CN115022348B - Intelligent factory cloud-level architecture data storage method for high-end battery - Google Patents
Intelligent factory cloud-level architecture data storage method for high-end battery Download PDFInfo
- Publication number
- CN115022348B CN115022348B CN202210614506.6A CN202210614506A CN115022348B CN 115022348 B CN115022348 B CN 115022348B CN 202210614506 A CN202210614506 A CN 202210614506A CN 115022348 B CN115022348 B CN 115022348B
- Authority
- CN
- China
- Prior art keywords
- space
- multicellular
- positive
- data
- local
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000013500 data storage Methods 0.000 title abstract description 11
- 238000003860 storage Methods 0.000 claims abstract description 23
- 238000005259 measurement Methods 0.000 claims description 82
- 239000011159 matrix material Substances 0.000 claims description 53
- 238000004519 manufacturing process Methods 0.000 claims description 16
- 238000012545 processing Methods 0.000 claims description 12
- 238000000354 decomposition reaction Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000001914 filtration Methods 0.000 abstract description 3
- 238000011160 research Methods 0.000 description 2
- 241000135164 Timea Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000011248 coating agent Substances 0.000 description 1
- 238000000576 coating method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 230000026676 system process Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1097—Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1095—Replication or mirroring of data, e.g. scheduling or transport for data synchronisation between network nodes
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q9/00—Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q2209/00—Arrangements in telecontrol or telemetry systems
- H04Q2209/20—Arrangements in telecontrol or telemetry systems using a distributed architecture
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q2209/00—Arrangements in telecontrol or telemetry systems
- H04Q2209/80—Arrangements in the sub-station, i.e. sensing device
- H04Q2209/84—Measuring functions
-
- 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]
Abstract
The invention discloses a cloud level architecture data storage method for an intelligent factory of a high-end battery, and belongs to the field of intelligent storage. According to the method, real-time data of a factory are transmitted to a cloud for storage by using each distributed sensor, the problem of data loss under cloud level is considered, timely data compensation is carried out after the local data analyzers detect the data loss, meanwhile, each local data analyzer carries out local state estimation in the distributed data analyzers by using a set member filtering algorithm under the disturbance of uncertain noise, the real feasible set range of the system state is jointly obtained, and data and state estimation results are transmitted back to the original cloud in real time and copied to the backup cloud.
Description
Technical Field
The invention relates to a cloud level architecture data storage method for an intelligent factory of a high-end battery, and belongs to the field of intelligent storage.
Background
Along with the rapid development of science and technology, the high-end battery intelligent factory workshops are more and more complex and larger in scale, on an intelligent factory production line, a wireless sensor network is formed by distributed sensors to collect operation data of each production process in real time, and a data analyzer is used for processing the collected operation data to obtain system state estimation of the factory, so that the method has important significance for subsequent monitoring and management.
The traditional data processing mode of the high-end intelligent factory is to transmit the data of each distributed sensor to a data analyzer, store the data in the data analyzer and process the data to complete state estimation, and the mode can fully utilize all data information, and has global optimality in state estimation accuracy. However, in the case of a large number of distributed sensors, when the data storage and processing are performed in one data analyzer, the data analyzer has a large load, which results in low calculation efficiency, and if the data analyzer fails, all the stored data are lost, which seriously affects the state estimation process; the cloud terminal is utilized to store data, the data analyzer acquires the data from the cloud terminal to process, the risk of losing all the data is greatly reduced by isolating the data storage from the data processing, meanwhile, the burden of a single data analyzer is reduced, and the calculation efficiency is higher, so that the cloud level architecture method of the high-end battery intelligent factory has deeper research significance.
However, some problems are faced by such cloud storage and data processing by a data analyzer, such as: while the data analyzer processes data, the measured system process noise and sensor measurement noise are unavoidable, and research of the traditional data processing algorithm assumes that the noise has Gaussian characteristics, such as a Kalman filtering algorithm; however, in the actual production process, on one hand, under some special conditions, the measurement data transmitted to the cloud level is insufficient, for example, part of the data is lost, so that accurate statistical characteristics cannot be obtained; on the other hand, the process noise and the measurement noise do not obey a certain random probability distribution, so that the existing cloud data storage mode has the problem that accurate storage data cannot be obtained due to data loss.
Disclosure of Invention
In order to solve the problem that accurate storage data cannot be obtained due to data loss in the current cloud data storage mode, the invention provides a high-end battery intelligent factory cloud architecture data storage method, which is applied to a high-end battery intelligent factory cloud architecture, wherein the high-end battery intelligent factory cloud architecture comprises the following steps: a plurality of sensors, a plurality of local data analyzers and a global data analyzer which are distributed on each production process of the high-end battery and used for collecting data in real time;
each sensor corresponds to a cloud end for storing the collected and processed data; the local data analyzers are used for acquiring data acquired by the sensors in real time from the cloud and carrying out local state estimation, and each local data analyzer sends local state estimation results acquired by the local data analyzers to the global data analyzer; the global data analyzer is used for carrying out global state estimation according to the local state estimation results of the local data analyzers, and transmitting the obtained global state estimation results back to the local data analyzers, and then the local data analyzers transmit the global state estimation results to the corresponding cloud ends for storage.
