CN117035297B - Campus intelligent task allocation method and system based on big data - Google Patents
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Abstract
The application relates to the technical field of internet of things park management, in particular to a method and a system for intelligent task allocation of a park based on big data, which belong to an intelligent park management method; according to the technical scheme provided by the embodiment of the application, the real-time data is classified, the abnormal data and the non-abnormal data are obtained according to the classified abnormal detection method, the hardware equipment corresponding to the abnormal data is removed, and the hardware equipment corresponding to the non-abnormal data is reserved to execute the issued task.
Description
Technical Field
The application relates to the technical field of internet of things park management, in particular to a park intelligent task distribution method and system based on big data.
Background
The management of the park is all-round and multi-level management. Generally, the park scale is large, the leader management radius and the management depth are large in relation, and the difficulty of making accurate decisions is greatly increased. The problems of step-by-step monitoring and management from projects, companies, industries and departments, the problems of adaptation and risk control of different market environments, the problems of resource integration and coordination brought by specialized division refinement and the like are all challenges facing management of a park.
However, the existing terminals of the smart park have the special problems of large data types and large data volumes, so that tasks cannot be executed due to data abnormality and operation abnormality of corresponding hardware devices when task allocation is performed on the data of the smart park.
Therefore, in order to solve the problem that the task cannot be executed due to the potential abnormality of the hardware device for executing the task in the prior art, it is necessary to provide a method and a system for intelligent task allocation in a park.
Disclosure of Invention
In order to solve the technical problems, the application provides a park intelligent task allocation method and system based on big data, which can realize the identification of potential abnormal conditions of hardware equipment and reduce the problem that tasks cannot be executed due to abnormal conditions of the hardware equipment by eliminating the hardware equipment for executing the tasks.
In order to achieve the above purpose, the technical scheme adopted by the embodiment of the application is as follows:
In a first aspect, a method for intelligent task allocation in a campus based on big data is provided, where the method is applied to a server, and a first storage unit, a second storage unit, a third storage unit and a task allocation unit are deployed in an edge service area where the server is located, and the method includes: determining a plurality of corresponding hardware devices based on task instructions issued by a task allocation unit, and acquiring a plurality of real-time data corresponding to the plurality of hardware devices; determining first data and second data according to the real-time data of the hardware equipment, wherein the first data is associated with the real-time data of the hardware equipment, the second data is associated with the real-time data of the hardware equipment, the first data is used for representing the information of the hardware equipment, and the second data is used for representing the behavior characteristics of the real-time data; determining an abnormality detection model based on the first data, detecting the second data based on the abnormality detection model, and determining the hardware equipment corresponding to the second data as abnormal equipment when the second data is abnormal; and eliminating the abnormal equipment and sending the task instruction to any other equipment.
Further, determining the first data and the second data according to the real-time data of the hardware device includes: and determining a mapping model based on the first data, and mapping the real-time data based on the mapping model to obtain first data and second data.
Further, the mapping model comprises a first mapping model and a second mapping model, the second mapping model comprising a plurality of second mapping sub-models; determining that the real-time data is mapped based on the mapping model to obtain first data and second data, wherein the mapping process comprises the following steps: and obtaining the first data through the first mapping model by the real-time data, and obtaining the second data through a corresponding second mapping sub-model in the second mapping model.
Further, obtaining the first data from the real-time data through the first mapping model includes: acquiring at least part of first data in the real-time data; mapping at least part of first data to a vector space through the first mapping model to obtain first vector data, wherein the first vector data comprises a multi-dimensional first vector; and performing dimension reduction compression on the first vector data to obtain second vector data, wherein the second vector data is the first data.
Further, the obtaining the second data through the corresponding second mapping sub-model in the second mapping model includes: acquiring at least part of second data in the real-time data; mapping at least part of the second data to a vector space through a corresponding second mapping sub-model to obtain third vector data, wherein the third vector data comprises a multidimensional third vector; and performing dimension reduction compression on the third vector data to obtain fourth vector data, wherein the second vector data is the second data.
