CN114237098B - Intelligent digital management system of electrical product - Google Patents
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Abstract
The invention provides an intelligent digital management system of an electrical product, which comprises: a monitoring module: the monitoring system is used for monitoring the working state of the electrical products in the preset area in real time based on preset monitoring equipment; a detection module: the device is used for detecting whether the working state is normal or not based on a preset big data processing center and determining a detection result; a control module: the intelligent control system is used for intelligently controlling the corresponding electric products through the detection results and generating corresponding control results; a statistic management module: the system is used for counting the electric products in the preset area range, determining the counting result, intelligently managing the counting result and determining the management result.
Description
Technical Field
The invention relates to the technical field of digital management and automatic management systems of electrical products, in particular to an intelligent digital management system of an electrical product.
Background
At present, with the electrical engineering technology spread over the aspects of people's lives, people have higher and higher requirements for electrical products, and improving the comfort level of space and life in a limited environment becomes a main target of people by modifying products in the technical fields of electric energy, electrical equipment, electrical technology and the like.
In the existing environment, the electrical products are generally checked and managed manually, but the electrical products are various in types, and if the electrical products are not technicians, the functional purposes of the electrical products cannot be identified, and meanwhile, whether the electrical products are unqualified or not is more difficult to observe visually.
The patent CN 102723243 a in the prior art discloses an anti-misloading system for assembling electrical products, but is only limited to electrical anti-misloading, and cannot comprehensively and flexibly perform intelligent comprehensive management on electrical products.
Disclosure of Invention
The invention provides an intelligent digital management system of an electrical product, which aims to solve the problems.
The invention provides an intelligent digital management system of an electrical product, which is characterized by comprising the following components:
a monitoring module: the monitoring system is used for monitoring the working state of the electrical products in the preset area in real time based on preset monitoring equipment;
a detection module: the big data processing center is used for transmitting the working state to a preset big data processing center, detecting whether the working state is normal or not and determining a detection result;
a control module: the automatic control system is used for acquiring a corresponding automatic control instruction based on the detection result, intelligently controlling a corresponding electric product through the automatic control instruction and generating a corresponding control result;
the digital management module: the system is used for carrying out digital statistics on the electrical products in the preset area range, determining a statistical result, carrying out intelligent management on the statistical result and determining a management result.
As an embodiment of the present technical solution, the monitoring module includes:
a classification unit: the system comprises a function classification tag, a classification result and a control module, wherein the function classification tag is used for classifying the functions of the electric products in a preset area based on a preset function classification label and determining the classification result;
reference unit: the electric product classifying device is used for classifying electric products in a preset area range based on the classification result, determining the classification result, labeling the classification result and determining the labeling sequence; wherein,
the statistical data at least comprises a product model, rated parameters and a use position;
working state data unit: the device is used for acquiring working state data corresponding to the electrical product according to the label sequence based on preset detection equipment;
a working state unit: and the device is used for determining the working state of the corresponding electric product through the working state data.
As an embodiment of the present technical solution, the working status unit includes:
generating a time subunit: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for receiving working state data of an electrical product and acquiring generation time of the working state data;
the working state data set subunit: the data acquisition module is used for acquiring the generation time and the corresponding working state data of the electrical product;
analysis results subunit: the system is used for dynamically deducing the working state data set based on a preset data analysis system, performing multi-scale hierarchical analysis and determining an analysis result;
the working state subunit: and the device is used for determining the working state of the electrical product according to the analysis result.
As an embodiment of the present technical solution, the detection module includes:
a judging unit: the system is used for recording and judging whether the electrical product is abnormal in the working process in real time according to the working state of the electrical product;
normal operating state data unit: the device is used for determining a normal result when the judgment result indicates that the electrical product is not abnormal in the working process, and acquiring the normal working state data of the corresponding electrical product;
abnormal working state data unit: the device is used for determining an abnormal result when the judgment result is that the electrical product is abnormal in the working process, and simultaneously recording abnormal working state data;
a detection result unit: and the data processing module is used for transmitting the normal working state data and the abnormal working state data to a preset big data processing center and generating corresponding detection results.