Optionally, the method includes:
step 1: acquiring measurement data of each distributed sensor k time in a high-end battery production process, transmitting the measurement data to a corresponding cloud for storage, and simultaneously, downloading the measurement data stored in the cloud by utilizing each local data analyzer;
step 2: acquiring a linear state space expression of a high-end battery intelligent factory workshop, and determining an initial positive multicell space and an initial full-symmetry multicell space; the state space expression comprises a measurement equation and a state equation;
step 3: according to a measurement equation in the state space expression, each local data analyzer forms the downloaded measurement data at k time into measurement zone information; judging whether the measured data is lost or not, if so, when k=1, no processing is needed, and when k is not equal to 1, the measured information at the moment k-1 is taken as the measured information at the moment k; and transmitting the data to a corresponding cloud;
step 4: according to the state equation of the state space expression in the step 2 and the measurement zone information of the step 3, the local state estimation of each local data analyzer at the moment k is obtained:
solving a predicted full-symmetrical multicellular space at the moment k+1 according to a state equation in a state space expression, and converting the predicted full-symmetrical multicellular space into a predicted positive multicellular space; then, the positive multicell space at the moment k+1 is obtained by utilizing the measurement band information at the moment k, and the local state estimation of each local data analyzer is completed;
step 5: transmitting the local state estimation obtained by each local data analyzer to a global data analyzer, and transmitting a global state estimation result to each local data analyzer by utilizing Minkowski and obtaining the final global state estimation, and transmitting the global state estimation result to each corresponding cloud end by each local data analyzer;
step 6: and each local data analyzer transmits the acquired measurement data, the local state estimation result obtained according to the measurement data and the global state estimation result to the backup cloud for backup storage.
Optionally, the step 2 includes:
the n-dimensional linear state space expression of the high-end battery intelligent factory workshop is obtained as follows:
x k+1 ＝Ax k +Bu k +w k (1)
y i,k ＝C i x k +v i,k (2)
equations (1) and (2) are a state equation and a measurement equation of the system, respectively, wherein k represents time, k=1, …, N; x is x k A system state value representing the time k; x is x k+1 A system state value representing the time k+1; u (u) k A system input value representing the time k; a, B represent the system matrix and input matrix separately; w (w) k Representing process noise of the system; i represents a distributed sensor number, i=1, …, M, and M represents that M distributed sensors in the wireless sensor network perform measurement; y is i,k Representing data acquired at the moment k of the ith sensor; c (C) i Representing an output matrix of the ith sensor; v i,k Representing measurement noise of the ith sensor, v i,k ∈[-δ k ,δ k ]，δ k Maximum noise margin value at time k, -delta k The noise minimum boundary value at the moment k;
defining the positive multicellular space as:
wherein ,
is the center point of the positive multicellular space, d is the generator matrix of the positive multicellular space, diag (d) is the diagonal matrix with the diagonal value equal to d, x is the range of feasible sets of positive multicellular space packages, m is an intermediate variable which is used as a reference, I ∞ Represents an infinite norm;
defining a fully symmetric multicellular space as:
wherein p is the center point of the holomorphic multicellular space, H is the generating matrix of the holomorphic multicellular space, B n ∈[-1,1] n For n unit intervals [ -1,1 [ -1 ]]The unit box is formed by the components,
for Minkowski and z is an intermediate variable;
determining the initial moment positive multicellular space as
And an initial holohedral multicellular space Z (p 1 ,H 1 ) And let->
I.e.
diag{d 1 }＝H 1 (6)
wherein ,
is the center point of the initial positive multicellular space, d 1 Generating a matrix for the initial positive multicellular space; p is p 1 Is the central point of the initial holohedral multicellular space, H 1 Is a generator matrix of an initial fully symmetric multicellular space.
Optionally, the step 3 includes:
defining the local data analyzer to be S (p i,k ,c i,k )；
wherein ,ci,k The center point with information is measured for sensor i at time k,
p i,k measuring the directional vector with information for the sensor i at time k,/>
θ s Estimating a feasible set range for the measurement zone information;
if the measured data is lost, when k=1, let
S(p i,k ,c i,k )＝0 (8)
When k is not equal to 1, the measurement information at k-1 is regarded as the measurement information at k, and
S(p i,k ,c i,k )＝S(p i,k-1 ,c i,k-1 ) (9)。
optionally, the step 4 includes:
solving a predicted fully-symmetrical multicellular space at the moment k+1 according to a state equation in a state space expression, and converting the predicted fully-symmetrical multicellular space into a predicted positive multicellular space; then, the positive multicell space at the moment k+1 is obtained by utilizing the measurement band information at the moment k, and the local state estimation of each local data analyzer is completed;
according to the state equation of the state space expression of the formula (1) and the measurement zone information shown in the formula (7), the local state estimation at the moment k of the local data analyzer corresponding to each cloud is obtained:
in the system state equation of equation (1), the process noise is limited to an unknown but bounded range, i.e., the process noise w k P (0, g), g being the boundary value of the noise;
using a fully symmetrical multicell space Z (p k ,H k ) Representing positive multicellular space at time k
I.e.
