Driver assistance application program, driver assistance system, and driver assistance method

Abstract

To provide maintenance work on equipment at the appropriate time. [Solution] The operation support application program 100 is an operation support application program that operates on a computer 10 capable of sending and receiving information via a communication network 8 with a heat source device 21 that performs at least one of heating and cooling of a heat medium by a vapor compression refrigeration cycle. The computer 10 is instructed to perform the following: generate predicted information 52 of virtual heat demand for each unit load time period on the day of operation using a heat demand prediction model 70 (operation support application program) stored in the computer 10; acquire actual information 53 of actual heat demand for each unit load time period on the day of operation; calculate the degree of deviation 54 of the actual heat demand to the virtual heat demand for the same load time period; and generate a maintenance recommendation task 80 for the heat source device 21 when generation conditions are met, including the degree of deviation 54 reaching a threshold.

Description

Technical Field

[0001] The technology disclosed in this specification relates to a driving support application program, a driving support system, and a driving support method.

Background Art

[0002] In the technical field related to industrial equipment, a system that supplies heat to load equipment such as production equipment by a heat source device is used. For example, Patent Document 1 discloses a hot water production system that supplies hot water heated by a first heating device including an electric heat pump and a second heating device including a combustion boiler to load equipment.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Generally, there is a large difference in the scale of production activities in a factory between peak seasons and slack seasons, and there are also large fluctuations in the consumption of thermal energy by load equipment. The fluctuations in the consumption of thermal energy affect the operating time and operating conditions of the heat source device. The fluctuations in the operating time and operating conditions affect the lifespan of the heat source device. In order for the heat source device to achieve the designed expected lifespan, it is desirable to provide maintenance work at appropriate times.

[0005] The technology disclosed in this specification aims to provide maintenance work to equipment at appropriate times.

Means for Solving the Problems

[0006] This specification provides an operation support application program. The operation support application program is an operation support application program that operates on a computer capable of sending and receiving information via a communication network with a heat source device that performs at least one of heating and cooling of a heat medium by a vapor compression refrigeration cycle, and causes the computer to perform the following: generate predicted information on virtual heat demand for each unit load time period on the day of operation using a heat demand change pattern model or a trained inference model stored in the computer; obtain actual information on the actual heat demand for each unit load time period on the day of operation; calculate the degree of deviation of the actual heat demand to the virtual heat demand for the same load time period, and generate a maintenance recommendation task for the heat source device when generation conditions are met, including the degree of deviation reaching a threshold. [Effects of the Invention]

[0007] The technology disclosed herein makes it possible to provide maintenance work to equipment at an appropriate time. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 is a schematic diagram illustrating a driver assistance system according to an embodiment. [Figure 2] Figure 2 is a schematic diagram showing the heat supply equipment and load equipment according to the embodiment. [Figure 3] Figure 3 is a hardware configuration diagram showing an information processing device according to an embodiment. [Figure 4] Figure 4 is a diagram illustrating the hierarchical structure of the information processing device according to the embodiment. [Figure 5] Figure 5 is a functional block diagram showing an information processing device according to an embodiment. [Figure 6] Figure 6 illustrates the process flow for generating maintenance recommendation tasks. [Figure 7] Figure 7 illustrates the process for generating forecast information for virtual heat demand. [Figure 8] Figure 8 is a diagram illustrating the concept of deviation. [Figure 9] Figure 9 is a schematic diagram illustrating the heat drop between the heat transfer medium generated in the heat source device and the heat source fluid used in the heat source device. [Figure 10] Figure 10 is a schematic diagram illustrating the cumulative operating time. [Figure 11] Figure 11 is a schematic diagram illustrating the cumulative heat transfer rate. [Figure 12] Figure 12 is a schematic diagram showing a first configuration example of a heat supply system according to an embodiment. [Figure 13] Figure 13 is a schematic diagram showing a second configuration example of the heat supply equipment according to the embodiment. [Figure 14] Figure 14 is a schematic diagram showing a third configuration example of the heat supply equipment according to the embodiment. [Figure 15] Figure 15 is a schematic diagram showing a fourth configuration example of the heat supply equipment according to the embodiment. [Figure 16] Figure 16 is a flowchart illustrating a driving assistance method according to an embodiment. [Modes for carrying out the invention]

[0009] [1] Driver assistance systems Figure 1 is a schematic diagram showing the operation support system 1 according to an embodiment. The operation support system 1 is an operation support system for the heat supply equipment 2 that provides thermal energy to the heat transfer medium used by the load equipment LE within the business premises 3. The operation support system 1 acquires and stores environmental information and operational information of the heat supply equipment 2 at the business premises 3. The operation support system 1 supports the operation of the heat supply equipment 2 using the environmental information and operational information.

[0010] The equipment refers to the devices installed in buildings such as factories. The devices are the general terms for machines, instruments, and appliances. The heat supply equipment 2 refers to the mechanical appliances that supply heat to the load equipment LE. The heat supply equipment 2 is composed of including a heat source device that performs at least one of heating and cooling of the heat medium HM supplied to the load equipment LE. The heat supply equipment 2 includes one or more heat source devices. The heat supply equipment 2 supplies heat to the load equipment LE by the heated heat medium HM.

[0011] The heat supply equipment 2 uses the primary utility to heat the heat medium HM as the secondary utility. Examples of the primary utility input to the heat supply equipment 2 include fuel (gas, oil), electricity, and raw water. The heat medium refers to an energy source or fluid necessary for industrial activities. Examples of the heat medium HM output from the heat supply equipment 2 include steam, heat medium oil, hot water, and cold water.

[0012] Examples of the heat source device include a combustion type steam boiler, an electric heater type steam boiler, a heat recovery type steam boiler, a combustion type heat medium boiler, a combustion type hot water boiler, an electric heater type hot water boiler, a heat recovery type hot water boiler, an electric type heat pump, an electric type chiller, an electric type heat pump chiller, an absorption type refrigerator, an adsorption type refrigerator, and a heat recovery type air compressor.

[0013] The load equipment LE uses the heat medium HM output from the heat supply equipment 2. The load equipment LE uses the heat medium HM as a heat source for various production processes, treatment processes, hot water supply, or air conditioning, for example.

[0014] In addition, the load equipment LE may include medical mechanical appliances used within a series of processes from receiving to discharging the object to be washed and sterilized, washing mechanical appliances used within a series of processes from collecting to shipping the objects to be washed, and food and beverage manufacturing mechanical appliances used within a series of processes from receiving raw materials to storing products.

[0015] Examples of medical equipment include washers and sterilizers. Examples of washers include vacuum boiling washers and ultrasonic washers. Examples of sterilizers include steam sterilizers and gas sterilizers. Medical equipment is installed in the central sterile supply department of a medical institution.

[0016] Examples of laundry machinery include washing machines, dryers, and finishing machines. Examples of washing machines include continuous washing machines, water washing machines, and dry cleaning machines. Examples of dryers include gas dryers and steam dryers. Examples of finishing machines include gas roll ironers and steam roll ironers. Laundry machinery is installed in laundry factories.

[0017] Examples of machinery and equipment for food and beverage manufacturing include thawers, cooking machines, coolers, and sterilizers. Examples of thawers include vacuum steam thawers, microwave thawers, high-frequency thawers, and running water thawers. Examples of cooking machines include steam kneaders, steam kettles, and saturated steam cookers. Examples of coolers include vacuum coolers, chilled water coolers, and cold air coolers. Examples of sterilizers include retort sterilizers and pasteurizers. Machinery and equipment for food and beverage manufacturing are installed in food and beverage factories.

[0018] The load equipment LE is installed at establishment 3. Establishment 3 refers to individual locations where the production of goods or the provision of services is carried out as a business. Establishment 3 where goods are produced, etc., includes factories 4. The load equipment LE is installed at factories 4. Examples of factories 4 include food factories, beverage factories, metal product factories, plastic product factories, textile factories, and laundry factories.

[0019] Furthermore, a factory 4 is not required to be established at business establishment 3, which provides the services. The business conducted at business establishment 3 may include public health services. Examples of public health services include hospitals, clinics, and public health centers. Business establishment 3 may also include a school lunch center.

[0020] In the example shown in Figure 1, a certain business operator has three business establishments 3. Business establishments 3 include the first business establishment 3A, the second business establishment 3B, and the third business establishment 3C. A factory 4 is located in the second business establishment 3B. The factory 4 located in the second business establishment 3B includes the first factory 4A and the second factory 4B. Alternatively, a factory 4 may be located in either the first business establishment 3A or the third business establishment 3C, or both.

[0021] The heat supply equipment 2 is installed in both the first factory 4A and the second factory 4B. Data collection terminals 7 are installed in both the first factory 4A and the second factory 4B. Data collection terminals 7 is a general term for devices used for data collection at the business establishment 3.

[0022] The driving support system 1 comprises a sensor group consisting of one or more environmental sensors 5 placed in the heat supply equipment 2, a controller 61 that controls the operation of the heat source device 21, a communication network 8 that transmits information, and an information processing device 6 configured to acquire environmental information 50 detected by the sensor group and operational information 51 generated by the controller 61 via the communication network 8.

[0023] The environmental sensor 5 detects environmental information 50 of the heat supply equipment 2. Environmental information 50 of the heat supply equipment 2 refers to the environmental state or conditions of the space in which the heat supply equipment 2 operates. Environmental information 50 of the heat supply equipment 2 includes environmental information 50 of the business establishment 3 (factory 4) where the heat supply equipment 2 is installed. Environmental information 50 includes physical parameters of the heat supply equipment 2 and its surroundings. Some of the detection data from the environmental sensor 5 is used for the operation or control of the heat supply equipment 2. Examples of environmental sensors 5 include temperature sensors, humidity sensors, pressure sensors, water level sensors, flow rate sensors, electrical conductivity sensors (EC sensors), power sensors, distance sensors, image sensors, and force sensors.

[0024] The environmental sensor 5 is connected to the controller 61 of the heat supply equipment 2 and the controller 61 of the data collection terminal 7, respectively.

[0025] Figure 2 is a schematic diagram showing the heat supply equipment 2 and load equipment LE according to the embodiment. In the example of Figure 2, the heat supply equipment 2 and load equipment LE are installed inside the building of the factory 4 (factory building). The load equipment LE is, for example, production equipment. The load equipment LE includes various production machinery and equipment and utilizes the heat transfer medium HM supplied from the heat supply equipment 2 during production operation.

[0026] In this embodiment, the heat supply equipment 2 includes a heat source device 21 that performs at least one of heating and cooling of a heat transfer medium by a vapor compression refrigeration cycle. The heat supply equipment 2 uses the heat source device 21 to perform at least one of heating and cooling of a heat transfer medium HM in accordance with the heat demand of the load equipment LE. The heat transfer medium HM is, for example, water (water).