As an embodiment of the present technical solution, the determining unit is configured to record and determine whether an abnormality occurs in an electrical product in real time according to a working state of the electrical product, and further includes the following steps:
step S01: classifying the working state corresponding to the electrical product according to preset conditions to obtain a preset working state data classification group { gamma } 1 ,γ 2 ,…,γ z Classifying the working state data into corresponding working state data information groups through the data classification groups
Step S02:calculating similarity between different working state data
Wherein d is the total data number of the working state data information group, d is a constant and is more than or equal to 1, z is the total data number of the working state data classification groups and is more than or equal to 1,
the similarity between the pth working state data and the qth working state data in the pth working state data classification group is represented by p and q, wherein p is a constant, and p is not equal to q, and is not more than 1 and not more than p and q;
classifying the p-th working state data in the group for the l-th working state data,
classifying the q-th working state data in the I-th working state data classification group, wherein tau is an analogous parameter related to the working state data, and e is a natural base number;
step S03: calculating the average interval value of the working state data according to the classification group of the working state data and the corresponding information group of the working state data
Wherein mu is a constant, and d is more than or equal to 1 and less than or equal to mu;
average interval of working state data for I-th group working state data classification group；
Step S04: determining similarity between different operating status data of the electrical product
Average interval value with working state data
And if the voltage is within the threshold value range, determining whether the electrical product is abnormal in the working process.
As an embodiment of the present technical solution, the control module further includes:
a detection result acquisition unit: the device is used for acquiring a detection result;
a normal product control unit: the cloud server is used for acquiring normal working state data when the detection result is that the electrical product is normal, regularly packaging the normal working state data, determining packaged data and compressing the packaged data to a preset cloud server;
an abnormal product control unit: the intelligent control system is used for acquiring abnormal working state data when the detection result is that the electrical product is abnormal, triggering a preset protection mechanism based on the abnormal working state data, generating a fault signal and transmitting the fault signal to a preset control terminal, and intelligently controlling the electrical product through the control terminal; wherein,
the intelligent control at least comprises brake-separating control, closing control and automatic cut-off circuit control;
an uploading unit: the system is used for uploading the model of the electric product and the corresponding abnormal working state data to a preset control terminal.
As an embodiment of the present technical solution, the statistics management module includes:
a statistic unit: the device is used for counting the electrical products in the preset area range and determining the counting result;
a management unit: and the intelligent management system is used for intelligently managing the statistical result based on a preset intelligent management system.
As an embodiment of the present technical solution, the statistical unit includes:
a statistical data unit: the system comprises a function classification module, a data processing module and a data processing module, wherein the function classification module is used for acquiring a function classification result of an electric product, dividing and counting the electric product in a preset area range according to the function classification result, and determining corresponding statistical data; wherein,
the statistical data at least comprises a product model, rated parameters and a use position;
a statistical database unit: the system comprises a statistical database, a data processing module and a data processing module, wherein the statistical database is used for acquiring the corresponding relation between an electrical product and corresponding statistical data and establishing a statistical database through the corresponding relation;
a visualization result unit: and the statistical database is used for performing visual processing on the statistical database based on a preset big data processing center to determine a statistical result.
As an embodiment of the present technical solution, the management unit includes:
storing a fitting subunit: the statistical result is subjected to storage fitting, correction calculation and optimal distribution data generation;
the logic management networking subunit: the system is used for carrying out logic management on the statistical result and constructing a corresponding logic management network;
a scheduling instruction subunit: the system comprises a logic management network and a plurality of management nodes, wherein the logic management network is used for acquiring each management node in the logic management network based on the optimal distribution data and receiving a corresponding scheduling instruction through the management nodes;
intelligent management subunit: and the management node is used for determining the corresponding relation between each scheduling instruction and the corresponding management node and intelligently managing the statistical result based on the corresponding relation.