H k ＝diag{d k } (12)
Will Z (p) k ,H k ) Substituting the predicted holohedral symmetry multicellular space into the state equation of (1) to obtain the k+1 time
wherein ,
to predict the center point of the holohedral multicellular space, < >>
Generating a matrix for predicting the holohedral multicellular space;
at this time at
Adding process noise w k P (0, g) and using the most compact prediction of positive multicellular space
Unwrapping predicted holohedral multicellular space, +.>
The calculation formula of (2) is as follows:
wherein ,
to predict the center point of the positive multicellular space, < >>
To predict the generating matrix of the positive multicellular space,
for diagonal value equal +.>
Diagonal matrix of>
For diagonal value equal +.>
Diagonal matrix of n H Is->
The dimension of the matrix, represents the norm, l, h is an intermediate variable;
predicted positive multicellular space determined in equations (15) and (16)
Decomposition into n constraint equations
wherein xj To predict the feasible set variable of the j-th dimension of the positive multicellular space,
to predict the feasible set minimum in the j-th dimension of the positive multicellular space,/for example>
For predicting the maximum value of a feasible set in the j-th dimension of the positive multicell space, j is a dimension variable;
next, the measurement band information S (p) of the ith data parser at the k time obtained according to the formula (7) i,k ,c i,k ) Updating to obtain positive multicellular space at k+1 time
The method comprises the following steps:
wherein ,
wherein xs For feasible set variables in the constraint ∈ represents intersection, α j (k+1) is the maximum value of the j-th dimension of the feasible set range,
α j+n (k+1) is the minimum value of the j-th dimension of the feasible set range, max represents taking the maximum value, min represents taking the minimum value,
transpose representing the j-th dimension of the unit diagonal matrix, is->
Value of j-th dimension of positive multicellular space center point,>
generating values of the j-th dimension of the matrix for positive multicellular space,>
for diagonal value equal +.>
Is a diagonal matrix of (a). />
The final local state estimate is for each data analyzer.
Optionally, the step 5 includes:
local state estimation results of each data analyzer according to formulas (17) to (22)
Solving M positive multicellular spaces in a global data analyzer using Minkowski and +.>
Orthomulticellular space with M partial estimates
Decomposition into n constraint equations according to equation (17)>
Then the constraint-bearing equation in the global estimate is defined as +.>
Then
The method comprises the following steps:
wherein ,
wherein xsum Is that
Feasible set of variables, alpha j,sum (k+1) is the maximum value of the j-th dimension of the feasible set range;
α j+n,sum (k+1) is the minimum value of the j-th dimension of the feasible set range,
is a global positive multicellular spatial center point,
a value d of the j-th dimension of the global positive multicellular space center point k+1 Generating a matrix for a global positive multicellular space, < >>
Generating a value of the j-th dimension of the matrix for the positive multicellular space, the global positive multicellular space +.>
The range of the expressed feasible set is the final global estimation result, the global state estimation result is transmitted to each local data analyzer, and each local data analyzer transmits the global state estimation result to each corresponding cloud.
Optionally, the local data analyzer and the global data analyzer are implemented by using a computer server.
The invention has the beneficial effects that:
according to the invention, the measurement data of the distributed sensor k moment in the high-end battery production process are acquired and transmitted to the corresponding cloud end for storage, and meanwhile, the measurement data stored in the cloud end are downloaded by utilizing a data analyzer; acquiring a linear state space expression of a high-end battery intelligent factory workshop, and determining an initial positive multicell space and an initial full-symmetry multicell space; the measurement equation in the state space expression is used for forming measurement zone information from the measurement data of k moment downloaded by the data analyzer; if the measured data is lost, when k=1, no processing is needed, and when k is not equal to 1, the measured information at the moment k-1 is taken as the measured information at the moment k; and transmitting the data to the original cloud; according to the state equation and the measurement zone information of the state space expression, solving the local state estimation at the moment k of the data analyzer corresponding to each cloud; transmitting the local state estimation obtained by each data analyzer to a total data analyzer, and transmitting the global state estimation result to each data analyzer by utilizing Minkowski and obtaining the final global state estimation, and transmitting the global state estimation result to each original cloud end by the data analyzer; and transmitting the data of each data analyzer and the global state estimation result to the backup cloud for storage. According to the battery intelligent factory cloud level architecture method, factory data are transmitted to the cloud for storage by using the distributed sensor, so that the problem of data loss under cloud level is considered, timely data compensation is performed after the data analyzer detects the data loss, meanwhile, local state estimation is performed in the distributed data analyzer by using a member filtering algorithm under the disturbance of uncertain noise, the production reality is met, the real feasible set range of the system state is obtained in a combined mode, and data and state estimation results are transmitted back to the original cloud in real time and copied to the backup cloud.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a high-end battery intelligent factory cloud architecture method disclosed in one embodiment of the present application.
