted heating.

[0013] Optionally, the method of delivering the hot water in the target boiler device to each of the final delivery devices through a preset thermal pipeline specifically includes: Selecting the maximum first product from the first products of the heating sub-areas to be selected within the key coverage area of ​​each of the final delivery equipment; According to the maximum first product, determining the hot water delivery order of the corresponding final delivery equipment, the larger the maximum first product is, the earlier the corresponding hot water delivery order is; Determine the hot water delivery time of the corresponding final delivery device based on the current time and the key time period corresponding to each of the maximum first products; Based on the hot water delivery time and hot water delivery sequence of the same final delivery device, the hot water in the target boiler device is delivered to the corresponding final delivery device through a preset thermal pipeline.

[0014] By adopting the above technical solution, the heating candidate sub-area corresponding to the maximum first product is the sub-area with the greatest possibility of gathering of people within the key coverage area. Then, according to the maximum first product corresponding to each final delivery device, the hot water delivery order of the corresponding final delivery device is determined. The larger the maximum first product, the more likely it is that people will gather within the key coverage area of ​​the corresponding final delivery device, and the higher the corresponding hot water delivery order, the hot water delivery will be given priority, and the area with a greater possibility of gathering of people will be heated in time. Furthermore, the hot water delivery time is determined according to the maximum first product corresponding to each final delivery device, which can meet the heating needs of the sub-area with the greatest possibility of gathering of people as much as possible.

[0015] Optionally, the method further includes: Obtain a second historical period in which the outdoor temperature of the area to be heated is lower than a preset first temperature threshold value during the same period, count the first occurrence frequency of each of the second historical periods, and select the third second historical period from each of the second historical periods in descending order of the first occurrence frequency to determine it as a low temperature prone period; Acquire the second historical sub-regions whose temperature is higher than the second temperature threshold in each of the prone low temperature periods, and count the second occurrence frequencies of each of the second historical sub-regions, and select the second historical sub-regions with the fourth number from each of the second historical sub-regions in descending order of the second occurrence frequencies to determine as the easily comfortable temperature sub-regions of the corresponding prone low temperature period; Calculating a third weight of each of the easy low temperature time periods and a fourth weight of each of the corresponding easy comfortable temperature sub-areas, wherein the third weight is a ratio of a first occurrence frequency of each easy low temperature time period to a sum of first occurrence frequencies of all easy low temperature time periods, and the fourth weight is a ratio of a second occurrence frequency of a single easy comfortable temperature sub-area corresponding to the easy low temperature time period to a sum of second occurrence frequencies of all corresponding easy comfortable temperature sub-areas; Performing a rationality check on the heating candidate sub-area according to the third weight and the corresponding fourth weights; The step of determining the final delivery device from each of the heat dissipation devices according to each of the heating coverage areas and each of the heating selected sub-areas specifically includes: After the rationality check of all the heating candidate sub-areas is passed, the final delivery equipment is determined from the heat dissipation equipment according to each heating coverage area and each checked heating candidate sub-area.

[0016] By adopting the above technical solution, the greater the first occurrence frequency, the more likely the outdoor temperature of the area to be heated in the corresponding second historical period is to be lower, thereby determining the low temperature period; the greater the second occurrence frequency, the more likely the temperature of the corresponding second historical sub-area in the low temperature period is to be relatively comfortable, thereby determining the comfortable temperature sub-area. Finally, combined with the third weight and the corresponding fourth weight, the possibility of concentrated distribution of personnel in each heating candidate sub-area due to cold weather is analyzed, so as to perform rationality verification on the heating candidate sub-area, thereby achieving more accurate determination of the final transportation equipment in the subsequent period.

[0017] Optionally, performing a rationality check on the heating candidate sub-area according to the third weight and the corresponding fourth weights specifically includes: Determine the low temperature period in which there is a heating sub-region to be selected in each corresponding easy-to-adjust temperature sub-region as an important period, calculate the second product of the third weight of the important period after the current time and the fourth weight of the corresponding single heating sub-region to be selected, and sum them to obtain the corresponding sum of the third products; Calculate and sum the third product of the third weight of the important time period before the current time and the fourth weight of the corresponding single heating to-be-selected sub-area to obtain the corresponding sum of the fourth products; If the sum of the third products corresponding to the same sub-area to be selected for heating is greater than the sum of the corresponding fourth products, it is determined that the rationality check of the corresponding sub-area to be selected for heating has passed.

[0018] By adopting the above technical solution, the larger the second product is, the greater the possibility that the outdoor temperature is low and the temperature in the heating sub-area is comfortable during the corresponding important period, so when it is cold outside, people are more likely to be distributed in the heating sub-area. Furthermore, the larger the sum of the third products is, the more likely people are to be distributed in the corresponding heating sub-area after the current time. The larger the sum of the fourth products is, the more likely people are to be distributed in the corresponding heating sub-area before the current time. If for the same heating sub-area, the corresponding sum of the third products is greater than the corresponding sum of the fourth products, it means that the possibility of people gathering in the corresponding heating sub-area after the current time is greater than before the current time, and then it is determined that there is a high probability of people gathering in this heating sub-area after the current time, and there is a possibility of heating demand, and it is determined that the rationality check of the corresponding heating sub-area has passed.