As an embodiment of the present technical solution, the storage fitting subunit is configured to perform storage fitting on the statistical result, and includes the following steps:
step 1: acquiring a statistical data set beta of a statistical result;
β＝{β 1 ,β 2 ,…,β n }
wherein, beta is a statistical data set, and n is the variable number in the statistical data setTotal number of data,. beta 1 For the 1 st variable data in the dynamic data set, β 2 For the 2 nd variable data in the dynamic data set, beta n The nth variable data in the dynamic data set;
step 2: acquiring storage prediction influence parameters of a statistical data set beta, and constructing a data storage prediction function through the storage prediction influence parameters:
wherein f is ω (β) represents a stored prediction function for the statistical data set β under the influence of stored prediction influence variable data ω, ψ being a stored prediction influence variable for the stored prediction function and 0 ≦ ψ, α being a first stored prediction influence parameter for the statistical data set β and 0.8 ≦ α ≦ 1.2, e being a natural base;
and step 3: for memory prediction function f ψ (β) performing a correction process to construct a data storage correction function g (ψ):
wherein epsilon is the first serial number of the dynamic data in the dynamic data set, epsilon is more than or equal to 1 and less than or equal to n, and 1<n, a is the second serial number of the dynamic data in the dynamic data set, and a is more than or equal to 1 and less than or equal to n, beta ε For the epsilon-th data variable, gamma, in the statistical data set ε For the epsilon-th data variable beta in the statistical data set ε Positive variable parameter of f ω (β ε ) Representing data variable beta under the influence of storing predicted influence variable data omega ε The stored prediction function of (a); beta is a a Is the a-th data variable, gamma, in the statistical data set a Negative variable parameter, f, for the a-th data variable in the statistical data set ω (β a ) Representing data variable beta under the influence of storing predicted influence variable data omega a Epsilon ≠ a;
and 4, step 4: carrying out minimization calculation on the data storage correction function g (psi), and calculating a minimum storage prediction influence variable psi min ：
Wherein, ω is min Predicting the influencing variable, ω, for minimum storage m For the mth storage prediction influencing variable, ρ is a second storage prediction influencing parameter with respect to the data storage modification function g (ω), m is a constant, and 1. ltoreq. m.ltoreq.n, β t For the t-th data variable in the data variable group, f ω (β t ) Representing data variable beta under the influence of storing predicted influence variable data omega t Of a memory prediction function, gamma t The negative variable parameter of the t-th data variable in the data variable group,
as a data variable beta t To the m-th power;
and 5: judging the minimum storage prediction influence variable omega min Whether the storage prediction influence variable is larger than a preset optimal storage prediction influence variable W or not is judged;
step 6: when the judgment result is the minimum storage prediction influence variable omega min If the storage capacity is larger than the preset optimal storage prediction influence variable W, segmenting the statistical result according to a preset size, determining a segmented statistical result, and transmitting the segmented statistical result to the step 1 in batches;
and 7: when the judgment result is the minimum storage prediction influence variable omega min And storing and fitting the statistical result when the predicted influence variable is less than or equal to a preset optimal storage prediction influence variable W.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a block diagram of an intelligent digital management system for electrical products according to an embodiment of the present invention;
FIG. 2 is a block diagram of an intelligent digital management system for electrical products according to an embodiment of the present invention;
fig. 3 is a block diagram of an intelligent digital management system of an electrical product according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
It will be understood that when an element is referred to as being "secured to" or "disposed on" another element, it can be directly on the other element or be indirectly on the other element. When an element is referred to as being "connected to" another element, it can be directly or indirectly connected to the other element.
It is to be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are used in an orientation or positional relationship indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the device or element so referred to must be in a particular orientation, constructed or operated in a particular orientation, and is not to be construed as limiting the invention.
Moreover, it is noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions, and "a plurality" means two or more unless specifically limited otherwise. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Example 1:
according to fig. 1, an embodiment of the present invention provides an intelligent digital management system for electrical products, including:
a monitoring module: the monitoring system is used for monitoring the working state of the electrical products in the preset area in real time based on preset monitoring equipment;
a detection module: the big data processing center is used for transmitting the working state to a preset big data processing center, detecting whether the working state is normal or not and determining a detection result;
a control module: the automatic control system is used for acquiring a corresponding automatic control instruction based on the detection result, intelligently controlling a corresponding electric product through the automatic control instruction and generating a corresponding control result;
a digital management module: the system is used for carrying out digital statistics on the electrical products in the preset area range, determining a statistical result, carrying out intelligent management on the statistical result and determining a management result.
The working principle and the beneficial effects of the technical scheme are as follows:
the embodiment of the invention provides an intelligent digital management system of an electrical product, which comprises a monitoring module, a detection module, a control module and a statistical management module, wherein the monitoring module is used for monitoring the working state of the electrical product in a preset area in real time based on preset monitoring equipment, the electrical product is generally used for the improvement of people in living, the automatic electrical equipment is monitored by setting a monitoring area, such as an office area, a small area or a commercial building, and the detection module is used for detecting whether the working state is normal or not based on a preset big data processing center, determining the detection result, judging the monitored data, and avoiding the potential safety hazard caused by improper application or long-term maintenance of the electrical product, the control module is used for intelligently controlling the corresponding electrical product according to the detection result, and a corresponding control result is generated, through control such as closing and opening of the electric products, operation of special personnel is avoided being required to be set for each building, labor cost is reduced, implementation efficiency is improved, the statistics management module is used for carrying out statistics on the electric products in a preset area range, a statistics result is determined, intelligent management is carried out on the statistics result, a management result is determined, and an automatic and intelligent management system is provided through intelligent management on the electric products.