Fig. 2 is a cloud-level architecture diagram of a high-end battery intelligent factory disclosed in one embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings.
Embodiment one:
the embodiment provides a data storage method of a high-end battery intelligent factory cloud architecture, which is applied to the high-end battery intelligent factory cloud architecture, and referring to fig. 1, the method comprises the following steps:
step 1: acquiring measurement data of each distributed sensor k time in a high-end battery production process, transmitting the measurement data to a corresponding cloud for storage, and simultaneously, downloading the measurement data stored in the cloud by utilizing each local data analyzer;
step 2: acquiring a linear state space expression of a high-end battery intelligent factory workshop, and determining an initial positive multicell space and an initial full-symmetry multicell space; the state space expression comprises a measurement equation and a state equation;
step 3: according to a measurement equation in the state space expression, each local data analyzer forms the downloaded measurement data at k time into measurement zone information; judging whether the measured data is lost or not, if so, when k=1, no processing is needed, and when k is not equal to 1, the measured information at the moment k-1 is taken as the measured information at the moment k; and transmitting the data to a corresponding cloud;
step 4: according to the state equation of the state space expression in the step 2 and the measurement zone information of the step 3, the local state estimation of each local data analyzer at the moment k is obtained:
solving a predicted full-symmetrical multicellular space at the moment k+1 according to a state equation in a state space expression, and converting the predicted full-symmetrical multicellular space into a predicted positive multicellular space; then, the positive multicell space at the moment k+1 is obtained by utilizing the measurement band information at the moment k, and the local state estimation of each local data analyzer is completed;
step 5: transmitting the local state estimation obtained by each local data analyzer to a global data analyzer, and transmitting a global state estimation result to each local data analyzer by utilizing Minkowski and obtaining the final global state estimation, and transmitting the global state estimation result to each corresponding cloud end by each local data analyzer;
step 6: and each local data analyzer transmits the acquired measurement data, the local state estimation result obtained according to the measurement data and the global state estimation result to the backup cloud for backup storage.
Embodiment two:
the embodiment provides a data storage method of a high-end battery intelligent factory cloud architecture, which is applied to the high-end battery intelligent factory cloud architecture, please refer to fig. 2, and the high-end battery intelligent factory cloud architecture comprises: a plurality of sensors, a plurality of local data analyzers and a global data analyzer which are distributed on each production process of the high-end battery and used for collecting data in real time; each sensor corresponds to a cloud end for storing the collected and processed data; the local data analyzers are used for acquiring data acquired by the sensors in real time from the cloud and carrying out local state estimation, and each local data analyzer sends local state estimation results acquired by the local data analyzers to the global data analyzer; the global data analyzer is used for carrying out global state estimation according to the local state estimation results of the local data analyzers, and transmitting the obtained global state estimation results back to the local data analyzers, and then transmitting the global state estimation results to the corresponding cloud ends for storage by the local data analyzers; the method comprises the following steps:
step 101, acquiring measurement data of each distributed sensor k time in a high-end battery production process, transmitting the measurement data to a corresponding cloud for storage, and simultaneously, downloading the measurement data stored in the cloud by utilizing each local data analyzer;
each distributed sensor corresponds to one cloud for storage, and each cloud corresponds to one local data analyzer for data processing.
Step 102, acquiring a linear state space expression of a high-end battery intelligent factory workshop, and determining an initial positive multicell space and an initial full-symmetry multicell space; the state space expression comprises a measurement equation and a state equation;
the n-dimensional linear state space expression of the high-end battery intelligent factory workshop is obtained as follows:
x k+1 ＝Ax k +Bu k +w k (1)
y i,k ＝C i x k +v i,k (2)
equations (1) and (2) are a state equation and a measurement equation of the system, respectively, wherein k represents time, k=1, …, N; x is x k A system state value representing the time k; x is x k+1 A system state value representing the time k+1; u (u) k A system input value representing the time k; a, B represent the system matrix and input matrix separately; w (w) k Representing process noise of the system; i represents a distributed sensor number, i=1, …, M, and M represents that M distributed sensors in the wireless sensor network perform measurement; y is i,k An observation value indicating the i-th sensor k time; c (C) i Representing an output matrix of the ith sensor; v i,k Representing measurement noise of the ith sensor, v i,k ∈[-δ k ,δ k ]，δ k Maximum noise margin value at time k, -delta k Noise minimum edge at k timeA boundary value;
the system state value refers to the state value of each working procedure of battery production, namely the production state value of the working procedures of mixing, coating, rolling, baking, assembling and forming on a production line; the system input value refers to the quantity of battery production raw materials;
defining the positive multicellular space as:
wherein ,
is the center point of the positive multicellular space, d is the generator matrix of the positive multicellular space, diag (d) is the diagonal matrix with the diagonal value equal to d, x is the range of feasible sets of positive multicellular space packages, m is an intermediate variable which is used as a reference, I ∞ Represents an infinite norm;
defining a fully symmetric multicellular space as:
wherein p is the center point of the holomorphic multicellular space, H is the generating matrix of the holomorphic multicellular space, B n ∈[-1,1] n For n unit intervals [ -1,1 [ -1 ]]The unit box is formed by the components,
for Minkowski and z is an intermediate variable, it can be seen that when H is a diagonal matrix and h=diag { d }, the fully-symmetric multicellular space becomes a positive multicellular space, and thus, the positive multicellular space is a special fully-symmetric multicellular space;
determining the initial moment positive multicellular space as
And an initial holohedral multicellular space Z (p 1 ,H 1 ) And let->
I.e.
