CN114442583B - Method, apparatus and medium for controlling a plurality of controlled apparatuses - Google Patents

Method, apparatus and medium for controlling a plurality of controlled apparatuses Download PDF

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CN114442583B
CN114442583B CN202210374528.XA CN202210374528A CN114442583B CN 114442583 B CN114442583 B CN 114442583B CN 202210374528 A CN202210374528 A CN 202210374528A CN 114442583 B CN114442583 B CN 114442583B
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state
data
time
state data
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CN114442583A (en
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雷翔
周子叶
沈国辉
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Guangdong Mushroom Iot Technology Co ltd
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Mogulinker Technology Shenzhen Co Ltd
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32252—Scheduling production, machining, job shop
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

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Abstract

Embodiments of the present disclosure relate to methods, apparatuses, and media for controlling a plurality of controlled apparatuses. According to the method, a plurality of state data collected at a predetermined sampling frequency are acquired; if the change relation of the plurality of state data along with the time has a periodic change rule, dividing the plurality of state data into a plurality of data groups according to the time; performing cluster analysis on the plurality of data packets to divide the plurality of data packets into a plurality of clusters; for each cluster, determining a plurality of state mutation time points and corresponding state data difference values for the state change type represented by the cluster based on the data grouping of the cluster center as the cluster; and determining which one or more controlled devices need to be powered on or powered off at the corresponding time point based on the corresponding state data difference value determined for the associated state change type in the current time period. This makes it possible to determine in advance what kind of control needs to be executed for each controlled device before the state changes abruptly.

Description

Method, apparatus and medium for controlling a plurality of controlled apparatuses
Technical Field
Embodiments of the present disclosure relate generally to the field of control, and more particularly, to a method, apparatus, and medium for controlling a plurality of controlled devices.
Background
In an industrial control system, a controlled system generally includes a plurality of controlled devices, and there is often mutual coupling effect between the controlled devices, and the control on each controlled device affects the control output of the whole controlled system. If the control scheme for the controlled devices is not appropriate, the controlled devices are easily started and stopped frequently, so that the output state generates large fluctuation, the output state cannot be maintained in a stable interval, and the energy is wasted. If the change situation of the output state of the controlled equipment can be judged in advance and corresponding control action is made in advance before the change situation is changed suddenly, the fluctuation of the output state can be slowed down, the possibility of accidents is reduced, and the energy-saving effect is achieved.
One such controlled system is a compressed air system, which typically includes an air compression station (hereinafter referred to simply as an "air compression station"), and the air compression station may include a plurality of air compressors (hereinafter referred to simply as "air compressors"). When the control scheme for the air compressors is inappropriate, the difference between the air supply and demand of the air compression station and a production workshop is easily caused, so that each air compressor is frequently started and stopped, the output pressure and flow are caused to generate large fluctuation, the fluctuation cannot be maintained in a stable interval, and the energy waste is further caused. At present, the control of the air compressors in the air compression station generally includes setting upper and lower limits of pressure of a pipe network according to the minimum production demand, pipeline pressure drop, safety and other factors, and then controlling the start and stop of each air compressor according to the upper and lower limits, for example, starting the air compressor when the pressure reaches the lower limit, and closing the air compressor when the pressure reaches the upper limit. However, this method cannot perform a corresponding control operation in advance before the pressure abruptly changes due to a sudden change in the gas end demand, and therefore cannot reduce the fluctuation of the pressure and cannot narrow the pressure band.
Disclosure of Invention
In view of the above problems, the present disclosure provides a method, an apparatus, and a medium for controlling a plurality of controlled apparatuses, which enable to determine in advance which control actions need to be made to the plurality of controlled apparatuses before a state abruptly changes, thereby contributing to reducing the possibility of occurrence of an accident and achieving an energy saving effect.
According to a first aspect of the present disclosure, there is provided a method for controlling a plurality of controlled devices, comprising: acquiring a plurality of state data which are acquired at a preset sampling frequency and are related to a plurality of controlled devices of a controlled system; if the change relation of the plurality of state data along with the time has a periodic change rule, dividing the plurality of state data into a plurality of data groups according to the time by taking the associated time period as a unit; performing cluster analysis on the plurality of data packets to divide the plurality of data packets into a plurality of clusters, each cluster representing a type of state change in the change relationship; for each cluster, determining a plurality of state mutation time points and state data difference values before and after each state mutation time point for the state change type represented by the cluster based on a data group serving as a cluster center of the cluster; and determining which one or more of the plurality of controlled devices needs to be powered on or powered off at the corresponding time point of the current time period based on the state data difference values before and after each state mutation time point determined for the associated state change type in the current time period.