Example 2:
according to fig. 2, the present technical solution provides an embodiment, where the monitoring module includes:
a classification unit: the system comprises a function classification tag, a function classification module and a function classification module, wherein the function classification tag is used for classifying the functions of the electric products in a preset area based on a preset function classification label and determining a classification result;
reference unit: the electric product classifying device is used for classifying the electric products in the preset area range based on the classification result, determining the classification result, labeling the classification result and determining the labeling sequence; wherein,
the statistical data at least comprises a product model, rated parameters and a use position;
working state data unit: the device is used for acquiring working state data corresponding to the electrical product according to the label sequence based on preset detection equipment;
a working state unit: and the device is used for determining the working state of the corresponding electric product through the working state data.
The working principle and the beneficial effects of the technical scheme are as follows:
the monitoring module comprises a classification unit, a labeling unit, a working state data unit and a working state unit, wherein the classification unit is used for carrying out function classification on electric products in a preset area based on a preset function classification label, determining a classification result, classifying the electric products with different functions and facilitating statistics of electric working data in the follow-up process; the working state data sheet is used for acquiring working state data corresponding to the electrical product according to the label sequence based on preset detection equipment, original data are provided for a subsequent detection module through the acquisition of the working state data of the electrical product, the working state unit is used for determining the working state of the corresponding electrical product through the working state data, the set formed by the whole working state data is informationized, and the working state data is related to the working state of the electrical product, and the statistics module is used for counting the information of the electrical product, so that follow-up calling, use and visualization are facilitated, and workers can check and manage the working state data.
Example 3:
this technical scheme provides an embodiment, the operating condition unit includes:
generating a time subunit: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for receiving working state data of an electrical product and acquiring generation time of the working state data;
the working state data set subunit: the data acquisition module is used for acquiring the generation time and the corresponding working state data of the electrical product;
analysis results subunit: the system is used for dynamically deducing the working state data set based on a preset data analysis system, performing multi-scale hierarchical analysis and determining an analysis result;
the working state subunit: and the device is used for determining the working state of the electrical product according to the analysis result.
The working principle and the beneficial effects of the technical scheme are as follows:
the technical scheme provides an embodiment, the working state unit comprises a generation time subunit, a working state data set subunit, an analysis result subunit and a working state subunit, wherein the generation time subunit is used for receiving the working state data of the electrical product and acquiring the generation time of the working state data; the working state data set subunit is used for creating a working state data set related to the electrical product by generating time and corresponding working state data; the analysis result subunit is used for performing dynamic deduction on the working state data set based on a preset data analysis system, performing multi-scale hierarchical analysis and determining an analysis result; the working state data set of the electric product is also a data set generated by the dynamic working of the electric product, so that the state data set is transmitted to a preset working condition simulation system, the state data set can be deduced and analyzed, and a plurality of layers of analysis layers are divided based on different analysis indexes, so that the multi-scale data set is analyzed, and a more accurate analysis result is obtained. The working state subunit is used for determining the working state of the electrical product through the analysis result, and providing original data for the detection module through information acquisition of the working state of the electrical product.
Example 4:
this technical scheme provides an embodiment, the detection module includes:
a judging unit: the system is used for recording and judging whether the electrical product is abnormal in the working process in real time according to the working state of the electrical product;
normal operating state data unit: the device is used for determining a normal result when the judgment result is that the electrical product is not abnormal in the working process, and acquiring the normal working state data of the corresponding electrical product;
abnormal working state data unit: the device is used for determining an abnormal result when the judgment result is that the electrical product is abnormal in the working process, and simultaneously recording abnormal working state data;
a detection result unit: and the data processing module is used for transmitting the normal working state data and the abnormal working state data to a preset big data processing center and generating corresponding detection results.
The working principle and the beneficial effects of the technical scheme are as follows:
the detection module of the technical scheme records and judges whether the electrical product is abnormal in the working process in real time, and acquires the normal working state data of the corresponding electrical product when the electrical product is not abnormal in the working process; when the judgment result is that the electrical product is abnormal in the working process, the abnormal result is determined, meanwhile, the abnormal working state data is recorded, and the normal working state data and the abnormal working state data generate corresponding detection results, so that the electrical product can be accurately monitored.