diag{d 1 }＝H 1 (6)
Step 103, according to a measurement equation in the state space expression, each local data analyzer forms the downloaded measurement data at the k moment into measurement zone information; judging whether the measured data is lost or not, if so, when k=1, no processing is needed, and when k is not equal to 1, the measured information at the moment k-1 is taken as the measured information at the moment k; and transmitting the data to a corresponding cloud;
defining the measurement zone information of the sensor i in the k-time data analyzer as S (p i,k ,c i,k )；
wherein ,ci,k The center point with information is measured for sensor i at time k,
p i,k measuring the directional vector with information for the sensor i at time k,/>
θ s A range of feasible sets is estimated for the measurement band information.
If the measured data is lost, when k=1, let
S(p i,k ,c i,k )＝0 (8)
When k is not equal to 1, the measurement information at k-1 is regarded as the measurement information at k, and
S(p i,k ,c i,k )＝S(p i,k-1 ,c i,k-1 ) (9)
step 104, obtaining local state estimation at each local data analyzer k moment according to the state equation of the state space expression in step 102 and the measurement zone information of step 103:
solving a predicted full-symmetrical multicellular space at the moment k+1 according to a state equation in a state space expression, and converting the predicted full-symmetrical multicellular space into a predicted positive multicellular space; then, the positive multicell space at the moment k+1 is obtained by utilizing the measurement band information at the moment k, and the local state estimation of each local data analyzer is completed;
according to the state equation of the state space expression of the formula (1) and the measurement zone information of the formula (7), the local state estimation at the moment k of the local data analyzer corresponding to each cloud is obtained:
in the system state equation of equation (1), the process noise is limited to an unknown but bounded range, i.e., the process noise w k P (0, g), g being the boundary value of the noise;
using a fully symmetrical multicell space Z (p k ,H k ) Representing positive multicellular space at time k
I.e.
H k ＝diag{d k } (12)
Will Z (p) k ,H k ) Substituting the predicted holohedral symmetry multicellular space into the state equation of (1) to obtain the k+1 time
wherein ,
to predict the center point of the holohedral multicellular space, < >>
Generating a matrix for predicting the holohedral multicellular space;
at this time at
Adding process noise w k P (0, g) and using the most compact prediction of positive multicellular space
Unwrapping predicted holohedral multicellular space, +.>
The calculation formula of (2) is as follows:
wherein ,
to predict the center point of the positive multicellular space, < >>
Generating a matrix for predicting the positive multicellular space, < >>
For diagonal value equal +.>
Diagonal matrix of>
For diagonal value equal +.>
Diagonal matrix of n H Is->
The dimension of the matrix, represents the norm, l, h are intermediate variables.
Predicted positive multicellular space determined in equations (15) and (16)
Decomposition into n constraint equations
wherein xj To predict the feasible set variable of the j-th dimension of the positive multicellular space,
to predict the feasible set minimum in the j-th dimension of the positive multicellular space,/for example>
For predicting the maximum value of a feasible set in the j-th dimension of the positive multicell space, j is a dimension variable;
next, the measurement band information S (p) of the ith data parser at the k time obtained according to the formula (7) i,k ,c i,k ) Updating to obtain positive multicellular space at k+1 time
The method comprises the following steps:
wherein ,
wherein xs For feasible set variables in the constraint ∈ represents intersection, α j (k+1) is the maximum value of the j-th dimension of the feasible set range,
α j+n (k+1) is the minimum value of the j-th dimension of the feasible set range, max represents taking the maximum value, min represents taking the minimum value,
transpose representing the j-th dimension of the unit diagonal matrix, is->
Value of j-th dimension of positive multicellular space center point,>
generating values of the j-th dimension of the matrix for positive multicellular space,>
for diagonal value equal +.>
Is a diagonal matrix of (a). />
The final local state estimate is for each data analyzer.