[0027] The configuration of the heat source device 21, which performs at least one of heating and cooling of the heat transfer medium by a vapor compression refrigeration cycle, is not particularly limited, but examples include a heat pump that generates hot water, a chiller that generates cold water, and a heat pump chiller that generates hot and cold water simultaneously. In the example shown in Figure 2, the heat source device 21 is a heat pump that heats the heat transfer medium HM by a vapor compression refrigeration cycle. The heat source device 21 heats the water used to generate hot water as the heat transfer medium HM.

[0028] In the example shown in Figure 2, the heat source device 21 consists of a vapor compression type heat pump including a refrigerant evaporator 211, a refrigerant condenser 212, a refrigerant compressor 213, and an expansion valve 214. The refrigerant evaporator 211, refrigerant condenser 212, refrigerant compressor 213, and expansion valve 214 are connected by a refrigerant circulation line 215 that circulates the refrigerant. The high-temperature, high-pressure refrigerant, which is depressurized in the expansion valve 214, evaporates by absorption in the refrigerant evaporator 211, and is compressed in the refrigerant compressor 213, condenses in the refrigerant condenser 212 by releasing heat. The refrigerant condenser 212 heats the water (heat transfer medium HM) by heat exchange with the high-temperature, high-pressure refrigerant. When an intermediate heat transfer medium is used, the heat transfer medium HM may be heated by further heat exchange between the intermediate heat transfer medium heated in the refrigerant condenser 212 and the heat transfer medium HM. The heat source from which the refrigerant evaporator 211 absorbs heat is not particularly limited and may be, for example, air or water. In one example, the heat source device 21 is an air-source heat pump that generates hot water using air as a heat source.

[0029] The heat supply equipment 2 includes a water tank 23 for storing water (hot water), which is the heat transfer medium HM. The water tank 23 stores water heated by the heat source device 21. The water tank 23 and the heat source device 21 are connected by a water line 24. In this specification, "line" refers to any line through which fluids can flow, such as a flow path, route, or pipeline.

[0030] The water supply line 24 is a passage for circulating water, which is the heat transfer medium HM. The water heated by the heat source device 21 is supplied to the water supply tank 23 via the water supply line 24.

[0031] Heated water is stored in the water tank 23. The water line 24 connects the water tank 23 to the load equipment LE. The water (heat transfer medium HM) stored in the water tank 23 is supplied to the load equipment LE via the water line 24. The load equipment LE either uses the supplied water as is or uses the heat extracted from the water. The water line 24 may be equipped with a pump to circulate the heat transfer medium HM.

[0032] The environmental sensor 5 is installed in the heat supply equipment 2. The environmental sensor 5 can be installed in one or more of the heat source device 21, the water line 24, or the water tank 23. The environmental sensor 5 installed in the heat supply equipment 2 includes a temperature sensor and a flow rate sensor. The environmental sensor 5 may also be installed in the load equipment LE.

[0033] The controller 61 of the heat supply equipment 2 has functions to control the operation of the heat supply equipment 2 and to control data collection. The controller 61 is mainly used for autonomous operation control of the heat source device 21. The controller 61 of the heat supply equipment 2 is connected to environmental sensors 5 installed in the heat source device 21, the water line 24, or the water tank 23. The controller 61 of the heat supply equipment 2 may also be configured as part of a data collection terminal 7 that is specialized for collecting information from the environmental sensors 5.

[0034] As shown in Figure 1, the controller 61 is incorporated into the heat supply equipment 2, the load equipment LE, and the data acquisition terminal 7. Examples of the controller 61 include a microcomputer 61A and a programmable logic controller 61B (PLC). The microcomputer 61A is an example of the controller 61 incorporated into the data acquisition terminal 7.

[0035] The controller 61 of the heat supply equipment 2 uses the environmental information 50 collected from the environmental sensor 5 to control the operation of the heat supply equipment 2 and records it for operational management.

[0036] The controller 61 of the data collection terminal 7 is connected to each of the multiple environmental sensors 5 scattered throughout the business premises 3. The controller 61 of the data collection terminal 7 is connected to multiple environmental sensors 5 of different types.

[0037] The controller 61 generates operating information 51 for the heat source device 21. The controller 61 may also generate the operating information 51 for the heat source device 21 based on the detection data of the environmental sensor 5.

[0038] [2] Hardware configuration of the information processing device As shown in Figure 1, the driver assistance system 1 has a plurality of information processing devices 6. The information processing devices 6 include a controller 61, an edge computer 62, a gateway 63, a guest computer 64, and a host computer 65.

[0039] Figure 3 is a hardware configuration diagram showing an information processing device 6 according to an embodiment. The information processing device 6 includes a computer 10. The controller 61, edge computer 62, gateway 63, guest computer 64, and host computer 65 each include the computer 10. The computer 10 has a processor 11, a storage device 12, a communication interface 13, and an input / output interface 14. The information processing device 6 also has a power supply (not shown).

[0040] The processor 11 includes a CPU (Central Processing Unit). The processor 11 may also include a GPU (Graphics Processing Unit). The storage device 12 includes a recording medium on which computer programs and data are recorded in a readable format by the processor 11. The storage device 12 includes onboard system memory such as RAM (Random Access Memory) or ROM (Read Only Memory), high-capacity flash memory such as an SD card or USB memory, and high-capacity storage such as an HDD (Hard Disk Drive) or SSD (Solid State Drive).

[0041] The communication interface 13 communicates via the communication network 8. Computer 10 sends data to an external computer via the communication network 8. Computer 10 receives data from an external computer via the communication network 8. Computer 10 connects to an external device via the input / output interface 14.

[0042] The communication network 8 is a general term for communication paths and devices that enable communication so that multiple computers 10 can send and receive information. Examples of communication networks 8 include local area networks (LANs), wide area networks (WANs), and commercial networks such as the Internet. A local area network may be a wired LAN or a wireless LAN. A wide area network may include mobile lines or satellite communication lines.

[0043] The storage device 12 stores various software programs. The storage device 12 stores a driver assistance application program 100 according to an embodiment. The processor 11 reads the software program from the storage device 12, loads it into system memory, and executes processing according to the software program. That is, the processor 11 can be considered to have multiple functional units. The functions of the processor 11 are realized by the software program. The software program may be distributed to the computer 10 via the communication network 8.

[0044] A software program that implements a specific function on a computer is called an application or engine (hereinafter referred to as "application, etc."), and the functional unit of an application, etc. is called a module. An engine may be installed on a computer as a single software package containing all its functions, but it is preferable that it be installed on a computer as individual software modules, each for each functional unit of an application, etc. Modularizing the functional units of an engine makes it easier to update when functional modifications are made. An application that runs on an edge computer 62 is sometimes called an edge application.

[0045] The environmental sensor 5 is connected to the input / output interface 14 of the controller 61. Multiple environmental sensors 5 are connected to one controller 61. The controller 61 collects environmental information 50 from the environmental sensors 5 in real time. The communication interface 13 of the controller 61 transmits the real-time environmental information 50 and real-time operational information 51 to the edge computer 62 via the communication network 8 (LAN).

[0046] The heat source device controller 61 can function not only as a local controller for the heat source device 21, but also as a system controller for controlling peripheral equipment (such as water pumps and water supply valves).

[0047] The edge computer 62 is installed in factory 4. One or more edge computers 62 are installed in each factory 4. The communication interface 13 of the edge computer 62 communicates with each of the multiple controllers 61 belonging to factory 4 where the edge computer 62 is located, via the communication network 8 (LAN). The edge computer 62 receives environmental information 50 and operational information 51 from the controllers 61 via the communication network 8.

[0048] The edge computer 62 has approximately 5GB of onboard memory as storage device 12 so as to be able to store a sufficient amount of information. The edge computer 62 may also have an AI engine (Neural Network Processing Unit: NPU) so as to be able to perform the training and inference phases in machine learning.

[0049] Gateway 63 constitutes a connection node between the local area network and the internet and has performance and specifications equivalent to that of the edge computer 62. Gateway 63 is installed at business establishment 3. One or more gateways 63 are installed at a single business establishment 3. The communication interface 13 of gateway 63 communicates with each of the multiple edge computers 62 belonging to business establishment 3 where gateway 63 is located, via the communication network 8 (LAN). Gateway 63 receives environmental information 50 and operational information 51 from the edge computers 62 via the communication network 8. If the factory 4 located at business establishment 3 is a single building, the edge computer 62 and gateway 63 may be integrated and configured as a single computer.

[0050] The guest computer 64 is located outside of the business premises 3. For example, the guest computer 64 is installed at a regional base of a service provider that undertakes tasks such as monitoring the status and maintenance of the heat supply equipment 2 installed at business premises 3. One guest computer 64 is installed at each management base. The guest computer 64 includes a local server. The communication interface 13 of the guest computer 64 communicates with the gateway 63 belonging to business premises 3 via the communication network 8 (Internet). The guest computer 64 receives environmental information 50 and operational information 51 from the gateway 63 via the communication network 8. If a guest computer 64 is not installed, the gateway 63 and the host computer 65 (described later) will be connected via the communication network 8.

[0051] The host computer 65 is located outside of the business premises 3. The host computer 65 is installed, for example, at the central hub of a service provider. The host computer 65 includes a cloud server. The communication interface 13 of the host computer 65 communicates with each of the multiple guest computers 64 via the communication network 8 (Internet). The host computer 65 receives environmental information 50 and operational information 51 from the guest computers 64 via the communication network 8.

[0052] [3] Hierarchical structure of information processing equipment Figure 4 is a diagram illustrating the hierarchical structure of the information processing device 6 according to the embodiment. The multiple information processing devices 6 have a hierarchical structure that transmits environmental information 50 detected by the environmental sensor 5 and operational information 51 generated by the controller 61 from the downstream side to the upstream side. Of the multiple information processing devices 6, the controller 61 to which the environmental sensor 5 is connected is the most downstream (lower layer, lower level) information processing device 6, followed by the edge computer 62 as the next downstream information processing device 6, followed by the gateway 63 as the next downstream information processing device 6, followed by the guest computer 64 as the next downstream information processing device 6, and the host computer 65 as the most upstream (upper layer, higher level) information processing device 6.

[0053] The information processing devices 6 are connected to each other via a communication network 8. Inside the building, a local area network is used as the communication network 8, while outside the building, a commercial wide area network such as an internet connection or a mobile network is used as the communication network 8.

[0054] Environmental information 50 is detected by the environmental sensor 5. The controller 61, which is a lower-level information processing device in the hierarchical structure, acquires the environmental information 50 from the environmental sensor 5. The environmental information 50 detected by the environmental sensor 5 and collected by the controller 61 is transmitted from the controller 61 to the edge computer 62 via the communication network 8. Operational information 51 generated by the controller 61 is also transmitted from the controller 61 to the edge computer 62 via the communication network 8. The environmental information 50 and operational information 51 transmitted to the edge computer 62 are then transmitted from the edge computer 62 to the gateway 63 via the communication network 8. The environmental information 50 and operational information 51 transmitted to the gateway 63 are then transmitted from the gateway 63 to the guest computer 64 via the communication network 8. The environmental information 50 and operational information 51 transmitted to the guest computer 64 are then transmitted from the guest computer 64 to the host computer 65 via the communication network 8.