According to a second aspect of the present disclosure, there is provided a computing device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect of the disclosure.
In a third aspect of the present disclosure, a non-transitory computer readable storage medium is provided having stored thereon computer instructions for causing a computer to perform the method of the first aspect of the present disclosure.
In some embodiments, the method further comprises: performing exception processing on the plurality of state data to remove the state data with exception in the plurality of state data; and smoothing a plurality of state data left after exception handling.
In some embodiments, clustering the plurality of data packets to partition the plurality of data packets into a plurality of clusters comprises: determining a number of the plurality of clusters; for each of the plurality of data packets, determining a distance between the data packet and a current cluster center of each of the plurality of clusters as a distance between the data packet and the respective cluster, an initial cluster center of each cluster being randomly determined; assigning each data packet to a cluster of the plurality of clusters that is closest thereto; after the plurality of data packets are all allocated to the corresponding clusters, updating a current cluster center of each cluster to a mean value between all data packets included in the cluster; and for each cluster, if the distance between the current cluster center of the cluster and the previous cluster center of the cluster is greater than or equal to the predetermined threshold, returning to the previous second step and continuing to execute until the distances between the current cluster center of the cluster and the previous cluster center of the cluster are all less than the predetermined threshold.
In some embodiments, for each cluster, determining a plurality of state transition time points for the type of state change represented by the cluster based on the data packet that is the cluster center of the cluster comprises: setting a plurality of time parameters for predicting the plurality of state mutation time points in a mathematical model pre-constructed for the state change type as corresponding initial time values, respectively, so as to determine a plurality of model parameters of the mathematical model by a least square method based on each state data in a data packet as a clustering center of the cluster; after determining a plurality of model parameters of the mathematical model, determining final time values of the plurality of time parameters of the mathematical model based on a differential evolution algorithm; for each of the plurality of determined time parameters, if the slope of a connection line between the state data at the time point corresponding to the time parameter and the state data at the time point corresponding to one of the previous time parameter or the next time parameter is greater than a predetermined threshold, determining the time point corresponding to the time parameter as a state abrupt change time point of the state change type.
In some embodiments, determining the state data difference before and after each state mutation time point for the type of state change represented by the cluster comprises: for each of the plurality of state mutation time points, determining a difference between state data acquired immediately before the state mutation time point and state data acquired immediately after the state mutation time point in a data packet that is a cluster center of the cluster as a state data difference before and after each state mutation time point.
In some embodiments, the controlled system is a compressed air system, the plurality of controlled devices are a plurality of air compressors in an air compression station of the compressed air system, and each status data is a total exhaust flow rate of the air compression station at a respective acquisition time.
In some embodiments, each change of state type is a gas usage type of the air compression station.
In some embodiments, the time period is 1 day.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
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The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements.
Fig. 1 shows a schematic diagram of an exemplary system 100 for implementing a method for controlling a plurality of controlled devices according to an embodiment of the present invention.
FIG. 2 shows a schematic view of an air compression system according to an embodiment of the present disclosure.
Fig. 3 shows a flow chart of a method 300 for controlling a plurality of controlled devices according to an embodiment of the present disclosure.
FIG. 4 illustrates an exemplary diagram of a plurality of status data over time, according to an embodiment of the disclosure.
Fig. 5 shows a flow chart of a method 500 for determining a plurality of state transition time points for the state change types represented by the respective clusters according to an embodiment of the present disclosure.
Fig. 6 illustrates a block diagram of an electronic device 600 in accordance with an embodiment of the disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The term "include" and variations thereof as used herein is meant to be inclusive in an open-ended manner, i.e., "including but not limited to". Unless specifically stated otherwise, the term "or" means "and/or". The term "based on" means "based at least in part on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first," "second," and the like may refer to different or the same object. Other explicit and implicit definitions are also possible below.