Example 5:
this technical scheme provides an embodiment, the judgement unit is used for through the operating condition of electric product, real-time recording and judge whether electric product takes place unusually at the course of the work, still includes following step:
step S01: classifying the working state corresponding to the electrical product according to preset conditions to obtain a preset working state data classification group { gamma } 1 ,γ 2 ,…,γ z Classifying the working state data into corresponding working state data information groups through the data classification groups
Step S02: calculating similarity between different working state data
Wherein d is the total data number of the working state data information group, d is a constant and is more than or equal to 1, z is the total data number of the working state data classification groups and is more than or equal to 1,
the similarity between the pth working state data and the qth working state data in the pth working state data classification group is represented by p and q, wherein p is a constant, and p is not equal to q, and is not more than 1 and not more than p and q;
classifying the p-th working state data in the group for the l-th working state data,
classifying the q-th working state data in the I-th working state data classification group, wherein tau is an analogous parameter related to the working state data, and e is a natural base number;
step S03: calculating the average interval value of the working state data according to the classification group of the working state data and the corresponding information group of the working state data
Wherein mu is a constant and is more than or equal to 1 and less than or equal to d;
averaging the working state data intervals of the I group of working state data classification groups;
step S04: determining similarity between different operating status data of the electrical product
Average interval value with working state data
And if the voltage is within the threshold value range, determining whether the electrical product is abnormal in the working process.
The working principle of the technical scheme is as follows:
different from the prior art that the data types of the electrical data are screened for errors, in the technical scheme, the data are grouped according to the classes, and then the similarity between different data in each group is calculated respectively
Then calculate the average interval value of data
Abnormal data are screened and judged through the similarity and average interval value, deviation data are finally obtained, and the similarity between different working state data of the electric product is judged
Average interval value with working state data
And if the voltage is within the threshold value range, determining whether the electrical product is abnormal in the working process.
The beneficial effects of the above technical scheme are: through the similarity calculation, the accuracy between the data is improved, when the electric data transmission is unstable, the error risk data can be timely acquired, and compared with the common method of directly setting a threshold value for interception, the accuracy of data judgment is improved through the calculation of the average interval value of the data.
Example 6:
this technical solution provides an embodiment, and the control module further includes:
a detection result acquisition unit: the device is used for acquiring a detection result;
a normal product control unit: the cloud server is used for acquiring normal working state data when the detection result is that the electrical product is normal, regularly packaging the normal working state data, determining packaged data and compressing the packaged data to a preset cloud server;
an abnormal product control unit: the intelligent control system is used for acquiring abnormal working state data when the detection result is that the electrical product is abnormal, triggering a preset protection mechanism based on the abnormal working state data, generating a fault signal and transmitting the fault signal to a preset control terminal, and intelligently controlling the electrical product through the control terminal; wherein,
the intelligent control at least comprises brake-separating control, closing control and automatic cut-off circuit control;
an uploading unit: the system is used for uploading the electric product model and the corresponding abnormal working state data to a preset control terminal.
The working principle and the beneficial effects of the technical scheme are as follows:
the control module of the technical scheme also comprises a detection result acquisition unit, a normal product control unit, an abnormal product control unit and an uploading unit, wherein the detection result acquisition unit is used for acquiring a detection result, the detection result comprises a normal detection result and an abnormal detection result, and the detection result is transmitted to different processing units according to different detection results, the normal product control unit is used for acquiring normal working state data when the detection result is that an electrical product is normal, regularly packaging the normal working state data, determining the packaged data, compressing the packaged data to a preset cloud server, packaging a large amount of normal data, facilitating the calling of workers, or facilitating historical review data to perform investigation after an emergency occurs, the abnormal product control unit is used for acquiring abnormal working state data when the detection result is that the electrical product is abnormal, triggering a preset protection mechanism based on the abnormal working state data, generating a fault signal and transmitting the fault signal to a preset control terminal, and intelligently controlling the electrical product through the control terminal; the intelligent control at least comprises brake-separating control, switch-on control and automatic cut-off circuit control; the uploading unit is used for uploading the electric product model and the corresponding abnormal working state data to a preset control terminal, so that the control terminal is timely reminded, and the staff or the user of the control terminal can be conveniently and timely checked and maintained.
Example 7:
this technical solution provides an embodiment, the statistics management module includes:
a statistic unit: the device is used for counting the electrical products in the preset area range and determining a counting result;
a management unit: and the intelligent management system is used for intelligently managing the statistical result based on a preset intelligent management system.