[0019] In a second aspect of the present application, a boiler room thermal system energy-saving operation system based on big data is provided, which specifically includes: An information acquisition module is used to obtain the heating coverage of at least one heat dissipation device in the area to be heated; An area determination module is used to divide the area to be heated into a plurality of sub-areas, and determine at least one sub-area to be heated in the area to be heated after the current time based on a target sub-area in the area to be heated where people are likely to gather and a corresponding target time period, wherein the target time period is a time period in which people are likely to gather in the corresponding target sub-area; An equipment screening module, used for determining a final delivery equipment from each of the heat dissipation equipment according to each of the heating coverage areas and each of the heating selected sub-areas; The thermal operation module is used to transport the hot water in the target boiler equipment to each of the final transportation devices through a preset thermal pipeline, and the target boiler equipment is the boiler equipment matched with the area to be heated.

[0020] By adopting the above technical solution, the information acquisition module obtains the heating coverage range of the heat dissipation equipment, and then the area determination module determines at least one heating candidate sub-area in the area to be heated after the current time. Then, the equipment screening module determines the final delivery equipment from each heat dissipation equipment according to each heating coverage range and each heating candidate sub-area. Finally, the thermal operation module delivers the hot water in the target boiler equipment to each final delivery equipment through the preset thermal pipeline.

[0021] In a third aspect of the present application, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is loaded and executed by a processor, the method steps described in any one of the first aspects are performed.

[0022] In a fourth aspect of the present application, an electronic device is provided, specifically comprising: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the processor is used to load and execute the computer program stored in the memory so that the electronic device performs the method as described in any one of the first aspects.

[0023] In summary, the present application includes at least one of the following beneficial technical effects: after obtaining the heating coverage range of each heat dissipation device, based on the target sub-areas where people are likely to gather in each sub-area of ​​the area to be heated and the corresponding target time period, the possibility of people gathering in the sub-areas of the area to be heated after the current time is analyzed, and then the heating sub-areas with heating needs are determined. Then, the final delivery equipment is screened out from each heat dissipation device to determine the heat dissipation equipment with a high probability of covering the sub-areas where people gather, and finally the hot water is delivered to this final delivery equipment in a targeted manner, so that the heat source provided by the target boiler equipment can be used in a targeted manner for heating in places where people are likely to gather, thereby improving the utilization efficiency of the heat source provided by the boiler room and avoiding energy waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of a method for energy-saving operation of a boiler room thermal system based on big data provided in an embodiment of the present application; Figure 2 It is a flow chart of another energy-saving operation method of a boiler room thermal system based on big data provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of a boiler room thermal system energy-saving operation system based on big data provided in an embodiment of the present application; Figure 4 It is a structural schematic diagram of another energy-saving operation system of a boiler room thermal system based on big data provided in an embodiment of the present application.

[0025] Explanation of the accompanying drawings: 11. Information acquisition module; 12. Area determination module; 13. Equipment screening module; 14. Thermal operation module; 15. Area verification module. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0027] In the description of the embodiments of the present application, words such as "illustrative", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "illustrative", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "illustrative", "for example" or "for example" is intended to present related concepts in a concrete way.

[0028] In the description of the embodiments of the present application, the term "and / or" is only a kind of association relationship describing the associated objects, indicating that there may be three kinds of relationships, for example, A and / or B, which can represent: A exists alone, B exists alone, and A and B exist at the same time. In addition, unless otherwise specified, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" can explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0029] See also Figure 1 The embodiment of the present application discloses a flow chart of a method for energy-saving operation of a boiler room thermal system based on big data, which can be implemented by a computer program and can also be run on a boiler room thermal system energy-saving operation system based on big data based on the von Neumann system. The computer program can be integrated into an application or run as an independent tool application, specifically including: S101: Obtain the heating coverage of at least one heat dissipation device in the area to be heated.

[0030] Specifically, in an embodiment of the present application, the area to be heated may be a factory building, or may be an area such as a public hall of a commercial building. The heat dissipation device is preset in the area to be heated, and is used to transfer the heat of the hot water generated by the target boiler device matching the area to be heated to the area to be heated, so as to achieve the heating effect. The heating coverage is the size of the area where the heat dissipation device can effectively dissipate heat. Among them, the target boiler device is a component of the thermal system of the boiler room and is located in the boiler room. After the target boiler device heats the water to a certain temperature, the hot water is transported to the area to be heated through the thermal pipeline by the circulating pump in the thermal system of the boiler room, and then the heat dissipation device transfers the heat of the hot water to various locations in the area to be heated. The target boiler device may be a gas boiler. In other embodiments, the target boiler device may also be an electric heating boiler. In addition, the heat dissipation device may be a plate heat exchanger or a cast iron radiator.