As described above, in an industrial control system, a controlled system generally includes a plurality of controlled devices, and there is often a mutual coupling effect between the controlled devices, and the control of each controlled device affects the control output of the entire controlled system. If the control scheme for the controlled devices is not appropriate, the controlled devices are easily started and stopped frequently, so that the output state generates large fluctuation, the output state cannot be maintained in a stable interval, and further energy is wasted.
To address at least in part one or more of the above issues and other potential issues, an example embodiment of the present disclosure proposes a method for controlling a plurality of controlled devices, comprising: acquiring a plurality of state data which are acquired at a preset sampling frequency and are related to a plurality of controlled devices of a controlled system; if the change relation of the plurality of state data along with the time has a periodic change rule, dividing the plurality of state data into a plurality of data groups according to the time by taking the associated time period as a unit; performing cluster analysis on the plurality of data packets to divide the plurality of data packets into a plurality of clusters, each cluster representing a type of state change in the change relationship; for each cluster, determining a plurality of state mutation time points and state data difference values before and after each state mutation time point for the state change type represented by the cluster based on a data group serving as a cluster center of the cluster; and determining which one or more of the plurality of controlled devices needs to be powered on or powered off at the corresponding time point of the current time period based on the state data difference values before and after each state mutation time point determined for the associated state change type in the current time period. In this way, it is possible to determine in advance which control actions need to be made to a plurality of controlled devices before the state abruptly changes, thereby contributing to a reduction in the possibility of occurrence of an accident and achieving an energy-saving effect.
Fig. 1 shows a schematic diagram of an exemplary system 100 for implementing a method for controlling a plurality of controlled devices according to an embodiment of the present invention. As shown in fig. 1, system 100 includes a server 110 and a controlled system 120. The server 110 is communicatively coupled to the controlled system 120 and is configured to control these controlled devices. The server 110 may be, for example, an edge server, which performs data interaction with each controlled device 1201 in the controlled system, for example, via a gateway (such as an internet of things gateway, not shown in the figure) to realize control of the controlled devices. For example, each controlled device 1201 may be connected with a gateway, e.g. via an industrial bus (such as an RS232 bus or an RS485 bus), to send data to the gateway via the industrial bus for the gateway to forward to the server 110, or to receive data from the server 110 forwarded by the gateway via the industrial bus. The gateway may interact with the server 110 via a wired connection or a wireless connection, whereby the gateway may communicate with the server 110 via wired or wireless communication. In some embodiments, the gateway may communicate (such as 4G or 5G communication) with the server 110 via one or more base stations (such as 4G or 5G base stations) (not shown). The server 110 may be implemented by one or more computing devices, such as a desktop, laptop, notebook, industrial control computer, and the like, which may include at least one processor 1101 and at least one memory 1102 coupled to the at least one processor 1101, the memory 1102 having stored therein instructions executable by the at least one processor 1102 which, when executed by the at least one processor 1101, perform the method 300 as described below. The specific structure of the server 110 may be, for example, the electronic device 600 described below in conjunction with fig. 6.
One example of the controlled system 120 shown in fig. 1 is a compressed air system as shown in fig. 2, and the plurality of controlled objects are a plurality of air compressors (i.e., air compressors) in an air compression station of the compressed air system. It should be appreciated that although only four air compressors are shown to be included in the air compression station in fig. 2, more or fewer air compressors may be included in the air compression station in actual use while remaining within the scope of the present disclosure.
Fig. 3 shows a flow chart of a method 300 for controlling a plurality of controlled devices according to an embodiment of the present disclosure. The method 300 may be performed by the server 110 as shown in FIG. 1, or may be performed at the electronic device 600 shown in FIG. 6. It should be understood that method 300 may also include additional blocks not shown and/or may omit blocks shown, as the scope of the disclosure is not limited in this respect.
At step 302, a plurality of status data collected at a predetermined sampling frequency about a plurality of controlled devices of a controlled system is acquired.
In the present disclosure, the sampling frequency may be to collect state data every minute or every few minutes, so each state data is state data about the controlled system collected at a different sampling time.
In the example where the controlled system is a compressed air system as shown in fig. 2, the plurality of controlled devices may be a plurality of air compressors in an air compression station. In this example, the plurality of status data mentioned in step 302 may be a plurality of total exhaust gas flow rates of the air compression station collected by the air compression station at corresponding sampling times, i.e., instantaneous mother pipe flow rate data collected by the gas production end flow meter shown in fig. 2 at a predetermined sampling frequency.