Step 105, transmitting the local state estimation obtained by each local data analyzer to a global data analyzer, and transmitting the global state estimation result to each local data analyzer by using Minkowski and obtaining the final global state estimation, and transmitting the global state estimation result to each corresponding cloud by each local data analyzer;
local state estimation results of each data analyzer according to formulas (17) to (22)
Solving M positive multicellular space sums for global positive multicellular space +.>
orthomulticellular space with M partial estimates
Decomposition into n constraint equations according to equation (17)>
Then the constraint-bearing equation in the global estimate is defined as +.>
Then
The method comprises the following steps:
wherein ,
wherein xsum Is that
Feasible set of variables, alpha j,sum (k+1) is the maximum value of the j-th dimension of the feasible set range,
α j+n,sum (k+1) is the minimum value of the j-th dimension of the feasible set range,
is a global positive multicellular spatial center point,
a value d of the j-th dimension of the global positive multicellular space center point k+1 Generating a matrix for a global positive multicellular space, < >>
Generating a value of the j-th dimension of the matrix for the positive multicellular space, the global positive multicellular space +.>
The expressed feasible set range is the final global estimation result, and the global state estimation result is transmitted to each data analyzer and is transmitted to each original cloud end by the data analyzer.
And 106, transmitting the acquired measurement data, the local state estimation result obtained according to the measurement data and the global state estimation result to a backup cloud by each local data analyzer for backup storage.
In order to further ensure the safety of data, in the scheme of the application, the data of each local data analyzer upload the acquired sensor measurement data to only the backup cloud for storage, and local state estimation results and global estimation results obtained from the global data analyzer are obtained according to the measurement data; the number of backup clouds may be determined according to the actual situation.
Some steps in the embodiments of the present invention may be implemented by using software, and the corresponding software program may be stored in a readable storage medium, such as an optical disc or a hard disk.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (6)
1. The method is applied to the high-end battery intelligent factory cloud architecture, and is characterized by comprising the following steps: a plurality of sensors, a plurality of local data analyzers and a global data analyzer which are distributed on each production process of the high-end battery and used for collecting data in real time;
each sensor corresponds to a cloud end for storing the collected and processed data; the local data analyzers are used for acquiring data acquired by the sensors in real time from the cloud and carrying out local state estimation, and each local data analyzer sends local state estimation results acquired by the local data analyzers to the global data analyzer; the global data analyzer is used for carrying out global state estimation according to the local state estimation results of the local data analyzers, and transmitting the obtained global state estimation results back to the local data analyzers, and then transmitting the global state estimation results to the corresponding cloud ends for storage by the local data analyzers;
the method comprises the following steps:
step 1: acquiring measurement data of each distributed sensor k time in a high-end battery production process, transmitting the measurement data to a corresponding cloud for storage, and simultaneously, downloading the measurement data stored in the cloud by utilizing each local data analyzer;
step 2: acquiring a linear state space expression of a high-end battery intelligent factory workshop, and determining an initial positive multicell space and an initial full-symmetry multicell space; the state space expression comprises a measurement equation and a state equation;
step 3: according to a measurement equation in the state space expression, each local data analyzer forms the downloaded measurement data at k time into measurement zone information; judging whether the measured data is lost or not, if so, when k=1, no processing is needed, and when k is not equal to 1, the measured information at the moment k-1 is taken as the measured information at the moment k; and transmitting the data to a corresponding cloud;
step 4: according to the state equation of the state space expression in the step 2 and the measurement zone information of the step 3, the local state estimation of each local data analyzer at the moment k is obtained:
solving a predicted full-symmetrical multicellular space at the moment k+1 according to a state equation in a state space expression, and converting the predicted full-symmetrical multicellular space into a predicted positive multicellular space; then, the positive multicell space at the moment k+1 is obtained by utilizing the measurement band information at the moment k, and the local state estimation of each local data analyzer is completed;
step 5: transmitting the local state estimation obtained by each local data analyzer to a global data analyzer, and transmitting a global state estimation result to each local data analyzer by utilizing Minkowski and obtaining the final global state estimation, and transmitting the global state estimation result to each corresponding cloud end by each local data analyzer;
step 6: and each local data analyzer transmits the acquired measurement data, the local state estimation result obtained according to the measurement data and the global state estimation result to the backup cloud for backup storage.