[0031] Furthermore, the execution subject of a method for energy-saving operation of a boiler room thermal system based on big data disclosed in an embodiment of the present application is a server, and the server communicates wirelessly with a terminal. A client related to the control of the boiler room thermal system is installed in the terminal, and the server is a background server of the client, which can be a physical server. Furthermore, a feasible way to determine the heating coverage range of the heat dissipation device is: when the heat dissipation device is working, the heating effect of the heat dissipation device on the surrounding area can be intuitively determined by a thermal imager, and then the heating coverage range can be determined based on the generated heat distribution image. In other embodiments, the heating coverage range of the heat dissipation device can also be simulated and determined by computational fluid dynamics (CFD) simulation technology and based on parameters such as air flow rate.

[0032] S102: Divide the area to be heated into a plurality of sub-areas, and determine at least one sub-area to be heated in the area to be heated after the current time based on a target sub-area where people are likely to gather and a corresponding target time period.

[0033] Specifically, after the heating coverage of each heat dissipation device is determined, the area to be heated is divided into multiple sub-areas. Specifically, the area segmentation tool of ArcGIS can be used to randomly divide the area based on the plan of the area to be heated to obtain multiple sub-areas. Further, from the historical monitoring videos of the cameras equipped in the area to be heated, the historical monitoring videos of the same period as the current period are screened out. For example, if the current period is a working day in September, the historical monitoring videos of the working days in September are screened out. Then, based on the screened historical monitoring videos, the first historical sub-area in which the number of personnel distribution exceeds the preset number threshold is determined. A feasible determination method is: using HOG features combined with SVM classifiers or deep learning models to detect pedestrians in each frame image of the historical monitoring video, and then using the foreground and background segmentation technology to distinguish pedestrians in the foreground from the background, and then detecting and counting, that is, identifying the number of personnel distribution. If the number of personnel distribution in a sub-area exceeds the number threshold, it means that there are more people gathered in the corresponding sub-area, and then the corresponding sub-area is determined as the first historical sub-area. Then, the first occurrence number of each first historical sub-area is counted. The larger the first occurrence number, the more likely it is that the corresponding first historical sub-area will have a gathering of people. According to the order of the first occurrence number from large to small, the first historical sub-area with the first number is selected from each first historical sub-area to be determined as the target sub-area, that is, the sub-area in the area to be heated where people are likely to gather. Then, the first historical period in which a single target sub-area is located when the number of people distributed exceeds the number threshold is obtained, and the first historical period in which it is located is determined by specifically identifying the time node corresponding to the single-frame image of the number of people distributed. Then, the second occurrence number of each first historical period is counted. The larger the second occurrence number, the more likely it is that the target sub-area will have a gathering of people in the corresponding first historical period. According to the order of the second occurrence number from large to small, the second historical period is selected from each first historical period to be determined as the target period of the corresponding target sub-area, that is, the period in which the corresponding target sub-area is likely to have a gathering of people.

[0034] Furthermore, the first weight of each target sub-region and the second weight of each corresponding target time period are calculated, the first weight being the ratio of the first occurrence number of each target sub-region to the sum of the first occurrence numbers of all target sub-regions, and the second weight being the ratio of the second occurrence number of a single target time period corresponding to the target sub-region to the sum of the second occurrence numbers of all corresponding target time periods. Then, according to the first weight and the corresponding second weights, the sub-area to be heated in the area to be heated after the current time is determined. In the present application, a feasible determination method is: determine the target sub-area containing the current time in the corresponding target time periods as the key sub-area, and determine the target time period containing the current time as the key time period. Then, calculate the first product of the first weight of each key sub-area and the second weight of the corresponding key time period. The larger the first product, the more likely it is that the corresponding key sub-area will have a gathering of people after the current time. The first products are summed to obtain the sum of the first products. The larger the sum of the first products, the greater the overall possibility of gathering of people after the current time. If the sum of the first products is greater than the preset first product sum threshold, the overall possibility of gathering of people in the area to be heated after the current time is relatively large, then it is necessary to specifically determine the sub-area with a high probability of gathering of people after the current time, and compare each first product with the preset product threshold. If the first product is greater than the product threshold, it means that the corresponding key sub-area is more likely to have a gathering of people after the current time, and targeted heating may be required, so it is determined as the sub-area to be heated.

[0035] S103: Determine the final delivery device from the heat dissipation devices according to the heating coverage areas and the heating sub-areas to be selected.