[0055] The controller 61 is a lower-level information processing unit in the hierarchical structure. The edge computer 62, gateway 63, and guest computer 64 are intermediate information processing units in the hierarchical structure. The host computer 65 is a higher-level information processing unit in the hierarchical structure.

[0056] The lower-level information processing unit functions as an IoT device to which the environmental sensor 5 is connected. The lower-level information processing unit can transmit various environmental information 50 and operational information 51 to the intermediate information processing unit. The intermediate information processing unit functions as a relay between the lower-level information processing unit and the upper-level information processing unit. The intermediate information processing unit can receive various environmental information 50 and operational information 51 from the lower-level information processing unit and can transmit various environmental information 50 and operational information 51 to the upper-level information processing unit. The upper-level information processing unit can receive various environmental information 50 and operational information 51 from the intermediate information processing unit.

[0057] [4] Software configuration of the information processing device Figure 5 is a functional block diagram showing an information processing device 6 according to an embodiment. The information processing device 6 includes an information processing module 31, a prediction information generation module 32, a performance information generation module 33, a deviation degree evaluation module 34, a message generation module 35, an information storage platform 36, a database 37, and a UI provision module 38. The database 37 may be a group of databases that handle a wide variety of information, or it may be a component of the information storage platform 36. The information processing device 6 does not necessarily have to include the message generation module 35 and the UI provision module 38.

[0058] A user terminal 40 is connected to an information processing device 6. Examples of user terminals 40 include personal computers, tablet devices, and smartphones. The user terminal 40 includes a display device such as a liquid crystal display or an organic EL display.

[0059] The multiple information processing devices 6 (61, 62, 63, 64, 65) each have, at one or more layers, an information processing module 31, a prediction information generation module 32, a performance information generation module 33, a deviation degree evaluation module 34, a message generation module 35, an information storage platform 36, a database 37, and a UI providing module 38. That is, each of the information processing module 31, prediction information generation module 32, performance information generation module 33, deviation degree evaluation module 34, message generation module 35, information storage platform 36, database 37, and UI providing module 38 can be a functional unit at one or more layers of the multiple information processing devices 6 (61, 62, 63, 64, 65).

[0060] Some or all of these functional units are realized by causing the computer 10 to perform information processing according to the driver assistance application program 100 according to the embodiment. That is, the driver assistance application program 100 has program modules for operating the computer 10 as an information processing module 31, a prediction information generation module 32, a performance information generation module 33, a deviation degree evaluation module 34, a message generation module 35, an information storage platform 36, a database 37, and a UI provision module 38, respectively. When the computer 10 executes the respective program modules corresponding to the information processing module 31, the prediction information generation module 32, the performance information generation module 33, the deviation degree evaluation module 34, the message generation module 35, the information storage platform 36, the database 37, and the UI provision module 38, the information processing of each of the functional units, the information processing module 31, the prediction information generation module 32, the performance information generation module 33, the deviation degree evaluation module 34, the message generation module 35, the information storage platform 36, the database 37, and the UI provision module 38, is realized.

[0061] In one example, it is preferable that the prediction information generation module 32, the actual information generation module 33, the deviation degree evaluation module 34, and the message generation module 35 be functional units of the edge computer 62. The information processing module 31 is preferably functional unit of the edge computer 62 or gateway 63, and the UI provision module 38, the information storage platform 36, and the database 37 are preferably functional units of the host computer 65. When the operation support system 1 is completed within the factory building (factory building) of the factory 4 at the request of the operations manager of the business site 3, all of the modules, platforms, and databases may be functional units of the edge computer 62.

[0062] <4-1> Information Processing Module The information processing module 31 performs predefined information processing on the acquired environmental information and operational information. The information processing performed by the information processing module 31 includes, for example, batch processing to adjust time-series environmental information and operational information to the required time granularity. The information processing performed by the information processing module 31 also includes, for example, grouping processing to integrate multiple different types of environmental information and operational information into an information set linked to the hierarchical level of industrial activity.

[0063] <4-1-1> Generation process of environmental information and operational information The information processing module 31 acquires and stores environmental information from the environmental sensor 5 at predetermined sampling intervals. The information processing module 31 has functions such as A / D conversion to convert analog signals from the environmental sensor 5 into digital signals, substitution of A / D values ​​with measurement sample values, selection of measurement sample values, moving average function of measurement sample values, and period or frequency measurement function of pulse signals, and uses these functions to calculate confirmed measurement values ​​in real time.

[0064] The information processing module 31 acquires and stores real-time operational information from the controller 61 at predetermined sampling intervals. For example, the controller 61 transmits an "operating" signal when the heat source device is operating and a "stopped" signal when it is stopped. The information processing module 31 uses the "operating" signal received via the input / output interface 14 to measure the uptime (actual operating time information) of the heat source device. The information processing module 31 also uses the "stopped" signal received via the input / output interface 14 to measure the downtime (non-operating time information) of the heat source device.

[0065] The time granularity of real-time environmental information and real-time operational information depends on the sampling interval or recording interval, and is generally quite fine (for example, the latest values ​​are updated at intervals of 10ms to 1s). The information processing module 31 transmits the environmental information, along with an identification number (measurement item ID, device ID, location ID, etc.) and the update time, to the higher-level information processing device 6.

[0066] <4-1-2> Batch Processing Real-time environmental information and real-time operational information are time-series data that are generated sequentially over time in the information processing module 31. Batch processing refers to the process of adjusting the time-series environmental information and operational information to the required time granularity. Time granularity is an indicator that represents the degree of fineness of time, and can be selected from, for example, seconds, minutes, hours, or days.

[0067] <4-1-3> Grouping process Grouping refers to the process of integrating multiple types of environmental and operational information (environmental information, batch environmental information, operational information, batch operational information) into an information set linked to the hierarchical level of industrial activity. As shown in Figure 4, the hierarchical level of industrial activity includes one or more levels from among the machine level, machine group level, cell level, line level, building level, and business establishment level.

[0068] <4-2> Information Storage Platform The information storage platform 36 stores various types of information, such as numerical data and images, and also provides a foundational environment for operating software (engines, applications) and hardware. The information storage platform 36 is composed of, for example, an operating system and a database, enabling centralized management of diverse big data and facilitating the smooth processing and utilization of information.

[0069] The information storage platform 36 stores acquired environmental information 50 and operational information 51. Specifically, the information storage platform 36 stores environmental information 50 and operational information 51 that have undergone prescribed information processing by the information processing module 31, as well as various registration information registered via the input device.

[0070] Environmental information 50 includes various types of information used to calculate the heat demand. Environmental information 50 includes, for example, the temperature and flow rate of the heat transfer medium HM supplied to the load equipment LE. Operational information 51 includes various types of information used to calculate the heat supply amount of the heat source device 21 of the heat supply equipment 2. Operational information 51 includes, for example, one or more of the following: heat output information, operating frequency information, and operating number information. Heat output information is information on the amount of heat that the heat source device 21 provides (outputs) to the heat transfer medium HM. Heat output information can be expressed, for example, as the amount of heat output per unit time (J) or as an index value indicating the amount of heat output. When the flow rate of the heat transfer medium HM (water) is kept constant, the temperature of the heat transfer medium HM (hot water outlet temperature) can be used as the index value for heat output information. Operating frequency information is information indicating the frequency with which the heat source device 21 performs heat output operations. Operating frequency information can be expressed, for example, as the number of transitions from a stopped state to an operating state (startup count) that occur per unit time. The operating unit information indicates the number of heat source devices 21 operating simultaneously. This information may be the numerical value of the number of operating heat source devices 21 themselves, or it may be expressed as an index value, such as the ratio of the operating unit to the base unit. For example, if the total number of heat pumps of the heat source devices 21 is the base unit, the ratio of the operating unit will be the operating rate of the heat source devices 21. Note that the operating rate referred to here is the instantaneous operating rate and is distinct from the time operating rate, which is the ratio of operating time to load time.

[0071] Thus, the computer 10 (information processing device 6) is configured to acquire environmental information 50 detected by the sensor group and operational information 51 generated by the controller 61 via the communication network 8, and also has an information storage platform 36 for storing the acquired environmental information 50 and operational information 51.

[0072] <4-3> Predictive Information Generation Module Figure 6 is a diagram illustrating the process flow for generating maintenance recommendation tasks 80. Figure 7 is a diagram illustrating the process for generating virtual heat demand forecast information 52. As shown in Figures 6 and 7, the forecast information generation module 32 uses the heat demand forecast model 70 stored in the computer 10 to generate virtual heat demand forecast information 52 for each unit load time period on the day of operation. Note that load time periods are time periods when heat load (heat demand) occurs, and for example, time periods when factory 4 is shut down are excluded from load time periods.

[0073] The prediction model 70 is a heat demand change pattern model or trained inference model generated by utilizing the environmental information 50 stored in the information storage platform 36. The information storage platform 36 collects and stores, for example, heat demand data for each unit load time period (e.g., every hour) throughout the year at the managed object (e.g., factory 4 in Figure 2).

[0074] <4-3-1> Model of changes in heat demand The heat demand change pattern model is a collection of statistical values ​​for heat demand for each unit load time period of the managed system. The heat demand change pattern model is created by applying statistical processing to the environmental information 50 stored in the information storage platform 36.

[0075] A model of the pattern of changes in heat demand can be created, for example, by following these steps. First, the year is divided into winter (December-February), intermediate season (March-May), summer (June-August), and intermediate season (September-November), and representative values ​​are extracted from 12 heat demand data points for the same time period and day of the week for each period. Since the distribution of heat demand data is biased and affected by outliers, the representative values ​​to be extracted are considered to be most appropriate in the order of mode, median, and mean.

[0076] Arrange the representative values ​​for each day of the week in order of time of day to create a pattern of change in representative values ​​for one week. Create this pattern of change in representative values ​​for one week for each of the four periods that divide the year.

[0077] The extracted representative values ​​are predicted values ​​of heat demand for a given period, a given day of the week, and a given unit load time period. In this specification, these predicted values ​​are referred to as "virtual heat demand." The change pattern of representative values ​​for one week for each period is calculated by determining the change pattern of the daily virtual heat demand for each period, broken down by day of the week. The heat demand change pattern model includes change pattern data for the daily virtual heat demand for each unit load time period in each of the four periods, broken down by day of the week. Therefore, once the period and day of the week to which the operating day belongs are specified, the virtual heat demand for each unit load time period on the operating day can be estimated from the change pattern data.

[0078] The length and timing of each period used to divide the year may vary depending on the region where the managed data is located. Furthermore, it is preferable to update the heat demand change pattern model by integrating and replacing data using additional historical information each year.

[0079] <4-3-2> Pre-trained inference model for heat demand The pre-trained inference model for heat demand is an inference model created to output a predicted value of heat demand (virtual heat demand) using machine learning with environmental information 50 stored in the information storage platform 36. The pre-trained inference model is created, for example, to output the virtual heat demand for the day of operation using regression analysis.

[0080] The training data used to create (learn) a pre-trained inference model consists of a dataset where features are used as explanatory variables and target labels are used as the target variable. Specifically, the training data is composed of three features: lunar phase, day of the week, and load time period, with the actual heat demand for the lunar phase, day of the week, and load time period identified by these features being used as the target labels. The training data includes a sufficient number of sample data of actual heat demand for each lunar phase, day of the week, and load time period. The actual heat demand is calculated from environmental information 50 measured on-site using environmental sensors 5 (in this case, temperature sensors and flow sensors). The actual heat demand for each lunar phase, day of the week, and load time period is calculated based on the environmental information 50 stored in the information storage platform 36.

[0081] The first explanatory variable, lunar phase, is defined as a variable from 1 to 12, for example, representing January to December. The second explanatory variable, day of the week, is defined as a variable from 1 to 7, for example, representing Sunday to Saturday (Thursday would be 5). The third explanatory variable, load time period, is defined as a variable from 1 to 24, dividing 24 hours into 1-hour intervals (0:00-1:00 is 1, 1:00-2:00 is 2, ..., 23:00-24:00 is 24).

[0082] The dependent variable, actual heat demand, may be expressed as either the absolute value of heat (in J) or the relative value of heat (in %). When using relative values, the sample data of actual heat demand is expressed as a percentage, with the maximum heat demand that can be generated by the load equipment LE set to 100%.

[0083] The trained inference model, through machine learning using the aforementioned training data, has acquired a regression equation that outputs a corresponding virtual heat demand (estimated heat demand) for any set of explanatory variables (lunar phase, day of the week, and load time period).

[0084] By inputting three explanatory variables for the day of operation into the created trained inference model, it becomes possible to predict the virtual heat demand for each unit load time period. If the virtual heat demand is output as a percentage, it can be converted to an energy value (in J) by multiplying that percentage value by the maximum heat demand that can be generated by the load equipment LE. Arranging the predicted virtual heat demand data over time yields data equivalent to a heat demand change pattern model.

[0085] Note that the explanatory variables (input variables) of the trained inference model are not limited to the date and time information mentioned above.

[0086] The explanatory variables (input variables) may, for example, be the output information of the load equipment LE per unit load time. For example, if the load equipment LE is production machinery, information on the production quantity per unit load time may be used as the explanatory variable. In this case, the training data is composed of actual values ​​of the production quantity per unit load time (e.g., per hour) as features, and the actual heat demand required for the production quantity identified by these features as the training label. Through machine learning, a trained inference model is obtained that outputs a corresponding virtual heat demand (estimated value of heat demand) for the input of explanatory variables (production quantity per unit load time). By inputting the planned values ​​of the production quantity for each unit load time period on the day of operation into the trained inference model, it becomes possible to predict the virtual heat demand for each unit load time period.

[0087] Furthermore, the explanatory variables (input variables) may be, for example, operational information of the load equipment LE during a unit load time 51. For example, information such as the equipment load rate and power load rate of the load equipment LE during a unit load time may be used as explanatory variables. The equipment load rate is the ratio (percentage) of the amount of processing when the load equipment LE operates under actual load conditions for one hour to the amount of processing when it operates under maximum load conditions for one hour. The power load rate is the ratio (percentage) of the amount of power consumed when the load equipment LE operates under actual load conditions for one hour to the amount of power consumed when it operates under maximum load conditions for one hour. In this case, the training data is constructed using the actual values ​​of the equipment load rate or power load rate as features, and the actual heat demand generated at the equipment load rate or power load rate identified by the features as the training labels. Through machine learning, a trained inference model is obtained that outputs a corresponding virtual heat demand (estimated value of heat demand) for each explanatory variable (equipment load rate or power load rate) input. By inputting planned values ​​for equipment load factor or power load factor for each unit load time period on the day of operation into a trained inference model, it becomes possible to predict the virtual heat demand for each unit load time period.

[0088] <4-3-3> Predictive Information The forecast information 52 is information on the virtual heat demand for each unit load time period on the day of operation. The forecast information generation module 32 generates the forecast information 52 of the virtual heat demand for each unit load time period on the day of operation using the heat demand forecast model 70 (i.e., the heat demand change pattern model or the trained inference model). That is, the forecast information generation module 32 obtains the virtual heat demand for each unit load time period on the day of operation from the change pattern model based on the period and day of the week to which the day of operation belongs. Alternatively, the forecast information generation module 32 obtains the virtual heat demand for each unit load time period on the day of operation by inputting information on explanatory variables for the day of operation into the trained inference model.

[0089] The forecast information 52 allows us to know in advance the changes in virtual heat demand for each unit load time period on the day of operation, before such changes occur. In other words, the forecast information 52 allows us to know in advance when and to what extent (heat quantity [J]) heat demand will occur in the load equipment LE.

[0090] The prediction model 70 may include both a heat demand change pattern model and a trained inference model. Furthermore, the prediction model 70 may include multiple types of change pattern models and multiple types of trained inference models. If the prediction model 70 includes multiple models, the driver assistance application program 100 (prediction information generation module 32) may have a function to select the model to be used to generate the virtual heat demand prediction information 52.

[0091] <4-4> Performance Information Generation Module As shown in Figure 6, the performance information generation module 33 uses the environmental information 50 stored in the information storage platform 36 to obtain performance information 53 of the actual heat demand for each unit load time period on the day of operation. As described above, the information storage platform 36 continuously collects and stores, for example, heat demand data for each unit load time period (e.g., every hour) throughout the year for the managed area. The performance information generation module 33 obtains performance information 53 of the actual heat demand for each unit load time period on the day of operation from the information storage platform 36.

[0092] Actual Information 53 is information (actual values) on the actual heat demand for each unit load time period on the day of operation. In other words, Actual Information 53 is historical data of the actual heat demand collected on the day of operation.

[0093] Actual data 53 is information on the actual heat demand that occurred for each unit load time period on the same day (the day of operation) as the virtual heat demand forecast data 52. Therefore, it can be said that forecast data 52 and actual data 53 have a relationship between the predicted value and the measured value of heat demand for the same unit load time period.

[0094] <4-5> Deviation Degree Evaluation Generation Module The deviation evaluation module 34 calculates the deviation 54 of the actual heat demand to the virtual heat demand during the same load period, and generates a maintenance recommendation task 80 for the heat source device 21 when the generation conditions 55, including the deviation 54 reaching a threshold, are met.

[0095] <4-5-1> Degree of deviation Figure 8 illustrates the concept of deviation 54. Deviation 54 represents the degree of difference between the actual heat demand and the virtual heat demand. Deviation 54 is calculated using the following formula, with the actual heat demand for each unit time period on the day of operation (newly acquired data) and the virtual heat demand for the same day and time period (model data). Degree of deviation [%] = (Actual heat demand - Virtual heat demand) ÷ Virtual heat demand × 100

[0096] Figure 8 shows a line graph illustrating the change in heat demand over time, with the vertical axis representing heat demand [J] and the horizontal axis representing time. In Figure 8, the actual heat demand DA is shown as a solid line, and the virtual heat demand DE is shown as a dotted line. As shown in Figure 8, the difference between the actual heat demand DA and the virtual heat demand DE during the same time period is represented by the deviation E. The ratio (percentage) of this deviation E to the virtual heat demand DE is the deviation degree 54. A deviation degree 54 can occur in cases where the actual heat demand is greater than the virtual heat demand, such as from time t1 to time t2, or in cases where the actual heat demand is less than the virtual heat demand, such as from time t3 to time t4. In this specification, a case where the actual heat demand is greater than the virtual heat demand is considered a positive deviation degree, and a case where the actual heat demand is less than the virtual heat demand is considered a negative deviation degree.

[0097] The deviation evaluation module 34 obtains virtual heat demand data and actual heat demand data for the same load period from the forecast information 52 and actual information 53. The deviation evaluation module 34 calculates the deviation 54 for each unit load time based on the above formula from the obtained virtual heat demand data and actual heat demand data. Note that the virtual heat demand DE, actual heat demand DA, and deviation 54 data are discrete values ​​for a constant unit time (e.g., 1 hour), but in Figure 8 they are shown in a simplified form as continuous values ​​(line graph).

[0098] A larger deviation of 54 indicates that the operating conditions of the load equipment LE, etc., deviate from those expected based on the annual heat demand at the business establishment 3 or factory 4. If the actual heat demand increases sharply, causing the positive deviation of 54 from the maximum value change pattern model to exceed a certain level, the heat source device 21 and load equipment LE will become overloaded and more prone to failure. On the other hand, if the actual heat demand decreases sharply, causing the negative deviation of 54 from the minimum value change pattern model to exceed a certain level, the start-stop frequency of the heat source device 21 and load equipment LE will increase, negatively impacting the lifespan of the components.

[0099] The threshold for deviation 54 is not particularly limited and is set to an appropriate value by estimating the degree of adverse effects caused by an increase in deviation 54. For example, the threshold for deviation 54 could be +20% on the positive side (i.e., actual heat demand is 20% greater than virtual heat demand) and -20% on the negative side (i.e., actual heat demand is 20% less than virtual heat demand).

[0100] <4-5-2> Generation conditions The generation condition 55 is the condition for generating the maintenance recommendation task 80. The deviation evaluation module 34 generates the maintenance recommendation task 80 if the generation condition 55 is met. The deviation evaluation module 34 does not generate the maintenance recommendation task 80 if the generation condition 55 is not met.

[0101] The generation condition 55 includes the fact that the deviation degree 54 has reached a threshold (hereinafter referred to as the first condition). The generation condition 55 includes at least the first condition and may include other conditions other than the first condition. The generation condition 55 may consist only of the first condition. For this reason, the deviation degree evaluation module 34 generates the maintenance recommendation task 80 triggered by at least the deviation degree 54 reaching a threshold. This allows the maintenance recommendation task 80 to be generated when the deviation between the virtual heat demand and the actual heat demand increases to a degree that could adversely affect the heat source device 21.

[0102] Examples of generation conditions 55, which include multiple conditions, are given below.

[0103] <4-5-2-1> First example of generation conditions In the first example, the generation condition 55 includes the fact that the degree of deviation 54 reaches a first threshold and the heat drop 56 reaches a second threshold. That is, in addition to the first condition, the condition that the heat drop 56 reaches a second threshold is added to the generation condition 55.

[0104] In this case, the deviation evaluation module 34 (computer 10) acquires environmental information 50, including the thermal drop 56 between the heat transfer medium HM generated in the heat source device 21 and the heat source fluid HS used in the heat source device 21. Figure 9 is a schematic diagram illustrating the thermal drop 56 between the heat transfer medium HM generated in the heat source device 21 and the heat source fluid HS used in the heat source device 21.

[0105] As shown in Figure 9, the heat source device 21 equipped with a vapor compression refrigeration cycle uses a heat source fluid HS to perform at least one of heating and cooling of the heat transfer medium HM. The heat source device 21 generates the heat transfer medium HM, which is hot water, by absorbing heat from air, which is the heat source fluid HS, and heating water. The heat drop 56 is the temperature difference between the temperature of the heat source fluid HS (inlet temperature in the heat source device 21) and the temperature of the heat transfer medium HM (outlet temperature). For example, the environmental sensor 5 includes a temperature sensor that detects the temperature of the heat source fluid HS and a temperature sensor that detects the temperature of the heat transfer medium HM. The heat drop 56 is the magnitude of the difference between the temperature X1 [°C] of the heat source fluid HS and the temperature X2 [°C] of the heat transfer medium HM, and is taken as a representative value, for example, per unit load time.

[0106] The larger the heat drop 56 of the heat source device 21, the greater the output of the refrigerant compressor 213 in the refrigeration cycle, which shortens the lifespan of components such as bearings and motors, and increases the likelihood of refrigerant leaks from the refrigerant piping. Therefore, in the first example, the deviation degree evaluation module 34 generates a maintenance recommendation task 80 when the deviation degree 54 reaches a first threshold and the heat drop 56 reaches a second threshold. This allows the maintenance recommendation task 80 to be generated when an operating condition occurs in which the deviation degree 54 is large and the output of the refrigerant compressor 213 is large. The second threshold is not particularly limited and is set to an appropriate value by estimating the degree of adverse effects caused by the increase in the heat drop 56.

[0107] <4-5-2-2> Second example of generation conditions In the second example, the generation condition 55 includes the fact that the deviation 54 has reached a first threshold and the cumulative operating time 57 has reached a third threshold. That is, in addition to the first condition, the condition that the cumulative operating time 57 has reached a third threshold is added to the generation condition 55.

[0108] The cumulative operating time 57 is the cumulative value of the operating time of the heat source device 21 since the last (most recent) maintenance service. In this case, the deviation evaluation module 34 (computer 10) acquires the operating information 51 of the heat source device 21, including the cumulative operating time 57 of the heat source device 21 since the last maintenance service. Figure 10 is a schematic diagram illustrating the cumulative operating time 57. In Figure 10, the vertical axis is a graph showing the operating state and stopped state of the heat source device 21 as binary data, and the horizontal axis represents time.

[0109] As shown in Figure 10, suppose that at a certain time t11, maintenance work is provided for the heat source device 21. After the maintenance is provided, the heat source device 21 repeatedly switches between an operating state and a stopped state in accordance with the operation of the factory 4, etc. Based on the operating information 51 of the heat source device 21, the deviation evaluation module 34 obtains the cumulative operating time 57 by accumulating the operating time of the heat source device 21 (the time in the hatched area of ​​Figure 10) from the time the maintenance work is provided to the day of operation (the present time).

[0110] The longer the cumulative operating time 57 of the heat source device 21, the longer the operating time of the refrigerant compressor 213 in the refrigeration cycle, which accelerates the deterioration of bearings and motors. Also, the longer the cumulative operating time 57 of the heat source device 21, the longer the flow time of the heat transfer medium HM and heat source fluid HS, which can cause the heat transfer surfaces of the heat exchangers (refrigerant evaporator 211 and refrigerant condenser 212) to become contaminated. In particular, if the heat source device 21 is operated frequently and for long periods of time, even if not much time has passed since maintenance was provided, the cumulative operating time 57 will increase rapidly compared to the elapsed time. Therefore, in the second example, the deviation evaluation module 34 generates a maintenance recommendation task 80 when the deviation 54 reaches the first threshold and the cumulative operating time 57 reaches the third threshold. This allows for the generation of a maintenance recommendation task 80 when a situation occurs where the deviation 54 is large and the cumulative operating time 57 of the heat source device 21 is long. The third threshold is not particularly limited and is set to an appropriate value that estimates the degree of adverse effects resulting from the increase in cumulative operating time of 57.

[0111] <4-5-2-3> Third example of generation conditions In the third example, the generation condition 55 includes the fact that the degree of deviation 54 has reached a first threshold and the cumulative heat transfer amount 58 has reached a fourth threshold. That is, in addition to the first condition, the condition that the cumulative heat transfer amount 58 has reached a fourth threshold is added to the generation condition 55.

[0112] The cumulative heat transfer amount 58 is the cumulative value of the amount of heat transferred (sum of heat absorption and heat compression) from the heat source fluid HS to the heat transfer medium HM by the heat source device 21 since the last (most recent) maintenance service. The cumulative heat transfer amount 58 can be determined from the heat drop 56 of the heat source device 21 shown in Figure 9 and the operating time of the heat source device 21 shown in Figure 10.

[0113] Figure 11 is a schematic diagram illustrating the cumulative heat transfer amount 58. The deviation degree evaluation module 34 acquires environmental information 50, including the heat drop 56 between the heat transfer medium HM generated in the heat source device 21 and the heat source fluid HS used in the heat source device 21, as shown in Figure 9. The deviation degree evaluation module 34 also acquires operating information 51 of the heat source device 21, including the operating time of the heat source device 21.

[0114] The deviation evaluation module 34 calculates an index value 56A of the heat drop 56 from the information of the heat drop 56, each time a predetermined operating time has elapsed since the last maintenance service. The length of the predetermined operating time may be the same as or different from the unit load time (e.g., 1 hour). The index value 56A may be the value of the heat drop 56 as is, or it may be an index value calculated by, for example, normalization. The index value 56A becomes larger as the heat drop 56 increases. The deviation evaluation module 34 calculates the cumulative heat transfer amount 58 by multiplying the index value 56A, which indicates the relative magnitude of the heat drop 56, by the operating time each time a predetermined operating time has elapsed since the last maintenance service. The cumulative heat transfer amount 58 is the cumulative value up to the day of operation of the heat transfer amount, which is expressed as the product of the index value 56A at each predetermined operating time and the predetermined length of that operating time.

[0115] The larger the cumulative amount of heat transferred (heat pumped) in the heat source device 21, the shorter the lifespan of the components of the refrigeration cycle and the more likely the heat transfer coefficient of the heat exchanger is to decrease. In particular, if the heat source device 21 is operated under high load or operated frequently for long periods of time, even if not much time has passed since maintenance was provided, the cumulative heat transfer amount 58 will increase rapidly. Therefore, in the third example, the deviation evaluation module 34 generates a maintenance recommendation task 80 when the deviation 54 reaches the first threshold and the cumulative heat transfer amount 58 reaches the fourth threshold. This allows the maintenance recommendation task 80 to be generated (regardless of the time elapsed since maintenance was provided) when a situation occurs where the deviation 54 is large and the cumulative heat transfer amount 58 of the heat source device 21 is large. The fourth threshold is not particularly limited and is set to an appropriate value by estimating the degree of adverse effects caused by the increase in cumulative heat transfer amount 58.

[0116] One or more generation conditions 55 can be set. For example, three generation conditions 55, from the first example to the third example, may be set in the driver assistance system 1. In this case, the deviation evaluation module 34 generates a maintenance recommendation task 80 when any one of the three generation conditions 55, from the first example to the third example, is met.

[0117] <4-5-3> Maintenance Recommended Tasks As shown in Figure 6, the maintenance recommendation task 80 is a task (process) for notifying that the implementation of equipment maintenance measures is recommended. When the maintenance recommendation task 80 is generated, the computer 10 (one of the modules) executes a pre-configured process corresponding to the maintenance recommendation task 80.

[0118] <4-5-3-1>Message recommending the provision of maintenance In this embodiment, the maintenance recommendation task 80 includes generating a message 81A recommending the provision of condition-based maintenance for at least one of the heat source device 21 and the load equipment LE.

[0119] Maintenance of the heat source device 21 and load equipment LE is carried out, for example, as maintenance work by a maintenance provider. The maintenance provider provides various maintenance services for the heat source device 21 and load equipment LE. Maintenance services include after-maintenance, which is performed after an abnormality occurs in the heat source device 21 or load equipment LE, and before-maintenance, which is performed before an abnormality occurs. Before-maintenance includes time-based maintenance and condition-based maintenance. Time-based maintenance is performed based on the elapsed time of a predetermined period. Condition-based maintenance is performed based on the condition and operating status of the equipment. Condition-based maintenance is considered the most preferable maintenance method because it reduces unnecessary work compared to time-based maintenance. Message 81A recommends providing this condition-based maintenance.

[0120] When a maintenance recommendation task 80 is generated, the message generation module 35 generates a message 81A recommending the provision of condition-based maintenance for at least one of the heat source device 21 and the load equipment LE. The generated message 81A is displayed to the user terminal 40 through a user interface provided by the UI provision module 38 (for example, a portal page for maintenance support).

[0121] Service personnel of the maintenance provider can perform condition-based maintenance by referring to message 81A displayed on the user terminal 40. This allows for the inspection of the heat source device 21 and load equipment LE, replacement and cleaning of parts, and replenishment of consumables, triggered by a deviation of 54 from the actual heat demand, thereby restoring equipment performance.

[0122] <4-5-3-2> A message recommending a review of the output condition values. In this embodiment, the maintenance recommendation task 80 includes generating a message 81B recommending a review of the output condition values ​​of the heat source device 21.

[0123] When the maintenance recommendation task 80 is activated, the message generation module 35 generates a message 81B recommending a review of the output condition values ​​of the heat source device 21. The generated message 81B is displayed on the user terminal 40 through the user interface provided by the UI provision module 38.

[0124] Service personnel of the maintenance provider can refer to the message 81B displayed on the user terminal 40 to revise the output condition values ​​of the heat source device 21. This triggers a change in the output condition values ​​of the heat source device 21 based on the deviation 54 from the actual heat demand, allowing it to transition to an appropriate operating state (an operating state that does not result in excessive or insufficient load) according to the actual heat demand.

[0125] <4-6> UI Provisioning Module The UI provision module 38 provides a user interface (UI) for displaying information such as the operational performance of the driver assistance system 1 on the user terminal 40. The user interface is a means of accessing the driver assistance system 1. The user interface mainly includes a function to display the UI screen on the user terminal 40 and a function to receive operation input for various functions provided via the UI screen.

[0126] The UI module 38 displays various types of information on the user terminal 40, such as environmental information 50, operating information 51 of the heat source device 21, operating conditions of the heat source device 21, operation plan of load equipment LE, virtual heat demand, and history of actual heat demand.

[0127] In this embodiment, the UI providing module 38 displays messages generated by the message generation module 35 on the user terminal 40. Specifically, the UI providing module 38 displays message 81A recommending the provision of condition-based maintenance and message 81B recommending a review of the output condition values ​​of the heat source device 21 on the user terminal 40.

[0128] The UI module 38 displays, for example, a maintenance support portal page (hereinafter simply referred to as the "portal page") for service personnel of a maintenance provider. The portal screen functions as a user interface for service personnel. On the portal page, the UI module 38 generates a screen that displays the daily action plan information of the service personnel in charge of the customer's equipment. When the UI module 38 generates a message 81A recommending the provision of condition-based maintenance or a message 81B recommending a review of the output condition values ​​of the heat source device 21, it reflects these messages on the screen that displays the daily action plan information. Service personnel can check messages 81A and 81B on the screen that displays the daily action plan information.

[0129] <4-7> Database Database 37 stores the operation plan of the load equipment LE, information on the load equipment LE, operating conditions of each heat source device 21, generation conditions 55, various thresholds for generation conditions 55, and equipment flow diagrams that depict the overall or partial configuration of the heat supply equipment 2 and the factory 4.

[0130] [5] Example of a heat supply system configuration The configuration of the heat supply equipment 2 according to this embodiment for supplying the heat transfer medium HM to the load equipment LE can take various forms and is not particularly limited. A typical example of the configuration of the heat supply equipment 2 will be described below.

[0131] <5-1> First Configuration Example Figure 12 is a schematic diagram showing a first configuration example of the heat supply equipment 2 according to the embodiment. In Figure 12, the load equipment LE indirectly uses heated water (heat transfer medium HM) (i.e., provides thermal output). In Figure 12, the heating of the heat transfer medium HM by the heat source device 21 takes place along the path through which the heat transfer medium HM is sent to the load equipment LE.

[0132] The heat supply equipment 2 includes a heat source device 21 that heats the water using an electric heat pump, an auxiliary heat source device 22 that heats the water using a combustion or electric boiler, and a water tank 23. The water line 24 includes a distribution line 241 and a return line 242. The heat supply equipment 2 heats the water used by the load equipment LE within the business premises as a heat transfer medium HM.

[0133] The water tank 23 stores water, which is the heat transfer medium HM, as stored water. The water tank 23 is, for example, an open-type tank. The water tank 23 is equipped with a water level sensor 231 as an environmental sensor 5. When the water level sensor 231 detects a decrease in the water level in the water tank 23, replenishment water is supplied to the water tank 23 through the replenishment water line 243. The water tank 23 may also be equipped with other environmental sensors 5. In this case, the environmental sensor 5 is a temperature sensor that detects the temperature of the water stored in the water tank 23.

[0134] The water distribution line 241 and the water return line 242 connect the water tank 23 and the load equipment LE, respectively. In Figure 12, the water distribution line 241 and the water return line 242 form a circulation line for the hot water loop system. A water supply pump 244 is provided in the water distribution line 241. The water distribution line 241 is the supply pipe that supplies water from the water tank 23 to the load equipment LE. The water stored in the water tank 23 is supplied to the load equipment LE via the water distribution line 241. In the load equipment LE, heat is removed from the water through heat exchange, and the water temperature decreases. The water return line 242 is the return pipe that returns water from the load equipment LE to the water tank 23. The water that has been used in the load equipment LE and whose temperature has decreased flows to the water return line 242.

[0135] The heat source device 21 consists of an air-source heat pump. In the first configuration example shown in Figure 12, the heat source device 21 further includes a sub-heat exchanger 216, a sub-circulation pump 217, and a sub-circulation line 218, in addition to the heat pump.

[0136] The sub-circulation line 218 connects the sub-heat exchanger 216 to the refrigerant condenser 212 (see Figure 2) of the heat source device 21. Intermediate heat transfer medium HM2 flows through the sub-circulation line 218. The intermediate heat transfer medium HM2 circulating in the sub-circulation line 218 may be water. A sub-circulation pump 217 is provided in the sub-circulation line 218. The sub-circulation pump 217 circulates the heat transfer medium HM flowing through the sub-circulation line 218.

[0137] The heat source device 21 absorbs heat from the air (heat source fluid HS) using the refrigerant evaporator 211. The heat source device 21 then releases (heats) the absorbed heat to the intermediate heat transfer medium HM2 circulating in the sub-circulation line 218 using the refrigerant condenser 212. The heat source device 21 also heats the water flowing through the return water line 242.

[0138] The sub-heat exchanger 216 performs heat exchange between the heat transfer medium HM flowing through the water supply line 24 and the intermediate heat transfer medium HM2 flowing through the sub-circulation line 218, thereby heating the heat transfer medium HM. In the first configuration example shown in Figure 12, the sub-heat exchanger 216 is located in the return water line 242 from the load equipment LE, and heats the water used as the heat transfer medium HM with the heat from the intermediate heat transfer medium HM2. The heated water is then sent to the water supply tank 23 via the return water line 242.

[0139] Environmental sensors 5 may be provided in the water supply line 24. The environmental sensors 5 are temperature sensors and may be provided in one or more locations in the water supply line 24. The installation locations of the environmental sensors 5 include, for example, the water distribution line 241 between the water supply tank 23 and the load equipment LE, the upstream location of the sub-heat exchanger 216 in the return water line 242, and the downstream location of the sub-heat exchanger 216 in the return water line 242. In Figure 12, the environmental sensors 5 are provided upstream of the location where the sub-heat exchanger 216 is located in the return water line 242. The upstream environmental sensors 5 are temperature sensors that detect the return temperature of the water. In Figure 12, the environmental sensors 5 are provided downstream of the location where the sub-heat exchanger 216 is located in the return water line 242. The downstream environmental sensors 5 are temperature sensors that detect the temperature of the water heated by the heat source device 21 (hot water temperature).

[0140] In Figure 12, a temperature sensor is provided as the environmental sensor 5 to detect the temperature of the air (heat source fluid HS) taken into the heat source device 21. The heat drop 56 can be determined from the air temperature and the hot water outlet temperature.

[0141] The auxiliary heat source device 22 heats the water in the water tank 23. The auxiliary heat source device 22 includes, for example, a group of steam boilers consisting of one or more steam boilers. The auxiliary heat source device 22 supplies steam ST to the steam line 25. The steam line 25 is equipped with a steam heater 251 and a steam supply valve 252.

[0142] The steam line 25 supplies steam ST from the steam boiler to the steam heater 251. After heat exchange in the steam heater 251, the steam ST is discharged from the steam heater 251 through the steam line 25. The steam heater 251 is installed inside the water tank 23. The steam heater 251 performs heat exchange between the water in the water tank 23 and the steam ST flowing through the steam line 25, heating the stored water. The steam supply valve 252 controls the flow state of the steam ST flowing through the steam line 25. Preferably, the steam supply valve 252 is a proportional control valve with an adjustable opening.

[0143] Thus, in the first configuration example, the heat source device 21 heats the water via a sub-heat exchanger 216 located in the return water line 242. The auxiliary heat source device 22 heats the water via a steam heater 251 located in the water tank 23. The heat source device 21 operates as a base load machine to meet the heat demand, and the auxiliary heat source device 22 operates as a peak load machine to meet the heat demand. The base load machine operates continuously at least during load periods to supply heat. The peak load machine operates when the heat demand exceeds the heat supply capacity of the base load machine and supplies heat to meet the excess heat demand that exceeds the heat supply capacity of the base load machine.

[0144] The heat source device 21 may also be configured such that the return water line 242 is directly connected to the refrigerant condenser 212. In this case, the heat source device 21 dissipates heat (heats the water) from the refrigerant circulating in the refrigerant circulation line 215 to the heat transfer medium HM (water) circulating in the return water line 242 via the refrigerant condenser 212. In this case, the sub-heat exchanger 216, sub-circulation pump 217, and sub-circulation line 218 can be omitted.

[0145] <5-2> Second Configuration Example Figure 13 is a schematic diagram showing a second configuration example of the heat supply equipment 2 according to the embodiment. In Figure 13, the load equipment LE indirectly uses heated water (heat transfer medium HM) (i.e., provides thermal output). The heating of the heat transfer medium HM by the heat source device 21 in Figure 13 differs from the first configuration example in that it is performed outside the path through which the heat transfer medium HM is sent to the load equipment LE. Note that in the second configuration example, explanations of configurations similar to those in the first configuration example may be omitted.

[0146] As shown in Figure 13, the heat source device 21 is not located in the return water line 242 of the heat supply equipment 2 in the second configuration example. The heat supply equipment 2 of the embodiment includes a circulation line 245 that circulates the water stored in the water tank 23. In the second configuration example, the heat source device 21 heats the water flowing through the circulation line 245 that circulates the water stored in the water tank 23. The auxiliary heat source device 22 heats the water stored in the water tank 23.

[0147] The circulation line 245 is connected at one end to the water tank 23 and is a line for circulating the water in the water tank 23. A sub-heat exchanger 216 is located in the circulation line 245. A circulation pump 26 is provided in the circulation line 245.

[0148] The sub-heat exchanger 216 performs heat exchange between the water flowing through the circulation line 245 and the intermediate heat transfer medium HM2 flowing through the sub-circulation line 218. The heat source device 21 heats the water (heat transfer medium HM) flowing through the circulation line 245 with the temperature of the intermediate heat transfer medium HM2.

[0149] Environmental sensors 5 are provided in the return water line 242 and the circulation line 245. These environmental sensors 5 are temperature sensors that detect the temperature of the water (heat transfer medium HM).

[0150] As described above, in the second configuration example, the heat source device 21 heats the water via a sub-heat exchanger 216 located in the circulation line 245. The circulation pump 26 sends the heated water from the circulation line 245 into the water tank 23, and sends the stored water in the water tank 23 to the sub-heat exchanger 216 in the circulation line 245. The water stored in the water tank 23 is heated by the circulation of the water.

[0151] <5-3> Third Configuration Example Figure 14 is a schematic diagram showing a third configuration example of the heat supply equipment 2 according to the embodiment. In Figure 14, the load equipment LE directly uses heated water (i.e., hot water output). In Figure 14, heating of the heat transfer medium HM by the heat source device 21 takes place on the path through which the heat transfer medium HM is sent to the load equipment LE. Note that in the third configuration example, the same configuration as in the first configuration example may be omitted from the explanation.

[0152] In the first and second configuration examples, a return water line 242 is provided to return water from the load equipment LE to the water tank 23. However, in the heat supply equipment 2 according to the third configuration example, the return water line 242 is not provided. In other words, in this third configuration example, the load equipment LE directly uses the heated water as, for example, washing water (i.e., hot water output). Since the water is consumed in the load equipment LE, it is not returned to the water tank 23.

[0153] As shown in Figure 14, in the heat supply equipment 2 according to the third configuration example, the water supply line 246 is connected to the water tank 23. A water supply pump 27 is installed in the water supply line 246.

[0154] The heat source device 21 heats the water (heat transfer medium HM) flowing through the water supply line 246. That is, the sub-heat exchanger 216 of the heat source device 21 is located in the water supply line 246. The auxiliary heat source device 22 heats the water in the water supply tank 23. Here, the configuration in which the heat source device 21 heats the water flowing through the water supply line 246 and the configuration in which the auxiliary heat source device 22 heats the water in the hot water tank are the same as in the first configuration example described above, so an explanation is omitted.

[0155] <5-4> Fourth Configuration Example Figure 15 is a schematic diagram showing a fourth configuration example of the heat supply equipment 2 according to the embodiment. In Figure 15, the load equipment LE directly uses heated water (i.e., hot water output). In Figure 15, heating of the heat transfer medium HM by the heat source device 21 is performed outside the path through which the heat transfer medium HM is sent to the load equipment LE. Note that in the fourth configuration example, explanations of configurations similar to those in the second configuration example may be omitted.

[0156] In the fourth configuration example, similar to the third configuration example, the load equipment LE directly uses the heated water (i.e., hot water output). On the other hand, in the fourth configuration example, similar to the second configuration example, the heat source device 21 heats the water in the circulation line 245 that circulates the water stored in the water tank 23. Note that in the fourth configuration example, explanations of configurations similar to those in the second configuration example may be omitted.

[0157] As shown in Figure 15, the sub-heat exchanger 216 of the heat source device 21 is not located in the water supply line 246 of the heat supply equipment 2 in this embodiment. In the fourth configuration example, the heat source device 21 heats the water flowing through the circulation line 245. That is, the sub-heat exchanger 216 of the heat source device 21 is located in the circulation line 245. The auxiliary heat source device 22 heats the water stored in the water tank 23. Here, the configuration in which the heat source device 21 heats the water (heat transfer medium HM) flowing through the circulation line 245 and the configuration in which the auxiliary heat source device 22 heats the water in the hot water tank are the same as in the second configuration example, so an explanation is omitted.

[0158] [6] Driving assistance methods and driving assistance application programs Figure 16 is a flowchart illustrating a driving assistance method according to an embodiment. Figure 16 is both a processing flowchart that the driving assistance application program 100 according to the embodiment causes the computer 10 to execute, and an operation flowchart of the driving assistance system 1 according to the embodiment.

[0159] The operation support method according to this embodiment is an operation support method for a heat supply equipment 2 that provides thermal energy to a heat supply equipment LE used by a load equipment LE within a business establishment 3, and is equipped with a heat source device 21 that performs at least one of heating and cooling of a heat transfer medium HM by a vapor compression refrigeration cycle.

[0160] As shown in Figure 16, the operation support method according to the embodiment includes the computer 10 generating predicted information 52 of virtual heat demand for each unit load time period on the day of operation using a heat demand change pattern model or a trained inference model (i.e., prediction model 70) stored in the computer 10 (step S10). The operation support application program 100 causes the computer 10 to operate as a prediction information generation module 32 and execute the process of generating predicted information 52 of virtual heat demand for each unit load time period.

[0161] The operation support method according to the embodiment includes obtaining actual heat demand information 53 for each unit load time period on the day of operation using the computer 10 (step S11). The operation support application program 100 causes the computer 10 to operate as a performance information generation module 33 and execute the process of obtaining actual heat demand information 53 for each unit load time period.

[0162] The operation support method according to the embodiment includes the following steps: the computer 10 calculates the degree of deviation 54 of the actual heat demand to the virtual heat demand during the same load period, and generates a maintenance recommendation task 80 for the heat source device 21 when the generation conditions 55, including the degree of deviation 54 reaching a threshold, are met (steps S12, S13, and S14). The operation support application program 100 causes the computer 10 to operate as a deviation degree evaluation module 34, thereby executing the calculation of the degree of deviation 54, the determination of whether the generation conditions 55 are met, and the generation of the maintenance recommendation task 80.

[0163] Specifically, the deviation evaluation module 34 calculates the deviation 54 based on the predicted information 52 for virtual heat demand and the actual information 53 for actual heat demand (step S12). The deviation evaluation module 34 also obtains indicators (such as heat drop 56, cumulative operating time 57, and cumulative heat transfer amount 58) to be used to determine the other conditions if the generation conditions 55 include conditions other than the first condition for the deviation 54. The deviation evaluation module 34 determines whether the generation conditions 55 have been met by comparing the calculated deviation 54 (and the indicators used to determine the other conditions) with a threshold (step S13). If the generation conditions 55 have been met (step S13; YES), the deviation evaluation module 34 generates a maintenance recommendation task 80. If the generation conditions 55 have not been met (step S13; NO), the deviation evaluation module 34 terminates processing without generating a maintenance recommendation task 80.

[0164] When a maintenance recommendation task 80 is generated, the message generation module 35 generates a corresponding message. The message generation module 35 outputs the generated message to the UI provision module 38. The UI provision module 38 displays the message on the screen of a user terminal 40 or the like, so that the message is known to the service personnel of the maintenance provider.

[0165] [7] Effects As described above, in this embodiment, the operation support application program 100 is an operation support application program that operates on a computer 10 capable of sending and receiving information via a communication network 8 with a heat source device 21 that performs at least one of heating and cooling of a heat transfer medium HM by a vapor compression refrigeration cycle. The computer 10 is instructed to perform the following: generate predicted information 52 of virtual heat demand for each unit load time period on the day of operation using a heat demand change pattern model or a trained inference model (prediction model 70) stored in the computer 10 (function of prediction information generation module 32); acquire actual information 53 of actual heat demand for each unit load time period on the day of operation (function of actual information generation module 33); calculate the degree of deviation 54 of the actual heat demand to the virtual heat demand for the same load time period, and generate a maintenance recommendation task 80 for the heat source device 21 when the generation conditions 55, including the degree of deviation 54 reaching a threshold, are met (function of deviation degree evaluation module 34).

[0166] According to this configuration, predicted information 52 of virtual heat demand for each unit load time period on the day of operation and actual information 53 of actual heat demand for each unit load time period on the day of operation are obtained. From the predicted information 52 of virtual heat demand and the actual information 53 of actual heat demand, the degree of deviation 54 of the actual heat demand to the virtual heat demand for the same load time period is calculated. Then, when the generation conditions 55 are met, including the fact that the degree of deviation 54 has reached a threshold, a maintenance recommendation task 80 for the heat source device 21 is generated. Here, if the actual heat demand increases sharply and the degree of deviation 54 exceeds a certain level and becomes large on the positive side, the heat source device 21 and load equipment LE become overloaded and more prone to failure. On the other hand, if the actual heat demand decreases sharply and the degree of deviation 54 exceeds a certain level and becomes large on the negative side, the start-stop frequency of the heat source device 21 and load equipment LE increases, negatively affecting the lifespan of the components. When the deviation of 54 reaches a threshold, a maintenance recommendation task 80 is generated, which allows for timely maintenance work to be provided to the heat source device 21.

[0167] In this embodiment, the driver assistance application program 100 further causes the computer 10 to acquire environmental information 50 including the temperature difference 56 between the heat transfer medium HM generated in the heat source device 21 and the heat source fluid HS used in the heat source device 21, and the generation conditions 55 may include the fact that the degree of deviation 54 has reached a first threshold and the temperature difference 56 has reached a second threshold. Here, the larger the temperature difference 56 of the heat source device 21, the greater the output of the refrigerant compressor 213 in the refrigeration cycle, which shortens the lifespan of bearings and motor components and makes refrigerant leaks from refrigerant piping more likely. In this configuration, a maintenance recommendation task 80 is generated on the condition that the degree of deviation 54 and the temperature difference 56 have reached their respective thresholds, so that maintenance work can be provided to the heat source device 21 at the time a situation occurs in which adverse effects on the heat source device 21 are a concern.

[0168] In this embodiment, the operation support application program 100 further causes the computer 10 to acquire operating information 51 of the heat source device 21, including the cumulative operating time 57 of the heat source device 21 since the last maintenance provision, and the generation conditions 55 may include the deviation degree 54 reaching a first threshold and the cumulative operating time 57 reaching a third threshold. Here, the longer the cumulative operating time 57 of the heat source device 21, the longer the operating time of the refrigerant compressor 213 in the refrigeration cycle, which makes the deterioration of bearings and motors more likely to progress. Also, the longer the cumulative operating time 57 of the heat source device 21, the longer the flow time of the heat transfer medium HM and heat source fluid HS, which makes the heat transfer surface of the heat exchanger more likely to get dirty. In this configuration, a maintenance recommendation task 80 is generated on the condition that the deviation degree 54 and the cumulative operating time 57 have reached their respective thresholds, so that maintenance work can be provided to the heat source device 21 at the time a situation occurs in which adverse effects on the heat source device 21 are a concern.

[0169] In this embodiment, the operation support application program 100 further causes the computer 10 to acquire environmental information 50 including the heat drop 56 between the heat transfer medium HM generated in the heat source device 21 and the heat source fluid HS used in the heat source device 21, acquire operation information 51 of the heat source device 21 including the operating time of the heat source device 21, and calculate the cumulative heat transfer amount 58 by multiplying an index value indicating the relative magnitude of the heat drop 56 by the operating time each predetermined operating time that has elapsed since the last maintenance provision, and the generation conditions 55 may include the fact that the degree of deviation 54 has reached a first threshold and the cumulative heat transfer amount 58 has reached a fourth threshold. Here, the larger the cumulative value of the heat transfer amount (heat pumped up) in the heat source device 21, the shorter the lifespan of the components of the refrigeration cycle and the more likely the heat transfer coefficient of the heat exchanger is to decrease. In this configuration, a maintenance recommendation task 80 is generated when the degree of deviation 54 and the cumulative heat transfer amount 58 reach their respective thresholds. Therefore, maintenance work can be provided to the heat source device 21 at the time a situation arises that raises concerns about adverse effects on the heat source device 21.

[0170] In this embodiment, the maintenance recommendation task 80 includes generating a message 81A recommending the provision of condition-based maintenance for at least one of the heat source device 21 and the load equipment LE (function of the message generation module 35). In this configuration, when the maintenance recommendation task 80 is generated, a message 81A recommending the provision of condition-based maintenance for at least one of the heat source device 21 and the load equipment LE is generated. Service personnel of the maintenance provider can refer to the generated message 81A and perform condition-based maintenance. This triggers inspection of the heat source device 21 and the load equipment LE, replacement and cleaning of parts, replenishment of consumables, etc., thereby restoring the performance of the equipment and contributing to achieving the expected design life.

[0171] In this embodiment, the maintenance recommendation task 80 includes generating a message 81B recommending a review of the output condition values ​​of the heat source device 21 (a function of the message generation module 35). In this configuration, when the maintenance recommendation task 80 is generated, a message 81B recommending a review of the output condition values ​​of the heat source device 21 is generated. Service personnel of the maintenance provider can refer to the generated message 81B and review the output condition values ​​of the heat source device 21. As a result, triggered by the deviation degree 54 of the actual heat demand, the settings of the output condition values ​​of the heat source device 21 are changed, allowing the system to transition to an appropriate operating state (an operating state that does not result in excessive or insufficient load) according to the actual heat demand.

[0172] In this embodiment, the operation support system 1 is an operation support system 1 for a heat supply equipment 2 that provides thermal energy to a heat supply equipment LE used by load equipment LE in a business premises 3, and includes a sensor group consisting of one or more environmental sensors 5 arranged in the heat supply equipment 2, a controller 61 that controls the operation of the heat source device 21, a communication network 8 that transmits information, and an information processing device 6 configured to acquire environmental information 50 detected by the sensor group and operational information 51 generated by the controller 61 via the communication network 8, wherein the information processing device 6 is an information storage platform that stores the acquired environmental information 50 and operational information 51. The system comprises: a 36; a prediction information generation module 32 that generates prediction information 52 of virtual heat demand for each unit load time period on the day of operation using a heat demand change pattern model or a trained inference model (prediction model 70) generated by utilizing environmental information 50 stored in the information storage platform 36; an actual information generation module 33 that acquires actual information 53 of actual heat demand for each unit load time period on the day of operation using environmental information 50 stored in the information storage platform 36; and a deviation degree evaluation module 34 that calculates the deviation degree 54 of the actual heat demand to the virtual heat demand for the same load time period, and generates a maintenance recommendation task 80 for the heat source device 21 when generation conditions 55, including the deviation degree 54 reaching a threshold, are met.

[0173] According to this configuration, predicted information 52 of virtual heat demand for each unit load time period on the day of operation and actual information 53 of actual heat demand for each unit load time period on the day of operation are obtained. The deviation evaluation module 34 calculates the deviation 54 of the actual heat demand to the virtual heat demand for the same load time period from the predicted information 52 of virtual heat demand and the actual information 53 of actual heat demand. Then, the deviation evaluation module 34 generates a maintenance recommendation task 80 for the heat source device 21 when the generation conditions 55, including the deviation 54 reaching a threshold, are met. Here, if the actual heat demand increases sharply and the deviation 54 exceeds a certain level and becomes large on the positive side, the heat source device 21 and load equipment LE become overloaded and more prone to failure. On the other hand, if the actual heat demand decreases sharply and the deviation 54 exceeds a certain level and becomes large on the negative side, the start-stop frequency of the heat source device 21 and load equipment LE increases, negatively affecting the lifespan of the components. When the deviation of 54 reaches a threshold, a maintenance recommendation task 80 is generated, which allows for timely maintenance work to be provided to the heat source device 21.

[0174] In one embodiment, the operation support method is an operation support method for a heat supply equipment 2 that provides thermal energy to a heat medium HM used by a load equipment LE in a business establishment 3, and is equipped with a heat source device 21 that performs at least one of heating and cooling of a heat medium HM by a vapor compression refrigeration cycle, and includes generating predicted information 52 of virtual heat demand for each unit load time period on the day of operation using a heat demand change pattern model or a trained inference model (prediction model 70) stored in a computer 10, obtaining actual information 53 of actual heat demand for each unit load time period on the day of operation, calculating the degree of deviation 54 of the actual heat demand to the virtual heat demand for the same load time period, and generating a maintenance recommendation task 80 for the heat source device 21 when generation conditions 55 are met, including that the degree of deviation 54 has reached a threshold.

[0175] According to this configuration, predicted information 52 of virtual heat demand for each unit load time period on the day of operation and actual information 53 of actual heat demand for each unit load time period on the day of operation are obtained. From the predicted information 52 of virtual heat demand and the actual information 53 of actual heat demand, the degree of deviation 54 of the actual heat demand to the virtual heat demand for the same load time period is calculated. Then, when the generation conditions 55 are met, including the fact that the degree of deviation 54 has reached a threshold, a maintenance recommendation task 80 for the heat source device 21 is generated. Here, if the actual heat demand increases sharply and the degree of deviation 54 exceeds a certain level and becomes large on the positive side, the heat source device 21 and load equipment LE become overloaded and more prone to failure. On the other hand, if the actual heat demand decreases sharply and the degree of deviation 54 exceeds a certain level and becomes large on the negative side, the start-stop frequency of the heat source device 21 and load equipment LE increases, negatively affecting the lifespan of the components. When the deviation of 54 reaches a threshold, a maintenance recommendation task 80 is generated, which allows for timely maintenance work to be provided to the heat source device 21.

[0176] [8] Contribution to the United Nations-led Sustainable Development Goals (SDGs) The operation support system described herein generates a maintenance recommendation task 80 for the heat source device 21 based on the deviation of 54 between the actual heat demand and the virtual heat demand. The generation of the maintenance recommendation task 80 enables the provision of maintenance work to the heat source device 21 at an appropriate time. As a result, it is possible to suppress the decrease in the energy efficiency of the heat source device and the increase in energy loss of load equipment, thereby contributing to the achievement of SDG (Sustainable Development Goal) Goal 7, "Affordable and Clean Energy." In addition, it is possible to reduce carbon dioxide emissions in conjunction with improvements in energy efficiency and energy loss, thereby contributing to the achievement of Goal 13, "Take urgent action to combat climate change and its impacts." [Explanation of Symbols]

[0177] 1…Operation support system, 2…Heat supply equipment, 3…Business premises, 3A…First business premises, 3B…Second business premises, 3C…Third business premises, 4…Factory, 4A…First factory, 4B…Second factory, 5…Environmental sensor, 6…Information processing device, 7…Data acquisition terminal, 8…Communication network, 10…Computer, 11…Processor, 12…Storage device, 13…Communication interface, 14…Input / output interface, 21…Heat source device, 22…Auxiliary heat source device, 23…Water tank, 24…Water line 25...Steam line, 26...Circulation pump, 27...Water supply pump, 31...Information processing module, 32...Predictive information generation module, 33...Actual information generation module, 34...Degree of deviation evaluation module, 35...Message generation module, 36...Information storage platform, 37...Database, 38...UI provision module, 40...User terminal, 50...Environmental information, 51...Operation information, 52...Predictive information, 53...Actual information, 54...Degree of deviation, 55...Generation conditions, 56...Heat drop, 56A...Index value, 57...Cumulative operating time, 58...Cumulative heat transfer amount, 61...Controller, 61A...Microcomputer, 61B...Programmable logic controller, 62...Edge computer, 63...Gateway, 64...Guest computer, 65...Host computer, 70...Predictive model, 80...Maintenance recommendation task, 81A...Message, 81B...Message, 100...Operation support application program, 211...Refrigerant evaporator, 2 12...Refrigerant condenser, 213...Refrigerant compressor, 214...Expansion valve, 215...Refrigerant circulation line, 216...Sub-heat exchanger, 217...Sub-circulation pump, 218...Sub-circulation line, 231...Water level sensor, 241...Water distribution line, 242...Return water line, 243...Makeup water line, 244...Water supply pump, 245...Circulation line, 246...Water supply line, 251...Steam heater, 252...Steam supply valve, E...Deviation, LE...Load equipment, HM...Heat transfer medium, HM2...Intermediate heat transfer medium, HS...Heat source fluid.

Claims

1. A heat source device that performs at least one of heating and cooling of a heat transfer medium by a vapor compression refrigeration cycle, and a driver assistance application program that operates on a computer capable of sending and receiving information via a communication network, To the aforementioned computer, Using a heat demand change pattern model or a trained inference model stored in the aforementioned computer, predictive information on virtual heat demand for each unit load time period on the day of operation is generated. To obtain actual heat demand data for each unit load time period on the day of operation, The system calculates the degree of deviation between the actual heat demand and the virtual heat demand during the same load period, and when the generation conditions, including the degree of deviation reaching a threshold, are met, it generates a maintenance recommendation task for the heat source device and executes the following: A driver assistance application program.

2. The computer is further instructed to acquire environmental information, including the heat drop between the heat transfer medium generated by the heat source device and the heat source fluid used by the heat source device. The generation conditions include the degree of deviation reaching a first threshold and the heat drop reaching a second threshold. The driver assistance application program according to claim 1.

3. The computer is further instructed to acquire operating information of the heat source device, including the cumulative operating time of the heat source device since the last maintenance service. The generation conditions include the degree of deviation reaching a first threshold and the cumulative operating time reaching a third threshold. The driver assistance application program according to claim 1.

4. To the aforementioned computer, To acquire environmental information including the heat drop between the heat transfer medium generated by the heat source device and the heat source fluid used by the heat source device, To acquire operating information of the heat source device, including the operating time of the heat source device, Since the last maintenance service, after a predetermined operating time has elapsed, the cumulative heat transfer amount is calculated by multiplying an index value indicating the relative magnitude of the heat drop by the operating time, and this is further performed. The generation conditions include the degree of deviation reaching a first threshold and the cumulative heat transfer amount reaching a fourth threshold. The driver assistance application program according to claim 1.

5. The maintenance recommendation task includes generating a message recommending the provision of condition-based maintenance for at least one of the heat source device and the load equipment. A driver assistance application program according to any one of claims 1 to 4.

6. The maintenance recommendation task includes generating a message recommending a review of the output condition values ​​of the heat source device. A driver assistance application program according to any one of claims 1 to 4.

7. An operation support system for a heat supply system that provides thermal energy to a heat transfer medium used in load equipment within a business premises, comprising a heat source device that performs at least one of heating and cooling of a heat transfer medium by a vapor compression refrigeration cycle, A sensor group consisting of one or more environmental sensors arranged in the heat supply equipment, A controller that controls the operation of the heat source device, A communication network that transmits information, The system includes an information processing device configured to acquire environmental information detected by the sensor group and operational information generated by the controller via the communication network, The aforementioned information processing device is An information storage platform for storing the acquired environmental information and operational information, A prediction information generation module that generates prediction information for virtual heat demand for each unit load time period on the day of operation, using a heat demand change pattern model or a trained inference model generated by utilizing the environmental information stored in the information storage platform, A performance information generation module that uses the environmental information stored in the information storage platform to acquire actual heat demand information for each unit load time period on the day of operation, The system includes a deviation degree evaluation module that calculates the deviation degree from the actual heat demand to the virtual heat demand during the same load period, and generates a maintenance recommendation task for the heat source device when generation conditions are met, including the deviation degree reaching a threshold. Driver assistance system.

8. A method for supporting the operation of a heat supply system that provides thermal energy to a heat transfer medium used in load equipment within a business premises, comprising a heat source device that performs at least one of heating and cooling of a heat transfer medium by a vapor compression refrigeration cycle, Using a heat demand change pattern model or a trained inference model stored in a computer, predictive information on virtual heat demand for each unit load time period on the day of operation is generated. To obtain actual heat demand data for each unit load time period on the day of operation, This includes calculating the degree of deviation between the actual heat demand and the virtual heat demand during the same load period, and generating a maintenance recommendation task for the heat source device when the generation conditions, including the degree of deviation reaching a threshold, are met. Driving assistance methods.

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