2. The method according to claim 1, wherein the step 2 comprises:
the n-dimensional linear state space expression of the high-end battery intelligent factory workshop is obtained as follows:
x k+1 ＝Ax k +Bu k +w k (1)
y i,k ＝C i x k +v i,k (2)
equations (1) and (2) are a state equation and a measurement equation of the system, respectively, wherein k represents time, k=1, …, N; x is x k A system state value representing the time k; x is x k+1 A system state value representing the time k+1; u (u) k A system input value representing the time k; a, B represent the system matrix and input matrix separately; w (w) k Representing process noise of the system; i represents a distributed sensor number, i=1, …, M, and M represents that M distributed sensors in the wireless sensor network perform measurement; y is i,k Representing data acquired at the moment k of the ith sensor; c (C) i Representing an output matrix of the ith sensor; v i,k Representing measurement noise of the ith sensor, v i,k ∈[-δ k ,δ k ]，δ k Maximum noise margin value at time k, -delta k The noise minimum boundary value at the moment k;
defining the positive multicellular space as:
wherein ,
is the center point of the positive multicellular space, d is the generator matrix of the positive multicellular space, diag (d) is the diagonal matrix with the diagonal value equal to d, x is the range of feasible sets of positive multicellular space packages, m is an intermediate variable which is used as a reference, I ∞ Represents an infinite norm;
defining a fully symmetric multicellular space as:
wherein p is the center point of the holomorphic multicellular space, H is the generating matrix of the holomorphic multicellular space, B n ∈[-1,1] n For n unit intervals [ -1,1 [ -1 ]]The unit box is formed by the components,
for Minkowski and z is an intermediate variable;
determining the initial moment positive multicellular space as
And an initial holohedral multicellular space Z (p 1 ,H 1 ) And order
I.e.
diag{d 1 }＝H 1 (6)
wherein ,
is the center point of the initial positive multicellular space, d 1 Generating a matrix for the initial positive multicellular space; p is p 1 Is the central point of the initial holohedral multicellular space, H 1 Is a generator matrix of an initial fully symmetric multicellular space.
3. The method according to claim 2, wherein the step 3 comprises:
defining the local data analyzer to be S (p i,k ,c i,k )；
wherein ,ci,k The center point with information is measured for sensor i at time k,
p i,k measuring the directional vector with information for the sensor i at time k,/>
θ s Estimating a feasible set range for the measurement zone information;
if the measured data is lost, when k=1, let
S(p i,k ,c i,k )＝0 (8)
When k is not equal to 1, the measurement information at k-1 is regarded as the measurement information at k, and
S(p i,k ,c i,k )＝S(p i,k-1 ,c i,k-1 ) (9)。
4. a method according to claim 3, wherein said step 4 comprises:
solving a predicted fully-symmetrical multicellular space at the moment k+1 according to a state equation in a state space expression, and converting the predicted fully-symmetrical multicellular space into a predicted positive multicellular space; then, the positive multicell space at the moment k+1 is obtained by utilizing the measurement band information at the moment k, and the local state estimation of each local data analyzer is completed;
according to the state equation of the state space expression of the formula (1) and the measurement zone information shown in the formula (7), the local state estimation at the moment k of the local data analyzer corresponding to each cloud is obtained:
in the system state equation of equation (1), the process noise is limited to an unknown but bounded range, i.e., the process noise
g is the boundary value of the noise;
using a fully symmetrical multicell space Z (p k ,H k ) Representing positive multicellular space at time k
I.e.
H k ＝diag{d k } (12)
Will Z (p) k ,H k ) Substituting the predicted holohedral symmetry multicellular space into the state equation of (1) to obtain the k+1 time
wherein ,
to predict the center point of the holohedral multicellular space, < >>
Generating a matrix for predicting the holohedral multicellular space;
at this time at
Add procedure noise on>
And using the most compact predicted positive multicellular space
Unwrapping predicted holohedral multicellular space, +.>
The calculation formula of (2) is as follows:
wherein ,
to predict the center point of the positive multicellular space, < >>
Generating a matrix for predicting the positive multicellular space, < >>
For diagonal value equal +.>
Diagonal matrix of>
For diagonal value equal +.>
Diagonal matrix of n H Is->
The dimension of the matrix, represents the norm, l, h is an intermediate variable;
predicted positive multicellular space determined in equations (15) and (16)
Decomposition into n constraint equations
wherein xj To predict the feasible set variable of the j-th dimension of the positive multicellular space,
to predict the feasible set minimum in the j-th dimension of the positive multicellular space,/for example>
For predicting the maximum value of a feasible set in the j-th dimension of the positive multicell space, j is a dimension variable;
next, the measurement band information S (p) of the ith data parser at the k time obtained according to the formula (7) i,k ,c i,k ) Updating to obtain positive multicellular space at k+1 time
The method comprises the following steps:
wherein ,
wherein xs For feasible set variables in the constraint ∈ represents intersection, α j (k+1) is the maximum value of the j-th dimension of the feasible set range,
α j+n (k+1) is the minimum value of the j-th dimension of the feasible set range, max represents taking the maximum value, min represents taking the minimum value,
transpose representing the j-th dimension of the unit diagonal matrix, is->
Value of j-th dimension of positive multicellular space center point,>
generating values of the j-th dimension of the matrix for positive multicellular space,>
for diagonal value equal +.>
Is a diagonal matrix of (a); />
The final local state estimate is for each data analyzer.