[0036] Specifically, after the heating sub-area to be selected in the heating area is determined, it is necessary to determine the final delivery equipment from each heat dissipation equipment based on this, that is, the heat dissipation equipment that delivers hot water to it. A feasible implementation method is: determine the heating coverage area including this heating sub-area to be selected as the key coverage area, and sum the first products corresponding to each heating sub-area to be selected in each key coverage area to obtain the corresponding sum of the second products. The larger the sum of the second products, the greater the overall possibility of personnel gathering in the heating sub-area to be selected in the corresponding key coverage area. If the sum of the second products is greater than the preset second product sum threshold, it means that the overall possibility of personnel gathering in the corresponding key coverage area is relatively large, and the probability of heat waste when the corresponding heat dissipation equipment is started to work for heating is relatively low, then the heat dissipation equipment belonging to the corresponding key coverage area is determined as the final delivery equipment.

[0037] S104: The hot water in the target boiler device is transported to each final transport device through a preset thermal pipeline.

[0038] Specifically, after the final delivery equipment is determined, the hot water heated by the target boiler equipment needs to be delivered to each final delivery equipment, and then the final delivery equipment transfers the heat of the hot water to the area within its heating coverage range, so as to provide heat in a targeted manner, realize accurate heating in the heating area, and avoid the waste of heat generated by the target boiler equipment. In the embodiment of the present application, a feasible delivery method is: from the first products of each heating selected sub-area within the key coverage range of a single final delivery equipment, the maximum first product is selected, and the heating selected sub-area corresponding to this maximum first product is the sub-area with the greatest possibility of personnel gathering within this key coverage range. Then, according to the maximum first product corresponding to each final delivery equipment, the hot water delivery order of the corresponding final delivery equipment is determined. The larger the maximum first product, the more likely it is that people will gather within the key coverage range of the corresponding final delivery equipment, and the more advanced the corresponding hot water delivery order is, then hot water delivery is given priority, and the area with a greater possibility of personnel gathering is heated in time. Then, for each final delivery equipment, the end time of the key period corresponding to its maximum first product is subtracted from the current time to obtain the hot water delivery time of the corresponding final delivery equipment. Finally, according to the hot water delivery time and hot water delivery sequence of the same final delivery equipment, the circulation pump is controlled to deliver the hot water in the target boiler equipment to the corresponding final delivery equipment through the preset thermal pipeline.

[0039] See also Figure 2 The embodiment of the present application discloses a flow chart of a method for energy-saving operation of a boiler room thermal system based on big data, which can be implemented by a computer program and can also be run on a boiler room thermal system energy-saving operation system based on big data based on the von Neumann system. The computer program can be integrated into an application or run as an independent tool application, specifically including: S201: Obtain the heating coverage of at least one heat dissipation device in the area to be heated.

[0040] S202: Divide the area to be heated into a plurality of sub-areas, and determine at least one sub-area to be heated in the area to be heated after the current time based on a target sub-area where people are likely to gather and a corresponding target time period.

[0041] For details, please refer to steps S101-S102, which will not be described in detail here.

[0042] S203: Obtain the second historical time periods during which the outdoor temperature of the area to be heated is lower than the preset first temperature threshold value, count the first occurrence frequency of each second historical time period, and select the third second historical time period from each second historical time period in descending order of the first occurrence frequency to determine it as the low temperature prone time period.

[0043] S204: Obtain the second historical sub-regions in which the temperature is higher than the second temperature threshold in each low temperature period, and count the second occurrence frequency of each second historical sub-region, and select the fourth number of second historical sub-regions from each second historical sub-region in descending order of the second occurrence frequency to determine as the corresponding easy comfortable temperature sub-region of the low temperature period.

[0044] Specifically, through the preset meteorological service tool, the second historical period in which the outdoor temperature of the area to be heated is lower than the preset first temperature threshold at the same time as the current period is obtained, that is, the period in which the outdoor temperature of the area to be heated is lower at the same historical period, and the first occurrence frequency of each second historical period is counted. The greater the first occurrence frequency, the more likely it is that the outdoor temperature of the area to be heated in the corresponding second historical period will be lower. Then, in order of the first occurrence frequency from large to small, the third second historical period is selected from each second historical period to be determined as a low temperature period, that is, a period when the outdoor temperature is likely to be low.

[0045] Furthermore, according to the historical monitoring records of the temperature of each sub-area in the heating area by the temperature sensor, the historical monitoring records include the temperature of each sub-area at each time in the same historical period. Then, the second historical sub-area whose temperature is higher than the second temperature threshold in each low temperature period is determined. The temperature is higher than the second temperature threshold, indicating that the temperature of the corresponding second historical sub-area is relatively comfortable. Then, the second occurrence frequency of each second historical sub-area is counted. The greater the second occurrence frequency, the more likely the temperature of the corresponding second historical sub-area in the low temperature period is to be a relatively comfortable temperature. Finally, in the order of the second occurrence frequency from large to small, the fourth number of second historical sub-areas is selected from each second historical sub-area to be determined as the corresponding easy-to-comfort sub-area in the low temperature period, that is, the sub-area with a relatively comfortable temperature. It should be noted that the second temperature threshold is greater than the first temperature threshold.

