Monitoring control device, monitoring control method, and monitoring control program

Abstract

To provide a monitoring controller capable of presenting a correlation between signals representing the condition of a facility.SOLUTION: A monitoring controller 1A includes a signal data acquisition unit 13 that acquires signal data representing the condition of a facility, a correlation learning unit 14 that acknowledges a correlation, in which a signal value of one signal varies through linkage with a variation in a signal value of the other signal, of signals on the basis of signal ledger data which represents a collection of signal attributes, outputs correlation data representing a result of the acknowledgement, and obtains a degree of correlation between the signals through learning based on the signal data and the signal ledger data, a conversion processing unit 15 that performs conversion processing for displaying a network structure, in which symbols representing the signals having the correlation are tied in a network manner, on the correlation data, and performs processing for displaying the degree of correlation together with the network structure, and a correlation editing unit 16 that edits the correlation indicated with the network structure according to an editing manipulation.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present disclosure relates to a monitoring control device, a monitoring control method, and a monitoring control program that monitor and control each of a plurality of facilities. [Background technology]

[0002] A monitoring control device is known that collectively monitors the status of multiple pieces of equipment installed in a building, such as power equipment, elevators, and air conditioning equipment, by acquiring signals indicating the status of each piece of equipment. The monitoring control device also controls each piece of equipment by sending commands to each piece of equipment. Such monitoring control devices not only monitor the status of each piece of equipment based on the signals from each piece of equipment, but also attempt to understand in advance the correlations between the signals being monitored, thereby helping to identify the cause of any abnormalities that may occur. Even when there are complex correlations between multiple signals, the monitoring control device can clearly present the correlations by displaying them on a screen.

[0003] Patent Document 1 discloses that a monitoring and control device that monitors and controls a power system generates association information that indicates the association between the facilities that make up the power system, and controls the power system through calculations using the association information. According to the technology in Patent Document 1, the hierarchical structure of each facility is identified from the hierarchical structure of the power system, and a facility code is set to individually identify each facility within the power system through coding according to rules. The monitoring and control device according to Patent Document 1 stores facility data including facility codes and attribute information that indicates the attributes of the facility, and generates association information based on the facility data. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2001-251763 Summary of the Invention [Problem to be solved by the invention]

[0005] According to the technology of Patent Document 1, rules for identifying the hierarchical level of each piece of equipment from the hierarchical structure of the power system must be determined in advance for the equipment code. Determining the rules for identifying the hierarchical level requires specialized knowledge of the configuration of the power system. Furthermore, according to the technology of Patent Document 1, if the hierarchical structure deviates even slightly from the predetermined rules, it is not possible to generate related information. Therefore, the technology of Patent Document 1 has a problem in that it may not be possible to present the correlation between signals representing the status of the equipment.

[0006] The present disclosure has been made in view of the above, and aims to provide a monitoring control device that can present the correlation between signals that represent the state of equipment. [Means for solving the problem]

[0007] In order to solve the above-mentioned problems and achieve the object, a monitoring control device according to the present disclosure includes a signal data collection unit that collects signal data including signal values ​​of signals that represent the state of a facility for each of a plurality of facilities that are the subject of monitoring and control, and a signal register data that compiles signal attributes for each of the plurality of signals, and identifies correlations between signals included in the plurality of signals in which a change in one signal value is linked to a change in the other signal value, based on the signal register data that summarizes the signal attributes for each of the plurality of signals, The correlation that is obvious from the contents of the signal register data Certification did outputting correlation data indicative of the results; and In the signal register data, the correlation between signals is not obvious. Correlation of Based on signal data and signal ledger data hand study To do By , the degree of correlation between signals whose correlation is not obvious in the signal register data The correlation learning unit includes a correlation learning unit to be determined, a conversion processing unit that performs conversion processing on the correlation data to display a network structure in which symbols of correlated signals are connected in a network shape, and also performs processing to display the degree of correlation together with the network structure, and a correlation editing unit that edits the correlation shown in the network structure in accordance with editing operations. [Effects of the Invention]

[0008] The monitoring control device according to the present disclosure has an effect of being able to present the correlation between signals that represent the state of equipment. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating a configuration example of a monitoring and control system including a monitoring and control device according to a first embodiment. [Figure 2] FIG. 1 is a diagram illustrating a configuration example of a monitoring control device according to a first embodiment; [Figure 3] FIG. 1 is a diagram for explaining a correlation recognized by the monitoring control device according to the first embodiment; [Figure 4] FIG. 1 is a diagram illustrating an example of signal data collected in the monitoring control device according to the first embodiment. [Figure 5] FIG. 1 is a diagram illustrating an example of signal register data stored in a monitoring control device according to a first embodiment; [Figure 6] FIG. 1 is a diagram illustrating an example of equipment ledger data input to the monitoring control device according to the first embodiment; [Figure 7] FIG. 1 is a diagram illustrating an example of a network structure converted from correlation data by the monitoring control device according to the first embodiment; [Figure 8] FIG. 10 is a diagram for explaining display of the correlation degree by the processing of the monitoring control device according to the first embodiment and editing of the correlation by the monitoring control device; [Figure 9] 1 is a flowchart showing an operation procedure of the monitoring and control device according to the first embodiment; [Figure 10] FIG. 10 is a diagram illustrating a configuration example of a monitoring control device according to a second embodiment. [Figure 11] FIG. 10 shows a first example of a graph displayed according to the second embodiment. [Figure 12] FIG. 10 is a diagram showing a second example of a graph displayed according to the second embodiment. [Figure 13] FIG. 10 is a diagram illustrating graph switching in the second embodiment. [Figure 14] FIG. 10 is a diagram illustrating a configuration example of a monitoring control device according to a third embodiment. [Figure 15] FIG. 10 is a diagram illustrating a configuration example of a monitoring control device according to a fourth embodiment. [Figure 16] FIG. 13 is a diagram illustrating a configuration example of a monitoring control device according to a fifth embodiment. [Figure 17] FIG. 1 is a diagram illustrating an example of a hardware configuration of a monitoring and control device according to first to fifth embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0010] A monitoring control device, a monitoring control method, and a monitoring control program according to embodiments will be described in detail below with reference to the accompanying drawings.

[0011] Embodiment 1 FIG. 1 is a diagram illustrating an example of the configuration of a monitoring and control system including a monitoring and control device 1A according to a first embodiment. The monitoring and control device 1A monitors and controls each of a plurality of pieces of equipment 2. In the first embodiment, the plurality of pieces of equipment 2 that are the targets of monitoring and control by the monitoring and control device 1A are pieces of equipment installed in a building such as a building. The plurality of pieces of equipment 2 include, for example, power equipment, elevators, air conditioning equipment, etc.

[0012] The monitoring and control device 1A is configured, for example, by one or more cloud servers. A cloud server is a server built in a cloud environment that includes computer resources provided by a cloud service platform. Note that the monitoring and control device 1A may be a server other than a cloud server, for example, it may be an on-premise server.

[0013] The monitoring and control device 1A and the terminal 3 constitute a monitoring and control system. The terminal 3 is a terminal device used by a user of the monitoring and control system. The user is a person who performs monitoring and control work for each facility 2 using the monitoring and control system. The number of terminals 3 connected to the monitoring and control device 1A is assumed to be arbitrary.

[0014] The terminal 3 is communicably connected to the monitoring control device 1A via a network, and transmits and receives information to and from the monitoring control device 1A. The network is, for example, a WAN (Wide Area Network) such as the Internet, but may also be a LAN (Local Area Network).

[0015] The storage device 4 is a device external to the monitoring and control system. The storage device 4 is, for example, a device of the business operator that owns the facility 2. In the first embodiment, the business operator that owns the facility 2 is a party other than the owner of the building in which the facility 2 is installed. The storage device 4 is communicatively connected to the monitoring and control device 1A via a network. In response to a request from the monitoring and control device 1A, the storage device 4 transmits the facility ledger data stored in the storage device 4 to the monitoring and control device 1A. The facility ledger data will be described later. Multiple storage devices 4, each storing facility ledger data for different facilities 2, may be connected to the monitoring and control device 1A. The number of storage devices 4 connected to the monitoring and control device 1A is arbitrary. The business operator that owns the facility 2 may be the same as the owner of the building.