At step 304, if the plurality of status data have a periodic variation law with time (e.g., with sampling time), the plurality of status data are divided into a plurality of data packets according to time (e.g., according to sampling time) in units of associated time periods.
The state data has periodicity if they exhibit similar variation laws at different times. In some embodiments, the time period associated with the periodic variation law is 1 day. For example, in the example shown in fig. 4, the status data (e.g., instantaneous parent flow data) exhibits a similar first type of status change on days 6.29 to 7.4, 7.6 to 7.11, and 7.13 to 7.18, and a similar second type of status change on days 7.5, 7.12, and 7.19, so it can be seen that in this example, a similar first type of status change is exhibited on the first 6 days of each week, and a similar second type of status change is exhibited on day 7 of the week. At this time, it can be considered that the change relationship of these state data with time has a periodic change rule, and the associated time period is 1 day in this example. Since the associated time period is 1 day, the status data may be divided into a plurality of data packets in units of days, i.e., the status data collected on the same day is divided into one data packet. While in the foregoing example the associated time period is 1 day, it should be appreciated that the time period is not limited to 1 day, and other time periods are possible, such as one or more worship, one or more hours, and so forth.
Since some abnormal values may exist in the state data collected at the sampling frequency, before step 304 is executed, abnormality processing may be performed on the state data to remove the state data in which an abnormality exists. For example, the state data having an abnormality in the collected plurality of state data may be identified in "quartering". Specifically, 0.25 quantiles q1 and 0.75 quantiles q3 of these state data can be determined, respectively, and then state data outside q1-1.5 Δ q, which is a quartile distance equal to q3-q1, and q3+1.5 Δ q, are regarded as state data in which an abnormality exists, and are removed.
The state data collected at the sampling frequency may have some "glitches" (e.g., the state data corresponding to the spikes in the variation shown in fig. 4), which may have a large effect on the later clustering. Therefore, in the present disclosure, smoothing may also be performed on a plurality of pieces of state data remaining after exception processing. For example, the smoothed state data may be obtained by selecting certain time windows (e.g., 3-15 minute time windows) and then taking the mean or median of the state data in each time window. Of course, such smoothing process may be omitted if the corresponding variation relationship does not have the glitch data.
At step 306, a cluster analysis is performed on the plurality of data packets (i.e., the plurality of data packets divided at step 304) to divide the plurality of data packets into a plurality of clusters, each cluster representing a type of state change in the change relationship at step 304.
For example, with respect to FIG. 4, by cluster analysis, data packets associated with day 6 prior to each week may all be clustered into the same first cluster because they all exhibit a similar first type of state change, and data packets associated with day 7 of each week may all be clustered into the same second cluster because they all exhibit a similar second type of state change.
In the present disclosure, a plurality of data packets may be cluster analyzed by dividing the plurality of data packets into a plurality of clusters (including determining a cluster center for each cluster).
First, how many clusters the plurality of data packets are to be divided into (i.e., the number of clusters the plurality of data packets are to be divided into) is determined.
Specifically, it may be assumed that the data packets need to be divided into two clusters (i.e., there are two cluster centers), then the data packets are subjected to cluster analysis to determine two cluster centers of the two clusters, respectively, then the distance between each of the data packets and the cluster center of the cluster into which the data packets are divided is calculated, and all the calculated distances are summed to obtain the distance sum D2. Then, the sum of distances D in the case of dividing the data packets into three clusters is obtained in a similar manner3Distance sum D in case of division into four clusters4Up to the sum of distances DnWhere n is theoretically the result of the division in step 304The number of the plurality of data packets may be an integer greater than a threshold in practical applications.
After determining the distance sum D2To DnThen, a second derivative is calculated for each sum of distances. Specifically, each distance sum D may be determined based on the following formulaiFirst derivative of
Figure DEST_PATH_IMAGE001
Then, the corresponding second derivative is determined from the corresponding first derivative based on the following formula
Figure 54333DEST_PATH_IMAGE002
Wherein i is an integer of 2 or more and n or less. After obtaining the sum D of each distanceiSecond derivative D ofi"thereafter, the determined D2To Dn"the number obtained by subtracting one from the number of cluster centers corresponding to the second derivative with the largest value is used as the optimal cluster center number. For example, if D3Second derivative D of3Is "D2To Dn"the largest second derivative, the number of clusters can be determined to be 2.
In the present disclosure, the distance between each data packet and the cluster center (in the present disclosure, a cluster center is also a data packet) may be, for example, the euclidean distance between the data packet and the cluster center, which may be calculated, for example, by the following formula:
Figure DEST_PATH_IMAGE003
. In this formula, xiCorresponding to the i-th status data, y, in the data packetiCorresponding to the i-th state data in the data packet that is the center of the cluster.
After determining the number of clusters, for each of the plurality of data packets (i.e., the plurality of data packets divided in step 304), a distance between the data packet and a current cluster center of each of the plurality of clusters may be determined as the distance between the data packet and the cluster, the initial cluster center of each cluster being randomly determined. The randomly determined initial cluster center may be any one of the aforementioned plurality of data packets, or may be any one of the aforementioned plurality of data packets in the time-varying relationship of the state data. Each data packet is then assigned to the cluster of the plurality of clusters that is closest thereto.
After the plurality of data packets are all assigned to the respective cluster, the current cluster center of each cluster is updated to the mean between all data packets included in the cluster. It should be appreciated that in the present disclosure, the mean between all data packets included in the cluster is also actually one data packet.
For each cluster, if the distance between the current cluster center of the cluster and the previous cluster center of the cluster (i.e., the cluster center determined in the previous cycle) is greater than or equal to the predetermined threshold, the previous step of determining the distance between the data packet and the current cluster center of each of the plurality of clusters continues until the distances between the current cluster center of the cluster and the previous cluster center of the cluster are both less than the predetermined threshold.
In step 308, for each cluster, a plurality of state transition time points and state data difference values before and after each state transition time point are determined for the state change type represented by the cluster based on the data packet as the cluster center of the cluster.
In the present disclosure, the state transition time point refers to, for example, a time point at which the state data abruptly changes from a steady oscillation to a rise or a fall.
The state data difference before and after each state transition time point refers to a difference between the state data acquired immediately before the state transition time point and the state data acquired immediately after the state transition time point in the data packet that is the cluster center of the corresponding cluster.
The method for determining a plurality of state transition time points for the state change types represented by the respective clusters will be described in further detail below with reference to fig. 5.
At step 310, at the current time period, it is determined which one or more of the plurality of controlled devices needs to be powered on or powered off at the corresponding time point of the current time period based on the state data difference value before and after each state mutation time point determined for the associated state change type.
In the present disclosure, the associated state change type refers to a state change type associated with a current time period. Since the change relationship of the plurality of state data over time (e.g., over the sampling time) acquired in step 302 has a periodic change rule, it is possible to infer which type of state change associated with the current time period is, from such a change rule and the data packets included in the respective clusters.
For example, in the example of a compressed air system, if the difference in total exhaust gas flow before and after a certain state transition time point, determined for the associated air usage type, is less than zero, this indicates that the air usage demand is less, and therefore it may be determined that one or more air compressors having a gas production rate matching the difference should be shut down at the corresponding time point of the current time period. On the contrary, if the difference is larger than zero, the air consumption demand is increased, so that it can be determined that one or more air compressors with the air production amount matched with the difference need to be started at the corresponding time point of the current time period.
Fig. 5 shows a flow chart of a method 500 for determining a plurality of state transition time points for the state change types represented by the respective clusters according to an embodiment of the present disclosure. The method 500 may be performed by the server 110 as shown in fig. 1, or may be performed at the electronic device 600 shown in fig. 6. It should be understood that method 500 may also include additional blocks not shown and/or may omit blocks shown, as the scope of the disclosure is not limited in this respect.
In step 502, a plurality of time parameters for predicting the plurality of state transition time points in a mathematical model pre-constructed for the state change type are respectively set to corresponding initial time values, so that a plurality of model parameters of the mathematical model are determined by a least square method based on each state data in a data packet as a clustering center of the cluster.
In the present disclosure, the mathematical model may be represented by the following formula (1):
Figure 892845DEST_PATH_IMAGE004
,
wherein
Figure DEST_PATH_IMAGE005
（1）
In the above formula (1), f (x) represents the state data, β, of the mathematical model for predicting the state change type at the time point x of the corresponding time period (for example, if the time period is 1 day, the time point may be, for example, a certain time in hours or minutes of the day)1To betan+1A plurality of model parameters, t, for the mathematical model1To tn+1For the plurality of time parameters for predicting the plurality of state transition time points, i is an integer of 1 or more and n +1 or less, and n is an optional integer.
Specifically, t in the above formula (1) may be expressed1To tn+1Set to a corresponding initial time value, e.g., in one example where the time period is optionally 1 day, i.e., 24 hours, t may be set, e.g., based on the following equation1To tn+1Initial time value of (a): t is tiAnd =24 × (i-1)/n, where i is an integer of 1 or more and n +1 or less. Of course, t can also be set based on other ways1To tn+1The initial time value of (a).
T in equation (1)1To tn+1After setting to the corresponding initial values, the respective model parameters β in equation (1) can be calculated1To betan+1It is used. Specifically, the plurality of model parameters of the mathematical model may be determined by performing regression on equation (1) by the least square method based on each state data in the data packet that is the cluster center of the cluster. In the least square method, a loss function expressed by the following formula (2) can be used to calculate a corresponding errorThe sum of squares, the model parameter corresponding to the minimum sum of squares of errors is the finally needed model parameter;
Figure 328374DEST_PATH_IMAGE006
（2）
in this equation (2), ssr represents the sum of squared errors of the whole, yiIs shown at time point xiIs the real state data of (which is the data packet at point x in time in the cluster center of the cluster)iThe collected state data), f (x)i) Represents the calculation at the time point x based on the formula (1) after the initial value of time is setiM denotes the number of state data included in the data packet as the cluster center of the cluster.
After the plurality of model parameters of the mathematical model are determined, final time values of the plurality of time parameters of the mathematical model are determined based on a Differential Evolution algorithm (Differential Evolution) at step 504.
The differential evolution algorithm is a common heuristic random search algorithm based on population difference, and the final time values of a plurality of time parameters of the attribute model can be efficiently determined through the algorithm.
After the specific values of the model parameter and the time parameter of the mathematical model shown in formula (1) are determined, it is equivalent to a mathematical model in which the type of state change represented by the corresponding cluster is determined based on the cluster center of the cluster.
In step 506, for each of the determined time parameters, if the slope of the connection line between the state data at the time point corresponding to the time parameter and the state data at the time point corresponding to one of the previous time parameter or the next time parameter is greater than a predetermined threshold, the time point corresponding to the time parameter is determined as a state transition time point of the state change type.
The former time parameter of each time parameter refers to the time parameter of the plurality of time parameters determined whose time value is immediately before the time value of the time parameter, and the latter time parameter of each time parameter refers to the time parameter of the plurality of time parameters determined whose time value is immediately after the time value of the time parameter.
In addition, in the present disclosure, the state data at the time point corresponding to each time parameter may be determined according to the mathematical model after the model parameters and the time parameters are determined through steps 504 and 506.
Since the number of the time parameters for predicting the state transition time points is optional when the model parameters and the time parameters in the formula (1) are calculated, the state transition time points calculated based on the time parameters may include a time point at which the state data change is small, and step 506 may further screen the time points to remove the state transition time points which are not satisfactory, that is, if the slopes of the connecting lines between the state data at the time point corresponding to the time parameter and the state data at the time points corresponding to the previous time parameter and the subsequent time parameter are both smaller than the predetermined threshold, it indicates that the flow transition does not occur, and therefore the time point corresponding to the time parameter may be removed instead of being the state transition time point of the corresponding state change type. After the time points corresponding to the time parameters which do not meet the requirements are removed, the finally needed state mutation time points are determined for the corresponding state change types.
By adopting the above means, the method can judge each time point at which the state abrupt change is possible in advance, and before the state abrupt change occurs, judge which control actions need to be performed on a plurality of controlled devices in advance, thereby being beneficial to alleviating the fluctuation of pressure, narrowing the pressure zone, being beneficial to reducing the possibility of accidents and achieving the effect of energy conservation.
FIG. 6 illustrates a schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure. For example, the fault diagnosis device 110 as shown in fig. 1 may be implemented by the electronic device 600. As shown, electronic device 600 includes a Central Processing Unit (CPU) 601 that may perform various suitable actions and processes according to computer program instructions stored in a Read Only Memory (ROM) 602 or loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the random access memory 603, various programs and data required for the operation of the electronic apparatus 600 can also be stored. The central processing unit 601, the read only memory 602, and the random access memory 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
A number of components in the electronic device 600 are connected to the input/output interface 605, including: an input unit 606 such as a keyboard, a mouse, a microphone, and the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The various processes and processes described above, such as methods 300 and 500, may be performed by the central processing unit 601. For example, in some embodiments, methods 300 and 500 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the read only memory 602 and/or the communication unit 609. When the computer program is loaded into the random access memory 603 and executed by the central processing unit 601, one or more of the actions of the methods 300 and 500 described above may be performed.
The present disclosure relates to methods, apparatuses, systems, electronic devices, computer-readable storage media and/or computer program products. The computer program product may include computer-readable program instructions for performing various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge computing devices. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the market, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims (10)

1. A method for controlling a plurality of controlled devices, comprising:
acquiring a plurality of state data which are acquired at a preset sampling frequency and are related to a plurality of controlled devices of a controlled system;
if the change relation of the plurality of state data along with the time has a periodic change rule, dividing the plurality of state data into a plurality of data groups according to the time by taking the associated time period as a unit;
performing cluster analysis on the plurality of data packets to divide the plurality of data packets into a plurality of clusters, each cluster representing a type of state change in the change relationship;
for each cluster, determining a plurality of state mutation time points and state data difference values before and after each state mutation time point for the state change type represented by the cluster based on a data group serving as a cluster center of the cluster; and
and determining which one or more of the plurality of controlled devices needs to be powered on or powered off at the corresponding time point of the current time period based on the state data difference values before and after each state mutation time point determined for the associated state change type.
2. The method of claim 1, further comprising:
performing exception processing on the plurality of state data to remove the state data with exception in the plurality of state data; and
and smoothing the plurality of state data left after exception processing.
3. The method of claim 1, wherein performing cluster analysis on the plurality of data packets to divide the plurality of data packets into a plurality of clusters comprises:
determining a number of the plurality of clusters;
for each of the plurality of data packets, determining a distance between the data packet and a current cluster center of each of the plurality of clusters as a distance between the data packet and the respective cluster, an initial cluster center of each cluster being randomly determined;
assigning each data packet to a cluster of the plurality of clusters that is closest thereto;
after the plurality of data packets are all allocated to the corresponding clusters, updating a current cluster center of each cluster to a mean value between all data packets included in the cluster; and
for each cluster, if the distance between the current cluster center of the cluster and the previous cluster center of the cluster is greater than or equal to a predetermined threshold, returning to the previous second step and continuing to execute until the distances between the current cluster center of the cluster and the previous cluster center of the cluster are all less than the predetermined threshold.
4. The method of claim 1, wherein for each cluster, determining a plurality of state transition time points for the type of state change represented by the cluster based on the data packet that is the cluster center of the cluster comprises:
setting a plurality of time parameters for predicting the plurality of state mutation time points in a mathematical model pre-constructed for the state change type as corresponding initial time values, respectively, so as to determine a plurality of model parameters of the mathematical model by a least square method based on each state data in a data packet as a clustering center of the cluster;
after determining a plurality of model parameters of the mathematical model, determining final time values of the plurality of time parameters of the mathematical model based on a differential evolution algorithm;
for each of the plurality of determined time parameters, if the slope of a connection line between the state data at the time point corresponding to the time parameter and the state data at the time point corresponding to one of the previous time parameter or the next time parameter is greater than a predetermined threshold, determining the time point corresponding to the time parameter as a state abrupt change time point of the state change type.
5. The method of claim 4, wherein determining a difference in state data before and after each state mutation time point for the type of state change represented by the cluster comprises:
for each of the plurality of state mutation time points, determining a difference between state data acquired immediately before the state mutation time point and state data acquired immediately after the state mutation time point in a data packet that is a cluster center of the cluster as a state data difference before and after each state mutation time point.
6. The method of claim 1, wherein the controlled system is a compressed air system, the plurality of controlled devices are a plurality of air compressors in an air compression station of the compressed air system, and each status data is a total exhaust flow rate of the air compression station at a respective acquisition time.
7. The method of claim 6, wherein each type of state change is a gas usage type of the air compression station.
8. The method of claim 1, wherein the time period is 1 day.
9. A computing device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor;
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.
10. A non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1-8.
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