[0046] S205: Calculate the third weight of each low temperature period and the fourth weight of each corresponding comfortable temperature sub-area.

[0047] S206: Performing a rationality check on the sub-areas to be selected for heating according to the third weight and the corresponding fourth weights.

[0048] Specifically, in the embodiment of the present application, the third weight is the ratio of the first occurrence frequency of each low temperature period to the sum of the first occurrence frequencies of all low temperature periods, and the fourth weight is the ratio of the second occurrence frequency of a single easy temperature sub-region corresponding to the low temperature period to the sum of the second occurrence frequencies of all the corresponding easy temperature sub-regions. Further, according to the third weight and the corresponding fourth weights, the rationality of each heating candidate sub-region is checked. A feasible verification method is: the low temperature period in which the heating candidate sub-region exists in each corresponding easy temperature sub-region is determined as an important period, and the second product of the third weight of the important period after the current time and the fourth weight of the corresponding single heating candidate sub-region is calculated. The larger the second product, the greater the possibility that the outdoor temperature is low and the temperature of the heating candidate sub-region is comfortable in the corresponding important period, so when the outdoor temperature is cold, the more people tend to be distributed in the heating candidate sub-region (the temperature is relatively comfortable). Further, the sum of each second product is summed to obtain the corresponding sum of the third products. The larger the sum of the third products, the more likely people are to be distributed in the corresponding heating candidate sub-region after the current time.

[0049] Similarly, the third product of the third weight of the important time period before the current time and the fourth weight of the corresponding single heating candidate sub-area is calculated and summed to obtain the corresponding sum of the fourth products. If the sum of the corresponding third products for the same heating candidate sub-area is greater than the sum of the corresponding fourth products, it means that the possibility of gathering of people in the corresponding heating candidate sub-area after the current time is greater than before the current time, and then it is determined that there is a high probability of gathering of people in this heating candidate sub-area after the current time, and there is a possibility of heating demand, and it is determined that the rationality check of the corresponding heating candidate sub-area has passed.

[0050] S207: After the rationality check of all the heating candidate sub-areas is passed, the final delivery equipment is determined from the heat dissipation equipment according to each heating coverage area and each verified heating candidate sub-area.

[0051] S208: The hot water in the target boiler device is transported to each final transport device through a preset thermal pipeline.

[0052] For details, please refer to steps S103-S104, which will not be described in detail here.

[0053] The implementation principle of the energy-saving operation method of the boiler room thermal system based on big data in the embodiment of the present application is as follows: after obtaining the heating coverage of each heat dissipation device, based on the target sub-areas where people are likely to gather in each sub-area of ​​the heating area and the corresponding target time period, analyze the possibility of people gathering in the sub-areas of the heating area after the current time, and then determine the heating sub-areas with heating needs. Then, from each heat dissipation device, select the final delivery device to determine the heat dissipation device with a high probability of covering the sub-areas where people gather, and finally deliver the hot water to this final delivery device in a targeted manner, so that the heat source provided by the target boiler device can be used in a targeted manner for heating in places where people are likely to gather, thereby improving the utilization efficiency of the heat source provided by the boiler room and avoiding energy waste.

[0054] The following is a system embodiment of the present application, which can be used to execute the method embodiment of the present application. For details not disclosed in the system embodiment of the present application, please refer to the method embodiment of the present application.

[0055] See also Figure 3 , which is a schematic diagram of the structure of the energy-saving operation system of the boiler room thermal system based on big data provided by the embodiment of the present application. The energy-saving operation system of the boiler room thermal system based on big data can be implemented as all or part of the system through software, hardware or a combination of both. The system includes an information acquisition module 11, a region determination module 12, an equipment screening module 13 and a thermal operation module 14.

[0056] The information acquisition module 11 is used to obtain the heating coverage of at least one heat dissipation device in the area to be heated; The area determination module 12 is used to divide the area to be heated into multiple sub-areas, and determine at least one sub-area to be heated in the area to be heated after the current time based on the target sub-area where people are likely to gather in the area to be heated and the corresponding target time period, and the target time period is the time period when people are likely to gather in the corresponding target sub-area; The equipment screening module 13 is used to determine the final delivery equipment from various heat dissipation equipment according to each heating coverage area and each heating selected sub-area; The thermal operation module 14 is used to transport the hot water in the target boiler equipment to each final transportation equipment through a preset thermal pipeline. The target boiler equipment is the boiler equipment matched with the area to be heated.