[0016] The monitoring control device 1A monitors the state of each of the multiple pieces of equipment 2 by collecting signal data including signal values ​​of signals that represent the state of the equipment 2. The monitoring control device 1A performs processing to visualize and display the state of each piece of equipment 2, and transmits the data for display to the terminal 3. The terminal 3 displays an image that represents the state of each piece of equipment 2. The monitoring control device 1A also generates commands in accordance with input operations made by a user to the terminal 3, and transmits the commands to each piece of equipment 2. The monitoring control device 1A controls each piece of equipment 2 by transmitting commands to each piece of equipment 2.

[0017] The monitoring control device 1A determines the correlation between signals collected from multiple pieces of equipment 2 based on signal ledger data stored inside the monitoring control device 1A. The signal ledger data will be described later. The monitoring control device 1A also reads out equipment ledger data from the storage device 4 and determines the correlation between the pieces of equipment 2 and the signals based on the signal ledger data. The monitoring control device 1A performs processing to display a network structure that shows the determined correlation. The terminal 3 receives the network structure data from the monitoring control device 1A and displays the network structure. The network structure will be described later in detail.

[0018] The monitoring control device 1A determines the degree of correlation between signals other than those for which a correlation has been confirmed by learning based on the signal data and signal register data. Furthermore, for equipment 2 and signals other than those for which a correlation has been confirmed, the monitoring control device 1A determines the degree of correlation between the equipment 2 and the signals by learning based on the signal data, signal register data, and equipment register data. The monitoring control device 1A performs processing to display the determined degree of correlation together with the network structure. The terminal 3 displays the degree of correlation together with the display of the network structure.

[0019] The terminal 3 accepts an editing operation by a user. The editing operation is an operation for editing the correlations shown in the network structure. The monitoring control device 1A edits the display of the correlations in the network structure in accordance with the editing operation.

[0020] Fig. 2 is a diagram illustrating a configuration example of a monitoring control device 1A according to the first embodiment. Fig. 2 illustrates the monitoring control device 1A and a storage device 4 connected to the monitoring control device 1A. The monitoring control device 1A includes a communication unit 10 that communicates with devices external to the monitoring control device 1A, a processing unit 11 that performs various processes, and a storage unit 12 that stores information. The processing unit 11 includes a signal data collection unit 13, a correlation learning unit 14, a conversion processing unit 15, and a correlation editing unit 16.

[0021] Signals representing the state of the equipment 2 are transmitted to the monitoring and control device 1A from the equipment 2 or from sensors that detect the state of the equipment 2. The sensors include sensors provided in the equipment 2 and sensors installed outside the equipment 2. The communication unit 10 sequentially receives signals representing the state of the equipment 2. The signal data collection unit 13 collects signal data including signal values ​​of signals representing the state of the equipment 2 for each of the multiple pieces of equipment 2 that are the targets of monitoring and control. The signals collected by the signal data collection unit 13 include signals representing environmental conditions such as the temperature or humidity of the location where the equipment 2 is installed. The collected signal data is stored in the memory unit 12. The collected signal data includes information about the state of the controller that controls the equipment 2. For example, for air conditioning equipment, the collected signal data may include information about the on / off state of the air conditioning equipment, information about the set temperature, information about the operation mode, etc.

[0022] The correlation learning unit 14 reads out the signal register data stored in the memory unit 12 and determines the correlations between signals included in the plurality of signals based on the signal register data. The correlations between signals included in the plurality of signals are relationships between signals collected by the signal data collecting unit 13, and are relationships in which a change in the value of one signal changes in conjunction with a change in the value of the other signal. The signal register data is data summarizing the signal attributes for each of the plurality of signals.

[0023] The correlation learning unit 14 acquires equipment ledger data from the storage device 4 and determines correlations between equipment 2 included in the plurality of equipment 2 based on the equipment ledger data. Alternatively, the correlation learning unit 14 determines correlations between equipment 2 included in the plurality of equipment 2 and signals included in the plurality of signals based on the equipment ledger data. The correlation between equipment 2 is a relationship between a signal possessed within one equipment 2 and a signal possessed within another equipment 2, where the value of one signal changes in conjunction with a change in the value of the other signal. The correlation between equipment 2 and a signal is a relationship between a signal possessed within equipment 2 and a signal that is the target of collection by the signal data collection unit 13, where the value of the other signal changes in conjunction with a change in the value of one signal. The signal possessed within equipment 2 is a signal that is monitored or generated within equipment 2 for control of equipment 2. The signals possessed within equipment 2 include signals that are the target of collection by the signal data collection unit 13 and signals that are not the target of collection by the signal data collection unit 13.

[0024] Fig. 3 is a diagram for explaining the correlations recognized by the monitoring control device 1A according to the first embodiment. Fig. 3 shows an example in which a chain of correlations between a plurality of pieces of equipment 2 and a plurality of signals is systematically developed and diagrammed. The "XX Building" shown in Fig. 3 is an example of a building equipped with a plurality of pieces of equipment 2 that are the targets of monitoring and control by the monitoring control device 1A.

[0025] The power equipment and elevators are examples of equipment 2 installed in the XX Building. The power equipment includes the equipment 2 of power receiving equipment, transformer equipment, distribution board A, distribution board B, and distribution board C. The elevator includes the equipment 2 of a control panel and a hall operating panel. The hall operating panel includes the equipment 2 of a hall operating panel installed on the first floor of the XX Building and a hall operating panel installed on the second floor of the XX Building. Inside distribution board A, a power consumption signal indicating the power consumption of distribution board A is possessed. Inside the elevator, a passenger number signal indicating the number of passengers in the elevator and a power consumption signal indicating the power consumption of the elevator are possessed.

[0026] In the first embodiment, correlations include three types: a dependency relationship, an ownership relationship, and a connection relationship. A dependency relationship is a relationship between signals when one signal changes due to control based on the other signal. An ownership relationship is a relationship between equipment 2 and a signal owned within equipment 2. A connection relationship is a relationship other than a dependency relationship or an ownership relationship. In FIG. 3, equipment 2 having a dependency relationship is represented by a solid line. Equipment 2 and signals having an ownership relationship are represented by a dashed line. Equipment 2 having a connection relationship is represented by a dot-dash line.

[0027] Because the power equipment and elevators are each controlled by the monitoring and control device 1A installed in the XX building, it can be said that there is a subordinate relationship between the XX building and the power equipment, and also a subordinate relationship between the XX building and the elevators. The power equipment, power receiving equipment, transformer equipment, distribution board A, distribution board B, and distribution board C form a hierarchical structure based on subordinate relationships. Distribution board A and the power consumption signal have an ownership relationship. The elevator, control panel, and hall operating panel form a hierarchical structure based on subordinate relationships. The elevator and the signals for passenger numbers and power consumption have an ownership relationship.

[0028] The platform operating panel installed on the first floor is connected to distribution board A and receives power from distribution board A. In this case, the relationship between distribution board A and the platform operating panel installed on the first floor is neither a subordinate relationship nor an ownership relationship, so it is considered to be a connected relationship. There is a connected relationship between distribution board A and the platform operating panel installed on the first floor. As the platform operating panel installed on the second floor receives power from distribution board B, there is a connected relationship between distribution board B and the platform operating panel installed on the second floor. As the control panel receives power from distribution board C, there is a connected relationship between distribution board C and the control panel.

[0029] The correlation learning unit 14 determines correlations that are obvious from the contents of the signal register data or the equipment register data by referring to the signal register data or the equipment register data. The correlation learning unit 14 outputs correlation data that indicates the results of determining the correlation. The correlation data is written to the storage unit 12.

[0030] The correlation learning unit 14 learns the correlation between signals for which the presence or absence of a correlation is not obvious in the signal register data or the equipment register data, and calculates the degree of correlation between the signals based on the learning results.The correlation learning unit 14 learns the correlation between pieces of equipment 2 for which the presence or absence of a correlation is not obvious in the signal register data or the equipment register data, and calculates the degree of correlation between the pieces of equipment 2 based on the learning results.The correlation learning unit 14 learns the correlation between pieces of equipment 2 and signals for which the presence or absence of a correlation is not obvious in the signal register data or the equipment register data, and calculates the degree of correlation between the pieces of equipment 2 and the signals based on the learning results.

[0031] The correlation learning unit 14 reads out the signal data stored in the memory unit 12 and determines the degree of correlation between signals other than those for which a correlation has been determined by learning based on the signal data and the signal register data. That is, the correlation learning unit 14 determines the degree of correlation between signals that represent the state of the equipment 2 and are collected by the signal data collecting unit 13. For example, in the example shown in FIG. 3, the signal for the amount of power consumption of the distribution board C and the signal for the amount of power consumption of the control panel are each assumed to be signals collected by the signal data collecting unit 13. In this case, the correlation learning unit 14 determines the degree of correlation between the signal for the amount of power consumption of the distribution board C and the signal for the amount of power consumption of the control panel.

[0032] Alternatively, the correlation learning unit 14 determines the degree of correlation between the pieces of equipment 2 included in the plurality of pieces of equipment 2 by learning based on the signal data, signal ledger data, and equipment ledger data. The degree of correlation between the pieces of equipment 2 is the degree of correlation between a signal owned by one piece of equipment 2 and a signal owned by the other piece of equipment 2. For example, in the example shown in FIG. 3, it is assumed that the signal for the amount of power consumption of distribution board A is the signal owned by distribution board A, and the signal for the number of people occupying an elevator is the signal owned by the elevator. In this case, the correlation learning unit 14 determines the degree of correlation between the signal for the amount of power consumption of distribution board A and the signal for the number of people occupying an elevator.

[0033] Alternatively, the correlation learning unit 14 determines the degree of correlation between equipment 2 included in the plurality of equipment 2 and signals included in the plurality of signals by learning based on the signal data, signal register data, and equipment register data. That is, the correlation learning unit 14 determines the degree of correlation between signals owned by equipment 2 and signals collected by the signal data collecting unit 13. For example, in the example shown in FIG. 3 , it is assumed that the signal of the power consumption amount of distribution board A is the signal owned by distribution board A, and the signal of the power consumption amount of distribution board C is the signal collected by the signal data collecting unit 13. In this case, the correlation learning unit 14 determines the degree of correlation between the signal of the power consumption amount of distribution board A and the signal of the power consumption amount of distribution board C.

[0034] Here, learning by the correlation learning unit 14 will be explained. Here, an example will be taken of determining the degree of correlation between signals collected by the signal data collecting unit 13. The correlation learning unit 14 observes a training data set including signal data and signal register data as state variables. For signals collected by the signal data collecting unit 13, the correlation learning unit 14 learns the tendency of one signal to change relative to a change in the other signal according to the training data set. The correlation learning unit 14 generates a model for calculating the degree of correlation, which is an index that indicates the tendency of one signal to change relative to a change in the other signal.

[0035] In the first embodiment, the correlation degree is a numerical representation of whether the manner of change between signals is similar. The value of the correlation degree takes into account whether one signal changes when the other signal changes, and the difference in the fluctuation range of the one signal value relative to the fluctuation range of the other signal value. The correlation learning unit 14 calculates the correlation degree from the input signal data and signal register data using the generated model. Furthermore, for signals having a subordinate relationship, the correlation learning unit 14 learns the dominant and subordinate signals based on the tendency that one signal changes with a delay following a change in the other signal.

[0036] Any learning algorithm may be used by the correlation learning unit 14. As an example, a case where reinforcement learning is applied to the learning algorithm used by the correlation learning unit 14 will be described. Reinforcement learning is a method in which an agent, which is an actor in a certain environment, observes the current state and decides on an action to take. The agent obtains a reward from the environment by selecting an action, and learns a policy that will obtain the most reward through a series of actions. Q-learning and TD-learning are known as representative methods of reinforcement learning.

[0037] When determining the degree of correlation between facilities 2 included in the plurality of facilities 2, the correlation learning unit 14 observes a training data set including signal data, signal register data, and facility register data as a state variable. For a signal owned by one facility 2 and a signal owned by another facility 2, the correlation learning unit 14 learns the tendency of a change in one signal relative to a change in the other signal, in accordance with the training data set. Furthermore, when determining the degree of correlation between a facility 2 included in the plurality of facilities 2 and a signal collected by the signal data collecting unit 13, the correlation learning unit 14 observes a training data set including signal data, signal register data, and facility register data as a state variable. For a signal owned by facility 2 and a signal collected by the signal data collecting unit 13, the correlation learning unit 14 learns the tendency of a change in one signal relative to a change in the other signal, in accordance with the training data set.

[0038] The correlation learning unit 14 may perform learning using, for example, a sparse modeling technique. The learning algorithm used by the correlation learning unit 14 may be a learning algorithm other than reinforcement learning. The learning algorithm may be supervised learning or semi-supervised learning. The correlation learning unit 14 may perform machine learning using, for example, a learning algorithm such as deep learning, a neural network, genetic programming, inductive logic programming, or a support vector machine.

[0039] The conversion processing unit 15 reads out the correlation data stored in the memory unit 12. The conversion processing unit 15 performs conversion processing on the correlation data to display a network structure. The network structure includes symbols representing signals collected by the signal data collection unit 13 and symbols representing facilities 2. In the network structure, for signals having a correlation, the symbols of one signal are connected to the symbols of the other signal. In the network structure, for facilities 2 having a correlation, the symbols of one facility 2 are connected to the symbols of the other facility 2. In the network structure, for facilities 2 and signals having a correlation, the symbols representing the facilities 2 are connected to the symbols representing the signals. In other words, in the network structure, for multiple signals and multiple facilities 2, the symbols of the signals having a correlation, the symbols of the facilities 2 having a correlation, and the symbols of the facilities 2 and the signals having a correlation are each connected in a network form.

[0040] In the correlation data, for signals included in the plurality of signals, the signal name of each signal is associated with information indicating whether or not there is a correlation. In the correlation data, for facilities 2 included in the plurality of facilities 2, the facility name of each facility 2 is associated with information indicating whether or not there is a correlation. In the correlation data, for facilities 2 included in the plurality of facilities 2 and signals included in the plurality of signals, the signal name of the signal, the facility name of the facility 2, and information indicating whether or not there is a correlation are associated. The conversion processing unit 15 converts the correlation data into, for example, a data format of a graph database. Note that the data format is not limited to a graph database. The correlation data may be converted into a data format other than a graph database.

[0041] By converting the correlation data into a network structure, the monitoring control device 1A can present the correlations to the user in an easy-to-understand manner even when there are complex correlations between multiple signals and multiple pieces of equipment 2. Furthermore, when a hierarchical structure having multiple layers based on correlations is constructed, the monitoring control device 1A can present the hierarchical structure to the user in an easy-to-understand manner.

[0042] The correlation learning unit 14 outputs the result of calculating the correlation degree to the conversion processing unit 15. The conversion processing unit 15 performs processing to display the correlation degree together with the network structure. The correlation learning unit 14 outputs data on the network structure and data for displaying the correlation degree. The communication unit 10 transmits the data on the network structure and data for displaying the correlation degree to the terminal 3 shown in FIG. 1. The terminal 3 displays the network structure shown in the received data. The terminal 3 displays the correlation degree together with the display of the network structure.

[0043] The user operates the terminal 3 while checking the network structure and the correlation degree displayed on the terminal 3, thereby instructing editing of the correlation in the network structure. The terminal 3 transmits a signal indicating the content of the editing operation to the monitoring control device 1A. When the signal indicating the content of the editing operation is received by the communication unit 10, the correlation editing unit 16 edits the correlation shown in the network structure in accordance with the editing operation. The correlation editing unit 16 updates the correlation data stored in the storage unit 12 by reflecting the edited correlation in the correlation data.

[0044] Next, details of the signal data collected by the monitoring control device 1A will be described. Fig. 4 is a diagram showing an example of signal data collected by the monitoring control device 1A according to the first embodiment. Fig. 4 shows, as an example of signal data, signal values ​​collected for a facility 2 with a facility name "A1" and signal values ​​collected for a facility 2 with a facility name "B1" among multiple facilities 2 that are targets of monitoring and control by the monitoring control device 1A. Hereinafter, the facility 2 with the facility name "A1" will be referred to as facility A1, and the facility 2 with the facility name "B1" will be referred to as facility B1.

[0045] The signals named "a11," "a12," "a13," "a14," and "a15" are signals that are to be collected by the signal data collecting unit 13 for facility A1. Hereinafter, the signals named "a11," "a12," "a13," "a14," and "a15" will be referred to as signal a11, signal a12, signal a13, signal a14, and signal a15. The signals named "b11," "b12," "b13," and "b14" are signals that are to be collected by the signal data collecting unit 13 for facility B1. Hereinafter, the signals named "b11," "b12," "b13," and "b14" will be referred to as signal b11, signal b12, signal b13, and signal b14.

[0046] The signal data includes at least a timestamp, which is information on the date and time when the monitoring and control device 1A acquired the signal value, a signal name, and a signal value. In the example shown in FIG. 4, the signal data includes a facility name. In the example shown in FIG. 4, the signal value is a numerical value or information indicating the on or off state of the signal. The signal data may include information other than the information used to determine the correlation in the first embodiment, or information other than the information used to learn the degree of correlation in the first embodiment.

[0047] Next, the signal register data stored in the monitoring control device 1A will be described in detail. Fig. 5 is a diagram showing an example of signal register data stored in the monitoring control device 1A according to the first embodiment. The signal register data is a database held in the monitoring control device 1A so that the monitoring control device 1A can manage signals input from a plurality of facilities 2.

[0048] The signal register data includes at least a signal name and information indicating the data format of the signal value. In the example shown in FIG. 5, the signal register data includes information indicating the type of facility 2, a floor name, a distribution board ID, a facility ID, and remarks. The floor name is the name of the floor on which facility 2 is installed. The distribution board ID is an identifier assigned to the distribution board that supplies power to facility 2. The facility ID is an identifier assigned to facility 2. The remarks include supplemental information about the signal. The supplemental information may be information about signal-specific rules or information about the object being monitored by the signal. The correlation learning unit 14 determines the ownership relationship between facility 2 and the signal owned within facility 2 based on the signal name and facility ID included in the signal register data. The signal register data may include information other than the information used to determine the correlation in the first embodiment or information other than the information used to learn the correlation in the first embodiment.

[0049] Next, details of the equipment ledger data input to the monitoring control device 1A will be described. Fig. 6 is a diagram showing an example of the equipment ledger data input to the monitoring control device 1A according to the first embodiment. The equipment ledger data is a database compiled by the owner of the equipment 2 for the construction of the equipment 2 installed in the building. The equipment ledger data may be updated in accordance with maintenance such as replacement of the equipment 2.

[0050] The equipment ledger data includes at least the equipment name and the equipment ID. In the example shown in FIG. 6, the equipment ledger data includes information indicating the type of equipment 2, the floor name, the distribution board ID, and collection protocol information. The collection protocol information is information about the communication standard for collecting signals. The equipment ledger data may include information other than the information used to determine the correlation in the first embodiment or information other than the information used to learn the correlation degree in the first embodiment. The correlation learning unit 14 may determine the ownership relationship between the equipment 2 and the signals owned within the equipment 2 based on the signal ledger data and the equipment ledger data.

[0051] Next, the network structure will be described in detail. Fig. 7 is a diagram illustrating an example of a network structure converted from correlation data by the monitoring control device 1A according to the first embodiment. In the network structure illustrated in Fig. 7, the symbol representing facility 2 is an oval shape with the facility name written on it. The symbol representing a signal is an oval shape with the signal name written on it. In the network structure illustrated in Fig. 7, an arrow with the character string "has" attached represents an ownership relationship between facility 2 and the signal. The example of the network structure illustrated in Fig. 7 represents that facility A1 has an ownership relationship with each of signals a11, a12, a13, a14, and a15, and facility B1 has an ownership relationship with each of signals b11, b12, b13, and b14.

[0052] Next, the display of the correlation degree and editing of the displayed correlation will be described. Fig. 8 is a diagram for explaining the display of the correlation degree by the processing of the monitoring control device 1A according to the first embodiment and editing of the correlation by the monitoring control device 1A. Fig. 8 shows an example of a screen displayed on the terminal 3. In the example shown in Fig. 8, the correlation degree between facility A1 and facility B1 is displayed together with the display of the network structure shown in Fig. 7.

[0053] The dashed line connecting the symbol for equipment A1 and the symbol for equipment B1 is an object indicating that they were the subject of the correlation calculation. The dashed line is also marked with the text "Correlation 56.7%," which indicates the calculation result of the correlation between equipment A1 and equipment B1. The user can refer to the display of the correlation between equipment A1 and equipment B1 to determine whether or not there is a correlation between equipment A1 and equipment B1.

[0054] The display of the correlation degree is not limited to the display of the correlation degree value, as long as the level of the correlation degree can be recognized. For example, the display of the correlation degree may be a display of an object that indicates the level of correlation by color or by color shading.

[0055] When a user selects a signal symbol from the network structure displayed on the screen, a graph showing the transition of the signal value for the selected signal is displayed on the screen. In the example shown in FIG. 8, when signal b14 is selected, a graph showing the transition of the signal value for signal b14 is displayed. The vertical axis of the graph represents the signal value, and the horizontal axis of the graph represents time. The user can check the transition of the signal value by selecting any signal from the network structure. The user can check whether there is an abnormality in the signal value from the transition of the signal value. Furthermore, if an abnormality occurs, the user can check the time when the abnormality occurred.

[0056] When signal b14 is selected, the symbol of signal b14 is displayed in a color different from that of the other symbols so as to be distinguishable from the other symbols. The symbol of the selected signal is not limited to being displayed in a color different from that of the other symbols, and may be displayed in any manner that allows it to be distinguished from the other symbols.

[0057] If the user determines that there is a correlation between facility A1 and facility B1, the user performs an editing operation to add a correlation between facility A1 and facility B1 in the network structure. In response to the editing operation to add the correlation, the correlation editing unit 16 adds information indicating the correlation between facility A1 and facility B1 to the correlation data stored in the storage unit 12. The information indicating the correlation added by the editing operation is accompanied by information for distinguishing it from correlations determined to be self-evident from the signal register data or the facility register data. In the network structure displayed on the screen of the terminal 3, the correlation added by the editing operation is displayed so as to be distinguishable from correlations determined to be self-evident. Objects indicating correlations added by the editing operation are different from objects indicating correlations determined to be self-evident. For example, objects indicating correlations determined to be self-evident are solid arrows, while correlations added by the editing operation are dashed arrows.

[0058] The user can also perform an editing operation to delete an added correlation. In response to the editing operation to delete a correlation, the correlation editing unit 16 deletes information indicating the correlation to be deleted from the correlation data.

[0059] The monitoring control device 1A may record a history of editing by the correlation editing unit 16. If editing operations can be performed by multiple users, the editing history may include information about the user who performed the editing. By checking the editing history, a user can perform editing work by utilizing the content of past edits. If there is an error in the past editing, the content of the edit at the time the error occurred can be confirmed from the editing history. Furthermore, by checking the editing history, it is possible to check whether tampering has occurred. The editing history may be stored in a device external to the monitoring control device 1A.

[0060] If multiple users can each perform editing operations, correlations added by editing operations may be weighted according to the user. Weightings for each user may be set based on pre-set conditions regarding the user's attributes, such as whether the user is a management company with specialized knowledge or the owner of the building or facility 2, and correlations entered and determined by users with higher weightings may be set with priority.

[0061] Next, the operation of the monitoring control device 1A will be described with reference to a flowchart of FIG.

[0062] In step S1, the monitoring and controlling device 1A collects signal data in the signal data collecting unit 13. The collected signal data is stored in the storage unit 12.

[0063] In step S2, the monitoring control device 1A determines in the correlation learning unit 14 whether or not correlation data is stored in the memory unit 12. If correlation data is stored (step S2, Yes), the monitoring control device 1A proceeds to step S3. If correlation data is not stored (step S2, No), the monitoring control device 1A proceeds to step S4.

[0064] In step S4, the monitoring and control device 1A performs new learning of correlations in the correlation learning unit 14. The correlation learning unit 14 reads out signal register data from the memory unit 12. The correlation learning unit 14 also acquires equipment register data from the storage device 4. The correlation learning unit 14 determines the correlations between signals based on the signal register data. The correlation learning unit 14 determines the correlations between pieces of equipment 2 based on the equipment register data. The correlation learning unit 14 determines the correlations between pieces of equipment 2 and signals based on the equipment register data. The correlation learning unit 14 outputs correlation data indicating the results of the determination to the memory unit 12. The correlation learning unit 14 also learns the correlations between signals and calculates the degree of correlation between the signals based on the learning results. The correlation learning unit 14 learns the correlations between pieces of equipment 2 and calculates the degree of correlation between pieces of equipment 2 based on the learning results. The correlation learning unit 14 learns the correlation between the equipment 2 and the signal, and calculates the degree of correlation between the equipment 2 and the signal based on the learning result.

[0065] In step S3, the monitoring and control device 1A executes relearning of correlations in the correlation learning unit 14. The correlation learning unit 14 recertifies the correlations between signals based on the signal ledger data. The correlation learning unit 14 recertifies the correlations between equipment 2 based on the equipment ledger data. The correlation learning unit 14 recertifies the correlations between equipment 2 and signals based on the equipment ledger data. The correlation learning unit 14 outputs correlation data indicating the results of the certification to the storage unit 12. In addition, the correlation learning unit 14 relearns the correlations between signals, and calculates the correlations between the signals based on the results of the relearning. The correlation learning unit 14 relearns the correlations between equipment 2, and calculates the correlations between equipment 2 based on the results of the relearning. The correlation learning unit 14 relearns the correlations between equipment 2 and signals, and calculates the correlations between equipment 2 and signals based on the results of the relearning.

[0066] In step S5 after step S3 or step S4, the monitoring control device 1A performs conversion processing of the correlation data in the conversion processing unit 15. The conversion processing unit 15 performs conversion processing on the correlation data to display the network structure. The conversion processing unit 15 also performs processing to display the degree of correlation together with the network structure.

[0067] In step S6, the monitoring control device 1A transmits network structure data, which is data indicating the network structure, to the terminal 3. The monitoring control device 1A transmits data for displaying the degree of correlation together with the network structure data to the terminal 3. The terminal 3 displays the network structure indicated in the network structure data on a screen. The terminal 3 also displays the degree of correlation in accordance with the data for displaying the degree of correlation.

[0068] The correlation editing unit 16 reads out the correlation data from the storage unit 12. In step S7, the monitoring control device 1A determines in the correlation editing unit 16 whether or not an editing operation has been performed on the terminal 3. If an editing operation has been performed (step S7, Yes), the monitoring control device 1A proceeds to step S8. If an editing operation has not been performed (step S7, No), the monitoring control device 1A proceeds to step S9.

[0069] In step S8, the monitoring control device 1A causes the correlation editing unit 16 to edit the correlations shown in the network structure in accordance with the editing operation, and stores the correlation data reflecting the edited content in the storage unit 12. The correlation editing unit 16 reflects the edited content in the correlation data read out from the storage unit 12, and writes the correlation data reflecting the edited content into the storage unit 12.

[0070] In step S9, the monitoring control device 1A stores the correlation data in the memory unit 12 using the correlation editing unit 16. The correlation editing unit 16 writes the read correlation data as is to the memory unit 12 without making any changes to the correlation data read from the memory unit 12. By completing step S8 or step S9, the monitoring control device 1A ends the operation according to the procedure shown in FIG.

[0071] According to the first embodiment, the monitoring control device 1A determines the correlation between signals that are the subject of collection in the signal data collection unit 13, based on the signal register data. The monitoring control device 1A determines the degree of correlation between signals by learning based on the collected signal data and signal register data. The monitoring control device 1A also determines the correlation between facilities 2, or the correlation between facilities 2 and signals, based on the facility register data. The monitoring control device 1A determines the degree of correlation between facilities 2, or the correlation between facilities 2 and signals, by learning based on the collected signal data, signal register data, and facility register data.

[0072] The monitoring control device 1A can present correlations based on signal data, signal ledger data, and facility ledger data, even if the user does not have specialized knowledge about the hierarchical structure consisting of multiple facilities 2 and multiple signals. Furthermore, since there is no need to determine rules in advance for identifying the hierarchical level of each facility 2, the monitoring control device 1A can avoid a situation in which correlations cannot be presented due to the hierarchical structure deviating from predetermined rules. As a result, the monitoring control device 1A has the effect of being able to present correlations between signals that represent the status of the facilities 2. Furthermore, the monitoring control device 1A has the effect of being able to present correlations between facilities 2, or correlations between the facilities 2 and signals.

[0073] Embodiment 2 Fig. 10 is a diagram showing an example of the configuration of a monitoring control device 1B according to the second embodiment. Fig. 10 shows the monitoring control device 1B and a storage device 4 connected to the monitoring control device 1B. The monitoring control device 1B has a configuration similar to that of the monitoring control device 1A according to the first embodiment, and also includes a label creation unit 17. In the second embodiment, the same components as those in the first embodiment are given the same reference numerals, and the following mainly describes the configuration that differs from that in the first embodiment.

[0074] The label creation unit 17 automatically creates labels based on the correlation data to represent the attributes of signals when displaying a graph showing changes in signals that represent the state of equipment. On the terminal 3, two or more signals are narrowed down from the multiple signals that are the subject of collection by the signal data collection unit 13, and graphs representing each of the narrowed down signals are simultaneously displayed. The labels represent the attributes used to narrow down the signals.

[0075] The label creation unit 17 reads out the correlation data from the storage unit 12. The label creation unit 17 determines an attribute to be used as a label based on the correlation between signals indicated in the correlation data. The label creation unit 17 may perform signal clustering and determine an attribute common to signals determined to be in the same cluster as an attribute to be used as a label.

[0076] For example, when displaying a graph of the amount of power consumed by each floor of a building, the labels are the amount of power consumed and the floor of the building. When displaying a graph of the amount of power consumed by each type of facility 2, the labels are the amount of power consumed and the type of facility 2. When displaying a graph of the amount of power consumed by each rated voltage of facility 2, the labels are the amount of power consumed and the rated voltage of facility 2.

[0077] Here, an example of a method for automatically creating labels based on correlation data will be described. For example, suppose that signals that are correlated contain a common keyword in their signal names. The label creation unit 17 determines that the common keyword is an attribute to be used as a label. To determine whether a common keyword is included, the signal names of each signal are first divided into words with character strings shorter than the signal names. For example, natural language processing techniques such as morphological analysis can be applied to divide the signal names. Next, whether a common keyword is included is determined by evaluating the similarity of character strings for each divided word. For example, methods such as the Levenshtein distance method or Gestalt pattern matching can be applied to evaluate the similarity of character strings. The label creation unit 17 can automatically create labels by extracting words determined to be common keywords from the signal names.

[0078] Fig. 11 is a diagram showing a first example of a graph displayed according to the second embodiment. The first example shown in Fig. 11 is an example of a graph showing the change in power consumption for each floor of a building. In the first example, a graph showing the change in power consumption for the entire first floor of the building and a graph showing the change in power consumption for the entire second floor of the building are displayed using the same vertical and horizontal axes. In other words, the graphs of power consumption for each floor are displayed simultaneously.

[0079] Fig. 12 is a diagram showing a second example of a graph displayed according to the second embodiment. The second example shown in Fig. 12 is an example of a graph showing the transition of power consumption for each type of facility 2. In the second example, a graph showing the transition of power consumption for all of the power facilities installed in the building and a graph showing the transition of power consumption for all of the elevators installed in the building are displayed using the same vertical and horizontal axes. In other words, graphs showing the power consumption for each type of facility 2 are displayed simultaneously.

[0080] The displayed graph may be switched by selecting a label from a list of multiple labels. Fig. 13 is a diagram for explaining switching of graphs in embodiment 2. Fig. 13 shows an example of a pull-down menu that is displayed on the screen of terminal 3 when a label is selected. The pull-down menu shown in Fig. 13 is displayed by a user's operation on terminal 3. The example shown in Fig. 13 is an example of a pull-down menu for further narrowing down labels when displaying a graph of power consumption. In Fig. 13, the option "by floor" represents the floor of the building. The option "by voltage" represents the rated voltage of facility 2. The option "by use" represents the type of facility 2.

[0081] Each of the multiple labels that are options is created by the label creation unit 17. When a user selects a label that is an option from a pull-down menu, a graph corresponding to the selected label is displayed on the screen. Note that a means other than the pull-down menu may be used to select a label.

[0082] According to the second embodiment, the monitoring control device 1B automatically creates labels based on correlation data, thereby eliminating the need for a user to manually create labels.

[0083] Embodiment 3 Fig. 14 is a diagram showing an example of the configuration of a monitoring control device 1C according to the third embodiment. Fig. 14 shows the monitoring control device 1C and a storage device 4 connected to the monitoring control device 1C. The monitoring control device 1C has a configuration similar to that of the monitoring control device 1A according to the first embodiment, and also includes an abnormality detection unit 18. In the third embodiment, the same components as those in the first or second embodiment are denoted by the same reference numerals, and the following mainly describes the configuration that differs from the first or second embodiment.

[0084] The anomaly detection unit 18 receives as input the results of the correlation calculations performed by the correlation learning unit 14. The anomaly detection unit 18 monitors the results of the correlation calculations performed by the correlation learning unit 14. The anomaly detection unit 18 may acquire past results of correlation calculations from the correlation data stored in the memory unit 12. For signals that are the subject of collection by the signal data collection unit 13, the anomaly detection unit 18 detects an anomaly in the tendency of changes in one signal relative to changes in the other signal based on changes in the results of the correlation calculations. In this way, the anomaly detection unit 18 automatically detects equipment 2 or signals that are elements that exhibit abnormal behavior within the correlations that have been determined in advance.

[0085] The anomaly detection unit 18 detects an anomaly, for example, when the fluctuation range of the calculation result of the correlation degree exceeds a preset threshold. When the anomaly detection unit 18 detects an anomaly, it outputs an alert indicating that an anomaly has occurred. The communication unit 10 transmits the alert output by the anomaly detection unit 18 to the terminal 3. When the terminal 3 receives the alert, it displays the alert on the screen. For example, the terminal 3 displays on the screen a message instructing an inspection of the element in which the anomaly has been detected. This enables a prompt response to the anomaly.

[0086] The abnormalities detected by the abnormality detection unit 18 include abnormalities caused by troubles such as breakdowns of the equipment 2, disconnections, clogged pipes, or water leaks. The abnormalities detected by the abnormality detection unit 18 are not limited to those caused by troubles. For example, an abnormality also includes a case where a change that cannot be confirmed from various data that can be acquired by the monitoring control device 1C, such as a change in the connection state of multiple pieces of equipment 2 and multiple signals, has occurred.

[0087] According to the third embodiment, the monitoring control device 1C automatically detects an abnormality in the tendency of one signal's change relative to the change in the other signal based on a change in the calculation result of the correlation degree, thereby enabling the user to quickly detect the abnormality.

[0088] Embodiment 4 Fig. 15 is a diagram showing an example of the configuration of a monitoring control device 1D according to the fourth embodiment. Fig. 15 shows the monitoring control device 1D and a storage device 4 connected to the monitoring control device 1D. The monitoring control device 1D has a configuration similar to that of the monitoring control device 1A according to the first embodiment, and also includes a network structure learning unit 19. In the fourth embodiment, the same components as those in the first to third embodiments are denoted by the same reference numerals, and the following description will mainly focus on the configuration that differs from the first to third embodiments.

[0089] The network structure learning unit 19 learns the characteristics of multiple network structures. The monitoring control device 1D collects correlation data for each site, such as each of multiple buildings or each of multiple floors within a building. The collected correlation data is stored in the memory unit 12. The network structure learning unit 19 uses the collected correlation data to learn characteristic structures from the network structures indicated in each correlation data. Based on the learning results, the network structure learning unit 19 estimates network structures for sites other than the site where the correlation data was collected.

[0090] The network structure characteristics learned by the network structure learning unit 19 include the hierarchical structure configuration, correlation trends, etc. For example, the network structure learning unit 19 learns from the collected correlation data a rule that signals whose signal names contain a specific character string have a subordinate relationship. Based on the results of this learning, the network structure learning unit 19 verifies whether this rule holds true even in a field where the correlation between signals is not understood.

[0091] The network structure learning unit 19 may estimate a correlation that is not grasped at a certain site based on a correlation defined in the network structure of another site. Here, an example of estimating an unrecognized correlation will be described. Assume that "Building X" and "Building Y" are each buildings included in multiple buildings. Assume that the signal register data for "Building X" indicates that a signal collection device is installed for each communication protocol, and that the network structure for "Building X" defines an ownership relationship in which the signal collection device owns facility 2. Meanwhile, assume that "Building Y" is installed with a signal collection device for each communication protocol, but that the signal collection device is treated as one of multiple facilities 2. In this case, the network structure learning unit 19 estimates that the ownership relationship between the signal collection device and facility 2 in "Building X" also holds between the signal collection device and facility 2 in "Building Y."

[0092] Based on this estimation, the network structure learning unit 19 inquires of the user of the monitoring control device 1D installed in "Building Y" as to whether an ownership relationship in which the signal collecting device owns the facility 2 that corresponds to the communication protocol of the signal collecting device is established between the signal collecting device in "Building Y" and the facility 2. If the inquired user acknowledges that an ownership relationship is established, the network structure learning unit 19 amends the correlation data for "Building Y" by adding an ownership relationship in which the signal collecting device owns the facility 2. In this way, the network structure learning unit 19 estimating the correlation confirms that the correlation is established, and then amends the correlation data.

[0093] According to embodiment 4, the monitoring control device 1D can learn the characteristics of multiple network structures and utilize the results of the learning to identify network structures at sites other than the site where the correlation data was collected.

[0094] As an application of the fourth embodiment, a cloud center connected to a plurality of monitoring control devices 1D may be provided, and the cloud center may collect rules regarding the characteristics of the network structure obtained by learning in each monitoring control device 1D. The cloud center may systematize the collected rules and distribute the systematized rules to each monitoring control device 1D. This allows each monitoring control device 1D to identify the network structure based on the systematized rules.

[0095] Embodiment 5. FIG. 16 is a diagram showing an example of the configuration of a monitoring and maintenance device 1E according to a fifth embodiment. The monitoring and maintenance device 1E receives inspection and maintenance data for a plurality of pieces of equipment 2, and learns the degree of correlation between items of the inspection and maintenance data and signals. In the fifth embodiment, the same components as those in the first to fourth embodiments are given the same reference numerals, and the configuration different from those in the first to fourth embodiments will be mainly described. The inspection and maintenance data includes at least one of the results of inspecting the equipment 2 and the results of performing maintenance work on the equipment 2. Maintenance work includes all work performed to continue using the equipment 2, such as repair, part replacement, and cleaning. Hereinafter, inspection and maintenance refers to at least one of inspection and maintenance.

[0096] The storage device 5 is a device external to the monitoring and control system. The storage device 5 is, for example, a device of a business operator that performs inspection and maintenance of multiple pieces of equipment 2. In the first embodiment, the business operator that performs inspection and maintenance is a party other than the owner of the building in which the pieces of equipment 2 are installed. The storage device 5 is communicably connected to the monitoring and control device 1E via a network. In response to a request from the monitoring and control device 1E, the storage device 5 transmits the inspection and maintenance data stored in the storage device 5 to the monitoring and control device 1E. FIG. 16 shows the monitoring and control device 1E, the storage device 4 connected to the monitoring and control device 1E, and the storage device 5 connected to the monitoring and control device 1E.

[0097] The correlation learning unit 14 performs learning similar to that in embodiment 1. In embodiment 5, the correlation learning unit 14 further obtains the degree of correlation between the inspection and maintenance items and the signal representing the state of the equipment 2 by learning based on the inspection and maintenance data and the signal data.

[0098] The correlation learning unit 14 acquires inspection and maintenance data from the storage device 5. The correlation learning unit 14 observes a training data set including signal data and inspection and maintenance data as state variables. The correlation learning unit 14 learns the tendency of signal changes in response to changes in the content of inspection and maintenance items according to the training data set. The correlation learning unit 14 generates a model for calculating the degree of correlation, which is an index that represents the tendency of signal changes in response to changes in the content of inspection and maintenance items. The correlation learning unit 14 outputs the result of calculating the degree of correlation between the inspection and maintenance items and the signal to the conversion processing unit 15.

[0099] The conversion processing unit 15 performs processing for displaying the degree of correlation, as in the case of embodiment 1. In embodiment 5, the conversion processing unit 15 further performs processing for displaying the degree of correlation between inspection and maintenance items and signals. On the screen of the terminal 3, a display of the degree of correlation between inspection and maintenance items and signals is added to the display similar to that in embodiment 1.

[0100] When the user determines that there is a correlation between the inspection and maintenance item and the signal, the user performs an editing operation to add a correlation between the inspection and maintenance item and the signal in the network structure. In response to the editing operation to add the correlation, the correlation editing unit 16 adds information indicating the correlation between the inspection and maintenance item and the signal to the correlation data stored in the storage unit 12.

[0101] According to the fifth embodiment, the monitoring and control device 1E determines the degree of correlation between the inspection and maintenance items and the signals that represent the state of the equipment 2 by learning based on the inspection and maintenance data and the signal data. Therefore, when an abnormality is found in the collected signal data, the user can search for the cause of the abnormality by taking the inspection and maintenance items into account. Furthermore, by adding a display of the degree of correlation between the inspection and maintenance items and the signals, the user can determine that an abnormality may have occurred when a change is observed in the signals and request inspection and maintenance.

[0102] The monitoring and control device 1E may output an alert to prompt inspection and maintenance when the fluctuation range of the signal value exceeds a preset threshold for an inspection and maintenance item and a signal for which a correlation has been added to the network structure. The communication unit 10 transmits the alert output by the anomaly detection unit 18 to the terminal 3. Upon receiving the alert, the terminal 3 displays the alert on its screen. For example, the terminal 3 displays a message on its screen instructing an inspection of the element in which an abnormality has been detected. This enables a prompt response to the abnormality.

[0103] Next, the hardware configuration of the monitoring control devices 1A, 1B, 1C, 1D, and 1E according to the first to fifth embodiments will be described. FIG. 17 is a diagram showing an example of the hardware configuration of the monitoring control devices 1A, 1B, 1C, 1D, and 1E according to the first to fifth embodiments. The monitoring control devices 1A, 1B, 1C, 1D, and 1E are realized by a computer system including a processing circuit 30 and a communication device 31. The processing circuit 30 includes a processor 32 and a memory 33. The processing circuit 30 is a circuit on which the processor 32 executes software.

[0104] The processing unit 11 of the monitoring and controlling devices 1A, 1B, 1C, 1D, and 1E is realized by software, firmware, or a combination of software and firmware. The software or firmware is written as a program and stored in memory 33. In the processing circuit 30, the processor 32 reads and executes the monitoring and control program stored in memory 33, thereby realizing each function of the processing unit 11. That is, the processing circuit 30 includes memory 33 for storing the monitoring and control program that results in the processing of the monitoring and controlling devices 1A, 1B, 1C, 1D, and 1E. The monitoring and control program can also be said to cause a computer to execute the procedures and methods of the monitoring and controlling devices 1A, 1B, 1C, 1D, and 1E.

[0105] The processor 32 is a CPU (Central Processing Unit, also referred to as a central processing unit, processing unit, arithmetic unit, microprocessor, microcomputer, processor, or DSP (Digital Signal Processor)). The memory 33 is, for example, a non-volatile or volatile semiconductor memory such as a RAM (Random Access Memory), a ROM (Read Only Memory), a flash memory, an EPROM (Erasable Programmable Read Only Memory), or an EEPROM (registered trademark) (Electrically Erasable Programmable Read Only Memory), a magnetic disk, a flexible disk, an optical disk, a compact disk, a minidisk, or a DVD (Digital Versatile Disc). The storage unit 12 of each of the monitoring and control devices 1A, 1B, 1C, 1D, and 1E is realized by the memory 33. The communication unit 10 of each of the monitoring and control devices 1A, 1B, 1C, 1D, and 1E is realized by the communication device 31. The monitoring and control devices 1A, 1B, 1C, 1D, and 1E may include an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The monitoring and control program may be stored in a recording medium such as a CD (Compact Disc)-ROM or DVD-ROM, and the recording medium may be provided to realize each embodiment.

[0106] The monitoring control devices 1A, 1B, 1C, 1D, and 1E may be configured with two or more devices, for example, two or more servers. The monitoring control devices 1A, 1B, 1C, 1D, and 1E may be configured with a processing server and a data server. When the monitoring control devices 1A, 1B, 1C, 1D, and 1E are configured with two or more devices, each of the two or more devices has, for example, the hardware configuration shown in FIG. 17. Note that communication between the two or more devices is performed via a communication device 31.

[0107] Terminal 3 is realized by a hardware configuration similar to that shown in Fig. 17. Terminal 3 further includes an input device operated by a user and a display device that displays a screen. The input device includes, for example, a keyboard, a mouse, a keypad, or a touch panel. The display device is, for example, an LCD (Liquid Crystal Display) or an organic EL (Electro-Luminescence) display.

[0108] The configurations shown in the above embodiments are examples of the contents of the present disclosure. The configurations of each embodiment can be combined with other known technologies. The configurations of each embodiment can also be combined as appropriate. Part of the configuration of each embodiment can be omitted or modified without departing from the gist of the present disclosure.

[0109] Various aspects of the present disclosure are summarized below as appendices.

[0110] (Appendix 1) a signal data collection unit that collects signal data including signal values ​​of signals representing the status of each of a plurality of facilities to be monitored and controlled; a correlation learning unit that determines a correlation between signals included in the plurality of signals, in which a signal value changes in conjunction with a change in the value of one of the signals, based on signal register data that summarizes the attributes of the signals for each of the plurality of signals, outputs correlation data that indicates the result of the determination, and obtains a degree of correlation between signals other than the signals for which the correlation has been determined, by learning based on the signal data and the signal register data; a conversion processing unit that performs a conversion process on the correlation data to display a network structure in which the symbols of the signals having the correlation are connected in a network shape, and that performs a process to display the degree of correlation together with the network structure; a correlation editing unit that edits the correlations shown in the network structure in accordance with an editing operation; A monitoring control device comprising:

[0111] (Appendix 2) The monitoring control device described in Appendix 1 is characterized in that the correlation learning unit acquires equipment ledger data that summarizes the attributes of each of the multiple pieces of equipment, and certifies the correlation between pieces of equipment included in the multiple pieces of equipment, or the correlation between equipment included in the multiple pieces of equipment and signals included in the multiple signals, based on the equipment ledger data.

[0112] (Appendix 3) The monitoring and control device described in Appendix 2 is characterized in that the correlation learning unit determines the degree of correlation between equipment included in the plurality of equipment, or the degree of correlation between an equipment included in the plurality of equipment and a signal included in the plurality of signals, by learning based on the signal data, the signal register data, and the equipment register data.

[0113] (Appendix 4) The monitoring and control device described in any one of appendices 1 to 3, characterized in that the correlation includes a dependency relationship, which is a relationship between signals when one signal changes due to control based on the other signal, an ownership relationship, which is a relationship between the equipment and a signal owned within the equipment, and a connection relationship, which is a relationship other than the dependency relationship and the ownership relationship.

[0114] (Appendix 5) A monitoring control device described in any one of appendices 1 to 4, characterized in that it includes a label creation unit that automatically creates labels that represent attributes of the signals based on the correlation data when displaying a graph that represents changes in the signals that represent the status of the equipment.

[0115] (Appendix 6) A monitoring and control device as described in any one of appendices 1 to 4, characterized in that it comprises an abnormality detection unit that detects an abnormality in the tendency of one of the signals to change relative to a change in the other of the signals based on a change in the calculation result of the correlation degree.

[0116] (Appendix 7) 5. The monitoring and control device according to any one of claims 1 to 4, further comprising a network structure learning unit that learns the characteristics of a plurality of the network structures.

[0117] (Appendix 8) The monitoring and control device described in any one of Appendices 1 to 4, characterized in that the correlation learning unit further determines the correlation between inspection or maintenance items and signals representing the status of the equipment by learning based on the signal data and inspection and maintenance data including at least one of the results of inspecting the equipment and the results of maintenance work performed on the equipment.

[0118] (Appendix 9) A monitoring and control method for monitoring and controlling each of a plurality of facilities using a monitoring and control device, comprising: collecting signal data for each of a plurality of pieces of equipment, the signal data including a signal value of a signal representative of a state of the equipment; a step of determining a correlation between signals included in the plurality of signals, in which a signal value changes in conjunction with a change in the value of one of the signals, based on signal register data summarizing the attributes of the signals for each of the plurality of signals, outputting correlation data indicating the determination result, and determining the degree of correlation between signals other than the signals for which the correlation has been determined, by learning based on the signal data and the signal register data; a step of performing a conversion process on the correlation data to display a network structure in which the symbols of the signals having the correlation are connected in a network form, and performing a process to display the degree of correlation together with the network structure; editing the correlations shown in the network structure according to an editing operation; A monitoring and control method comprising:

[0119] (Appendix 10) The monitoring and control method described in Appendix 9, characterized in that in the step of outputting the correlation data and determining the degree of correlation by learning, equipment register data summarizing the attributes of each of the plurality of pieces of equipment is obtained, and the correlation between pieces of equipment included in the plurality of pieces of equipment, or the correlation between pieces of equipment included in the plurality of pieces of equipment and signals included in the plurality of signals, is determined based on the equipment register data.

[0120] (Appendix 11) 11. The monitoring and control method according to claim 10, wherein in the step of outputting the correlation data and determining the degree of correlation by learning, the degree of correlation between equipment included in the plurality of equipment, or the degree of correlation between the equipment included in the plurality of equipment and the signals included in the plurality of signals, is determined by learning based on the signal data, the signal register data, and the equipment register data.

[0121] (Appendix 12) A monitoring and control method according to any one of appendices 9 to 11, characterized in that the correlation includes a dependency relationship, which is a relationship between signals when one signal changes due to control based on the other signal, an ownership relationship, which is a relationship between the equipment and a signal owned within the equipment, and a connection relationship, which is a relationship other than the dependency relationship and the ownership relationship.

[0122] (Appendix 13) 13. The monitoring and control method according to any one of appendices 9 to 12, further comprising a step of automatically creating labels representing attributes of the signals when displaying a graph showing changes in the signals representing the state of the equipment, based on the correlation data.