The working principle and the beneficial effects of the technical scheme are as follows:
through the statistics management to electric products, make things convenient for the staff to overhaul in the large tracts of land, alleviateed staff's burden, improved work efficiency.
Example 8:
this technical scheme provides an embodiment, the statistics unit includes:
a statistical data unit: the system comprises a function classification module, a data processing module and a data processing module, wherein the function classification module is used for acquiring a function classification result of an electric product, dividing and counting the electric product in a preset area range according to the function classification result, and determining corresponding statistical data; wherein,
the statistical data at least comprises a product model, rated parameters and a use position;
a statistical database unit: the system comprises a statistical database, a data processing module and a data processing module, wherein the statistical database is used for acquiring the corresponding relation between an electrical product and corresponding statistical data and establishing a statistical database through the corresponding relation;
a visualization result unit: and the statistical database is used for performing visual processing on the statistical database based on a preset big data processing center to determine a statistical result.
The working principle and the beneficial effects of the technical scheme are as follows:
the statistical unit of the technical scheme performs multi-dimensional statistics on the electric appliance products, improves management efficiency and makes management data clear at a glance.
Example 9:
this technical solution provides an embodiment, and the management unit includes:
storing a fitting subunit: the statistical result is subjected to storage fitting, correction calculation and optimal distribution data generation;
the logic management networking subunit: the system is used for carrying out logic management on the statistical result and constructing a corresponding logic management network;
a scheduling instruction subunit: the system comprises a logic management networking module, a scheduling module and a data processing module, wherein the logic management networking module is used for acquiring each management node in the logic management networking based on the optimal distribution data and receiving a corresponding scheduling instruction through the management node;
intelligent management subunit: and the management node is used for determining the corresponding relation between each scheduling instruction and the corresponding management node and intelligently managing the statistical result based on the corresponding relation.
The working principle and the beneficial effects of the technical scheme are as follows:
the management unit comprises a storage fitting subunit, a logic management networking subunit, a scheduling instruction subunit and an intelligent management subunit, wherein the storage fitting subunit is used for performing storage fitting on a statistical result, performing correction calculation and generating optimal distribution data; the logic management networking subunit is used for performing logic management on the statistical result and constructing a corresponding logic management networking; the scheduling instruction subunit is used for acquiring each management node in the logic management networking based on the optimal distribution data and receiving a corresponding scheduling instruction through the management node; the intelligent management subunit is used for determining the corresponding relationship between each scheduling instruction and the corresponding management node, and intelligently managing the statistical result based on the corresponding relationship.
Example 10:
the technical scheme provides an embodiment, wherein the storage fitting subunit is used for performing storage fitting on the statistical result, and comprises the following steps:
step 1: acquiring a statistical data set beta of a statistical result;
β＝{β 1 ,β 2 ,…,β n }
wherein beta is a statistical data set, n is the total number of variable data in the statistical data set, and beta 1 Is the 1 st variable data in the dynamic data set, beta 2 For the 2 nd variable data in the dynamic data set, beta n The nth variable data in the dynamic data set;
step 2: acquiring storage prediction influence parameters of a statistical data set beta, and constructing a data storage prediction function through the storage prediction influence parameters:
wherein, f ω (β) represents a stored prediction function with respect to the statistical data set β under the influence of stored prediction influence variable data ω, ω being a stored prediction influence variable of the stored prediction function and 0 ≦ ω, α being a first stored prediction influence parameter with respect to the statistical data set β and 0.8 ≦ α ≦ 1.2, e being a natural base;
and step 3: for memory prediction function f ω (β) performing a correction process to construct a data storage correction function g (ω):
wherein epsilon is the first serial number of the dynamic data in the dynamic data set, epsilon is more than or equal to 1 and less than or equal to n, and 1<n, a is the second serial number of the dynamic data in the dynamic data set, and a is more than or equal to 1 and less than or equal to n, beta ε For the epsilon-th data variable, gamma, in the statistical data set ε For the epsilon-th data variable beta in the statistical data set ε Positive variable parameter of f ω (β ε ) Representing data variable beta under the influence of storing predicted influence variable data omega ε The stored prediction function of (a); beta is a a Is the a-th data variable, gamma, in the statistical data set a Negative variable parameter, f, for the a-th data variable in the statistical data set ω (β a ) Representing the influence of storing predicted influence variable data omegaData variable beta a Epsilon ≠ a;
and 4, step 4: carrying out minimum calculation on the data storage correction function g (omega), and calculating a minimum storage prediction influence variable omega min ：
Wherein, ω is min Predicting the influencing variable, ω, for minimum storage m For the mth storage prediction influencing variable, ρ is a second storage prediction influencing parameter with respect to the data storage correction function g (ω), m is a constant, and 1. ltoreq. m.ltoreq.n, β t For the t-th data variable in the data variable group, f ω (β t ) Representing data variable beta under the influence of storing predicted influence variable data omega t Of a memory prediction function, gamma t The negative variable parameter of the t-th data variable in the data variable group,
as a data variable beta t To the m power of;
and 5: judging the minimum storage prediction influence variable omega min Whether the storage capacity is larger than a preset optimal storage prediction influence variable W or not is judged, and a judgment result is determined;
step 6: when the judgment result is the minimum storage prediction influence variable omega min If the storage capacity is larger than the preset optimal storage prediction influence variable W, segmenting the statistical result according to a preset size, determining a segmented statistical result, and transmitting the segmented statistical result to the step 1 in batches;
and 7: when the judgment result is the minimum storage prediction influence variable omega min And storing and fitting the statistical result when the predicted influence variable is less than or equal to a preset optimal storage prediction influence variable W.