[0057] Optionally, the region determination module 12 is specifically configured to: Obtain the first historical sub-regions in which the number of people distributed in the area to be heated exceeds a preset number threshold during the same period, count the first occurrence times of each first historical sub-region, and select the first historical sub-region with the first number from each first historical sub-region in descending order of the first occurrence times to determine it as the target sub-region; Obtain the first historical period of each target sub-area when the number of personnel distribution exceeds the number threshold, count the number of second occurrences of each first historical period, and select the first historical period with the second number from each first historical period in descending order of the number of second occurrences to determine it as the target period of the corresponding target sub-area; Calculate the first weight of each target sub-region and the second weight of each corresponding target period, the first weight is the ratio of the first occurrence number of each target sub-region to the sum of the first occurrence numbers of all target sub-regions, and the second weight is the ratio of the second occurrence number of a single target period corresponding to the target sub-region to the sum of the second occurrence numbers of all corresponding target periods; At least one sub-region to be heated in the region to be heated after the current time is determined based on the first weight and the corresponding second weights.

[0058] Optionally, the region determination module 12 is specifically configured to: Determine the target sub-regions including the current time in the corresponding target time periods as key sub-regions, determine the target time period including the current time as the key time period, and calculate the first product of the first weight of each key sub-region and the second weight of the corresponding key time period; Summing the first products to obtain a sum of the first products, and if the sum of the first products is greater than a preset first product sum threshold, comparing the first products with a preset product threshold; If the first product exceeds the product threshold, the corresponding key sub-area is determined as the sub-area to be selected for heating.

[0059] Optionally, the device screening module 13 is specifically used for: The heating coverage area including the heating candidate sub-area is determined as the key coverage area, and the first products of each heating candidate sub-area within each key coverage area are summed to obtain the sum of the corresponding second products; The sum of the second products is compared with a preset second product sum threshold. If the sum of the second products is greater than the second product sum threshold, the heat dissipation device belonging to the corresponding key coverage range is determined as the final delivery device.

[0060] Optionally, the thermal operation module 14 is specifically used for: Select the maximum first product from the first products of each heating sub-area to be selected within the key coverage area of ​​each final transmission equipment; According to the maximum first product, the hot water delivery order of the corresponding final delivery equipment is determined. The larger the maximum first product is, the earlier the corresponding hot water delivery order is. Determine the hot water delivery time of the corresponding final delivery device based on the current time and the key time period corresponding to each maximum first product; Based on the hot water delivery time and hot water delivery sequence of the same final delivery equipment, the hot water in the target boiler equipment is delivered to the corresponding final delivery equipment through a preset thermal pipeline.

[0061] Optional, such as Figure 4 As shown, the system further includes a region verification module 15, which is specifically used for: Obtain the second historical period in which the outdoor temperature of the area to be heated is lower than the preset first temperature threshold value during the same period, count the first occurrence frequency of each second historical period, and select the third second historical period from each second historical period in descending order of the first occurrence frequency to determine it as the low temperature prone period; Obtain the second historical sub-regions whose temperature is higher than the second temperature threshold in each low temperature period, and count the second occurrence frequency of each second historical sub-region, and select the second historical sub-region with the fourth number from each second historical sub-region in descending order of the second occurrence frequency to determine it as the easy comfortable temperature sub-region of the corresponding low temperature period; Calculate the third weight of each easy low temperature period and the fourth weight of each corresponding easy comfortable temperature sub-area, the third weight being the ratio of the first occurrence frequency of each easy low temperature period to the sum of the first occurrence frequencies of all easy low temperature periods, and the fourth weight being the ratio of the second occurrence frequency of a single easy comfortable temperature sub-area corresponding to the easy low temperature period to the sum of the second occurrence frequencies of all corresponding easy comfortable temperature sub-areas; According to the third weight and the corresponding fourth weights, the rationality of the sub-areas to be selected for heating is checked.

[0062] Optionally, the device screening module 13 is specifically used for: After the rationality check of all the heating candidate sub-areas is passed, the final delivery equipment is determined from the heat dissipation equipment according to each heating coverage area and each verified heating candidate sub-area.

[0063] Optionally, the area verification module 15 is specifically used for: Determine the low temperature period in which there is a heating sub-region to be selected in each corresponding easy-to-adjust temperature sub-region as an important period, calculate the second product of the third weight of the important period after the current time and the fourth weight of the corresponding single heating sub-region to be selected, and sum them to obtain the corresponding sum of the third products; Calculate and sum the third product of the third weight of the important time period before the current time and the fourth weight of the corresponding single heating to-be-selected sub-area to obtain the corresponding sum of the fourth products; If the sum of the third products corresponding to the same sub-area to be selected for heating is greater than the sum of the corresponding fourth products, it is determined that the rationality check of the corresponding sub-area to be selected for heating has passed.

[0064] It should be noted that the energy-saving operation system of a boiler room thermal system based on big data provided in the above embodiment only uses the division of the above functional modules as an example when executing the energy-saving operation method of a boiler room thermal system based on big data. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the equipment is divided into different functional modules to complete all or part of the functions described above. In addition, the energy-saving operation system of a boiler room thermal system based on big data provided in the above embodiment and the energy-saving operation method of a boiler room thermal system based on big data belong to the same concept. The implementation process is detailed in the method embodiment, which will not be repeated here.

[0065] An embodiment of the present application also discloses a computer-readable storage medium, and the computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, an energy-saving operation method of a boiler room thermal system based on big data of the above embodiment is adopted.

[0066] Among them, the computer program can be stored in a computer-readable medium, the computer program includes computer program code, the computer program code can be in the form of source code, object code, executable file or certain middleware, etc. The computer-readable medium includes any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the computer-readable medium includes but is not limited to the above-mentioned components.

[0067] Among them, through this computer-readable storage medium, an energy-saving operation method of a boiler room thermal system based on big data in the above embodiment is stored in a computer-readable storage medium, and is loaded and executed on a processor to facilitate the storage and application of the above method.

[0068] An embodiment of the present application also discloses an electronic device, in which a computer program is stored in a computer-readable storage medium. When the computer program is loaded and executed by a processor, the above-mentioned energy-saving operation method of a boiler room thermal system based on big data is adopted.

[0069] The electronic device may be a desktop computer, a laptop computer, a cloud server or other electronic device, and the electronic device includes but is not limited to a processor and a memory. For example, the electronic device may also include input and output devices, a network access device, and a bus.

[0070] Among them, the processor can adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be adopted. The general-purpose processor can adopt a microprocessor or any conventional processor, etc., and this application does not impose any restrictions on this.

[0071] Among them, the memory can be an internal storage unit of the electronic device, such as a hard disk or memory of the electronic device, or it can be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (SMC), a secure digital card (SD) or a flash memory card (FC) equipped on the electronic device. Moreover, the memory can also be a combination of an internal storage unit and an external storage device of the electronic device. The memory is used to store computer programs and other programs and data required by the electronic device. The memory can also be used to temporarily store data that has been output or is to be output, and this application does not impose any restrictions on this.

[0072] Among them, through this electronic device, an energy-saving operation method of a boiler room thermal system based on big data in the above-mentioned embodiment is stored in the memory of the electronic device, and is loaded and executed on the processor of the electronic device for easy use.

[0073] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. This application is intended to cover any variation, use or adaptive change of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for energy-saving operation of a boiler room thermal system based on big data, characterized in that: The method comprises: Obtaining the heating coverage of at least one heat dissipation device in the area to be heated; Divide the area to be heated into multiple sub-areas, and determine at least one sub-area to be heated in the area to be heated after the current time based on the target sub-area where people are likely to gather and the corresponding target time period, wherein the target time period is the time period when people are likely to gather in the corresponding target sub-area; Determine a final delivery device from each of the heat dissipation devices according to each of the heating coverage areas and each of the heating to-be-selected sub-areas; The hot water in the target boiler equipment is transported to each of the final transport devices through a preset thermal pipeline, and the target boiler equipment is a boiler equipment matched with the area to be heated.

2. The energy-saving operation method of the boiler room thermal system based on big data according to claim 1 is characterized in that: The step of determining at least one sub-region to be heated in the area to be heated after the current time based on the target sub-region where people are likely to gather and the corresponding target time period in the area to be heated specifically includes: Obtain the first historical sub-regions in which the number of people distributed in the area to be heated exceeds a preset number threshold during the same period, count the first occurrence times of each of the first historical sub-regions, and select the first historical sub-region with the first number from each of the first historical sub-regions in descending order of the first occurrence times to determine it as the target sub-region; Obtaining the first historical period in each of the target sub-areas when the number of personnel distribution exceeds the number threshold, counting the second occurrence times of each of the first historical period, and selecting the first historical period with the second number from each of the first historical periods in descending order of the second occurrence times to determine as the target period of the corresponding target sub-area; Calculating a first weight of each target sub-region and a second weight of each corresponding target time period, wherein the first weight is a ratio of a first occurrence number of each target sub-region to a sum of first occurrence numbers of all target sub-regions, and the second weight is a ratio of a second occurrence number of a single target time period corresponding to the target sub-region to a sum of second occurrence numbers of all corresponding target time periods; At least one sub-region to be heated in the region to be heated after the current time is determined based on the first weight and the corresponding second weights.

3. The energy-saving operation method of the boiler room thermal system based on big data according to claim 2 is characterized in that: The determining, based on the first weight and the corresponding second weights, at least one sub-area to be heated in the area to be heated after the current time specifically includes: Determine the target sub-regions including the current time in the corresponding target time periods as key sub-regions, determine the target time period including the current time as a key time period, and calculate a first product of a first weight of each key sub-region and a second weight of the corresponding key time period; summing up the first products to obtain a sum of first products, and if the sum of the first products is greater than a preset first product sum threshold, comparing the first products with a preset product threshold; If the first product exceeds the product threshold, the corresponding key sub-area is determined as a sub-area to be selected for heating.

4. The energy-saving operation method of the boiler room thermal system based on big data according to claim 3 is characterized in that: The step of determining the final delivery device from each of the heat dissipation devices according to each of the heating coverage areas and each of the heating selected sub-areas specifically includes: Determine a heating coverage range including the heating candidate sub-area as a key coverage range, and sum the first products of each heating candidate sub-area within each key coverage range to obtain a sum of corresponding second products; The sum of the second products is compared with a preset second product sum threshold. If the sum of the second products is greater than the second product sum threshold, the heat dissipation device belonging to the corresponding key coverage range is determined as the final delivery device.

5. The energy-saving operation method of the boiler room thermal system based on big data according to claim 4 is characterized in that: The method of delivering the hot water in the target boiler equipment to each of the final delivery equipment through a preset thermal pipeline specifically includes: Selecting the maximum first product from the first products of the heating sub-areas to be selected within the key coverage area of ​​each of the final delivery equipment; According to the maximum first product, determining the hot water delivery order of the corresponding final delivery equipment, the larger the maximum first product is, the earlier the corresponding hot water delivery order is; Determine the hot water delivery time of the corresponding final delivery device based on the current time and the key time period corresponding to each of the maximum first products; Based on the hot water delivery time and hot water delivery sequence of the same final delivery device, the hot water in the target boiler device is delivered to the corresponding final delivery device through a preset thermal pipeline.

6. The energy-saving operation method of the boiler room thermal system based on big data according to claim 1 is characterized in that: The method further comprises: Obtain a second historical period in which the outdoor temperature of the area to be heated is lower than a preset first temperature threshold value during the same period, count the first occurrence frequency of each of the second historical periods, and select the third second historical period from each of the second historical periods in descending order of the first occurrence frequency to determine it as a low temperature prone period; Acquire the second historical sub-regions whose temperature is higher than the second temperature threshold in each of the prone low temperature periods, and count the second occurrence frequencies of each of the second historical sub-regions, and select the second historical sub-regions with the fourth number from each of the second historical sub-regions in descending order of the second occurrence frequencies to determine as the easily comfortable temperature sub-regions of the corresponding prone low temperature period; Calculating a third weight of each of the easy low temperature time periods and a fourth weight of each of the corresponding easy comfortable temperature sub-areas, wherein the third weight is a ratio of a first occurrence frequency of each easy low temperature time period to a sum of first occurrence frequencies of all easy low temperature time periods, and the fourth weight is a ratio of a second occurrence frequency of a single easy comfortable temperature sub-area corresponding to the easy low temperature time period to a sum of second occurrence frequencies of all corresponding easy comfortable temperature sub-areas; Performing a rationality check on the heating candidate sub-area according to the third weight and the corresponding fourth weights; The step of determining the final delivery device from each of the heat dissipation devices according to each of the heating coverage areas and each of the heating selected sub-areas specifically includes: After the rationality check of all the heating candidate sub-areas is passed, the final delivery equipment is determined from the heat dissipation equipment according to the heating coverage areas and the checked heating candidate sub-areas.

7. The energy-saving operation method of the boiler room thermal system based on big data according to claim 6 is characterized in that: The performing a rationality check on the heating candidate sub-area according to the third weight and the corresponding fourth weights specifically includes: Determine the low temperature period in which there is a heating sub-region to be selected in each corresponding easy-to-adjust temperature sub-region as an important period, calculate the second product of the third weight of the important period after the current time and the fourth weight of the corresponding single heating sub-region to be selected, and sum them to obtain the corresponding sum of the third products; Calculate and sum the third product of the third weight of the important time period before the current time and the fourth weight of the corresponding single heating to-be-selected sub-area to obtain the corresponding sum of the fourth products; If the sum of the third products corresponding to the same sub-area to be selected for heating is greater than the sum of the corresponding fourth products, it is determined that the rationality check of the corresponding sub-area to be selected for heating has passed.

8. A boiler room thermal system energy-saving operation system based on big data, characterized in that: include: An information acquisition module (11) is used to acquire a heating coverage area of ​​at least one heat dissipation device in an area to be heated; An area determination module (12) is used to divide the area to be heated into a plurality of sub-areas, and determine at least one sub-area to be heated in the area to be heated after the current time based on a target sub-area in the area to be heated where people are likely to gather and a corresponding target time period, wherein the target time period is a time period in which people are likely to gather in the corresponding target sub-area; An equipment screening module (13) is used to determine a final delivery device from each of the heat dissipation devices according to each of the heating coverage areas and each of the heating selected sub-areas; The thermal operation module (14) is used to transport the hot water in the target boiler equipment to each of the final transport devices through a preset thermal pipeline, and the target boiler equipment is the boiler equipment matched with the area to be heated.

9. A computer-readable storage medium having a computer program stored therein, characterized in that: When the computer program is loaded and executed by a processor, the method according to any one of claims 1 to 7 is adopted.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor loads and executes the computer program, the method according to any one of claims 1 to 7 is adopted.

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