[0123] (Appendix 14) 13. A monitoring and control method according to any one of claims 9 to 12, further comprising the step of detecting an abnormality in the tendency of one of the signals to change relative to a change in the other of the signals based on a change in the calculation result of the correlation.

[0124] (Appendix 15) 13. The supervisory control method according to any one of claims 9 to 12, further comprising the step of learning characteristics of a plurality of the network structures.

[0125] (Appendix 16) The monitoring and control method according to any one of appendices 9 to 12, characterized in that in the step of outputting the correlation data and determining the degree of correlation by learning, the degree of correlation between the inspection or maintenance items and the signal representing the state of the equipment is determined by learning based on the signal data and inspection and maintenance data including at least one of the results of inspecting the equipment and the results of maintenance work performed on the equipment.

[0126] (Appendix 17) collecting signal data including signal values ​​of signals representing the status of each of a plurality of facilities to be monitored and controlled; a step of determining a correlation between signals included in the plurality of signals, in which a signal value changes in conjunction with a change in the value of one of the signals, based on signal register data summarizing the attributes of the signals for each of the plurality of signals, outputting correlation data indicating the determination result, and determining the degree of correlation between signals other than the signals for which the correlation has been determined, by learning based on the signal data and the signal register data; a step of performing a conversion process on the correlation data to display a network structure in which the symbols of the signals having the correlation are connected in a network form, and performing a process to display the degree of correlation together with the network structure; editing the correlations shown in the network structure according to an editing operation; A monitoring and control program that causes a computer to execute the above. [Explanation of symbols]

[0127] 1A, 1B, 1C, 1D, 1E Monitoring and control device, 2 Equipment, 3 Terminal, 4, 5 Storage device, 10 Communication unit, 11 Processing unit, 12 Memory unit, 13 Signal data collection unit, 14 Correlation learning unit, 15 Conversion processing unit, 16 Correlation editing unit, 17 Label creation unit, 18 Anomaly detection unit, 19 Network structure learning unit, 30 Processing circuit, 31 Communication device, 32 Processor, 33 Memory.

Claims

1. a signal data collection unit that collects signal data including signal values ​​of signals representing the status of each of a plurality of facilities to be monitored and controlled; a correlation learning unit that, based on signal register data summarizing the attributes of each of the plurality of signals, identifies a correlation between signals included in the plurality of signals, where a signal value of one signal changes in conjunction with a change in the value of the other signal, and outputs correlation data indicating the result of identifying a correlation that is obvious from the content of the signal register data, and that determines a degree of correlation between signals whose correlation is not obvious in the signal register data by learning the correlation between signals whose correlation is not obvious in the signal register data based on the signal data and the signal register data; a conversion processing unit that performs a conversion process on the correlation data to display a network structure in which symbols of the signals having a correlation are connected in a network form, and that performs a process to display the degree of correlation together with the network structure; a correlation editing unit that edits the correlations shown in the network structure in accordance with an editing operation; A monitoring control device comprising:

2. The monitoring and control device according to claim 1, characterized in that the correlation learning unit acquires equipment register data summarizing the attributes of each of the plurality of pieces of equipment, and determines the correlation between pieces of equipment included in the plurality of pieces of equipment based on the equipment register data, or determines the correlation between pieces of equipment included in the plurality of pieces of equipment and signals included in the plurality of signals based on the equipment register data and the signal register data.

3. The monitoring and control device according to claim 2, characterized in that the correlation learning unit determines the degree of correlation between equipment included in the plurality of equipment, or the degree of correlation between an equipment included in the plurality of equipment and a signal included in the plurality of signals, by learning based on the signal data, the signal register data, and the equipment register data.

4. A monitoring control device as described in claim 1 or 2, characterized in that the correlation includes a dependency relationship, which is the relationship between signals when one signal changes due to control based on the other signal, an ownership relationship, which is the relationship between the equipment and a signal owned within the equipment, and a connection relationship, which is a relationship other than the dependency relationship and the ownership relationship.

5. 3. The monitoring and control device according to claim 1, further comprising a label creation unit that automatically creates labels representing attributes of the signals based on the correlation data when displaying a graph showing changes in the signals representing the state of the equipment.

6. 3. The monitoring and control device according to claim 1, further comprising an abnormality detection unit that detects an abnormality in the tendency of one of the signals to change relative to a change in the other of the signals based on a change in the calculation result of the correlation degree.

7. 3. The monitoring and control device according to claim 1, further comprising a network structure learning unit that learns the characteristics of a plurality of said network structures.

8. The monitoring and control device described in claim 1 or 2, characterized in that the correlation learning unit further determines the correlation between inspection or maintenance items and signals representing the status of the equipment by learning based on the signal data and inspection and maintenance data including at least one of the results of inspecting the equipment and the results of maintenance work performed on the equipment.

9. A monitoring and control method for monitoring and controlling each of a plurality of facilities using a monitoring and control device, comprising: collecting signal data for each of a plurality of pieces of equipment, the signal data including a signal value of a signal representative of a state of the equipment; a step of determining a correlation between signals included in the plurality of signals, in which a signal value of one signal changes in conjunction with a change in the value of the other signal, based on signal register data summarizing the attributes of the plurality of signals, and outputting correlation data indicating the result of determining a correlation that is obvious from the content of the signal register data, and determining a degree of correlation between signals whose correlation is not obvious in the signal register data by learning the correlation between signals whose correlation is not obvious in the signal register data based on the signal data and the signal register data; a step of performing a conversion process on the correlation data to display a network structure in which the symbols of the signals having a correlation are connected in a network form, and performing a process to display the degree of correlation together with the network structure; editing the correlations shown in the network structure according to an editing operation; A monitoring and control method comprising:

10. 10. The monitoring and control method according to claim 9, wherein the step of outputting the correlation data and determining the degree of correlation by learning includes obtaining equipment ledger data summarizing attributes of each of the plurality of pieces of equipment, and determining the correlation between pieces of equipment included in the plurality of pieces of equipment based on the equipment ledger data, or determining the correlation between pieces of equipment included in the plurality of pieces of equipment and signals included in the plurality of signals based on the equipment ledger data and the signal ledger data.

11. 11. The monitoring and control method according to claim 10, wherein in the step of outputting the correlation data and determining the degree of correlation by learning, the degree of correlation between equipment included in the plurality of equipment, or the degree of correlation between an equipment included in the plurality of equipment and a signal included in the plurality of signals, is determined by learning based on the signal data, the signal register data, and the equipment register data.

12. A monitoring and control method as described in claim 9 or 10, characterized in that the correlation includes a dependency relationship, which is a relationship between signals when one signal changes due to control based on the other signal, an ownership relationship, which is a relationship between the equipment and a signal owned within the equipment, and a connection relationship, which is a relationship other than the dependency relationship and the ownership relationship.

13. The monitoring and control method according to claim 9 or 10, further comprising a step of automatically creating labels representing attributes of the signals when displaying a graph showing changes in the signals representing the state of the equipment, based on the correlation data.

14. 11. The monitoring and control method according to claim 9, further comprising a step of detecting an abnormality in the tendency of one of the signals to change relative to a change in the other of the signals based on a change in the calculation result of the correlation degree.

15. 11. The supervisory control method according to claim 9, further comprising the step of learning characteristics of a plurality of said network structures.

16. 11. The monitoring and control method according to claim 9 or 10, characterized in that in the step of outputting the correlation data and determining the degree of correlation by learning, the degree of correlation between the inspection or maintenance items and the signal representing the state of the equipment is determined by learning based on the signal data and inspection and maintenance data including at least one of the results of inspecting the equipment and the results of performing maintenance work on the equipment.

17. collecting signal data including signal values ​​of signals representing the status of each of a plurality of facilities to be monitored and controlled; a step of determining a correlation between signals included in the plurality of signals, in which a signal value of one signal changes in conjunction with a change in the value of the other signal, based on signal register data summarizing the attributes of the plurality of signals, and outputting correlation data indicating the result of determining a correlation that is obvious from the content of the signal register data, and determining a degree of correlation between signals whose correlation is not obvious in the signal register data by learning the correlation between signals whose correlation is not obvious in the signal register data based on the signal data and the signal register data; a step of performing a conversion process on the correlation data to display a network structure in which the symbols of the signals having a correlation are connected in a network form, and performing a process to display the degree of correlation together with the network structure; editing the correlations shown in the network structure according to an editing operation; A monitoring and control program that causes a computer to execute the above.

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