The working principle of the technical scheme is as follows: different from the prior art in which the partition transmission and storage are directly performed on the acquired data, the above technical scheme performs analysis and calculation on the stored data to construct the storage with better storage effectAccording to the scheme, firstly, according to statistical information, a prediction function f of data storage is constructed by storing a prediction influence variable and a data variable to the power of a natural base number e ω (β); for the prediction function f ω (beta) carrying out correction processing, combining the first serial number and the second serial number of the stored data, summing the product of the positive variable parameter and the prediction function, then summing the product of the positive variable parameter and the prediction function, carrying out difference calculation, constructing a data storage correction function g (omega), finally carrying out minimum calculation on the data storage correction function g (omega), and calculating a minimum influence variable omega min Generating an optimal data storage scheme according to the minimum influence variable;
the beneficial effects of the above technical scheme are: by constructing the storage prediction function, the effective rate of storage allocation is greatly improved, and the accuracy of storage management is enhanced by correcting the prediction function.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (8)
1. An intelligent digital management system for electrical products, comprising:
a monitoring module: the system comprises a monitoring device, a monitoring system and a control device, wherein the monitoring device is used for monitoring the working state of an electrical product in a preset area in real time based on preset monitoring equipment;
a detection module: the big data processing center is used for transmitting the working state to a preset big data processing center, detecting whether the working state is normal or not and determining a detection result;
a control module: the automatic control device is used for acquiring a corresponding automatic control instruction based on the detection result, intelligently controlling a corresponding electric product through the automatic control instruction and generating a corresponding control result;
a digital management module: the system is used for carrying out digital statistics on the electrical products in the preset area range, determining a statistical result, carrying out intelligent management on the statistical result and determining a management result;
the detection module comprises:
a judging unit: the system is used for recording and judging whether the electrical product is abnormal in the working process in real time according to the working state of the electrical product;
normal operating state data unit: the device is used for determining a normal result when the judgment result is that the electrical product is not abnormal in the working process, and acquiring the normal working state data of the corresponding electrical product;
abnormal working state data unit: the device is used for determining an abnormal result when the judgment result is that the electrical product is abnormal in the working process, and simultaneously recording abnormal working state data;
a detection result unit: the data processing device is used for transmitting the normal working state data and the abnormal working state data to a preset big data processing center and generating corresponding detection results;
the judging unit is used for recording and judging whether the electric product is abnormal in the working process in real time according to the working state of the electric product, and further comprises the following steps:
step S01: the method comprises the steps that data classification is carried out on the working state corresponding to the electric product according to preset conditions, a preset working state data classification group is obtained, and the working state data are classified into corresponding working state data information groups through the data classification group;
step S02: calculating the similarity between different working state data;
step S03: calculating an average interval value of the working state data according to the working state data classification group and the corresponding working state data information group;
step S04: determining similarity between different operating status data of the electrical product
And determining whether the average interval value with the working state data is within a threshold range or not, and determining whether the electrical product is abnormal in the working process or not.
2. An intelligent digital management system for electric products, according to claim 1, characterized in that said monitoring module comprises:
a classification unit: the system comprises a function classification tag, a classification result and a control module, wherein the function classification tag is used for classifying the functions of the electric products in a preset area based on a preset function classification label and determining the classification result;
reference unit: the electric product classifying device is used for classifying the electric products in the preset area range based on the classification result, determining the classification result, labeling the classification result and determining the labeling sequence; wherein,
working state data unit: the device is used for acquiring working state data corresponding to the electrical product according to the label sequence based on preset detection equipment;
a working state unit: and the device is used for determining the working state of the corresponding electric product through the working state data.
3. An intelligent digital management system for electric products, according to claim 2, characterized in that said operating status unit comprises:
generating a time subunit: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for receiving working state data of an electrical product and acquiring generation time of the working state data;
the working state data set subunit: the data acquisition module is used for acquiring the generation time and the corresponding working state data of the electrical product;
analysis results subunit: the system is used for dynamically deducing the working state data set based on a preset data analysis system, performing multi-scale hierarchical analysis and determining an analysis result;
the working state subunit: and the device is used for determining the working state of the electrical product according to the analysis result.
4. The intelligent digital management system for electric products according to claim 1, wherein said control module further comprises:
a detection result acquisition unit: the device is used for acquiring a detection result;
a normal product control unit: the cloud server is used for acquiring normal working state data when the detection result is that the electrical product is normal, regularly packaging the normal working state data, determining packaged data and compressing the packaged data to a preset cloud server;
an abnormal product control unit: the intelligent control system is used for acquiring abnormal working state data when the detection result is that the electrical product is abnormal, triggering a preset protection mechanism based on the abnormal working state data, generating a fault signal and transmitting the fault signal to a preset control terminal, and intelligently controlling the electrical product through the control terminal; wherein,
the intelligent control at least comprises brake-separating control, closing control and automatic cut-off circuit control;
an uploading unit: the system is used for uploading the model of the electric product and the corresponding abnormal working state data to a preset control terminal.
5. An intelligent digital management system of electric products, according to claim 1, characterized in that said digital management module comprises:
a statistic unit: the device is used for counting the electrical products in the preset area range and determining a counting result;
a management unit: and the intelligent management system is used for intelligently managing the statistical result based on a preset intelligent management system.
6. An intelligent digital management system for electric products, according to claim 5, characterized in that said statistical unit comprises:
a statistical data unit: the system comprises a function classification module, a data processing module and a data processing module, wherein the function classification module is used for acquiring a function classification result of an electric product, dividing and counting the electric product in a preset area range according to the function classification result, and determining corresponding statistical data; wherein,
the statistical data at least comprises a product model, rated parameters and a use position;
a statistical database unit: the system comprises a statistical database, a data processing module and a data processing module, wherein the statistical database is used for acquiring the corresponding relation between an electrical product and corresponding statistical data and establishing a statistical database through the corresponding relation;
a visualization result unit: and the statistical database is used for performing visual processing on the statistical database based on a preset big data processing center to determine a statistical result.
7. An intelligent digital management system for electric products, according to claim 5, characterized in that said management unit comprises:
storing a fitting subunit: the statistical result is subjected to storage fitting, correction calculation and optimal distribution data generation;
the logic management networking subunit: the system is used for carrying out logic management on the statistical result and constructing a corresponding logic management network;
the scheduling instruction subunit: the system comprises a logic management network and a plurality of management nodes, wherein the logic management network is used for acquiring each management node in the logic management network based on the optimal distribution data and receiving a corresponding scheduling instruction through the management nodes;
intelligent management subunit: and the management node is used for determining the corresponding relation between each scheduling instruction and the corresponding management node and intelligently managing the statistical result based on the corresponding relation.
8. An intelligent digital management system for electric products according to claim 7, wherein said storage fitting subunit is used for performing storage fitting on said statistical results, and comprises the following steps:
step 1: acquiring a statistical data set of statistical results;
step 2: acquiring storage prediction influence parameters of a statistical data set, and constructing a data storage prediction function through the storage prediction influence parameters;
and step 3: correcting the storage prediction function to construct a data storage correction function;
and 4, step 4: performing minimum calculation on the data storage correction function, and calculating a minimum storage prediction influence variable; and 5: judging whether the minimum storage prediction influence variable is larger than a preset optimal storage prediction influence variable or not, and determining a judgment result;
step 6: when the judgment result is that the minimum storage prediction influence variable is larger than a preset optimal storage prediction influence variable, segmenting the statistical result according to a preset size, determining a segmented statistical result, and transmitting the segmented statistical result to the step 1 in batches;
and 7: and when the judgment result is that the minimum storage prediction influence variable is less than or equal to the preset optimal storage prediction influence variable, performing storage fitting on the statistical result.
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