5. The method according to claim 4, wherein the step 5 comprises:
local state estimation results of each data analyzer according to formulas (17) to (22)
Using Minkowski sum to solve M positive multicell spaces and to be global positive multicell space in global data analyzer
orthomulticellular space with M partial estimates
Decomposition into n constraint equations according to equation (17)>
Then the constraint-bearing equation in the global estimate is defined as +.>
Then
The method comprises the following steps:
wherein ,
wherein xsum Is that
Feasible set of variables, alpha j,sum (k+1) is the maximum value of the j-th dimension of the feasible set range;
α j+n,sum (k+1) is the minimum value of the j-th dimension of the feasible set range,
is the global positive multicellular space center point, < >>
A value d of the j-th dimension of the global positive multicellular space center point k+1 Generating a matrix for a global positive multicellular space, < >>
Generating a value of the j-th dimension of the matrix for the positive multicellular space, the global positive multicellular space +.>
The range of the expressed feasible set is the final global estimation result, the global state estimation result is transmitted to each local data analyzer, and each local data analyzer transmits the global state estimation result to each corresponding cloud.
6. The method of claim 5, wherein the local data analyzer and the global data analyzer are implemented using a computer server.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210614506.6A CN115022348B (en) | 2022-05-27 | 2022-05-27 | Intelligent factory cloud-level architecture data storage method for high-end battery |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210614506.6A CN115022348B (en) | 2022-05-27 | 2022-05-27 | Intelligent factory cloud-level architecture data storage method for high-end battery |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115022348A CN115022348A (en) | 2022-09-06 |
| CN115022348B true CN115022348B (en) | 2023-04-28 |
Family
ID=83071678
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210614506.6A Active CN115022348B (en) | 2022-05-27 | 2022-05-27 | Intelligent factory cloud-level architecture data storage method for high-end battery |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115022348B (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112260867A (en) * | 2020-10-21 | 2021-01-22 | 山东科技大学 | State estimation method of event-triggered transmission complex network based on collective member estimation |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10846596B2 (en) * | 2015-11-23 | 2020-11-24 | Daniel Chonghwan LEE | Filtering, smoothing, memetic algorithms, and feasible direction methods for estimating system state and unknown parameters of electromechanical motion devices |
| CN109543143B (en) * | 2019-01-28 | 2023-08-18 | 杭州电子科技大学 | Multi-sensor fusion estimation method of nonlinear deviation system |
| CN113701742B (en) * | 2021-08-24 | 2024-04-26 | 江苏海事职业技术学院 | Mobile robot SLAM method based on cloud and edge fusion calculation |
| CN113950018B (en) * | 2021-10-13 | 2023-10-27 | 华东理工大学 | Asynchronous multi-sensor network system and global ellipsoid state estimation method |
-
2022
- 2022-05-27 CN CN202210614506.6A patent/CN115022348B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112260867A (en) * | 2020-10-21 | 2021-01-22 | 山东科技大学 | State estimation method of event-triggered transmission complex network based on collective member estimation |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115022348A (en) | 2022-09-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111900731B (en) | PMU-based power system state estimation performance evaluation method | |
| WO2023134188A1 (en) | Index determination method and apparatus, and electronic device and computer-readable medium | |
| CN114936504A (en) | Equipment residual life prediction method and system based on Bayesian multi-source data fusion | |
| CN111680398B (en) | Single machine performance degradation prediction method based on Holt-windows model | |
| CN110807508B (en) | Bus peak load prediction method considering complex weather influence | |
| CN109523077B (en) | Wind power prediction method | |
| CN117289668B (en) | Distributed speed reducer network cooperative control method, device, equipment and storage medium | |
| CN112580844A (en) | Meteorological data processing method, device, equipment and computer readable storage medium | |
| CN116207766B (en) | Dynamic threshold control method and system for lithium battery energy storage system | |
| CN113642812A (en) | Beidou-based micro-deformation prediction method, device, equipment and readable storage medium | |
| CN115022348B (en) | Intelligent factory cloud-level architecture data storage method for high-end battery | |
| CN112560981A (en) | Training method, apparatus, device, program and storage medium for generating countermeasure model | |
| CN115908051A (en) | Method for determining energy storage capacity of power system | |
| CN114093433B (en) | Observer-based method and system for evaluating prediction precision of single-ton energy consumption in rectification process | |
| CN114741822A (en) | Method, system and device for predicting power failure probability of power distribution network under natural disasters | |
| CN109753018B (en) | Error compensation system and dynamic compensation method based on cloud intelligence | |
| CN116233747B (en) | BLE positioning method and device based on transfer learning and fingerprint library updating | |
| CN110543724A (en) | Satellite structure performance prediction method for overall design | |
| CN111553565B (en) | Performance evaluation method and system for online monitoring device of transformer substation | |
| JP2020035042A (en) | Data determination device, method, and program | |
| JPH08163778A (en) | Power demand prediction system | |
| CN116882079A (en) | Water pump characteristic curve self-adaptive calibration and prediction method | |
| WO2022156743A1 (en) | Feature construction method and apparatus, model training method and apparatus, and device and medium | |
| CN115618743B (en) | State evaluation method and state evaluation system of sighting telescope system | |
| CN116701371B (en) | Method and device for interpolating missing values of atmospheric temperature data under covariance analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |