Information processing device, information processing method, and information processing program

Abstract

To easily specify an object facility having high priority from multiple uncontrollable object facilities.SOLUTION: An information processing device 100 includes a collection section 121, a probability density distribution calculation section 123, a difference calculation section 124, and a priority determination section 125. The collection section 121 collects measurement data of object facilities during each of multiple designated periods. The probability density distribution calculation section 123 calculates a probability density distribution during each designated period on the basis of measurement data collected by the collection section 121. The difference calculation section 124 calculates a difference between probability density distributions for the probability density distributions during multiple periods to be evaluated that are calculated by the probability density distribution calculation section 123. The priority determination section 125 determines an object facility having high priority to be treated from among multiple object facilities according to a difference calculated by the difference calculation section 124.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] In conventional building air conditioning systems, quickly detecting deterioration of the indoor environment due to control problems or other reasons is extremely important for maintaining the comfort of occupants. However, as the scale of a building increases, such as with the number of floors, it becomes difficult to monitor all indoor environments within the system using only human power.

[0003] Therefore, a system has been proposed that supports efficient building management by processing massive amounts of air conditioning data in real time using AI or rule-based judgment machines and notifying managers of areas where problems are occurring (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2013-164212 Summary of the Invention [Problem to be solved by the invention]

[0005] However, with conventional technology, it is not possible to easily identify equipment with a high priority among multiple pieces of equipment with poor control. For example, conventional technology is specialized in determining whether indoor control is good or bad at the time of measurement, and it is not possible to identify air conditioning equipment with a high priority among air conditioning equipment that is determined to be poorly controlled. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems, the information processing device of the present invention is characterized by having a collection unit that collects measurement data of target equipment for each of a plurality of specified periods; a probability density distribution calculation unit that calculates a probability density distribution for each of the specified periods based on the measurement data collected by the collection unit; a difference calculation unit that calculates the difference between the probability density distributions for the plurality of periods to be evaluated that are calculated by the probability density distribution calculation unit; and a priority determination unit that determines target equipment with a high priority to address from the plurality of target equipment based on the difference calculated by the difference calculation unit. [Effects of the Invention]

[0007] According to the present invention, it is possible to easily identify a target piece of equipment that has a high priority for addressing from among a plurality of target pieces of equipment that are poorly controlled. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing an air conditioning control system including an information processing device according to an embodiment. [Figure 2] FIG. 2 is a block diagram illustrating an example of the configuration of the information processing device according to the embodiment. [Figure 3] FIG. 3 is a diagram showing a specific example of data stored in the storage unit according to the embodiment. [Figure 4] FIG. 4 is a diagram showing the overall processing flow of the information processing device according to the embodiment. [Figure 5] FIG. 5 is a diagram showing a specific example of a display screen according to the embodiment. [Figure 6] FIG. 6 is a diagram showing a specific example of a display screen according to the embodiment. [Figure 7] FIG. 7 is a diagram showing a specific example of a display screen according to the embodiment. [Figure 8] FIG. 8 is a flowchart illustrating an example of a processing procedure of the information processing device according to the embodiment. [Figure 9] FIG. 9 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of the information processing device according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments of an information processing device, an information processing method, and an information processing program according to the present application will be described in detail with reference to the accompanying drawings. Note that the information processing device, the information processing method, and the information processing program according to the present application are not limited to these embodiments.

[0010] 1. Introduction First, an overview of a system including an information processing device 100 according to this embodiment will be described. Fig. 1 is a diagram showing an air conditioning control system including an information processing device according to this embodiment. Note that, below, an air conditioning control system will be taken as an example of a system according to this embodiment, and a case will be described in which the equipment for which priority is to be determined is an air conditioner having a VAV (Variable Air Volume) or the like, but the present invention is not limited to this, and can also be applied to equipment such as large machinery operating in a factory, for example.

[0011] 1, the information processing device 100 is connected to the air conditioning system monitoring device 200 by wire or wirelessly, and transmits and receives information to and from the air conditioning system monitoring device 200. The air conditioning system monitoring device 200 is also connected to air conditioning systems 210 and 220. Note that, although the air conditioning system monitoring device 200 is connected to two air conditioning systems in the example of FIG. 1, the number of air conditioning systems is not limited to this, and the number of air conditioning systems can be increased or decreased as desired.

[0012] Air conditioning system 210 has air conditioner 211, and VAV 212 and VAV 215 connected to air conditioner 211. VAV 212 is connected to room temperature sensor 213 and corresponds to air outlets 214-1 to 214-n. Similarly, VAV 215 is connected to room temperature sensor 216 and corresponds to air outlets 217-1 to 217-n. Air conditioning system 220 also has the same configuration as air conditioning system 210 from air conditioner 221 onwards. The configuration of the air conditioning system is not limited to the configuration shown in Fig. 1, and the number of air conditioners and VAVs can be increased or decreased as desired.

[0013] The information processing device 100 is a server device that receives indoor temperature measurements, VAV supply air volume, floor radiation temperature, etc. from the air conditioning system monitoring device 200 and indoor temperature sensors 213, 216, and stores the information as historical data, and is realized by a PC (Personal Computer), a cloud system, etc.

[0014] The air conditioning system monitoring device 200 monitors the control status of air conditioning equipment such as air conditioners 211 and VAVs 212 that belong to each air conditioning system. The air conditioning system monitoring device 200 then transmits information about the indoor environment, such as the supply air volume of each VAV and the temperature, to the information processing device 100.

[0015] The air conditioner 211 is an air conditioning facility configured with, for example, a cooling coil, a heating coil, and a blower, and its operation is monitored by the air conditioning system monitoring device 200. In addition, conditioned air blown out by the air conditioner 211 and the air conditioner 221 is supplied to each area via an air supply duct. The VAV 212 and the VAV 215 are air conditioning facilities that control the amount of conditioned air taken in from the air conditioner 211 so as to relieve the air conditioning load in each room. In addition, the VAV 212 and the VAV 215 adjust the amount of air supplied from each air outlet by controlling the opening degree of the damper.

[0016] The indoor temperature sensor 213 and the indoor temperature sensor 216 are measuring devices that measure nearby temperatures, and measure not only the indoor temperature but also the radiant temperatures of the walls, ceiling, and floor. The temperatures measured by each temperature sensor are transmitted to the information processing device 100 together with the time of measurement, etc.

[0017] Next, we will explain the problems with the conventional technology in detecting air conditioner defects according to this embodiment. Conventionally, air conditioner defect detection has focused on determining whether the condition is good or bad at the time of measurement, and it is difficult to make a judgment taking time series into consideration. Therefore, for example, if the current indoor control is bad, it is not possible to determine whether the bad condition has continued since before, or whether the condition was good before but has now become bad.

[0018] In the two cases of poor control described above, the administrator is required to prioritize dealing with air conditioners that were previously in good condition but have now become poor. This is because people generally have different perceptions of the indoor environment, and even if the judgment indicates poor control, the user may not feel particularly uncomfortable. For this reason, if the user has not previously taken action such as filing a complaint, it is likely that the user has tolerated the poor control to a certain extent. Therefore, even among air conditioners with poor control, if the same poor control has continued for some time, it can be determined that the priority for dealing with the problem is low.

[0019] In other words, with conventional technology, it is difficult to take time series into account when determining whether an air conditioner is faulty, so it is not possible to grasp changes in control failure over time, and it is not possible to identify an air conditioner with a high priority for response from among multiple air conditioners with control failures.

[0020] Next, an overview of the processing of the information processing device 100 according to this embodiment will be described. In consideration of the above-mentioned problems, the information processing device 100 according to this embodiment collects measurement data of target equipment for each of a plurality of specified periods and calculates a probability density distribution for each specified period based on the measurement data. The information processing device 100 then calculates the difference between the probability density distributions for the period to be evaluated for the plurality of probability density distributions, and determines target equipment with a high priority for addressing from among the plurality of target equipment based on the calculated difference.

[0021] For example, the information processing device 100 collects measurement data such as the indoor temperature, the set temperature, and the supply air volume for the target equipment for a predetermined period such as one month, and then calculates a probability density distribution for the specified period from the measurement data.

[0022] Then, for example, the information processing device 100 calculates the distance between the probability density distributions calculated for each specified period for the period to be evaluated, such as last month and this month, using Kullback-Leibler divergence (KL divergence) etc. Then, the information processing device 100 determines that the larger the calculated distance, the more the control state of the target equipment has changed during the evaluation period, and identifies the target equipment as having a high priority for handling.

[0023] As a result, the information processing device 100 can determine that the greater the distance between the probability density distributions for the evaluation period, the more the target equipment's control state has changed and the higher the priority of response, and therefore can easily identify target equipment with a high priority of response from multiple target equipment that is poorly controlled.

[0024] 2. Configuration of Information Processing Device 100 Next, the configuration of the information processing device 100 shown in Fig. 1 will be described with reference to Fig. 2. Fig. 2 is a block diagram showing an example of the configuration of the information processing device according to the embodiment. As shown in Fig. 2, the information processing device 100 according to the embodiment includes a communication unit 110, a control unit 120, and a storage unit 130.

[0025] The communication unit 110 is realized by, for example, a network interface card (NIC), etc. The communication unit 110 is connected to the air conditioning system monitoring device 200 by wire or wirelessly, and transmits and receives information.

[0026] The storage unit 130 is realized by a storage device such as a RAM (Random Access Memory) or a hard disk, for example. The storage unit 130 stores data and programs necessary for various processes by the control unit 120. The storage unit 130 stores measurement data relating to the control status of air conditioners such as VAVs, collected by the collection unit 121, which will be described later.

[0027] Here, the measurement data stored in the memory unit 130 will be described with reference to Fig. 3. Fig. 3 is a diagram showing a specific example of data stored in the memory unit according to the embodiment. As shown in Fig. 3, the memory unit 130 stores measurement data relating to items such as "date and time," "supply air temperature," "VAV airflow rate," and "room temperature." "Date and time" stores date and time information at the time of measurement, "supply air temperature" stores the measured temperature of the conditioned air supplied into the room by the VAV, "VAV airflow rate" stores the measured airflow rate of the conditioned air supplied into the room by the VAV, and "room temperature" stores the measured temperature of the room in which the VAV is installed (the room to be regulated).

[0028] The measurement data stored in the storage unit 130 is not limited to the data shown in FIG. 3, and any measurement data set in advance by a maintenance person or the like can be stored in order to determine whether there is a malfunction in the air conditioner.

[0029] Returning to the explanation of Fig. 2, the control unit 120 is realized by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like executing various programs stored in a storage device inside the information processing device 100 using RAM as a work area. The control unit 120 is also realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The control unit 120 has a collection unit 121, a determination unit 122, a probability density distribution calculation unit 123, a difference calculation unit 124, a priority determination unit 125, and a display unit 126.

[0030] The collection unit 121 collects measurement data of the target equipment for each of a plurality of specified periods. For example, the collection unit 121 collects measurement data of the VAV for each of a plurality of specified periods, such as last month and this month, from the air conditioning system monitoring device 200 and stores the data in the memory unit 130. Here, the collected measurement data is, for example, measurement data acquired by the air conditioning system monitoring device 200 periodically, such as at 10-minute intervals, for the VAV 212. All measurement data within the specified period may be collected together, or acquired data may be collected each time and stored in chronological order in the memory unit 130. It is assumed that the air conditioning system monitoring device 200 is capable of storing measurement data of each air conditioning equipment in an internal memory device.

[0031] The determination unit 122 determines the state of the target equipment for a specified period, calculated using the measurement data collected by the collection unit 121. For example, the determination unit 122 references the measurement data stored in the storage unit 130, determines whether each of all measurements for the specified period is good or bad, and calculates a failure rate by aggregating the determination results. Specifically, the determination unit 122 determines whether all measurement data collected at 10-minute intervals for the current month is good or bad, and calculates the rate of data determined to be bad among all measurements, thereby determining whether the VAV control state for this month is good or bad.

[0032] The probability density distribution calculation unit 123 calculates the probability density distribution for each specified period based on the measurement data collected by the collection unit 121. For example, the probability density distribution calculation unit 123 refers to the measurement data stored in the storage unit 130 and calculates the probability density distribution for the specified period using all the measurement data within the specified period. Note that the calculated probability density distribution may be approximated by a normal distribution.

[0033] Furthermore, the probability density distribution calculation unit 123 can calculate the probability density distribution for the target equipment determined to be defective by the determination unit 122. For example, if the defective rate of the VAV's control state during a specified period is equal to or greater than a preset threshold, the probability density distribution calculation unit 123 determines that the VAV is defective and calculates the probability density distribution of all measurement data during the specified period. As a result, for example, if the specified period is the current month, the priority determination process described below can be performed only on VAVs whose control state is defective this month.

[0034] The difference calculation unit 124 calculates the difference between the probability density distributions for the multiple periods to be evaluated, which are calculated by the probability density distribution calculation unit 123. For example, the difference calculation unit 124 calculates the distance between the probability density distributions for the multiple periods to be evaluated, such as this month and last month, using KL divergence.

[0035] Here, KL divergence is an index that calculates the similarity (degree of separation) between two probability distributions as a distance, and is 0 when the two probability distributions are identical, and the calculated distance increases as the distributions become more dissimilar. In other words, by using KL divergence, the difference calculation unit 124 can calculate the magnitude of the difference between the probability density distributions as the magnitude of the distance. Note that the method used by the difference calculation unit 124 to calculate the difference between the probability density distributions is not limited to the method using the KL divergence described above.

[0036] The information processing device 100 performs the processes from the probability density distribution calculation unit 123 to the difference calculation unit 124 to calculate the distance between the probability density distributions, thereby making it possible to more appropriately evaluate the change in the good / bad tendency of the VAV than by simply comparing the defect rates calculated by the determination unit 122. This allows the information processing device 100 to appropriately evaluate that the types of control failures are different, even if completely different types of control failures occurred last month and this month and the change in the defect rate itself is small.

[0037] The priority determination unit 125 determines target equipment having a high priority for addressing from among the plurality of target equipment, according to the difference calculated by the difference calculation unit 124. For example, the priority determination unit 125 determines that target equipment for which the distance between the probability distributions calculated by the difference calculation unit 124 is greater than a preset threshold is target equipment having a large change in control state during the evaluation target period, and determines that target equipment has a high priority for addressing.

[0038] Furthermore, the priority determination unit 125 can determine a higher priority for the target equipment as the difference calculated by the difference calculation unit 124 increases. For example, the priority determination unit 125 determines that the target equipment has a higher priority for handling in descending order of the distance value calculated by the difference calculation unit 124.

[0039] Furthermore, the priority determination unit 125 determines target equipment with a high priority for handling from among the plurality of target equipment based on the determination result by the determination unit 122 and the difference calculated by the difference calculation unit 124. For example, for a plurality of target equipment with the same distance calculated by KL divergence, the priority determination unit 125 may assign a higher handling priority to the target equipment with a higher current defect rate, or a lower handling priority to the target equipment with a higher previous defect rate.

[0040] The display unit 126 displays information about a plurality of target facilities in descending order of priority. For example, the display unit 126 refers to the response priorities determined by the priority determination unit 125 in descending order of distance for a plurality of VAVs managed by the administrator of the air conditioning control system, and displays information about the VAVs in descending order of response priority on the management screen.

[0041] 3. Examples Here, the overall flow of processing performed by the information processing device 100 will be described with reference to Fig. 4. Fig. 4 is a diagram showing the overall flow of processing performed by the information processing device according to the embodiment. Note that the processing content shown in Fig. 4 is an example, and the processing performed by the information processing device 100 is not limited to the processing content described below.

[0042] First, the collection unit 121 collects the measurement data for last month and this month for each piece of target equipment from the air conditioning system monitoring device 200 and stores them in the memory unit 130. Next, the determination unit 122 determines whether all of the measurement data for last month stored in the memory unit 130 is good or bad, and calculates a defect rate of "20%" for all of the measurement data. Since this is lower than the threshold value of 50%, the determination unit 122 determines that the control status of the target equipment for last month was "good." The determination unit 122 also performs the same process for this month, determining a defect rate of "60%" and a control status of "bad."

[0043] Furthermore, the probability density distribution calculation unit 123 calculates the probability density distribution for last month for the target equipment using all of the measurement data for last month stored in the storage unit 130. The probability density distribution calculation unit 123 also performs the same process for this month to calculate the probability density distribution for this month. As a result, the probability density distribution for last month and the probability density distribution for this month are calculated for each piece of target equipment.

[0044] Then, the difference calculation unit 124 calculates the distance between the probability density distributions by KL divergence, treating the calculated probability density distribution for last month and the probability density distribution for this month as a set. In this way, the distance between the probability density distributions is calculated for each piece of target equipment.

[0045] Next, the priority determination unit 125 comprehensively determines the magnitude of the calculated distance, the defect rate determined by the determination unit 122, and the control state for multiple pieces of target equipment, and determines the response priority. At this time, for multiple pieces of target equipment with the same calculated distance, the response priority may be increased if the defect rate this month is higher, or the defect rate last month was also higher if the defect rate was lower. Next, the display unit 126 sorts the target equipment in order of response priority and displays them on the management screen.

[0046] Through the series of processes described above, the information processing device 100 can allow the administrator to recognize, from among the target equipment, target equipment whose control status has changed significantly between this month and last month as target equipment with a high priority for response.

[0047] Next, examples of images displayed on the management screen by the display unit 126 will be described with reference to Fig. 5 to Fig. 7. Fig. 5 to Fig. 7 are diagrams showing specific examples of display screens according to the embodiment.

[0048] As shown in FIG. 5, the display unit 126 displays a screen showing information on "Floor," "AHU / FCU," "VAV," "Before," "After," and "Transition" for the VAV to be managed, for example.

[0049] "Floor" displays information about the floor on which the target VAV is installed, and "AHU / FCU" displays information about the air conditioner (corresponding to air conditioner 211 in Figure 1) connected to the target VAV. "VAV" displays information about the target VAV. "Before" displays the good / bad VAV judgment result for the earlier of the two periods being evaluated (e.g., last month), and "After" displays similar information for the other period (e.g., this month). "Transition" displays information showing the change in the results between "Before" and "After."

[0050] For example, among the VAVs that have become faulty this month, the display unit 126 displays that "VAV-O-13-1-19" connected to "AHU-O-13-1" on "Floor 13" was "Good" last month but has become "Abnormal" this month, and therefore the change in control status is "↓", allowing the user to understand that this is a VAV with a high priority for response.

[0051] 5, the display unit 126 sorts and displays the multiple VAVs with a control status of "↓" in descending order of priority determined by the priority determination unit 125. That is, the display unit 126 indicates that, among the multiple VAVs with a control status of "↓" shown in FIG. 5, "VAV-O-13-1-19" has the highest response priority, and the lower the display position, the lower the response priority.

[0052] This allows the display unit 126 to display on the management screen the changes in the control status of VAVs and the response priority for multiple VAVs that have poor control conditions this month and need to be addressed, allowing the administrator to easily grasp information about VAVs with high response priority.

[0053] Furthermore, by clicking on the display field of any VAV shown in Fig. 5, display unit 126 can transition to the screen shown in Fig. 6 or Fig. 7. In the graph shown in Fig. 6, the vertical axis indicates an index (Flow Rate (%)) showing the VAV supply air volume, and the horizontal axis indicates the difference between the measured temperature and the set temperature in the room to be adjusted (Room Temp Diff (Measured-Setpoint) [°C]).

[0054] Figure 6 shows a graph plotting all measurement data from last month (202203) and this month (202204). It is assumed that the air conditioning was set to cooling when Figure 6 was created. In Figure 6, the range where the temperature difference is "-1" or less and the airflow is "40%" or more (upper left of the graph) indicates that the room temperature is lower than the set temperature and the airflow is strong. Therefore, control data plotted in this range is considered to be defective data, as the room is overcooled and the airflow is strong. Similarly, data plotted in the range where the temperature difference is "1" or more and the airflow is "80%" or less (lower right of the graph) indicates that the room is not sufficiently cooled and the airflow is weak.

[0055] 6, areas other than the upper left or lower right of the graph are areas where there are no problems (are tolerable) with control, and the measurement data plotted in those areas is good data. In other words, display unit 126 allows the user to grasp the good / bad distribution of all measurement data for the target VAV for the last month and the current month from the graph shown in FIG.

[0056] The graph in Figure 7 is a histogram that shows the frequency of the contents of the scatter plot graph in Figure 6. The vertical axis is an index showing the frequency of the measurement data (Occurrence count [hour]), and the horizontal axis shows the items corresponding to each point shown in Figure 6. For example, the point in the upper left of the graph in Figure 6 (too cold and windy) corresponds to the "Cold / Excessive" item in Figure 7.

[0057] As shown in Figure 7, last month's measurement data for the target VAV showed a high frequency of measurement data in the "Cold / Adequate" range, indicating good measurement data, whereas this month's measurement data showed a high frequency of measurement data in the "Cold / Excessive" range, indicating bad measurement data. In other words, this month's measurement data shows an increase in the proportion of bad data in the "Cold / Excessive" range compared to last month's.

[0058] This allows the display unit 126 to display the frequency of good / bad measurement data between last month and this month, making it easy to understand the change in measurement data between last month and this month.

[0059] [4. Processing Procedures by Information Processing Device] Next, an example of a processing procedure by the information processing device 100 according to the embodiment will be described with reference to Fig. 8. Fig. 8 is a flowchart showing an example of a processing procedure by the information processing device according to the embodiment. Note that the steps in the flowchart shown in Fig. 8 may be executed in a different order, and some processing may be omitted. Furthermore, the processing shown in Fig. 8 shows the flow of processing content performed for a specific target facility, and the information processing device 100 is assumed to perform similar processing for each of multiple target facilities under management.

[0060] The collection unit 121 first collects measurement data of the target equipment for each of a plurality of designated periods (S101). Then, the determination unit 122 determines whether the control state of the target equipment for the designated periods is good or bad (S102).

[0061] If the target equipment is determined to be defective (S103; Yes), the probability density distribution calculation unit 123 calculates the probability density distribution for each specified period based on the measurement data (S104).On the other hand, if the target equipment is not determined to be defective (S103; No), the information processing device 100 ends the processing process for the target equipment.

[0062] After S104, the difference calculation unit 124 calculates the difference between the probability density distributions for the period to be evaluated using the KL divergence distance (S105). Then, the priority determination unit 125 determines the response priority of the target equipment based on the magnitude of the calculated difference (distance) (S106), and the information processing device 100 ends the processing steps.

[0063] 5. Effects of the embodiment As described above, the information processing device 100 according to this embodiment includes the collection unit 121, the probability density distribution calculation unit 123, the difference calculation unit 124, and the priority determination unit 125. The collection unit 121 collects measurement data of the target equipment for each of a plurality of specified periods. The probability density distribution calculation unit 123 calculates a probability density distribution for each of the specified periods based on the measurement data collected by the collection unit 121.

[0064] The difference calculation unit 124 calculates the difference between the probability density distributions for the multiple periods to be evaluated, which are calculated by the probability density distribution calculation unit 123. The priority determination unit 125 determines, from the multiple target facilities, a target facility with a high priority to be addressed, according to the difference calculated by the difference calculation unit 124.

[0065] As a result, the information processing device 100 can determine that target equipment with a large difference in control state during the period being evaluated is target equipment with a high priority for addressing, making it easy to identify target equipment with a high priority for addressing from multiple target equipment with poor control.

[0066] The information processing device 100 further includes a display unit 126. The display unit 126 displays information about a plurality of pieces of target equipment in descending order of priority. In this case, the priority determination unit 125 determines a higher priority for the target equipment as the difference calculated by the difference calculation unit 124 increases.

[0067] This allows the information processing device 100 to display information about multiple target facilities that are subject to management in descending order of priority, thereby providing useful information when determining the order in which to deal with target facilities with high response priorities.

[0068] The information processing device 100 further includes a determination unit 122. The determination unit 122 determines the state of the target equipment for a specified period calculated using the measurement data collected by the collection unit 121. In this case, the probability density distribution calculation unit 123 calculates a probability density distribution for the target equipment determined to be defective by the determination unit 122.

[0069] As a result, the information processing device 100 can determine the priority of response for target equipment judged as defective based on the good / bad judgment result of the target equipment determined by the measurement data for the period to be evaluated, so that a series of processes can be performed only for target equipment whose control state is poor and requires response, thereby reducing the processing load.

[0070] In addition, when the information processing device 100 has a judgment unit 122, the priority determination unit 125 determines, from among multiple target facilities, a target facility with a high priority to be addressed based on the judgment result by the judgment unit 122 and the difference calculated by the difference calculation unit 124.

[0071] As a result, the information processing device 100 determines the response priority of the target equipment by combining the good / bad judgment result of the target equipment with the distance of the probability density distribution, thereby enabling a more accurate response priority to be determined taking into account the condition of the target equipment.

[0072] [6. Hardware Configuration] The information processing device 100 according to the embodiment described above is realized, for example, by a computer 1000 configured as shown in Fig. 9. Fig. 9 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device according to the embodiment. The computer 1000 has a configuration in which a CPU 1100, a RAM 1200, a ROM 1300, an auxiliary storage device 1400, a communication I / F (interface) 1500, and an input / output I / F (interface) 1600 are connected by a bus 1800.

[0073] The CPU 1100 operates and controls each unit based on programs stored in the ROM 1300 or the auxiliary storage device 1400. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 starts up, programs that depend on the hardware of the computer 1000, and the like.

[0074] The auxiliary storage device 1400 stores programs executed by the CPU 1100, data used by the programs, etc. The communication I / F 1500 receives data from other devices via a predetermined communication network and sends it to the CPU 1100, and transmits data generated by the CPU 1100 to other devices via the predetermined communication network.

[0075] The CPU 1100 controls output devices such as a display and a printer, and input / output devices 1700 such as a keyboard and a mouse, via the input / output I / F 1600. The CPU 1100 acquires data from the input / output device 1700 via the input / output I / F 1600. The CPU 1100 also outputs generated data to the input / output device 1700 via the input / output I / F 1600.

[0076] For example, when the computer 1000 functions as the information processing device 100 according to this embodiment, the CPU 1100 of the computer 1000 executes a program loaded onto the RAM 1200 to realize the functions of the control unit 120 .

[0077] [7. Other] Of the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods. In addition, the information including the processing procedures, specific names, various data, and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown in the drawings.

[0078] Furthermore, the components of each device shown in the figure are conceptual functional components and do not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

[0079] The above-described components include those that can be easily imagined by a person skilled in the art, those that are substantially the same, and those that are within the scope of what is called equivalents. Furthermore, the above-described embodiments can be appropriately combined within the scope that does not cause contradictions in the processing content.

[0080] Furthermore, the aforementioned "section, module, unit" can be read as "means" or "circuit," etc. For example, a control section can be read as control means or a control circuit.

[0081] Although some embodiments of the present invention have been described in detail above with reference to the drawings, these are merely examples, and the present invention can be implemented in other forms that include the embodiments described in the Disclosure of the Invention section and that have been modified and improved in various ways based on the knowledge of those skilled in the art. [Explanation of symbols]

[0082] 100 Information processing device 110 Communications Department 120 control section 121 Collection Department 122 Judgment section 123 Probability density distribution calculation unit 124 Difference calculation part 125 Priority determination section 126 Display section 130 Storage section 200 Air conditioning system monitoring device 210, 220 Air conditioner system 211, 221 Air conditioner 212, 215 VAV 213, 216 Indoor temperature sensor 214-1~214-n, 217-1~217-n air outlet

Claims

1. a collection unit that collects measurement data of the target equipment for each of a plurality of specified periods; a probability density distribution calculation unit that calculates a probability density distribution for each of the specified periods based on the measurement data collected by the collection unit; a difference calculation unit that calculates a difference between probability density distributions for a plurality of periods to be evaluated, the probability density distributions being calculated by the probability density distribution calculation unit; a priority determination unit that determines, from among a plurality of pieces of target equipment, a piece of target equipment that has a high priority to be addressed in accordance with the difference calculated by the difference calculation unit; An information processing device comprising:

2. the priority determination unit determines the priority of the target equipment to be higher as the difference calculated by the difference calculation unit is larger, The system further includes a display unit that displays information about the plurality of target facilities in order of the priority.

2. The information processing apparatus according to claim 1, wherein:

3. The system further includes a determination unit that determines a state of the target equipment during the specified period, the state being calculated using the measurement data collected by the collection unit; The probability density distribution calculation unit calculates the probability density distribution for the target equipment determined to be defective by the determination unit.

2. The information processing apparatus according to claim 1, wherein:

4. The system further includes a determination unit that determines a state of the target equipment during the specified period, the state being calculated using the measurement data collected by the collection unit; The priority determination unit determines a target facility having a high priority to be addressed from among a plurality of target facilities based on the determination result by the determination unit and the difference calculated by the difference calculation unit.

2. The information processing apparatus according to claim 1, wherein:

5. An information processing method executed by an information processing device, a collection step of collecting measurement data of the target equipment for each of a plurality of specified periods; a probability density distribution calculation step of calculating a probability density distribution for each of the specified periods based on the measurement data collected by the collection step; a difference calculation step of calculating a difference between the probability density distributions for a plurality of periods to be evaluated, the probability density distributions being calculated in the probability density distribution calculation step; a priority determination step of determining, from among the plurality of target facilities, a target facility having a high priority to be addressed in accordance with the difference calculated by the difference calculation step; An information processing method comprising:

6. a collection procedure for collecting measurement data of the target equipment for each of a plurality of specified periods; a probability density distribution calculation step of calculating a probability density distribution for each of the specified periods based on the measurement data collected by the collection step; a difference calculation step of calculating a difference between probability density distributions for a plurality of time periods to be evaluated, the probability density distributions being calculated by the probability density distribution calculation step; a priority determination step for determining, from among a plurality of pieces of target equipment, a piece of target equipment having a high priority to be addressed in accordance with the difference calculated by the difference calculation step; An information processing program characterized by causing a computer to execute the above.

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