Further, determining an anomaly detection model based on the first data, and detecting the second data based on the anomaly detection model to obtain anomaly data, including: determining a data type to be detected based on the first data, and determining an anomaly detection model based on the data type to be detected, wherein the anomaly detection model is an anomaly state decision network meeting network convergence requirements; and processing the second data based on the abnormal state decision network to obtain abnormal state activity characteristics of abnormal state activity in the second data, and determining abnormal data.
Further, the method further comprises the steps of sending the abnormal data to a corresponding user terminal, storing the abnormal data to a first storage unit, determining a storage unit corresponding to the real-time data by the first data and the second data in the non-abnormal data, and storing the storage unit.
Further, determining and storing the first data and the second data in the non-abnormal data to a storage unit corresponding to the real-time data, including: acquiring the data quantity of the second data, assigning any one of the second data, and ranking based on the assigned second data; determining a data storage margin of the second storage unit; the first data and the second data to be stored to the second storage unit are determined based on the ranking and the data storage margin.
In a second aspect, a campus intelligent task allocation system based on big data is provided, which comprises a server, and a hardware device and a user side which are connected with the server through a gateway, wherein the hardware device is used for acquiring corresponding real-time data; the server is internally provided with a task allocation device, a plurality of hardware devices are allocated based on task instructions, and a data management device is used for processing and storing a plurality of real-time data and storing the real-time data into corresponding storage units.
Further, the task allocation device comprises an abnormality detection unit, which is used for detecting whether the real-time data is abnormal or not; the data management apparatus includes: the first storage unit is used for storing the abnormal data obtained through the abnormal detection unit, and the second storage unit and the third storage unit are used for storing the non-abnormal data.
In a third aspect, there is provided a terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing a method as claimed in any one of the preceding claims when executing the computer program.
In a fourth aspect, a computer readable storage medium is provided, the computer readable storage medium storing a computer program which, when executed by a processor, implements the method of any of the above.
According to the technical scheme provided by the embodiment of the application, the real-time data is classified, the abnormal data and the non-abnormal data are obtained according to the classified abnormal detection method, the hardware equipment corresponding to the abnormal data is removed, and the hardware equipment corresponding to the non-abnormal data is reserved to execute the issued task.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, 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 application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
The methods, systems, and/or programs in the accompanying drawings will be described further in terms of exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments, wherein the exemplary numbers represent like mechanisms throughout the various views of the drawings.
Fig. 1 is a schematic structural diagram of a campus intelligent task allocation system based on big data according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of an intelligent task allocation device according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of a data management device according to an embodiment of the present application.
Fig. 4 is a schematic flow chart of an intelligent task allocation method according to an embodiment of the present application.
Fig. 5 is a schematic flow chart of a data management method according to an embodiment of the present application.
Fig. 6 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Detailed Description
In order to better understand the above technical solutions, the following detailed description of the technical solutions of the present application is made by using the accompanying drawings and specific embodiments, and it should be understood that the specific features of the embodiments and the embodiments of the present application are detailed descriptions of the technical solutions of the present application, and not limiting the technical solutions of the present application, and the technical features of the embodiments and the embodiments of the present application may be combined with each other without conflict.
In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant teachings. It will be apparent, however, to one skilled in the art that the application can be practiced without these details. In other instances, well known methods, procedures, systems, components, and/or circuits have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the present application.
The present application uses a flowchart to illustrate the execution of a system according to an embodiment of the present application. It should be clearly understood that the execution of the flowcharts may be performed out of order. Rather, these implementations may be performed in reverse order or concurrently. Additionally, at least one other execution may be added to the flowchart. One or more of the executions may be deleted from the flowchart.
Before describing embodiments of the present invention in further detail, the terms and terminology involved in the embodiments of the present invention will be described, and the terms and terminology involved in the embodiments of the present invention will be used in the following explanation.
(1) In response to a condition or state that is used to represent the condition or state upon which the performed operation depends, the performed operation or operations may be in real-time or with a set delay when the condition or state upon which it depends is satisfied; without being specifically described, there is no limitation in the execution sequence of the plurality of operations performed.
(2) Based on the conditions or states that are used to represent the operations that are being performed, one or more of the operations that are being performed may be in real-time or with a set delay when the conditions or states that are being relied upon are satisfied; without being specifically described, there is no limitation in the execution sequence of the plurality of operations performed.
(3) Edge servers, which provide a user with a channel into the network and the ability to communicate with other server devices, are typically a group of servers that perform a single function.
(4) The internet of things (Internet of Things, ioT for short) refers to collecting any object or process needing to be monitored, connected and interacted in real time through various devices and technologies such as various information sensors, radio frequency identification technologies, global positioning systems, infrared sensors and laser scanners, collecting various needed information such as sound, light, heat, electricity, mechanics, chemistry, biology and positions of the object or process, and realizing ubiquitous connection of the object and people through various possible network access, thereby realizing intelligent sensing, identification and management of the object and the process. The internet of things is an information carrier based on the internet, a traditional telecommunication network and the like, and enables all common physical objects which can be independently addressed to form an interconnection network.
(5) Neural networks, artificial neural networks (ARTIFICIAL NEURAL NETWORK, ANN), simply referred to as neural networks or neural-like networks, are in the field of machine learning and cognitive sciences, a mathematical or computational model that mimics the structure and function of biological neural networks (the central nervous system of animals, particularly the brain) for estimating or approximating functions.
According to the technical scheme provided by the embodiment of the application, the main application scene is for intelligent park data management based on the Internet of things technology and providing corresponding services. The intelligent application is realized mainly by collecting data in various intelligent devices and intelligent management is realized for the intelligent park. At present, in the establishment, management and operation process of an intelligent park, a main technical real-time means is to install more intelligent hardware, such as a camera and a data acquisition device, and then upload acquired data to a sport implementation for remote visualization. Aiming at the problem that the task cannot be executed due to the abnormality of the data and the operation abnormality of the corresponding hardware equipment when the task is allocated to the data of the intelligent park by the hardware equipment according to the corresponding operation of the task.
Based on the technical background, the embodiment of the application provides a campus intelligent task distribution system based on big data, which comprises a server, and hardware equipment and a user side which are connected with the server through a gateway, wherein the hardware equipment is used for collecting corresponding real-time data; the server is internally provided with a task allocation device, a plurality of hardware devices are allocated based on task instructions, and a data management device is used for processing and storing a plurality of real-time data and storing the real-time data into corresponding storage units.
In this embodiment, the preference for servers is edge computing servers. In practical engineering application, compared with cloud computing, the edge computing server has the advantages of safety, time delay, reliability and the like. Because edge computing devices rarely transmit data to cloud-based systems, sensitive data is rarely attacked. The lack of transmission means that an attacker must directly access the device itself, but not attack, or spoof the server itself. Saving the data on the device also provides the designer with more opportunities to collect the data by using memory encryption and specialized security hardware to protect the data. The sensitive data may also be partially processed on the edge computing device and then sent to the cloud-based system for further processing, which helps blur the data, thereby reducing its usefulness to the attacker (i.e., a trained neural network is far less sensitive to visual data than a camera). And local data processing on the device also means that the delay of the data itself is greatly reduced, which is very beneficial for applications requiring fast results. The ability to locally process data may also eliminate the need for continuous connectivity over the internet, thereby improving the reliability of the design. The reliability of the internet still affects many places around the world, and the speed of the internet may vary greatly. The use of edge computation helps to increase the available bandwidth of the local network, thereby improving other services, such as local servers and other internet of things devices, thereby increasing the maximum number of devices on a single network (by doing so, more internet of things devices can be integrated). The edge computing server is preferentially selected for the processing environment of the present system.
Referring to fig. 1, a big data-based intelligent campus task allocation system provided by an embodiment of the present application includes a server, and a hardware device and a user terminal connected to the server through a gateway, where the hardware device is used to collect corresponding real-time data; the server is internally provided with a task allocation device, a plurality of hardware devices are allocated based on task instructions, and a data management device is used for processing and storing a plurality of real-time data and storing the real-time data into corresponding storage units.
Specifically, referring to fig. 2, the task allocation apparatus 200 includes a data processing unit 210, an anomaly detection unit 220, and a task allocation unit 230, configured to perform anomaly detection based on real-time data, and determine and allocate corresponding hardware devices according to a detection result.
Referring to fig. 3, the data management apparatus 300 includes: a first storage unit 310 for storing the abnormal data obtained through the abnormality detection unit, a second storage unit 320 and a third storage unit 330 for storing the non-abnormal data.
The task allocation device is used for allocating tasks, and the data management device is used for storing and managing data in the task allocation process.
Referring to fig. 4, the intelligent task allocation method of step S410-step S440 is executed for the task allocation device, and specifically includes the following steps:
Step S410, determining a plurality of corresponding hardware devices based on task instructions issued by the task allocation unit, and acquiring a plurality of real-time data corresponding to the plurality of hardware devices.
In this embodiment, the hardware device includes various sensors and data acquisition devices, including but not limited to sensing data corresponding to various sensors and data acquired by various data acquisition devices, but mainly includes three classifications according to classifications, namely image data, digital data and sound data, wherein communication between the hardware device and the server is implemented based on a gateway. Since data collection and communication between hardware devices, gateways and servers are prior art, detailed description is omitted in this embodiment.
The method mainly aims at hardware devices to realize task execution, the number of the hardware devices is multiple, namely, corresponding hardware types are firstly determined based on task instructions, then all hardware devices corresponding to the hardware types are traversed, the hardware devices in idle states are available hardware devices, and the hardware devices are hardware devices to be selected. And collecting a plurality of real-time data corresponding to the plurality of hardware devices to be selected.
And S420, determining first data and second data according to the real-time data of the hardware equipment.
In this embodiment, the first data is used to characterize the hardware device, and the second data is used to characterize the behavior of the real-time data. In this embodiment, since the data type of the real-time data to be processed, that is, the data type of the image data, the digital data, and the sound data needs to be determined, and the determination of the data type needs a larger processing amount if the feature extraction processing is directly performed on the data, the determination of the data type can be directly determined based on the type of the corresponding hardware device and the specific information of the hardware device, wherein the first data is used for explaining the setting reason of the hardware device, and the determination of the type of the corresponding hardware device and the name of the hardware device by the first data, and the determination of what type of the corresponding acquired second data is by the type of the hardware device and the name of the hardware device can be performed. For example, the device a may be determined to be an image capturing device in the early stage of the setting in the method, whether the device a is an image capturing device is determined by first data in data capturing of the device, and by determining that the device a is an image capturing device, it is able to determine that the second data is image data.
And a specific processing method for determining the first data and the second data based on the acquired real-time data includes the steps of:
and determining a mapping model based on the first data, and mapping the real-time data based on the mapping model to obtain first data and second data. The mapping model comprises a first mapping model and a second mapping model, wherein the first mapping model is used for processing first data, the second mapping model is used for processing second data, the setting of the mapping model is based on the structural characteristics of the first data and the second data, the first data is used for representing the device type, and the first data of the device type is more consistent in structure. That is, the device data is managed correspondingly when the device management is performed, so that the setting for the first mapping model is a common model. In the present embodiment, since the acquired data structure is different from the data structure to be processed later, the data structure that can be processed later is to be processed based on the acquired data, and the solution to this problem is realized by a data mapping model. However, since the data acquired in the present embodiment includes at least image data, sound data, and digital data, it is necessary to configure corresponding mapping models for the above three data types, respectively.
In this embodiment, since the data to be processed is public data, the above problems can be solved by setting the mapping model in order to ensure the guarantee of the privacy of the data during the data processing and the data transmission.
In this embodiment, therefore, the second mapping model includes a plurality of mapping sub-models configured independently for a plurality of second data types, whereas in this embodiment, the second data types include three data types, so that in this embodiment, the second mapping sub-model is also included for the second mapping model. The corresponding equipment information is determined through the first data, the type of the collected second data is determined based on the equipment information, the corresponding second mapping sub-model is determined based on the determined second data type, and the second data is mapped through the second mapping sub-model to obtain the second data of the data structure which can be processed subsequently.
The obtaining of the first data includes:
At least part of the first data in the real-time data is acquired.
Mapping at least part of the first data to a vector space through the first mapping model to obtain first vector data, wherein the first vector data comprises a multi-dimensional first vector.
And performing dimension reduction compression on the first vector data to obtain second vector data, wherein the second vector data is the first data.
The obtaining of the second data includes:
At least part of the second data in the real-time data is acquired.
And mapping at least part of the second data to a vector space through a corresponding second mapping sub-model to obtain third vector data, wherein the third vector data comprises a multidimensional third vector.
And performing dimension reduction compression on the third vector data to obtain fourth vector data, wherein the second vector data is the second data.
By the processing, the data to be processed with standard data structure, small data volume and privacy can be obtained.
In this embodiment, because the mapping model can achieve two technical effects, the method includes preprocessing unstructured data to obtain structured data and encrypting the structured data to obtain encrypted structured data, and converting unstructured data into structured data is mainly used for reducing unnecessary information of the data, reducing cost of data storage and improving utilization rate of storage space. Therefore, the pre-processing mapping model and the encryption mapping model are included for the mapping model, so the first pre-processing mapping sub-model, the second encryption mapping sub-model and the second encryption mapping sub-model are included for the first mapping sub-model and the second mapping sub-model, respectively.
Wherein a corresponding plurality of preprocessing mapping sub-models and a corresponding plurality of encryption mapping sub-models are set for the image data, the sound data, and the digital data.
The following description will be made with respect to the preprocessing map sub-model and the encryption map sub-model for different data.
Preprocessing is carried out on the image data, preprocessing mapping submodels are used for preprocessing, and preprocessing mapping functions are configured in the preprocessing mapping submodels corresponding to the image data, wherein the functions are specifically as follows:
d＝T(s)＝csγ；
Wherein c and gamma are constants, and the image data is processed based on the value range of gamma, specifically:
Firstly, gray processing is carried out on the image data to obtain gray image data, and then the gray image data is processed based on the preprocessing mapping function to obtain a gray image with high definition.
When gamma is larger than 1, the low gray scale range in the input image is compressed, and the high gray scale range is stretched, so that the image enhancement effect of reinforcing the bright part and compressing the dark part is realized. When gamma is less than 1, the high gray scale range in the input image is compressed, and the low gray scale range is stretched, so that the image enhancement effect of enhancing the dark part and compressing the bright part is realized. In this embodiment, the image data is processed by setting the above image data preprocessing mapping sub-model, so as to obtain image data with higher definition and smaller data size.
The method for obtaining the new image data and the encrypted data corresponding to the image data specifically comprises the following steps: and processing the gray image data to obtain initial image matrix data, then regenerating a random encryption matrix, summing the initial image matrix and the encryption matrix through circulation to obtain a new matrix, and outputting the initial matrix as encrypted image data for storage. In the decryption process for the subsequent reading of the image, the above matrix may be decrypted by generating a corresponding key after verification by the user in the management system, which will not be described in detail in the present embodiment.
The encryption method is a simple encryption method, and has the defect that original pictures are covered only by a random matrix mode, but the image encryption effect after encryption is unstable due to unstable quality of the random matrix. Therefore, in order to address the above drawbacks, another encryption method is provided, which is implemented based on the following encryption formula:
Wherein mod represents a modulo operation; Representing a bitwise exclusive or operation. r 1,r2,r3 is the product of the chaotic sequence value and 255, the key for replacing transformation is provided by an encryption system corresponding to r 1,r2,r3, multiple transformations can be carried out to search for better encryption effect, I (I, j) is the gray value of the image (I, j), and the gray value of I (I, j) at (I, j) after replacement is represented by the gray value of I (I, j) which is satisfied by 1.ltoreq.i.ltoreq.M and 1.ltoreq.j.ltoreq.N.
And the decryption formula for the settings is:
the processing procedure for the digital data also comprises the process of encrypting the digital data after the data volume reduction processing, and the following description is made for the application scenario, specifically comprising the following steps:
The unstructured data is subjected to structured data acquisition by normalization processing, which in this embodiment is based on the following formula:
p_new= (P-MI)/(MA-MI), where P is the original data, MI is the minimum value in this attribute, and MA is the maximum value in this attribute. By the above processing, all values are limited to 0-1, resulting in a standard structure of data.
The data of the standardized processing is in a matrix structure, namely, the data of the standardized processing is processed by an initial matrix to obtain a target matrix, and the method comprises the following steps of:
Zero-mean processing is carried out on the initial matrix C to obtain covariance matrixes of n characteristics, wherein a definition formula for covariance is as follows:
After zero-mean processing, the covariance definition formula is:
The covariance matrix corresponding to the initial moment C is:
In the present embodiment The initial data values for feature X and feature Y representing n samples.
The covariance matrix is processed to obtain n standard orthogonal eigenvectors of the covariance matrix, and the n standard orthogonal eigenvectors are arranged according to the size, and in the embodiment, the matrix P for diagonalizing the covariance matrix C is searched for in the processing process, specifically, the n standard orthogonal eigenvectors are obtained through the following formula:
Wherein PA is: wherein/> The n samples represented are valued under the newly constructed feature.
The new n eigenvalues are obtained by projecting the obtained diagonalized matrix P to the A direction, then the variance is obtained based on the new n eigenvalues, the eigenvalue with the largest variance corresponding to the obtained n eigenvalues is reserved, the larger the variance is, the larger the data distribution degree is, the larger the information content contained in the characteristics is, so that a group of data with larger variance is reserved, and the whole compression processing process is completed.
The compressed data is obtained according to the above process, and then encryption processing is carried out on the data, wherein the specific process is carried out based on an encryption mapping function, and the encryption mapping function is as follows: x n+1＝μxn(1-xn), a key space k= { x 0, μ }, where x 0 e (0, 1) is an initial value and μ is a control parameter.
The audio data is audio data, and the preprocessing of the audio data is realized by an encoder, and the method specifically comprises the following steps: the method comprises the steps of inputting a code stream in audio data to a decoder, decoding information such as bit rate, sampling rate and the like in the code stream, inputting a residual signal coding code stream to a next-step entropy decoder, decoding the residual signal by the entropy decoder according to code stream information transmitted by an upper stage, recovering original audio information by the residual signal and a prediction coefficient, recovering stereo left and right channel signals by channel setting information in the code stream, and outputting the left and right channels according to a required format.
The corresponding audio data is obtained through the processing method, and encryption processing is carried out on the obtained audio data, wherein the method comprises the following steps: obtaining the audio data obtained by the previous processing, obtaining the length of the audio data, carrying out Fourier transform on the audio data, extracting each carrier frequency in the audio data after Fourier transform, constructing a carrier sequence based on each obtained carrier frequency, obtaining a corresponding matrix, carrying out inversion processing on the matrix to obtain a processed matrix, constructing a direct current component, carrying out synchronous modulation on data based on the direct current component, and carrying out Fourier transform on the encrypted data to obtain the encrypted audio data.
And S430, determining an abnormality detection model based on the first data, detecting the second data based on the abnormality detection model, and determining the hardware device corresponding to the second data as an abnormal device when the second data is abnormal.
In this embodiment, the storage of the data is ranked based on the importance of the obtained data, and the information mainly managed in the management of the intelligent park or the information managed in real time is abnormal, that is, whether the data is abnormal or not is judged by the collected data, and the hardware configuration and the software configuration of the intelligent park are optimized after alarming and recording based on the abnormal situation.
Therefore, for this scenario, it is necessary to perform abnormality determination on the collected real-time data, and the object for abnormality determination is the second data, and since the second data is based on a different data type, the abnormality determination method is also different.
In the present embodiment, the detection of the abnormal data is mainly based on the configured abnormal detection model, and because the corresponding abnormal detection model is also different for different data types, it is necessary to determine the specific type and determine the corresponding abnormal detection model for different data types. And determining the type of the data collected by the equipment by determining the corresponding equipment information through the first data based on the first data aiming at the judgment of the data type and the corresponding abnormality detection model, and determining the corresponding abnormality detection model based on the data type. For example, if the determined data type is image data, the abnormality detection model is an abnormality detection model for the image.
In this embodiment, the abnormal state decision network meeting the network convergence requirement is based on the abnormal detection model, where the abnormal state decision network is a neural network obtained through training, and the training result is that the corresponding neural network is in a convergence state. The image neural network and the natural language neural network belong to the prior art, and in this embodiment, the configuration can be performed by adopting the existing neural network.
The specific method for the process is as follows: and processing the second data based on the abnormal state decision network to obtain abnormal state activity characteristics of abnormal state activity in the second data, and determining abnormal data.
And when the second data is abnormal, determining the hardware device corresponding to the second data as an abnormal device.
And sending the abnormal data to a corresponding user terminal, and storing the abnormal data to a first storage unit. In this embodiment, the processing of the abnormal data includes real-time alarming and storing after alarming, and the storage position of the abnormal data is the first storage unit, that is, the obtained abnormal data is stored in the first storage unit.
And S440, eliminating the abnormal equipment and issuing the task instruction to any other equipment.
In the embodiment of the application, aiming at the abnormal equipment determined in the step S130, the abnormal equipment needs to be removed from a plurality of hardware equipment to be selected, and steps S120-S130 are carried out in the plurality of hardware equipment, the method traverses the plurality of hardware equipment to determine the final executable hardware equipment, and then the final target hardware equipment is determined based on specific task cost factors.
Referring to fig. 5, the data management device performs step S510 to step S520, for implementing management of abnormal data, and specifically includes the following steps:
And S510, transmitting the abnormal data to a corresponding user terminal.
And S520, storing the abnormal data in a first storage unit, determining a storage unit corresponding to the real-time data by the first data and the second data in the non-abnormal data, and storing the first data and the second data.
For this step, specifically, it includes: acquiring the data quantity of the second data, assigning any one of the second data, and ranking based on the assigned second data; determining a data storage margin of the second storage unit; the first data and the second data to be stored to the second storage unit are determined based on the ranking and the data storage margin.
In the embodiment of the application, the non-abnormal data is stored in the second storage unit and the third storage unit.
The data size for such data is large, so that corresponding settings need to be made for the storage mechanism and the management mechanism, namely the following methods are included:
And acquiring the data quantity of the second data, assigning any one of the second data, and ranking based on the assigned second data.
In this embodiment, the assignment process for the second data is determined based on the device type information in the first data, and the specific value for the assignment process is based on the statistical data of the abnormal situation occurring in the history data, that is, when the abnormal situation of the data collected by a certain device is relatively high, the corresponding device, that is, the corresponding first data and the second data corresponding to the first data, is relatively high, and the specific value for the assignment may be obtained according to expert experience, which is not described in detail in this embodiment, and may be implemented by means of manual marking.
And determining the data storage allowance of the second storage unit. The value of the data amount that can be stored is determined by the remaining data storage margin of the second storage unit, and in this embodiment, a certain redundancy is set for the data storage margin in order to ensure the system processing correctness, that is, when the actual data storage margin is M, the available storage space is m×80%, or other values, and the setting for the specific value may be obtained through historical data or may be obtained through an expert system, which is not described in detail in this embodiment.
The first data and the second data to be stored to the second storage unit are determined based on the ranking and the data storage margin. And storing the second data in a ranking mode, and storing the rest second data to a third storage unit when the storage space of the second storage unit reaches a storage threshold value.
In order to further optimize the storage method, the historical data in the second storage unit needs to be updated and optimized, specifically: and comparing the historical second data with the historical second data to obtain a ranking, and storing the historical second data, the historical first data, the real-time second data and the real-time first data into the second storage unit and the third storage unit based on the data of the second storage unit and the margin in a time sequence arrangement mode.
Referring to fig. 6, the above intelligent task allocation method can be integrated into a terminal device 600 comprising a memory 610, a processor 620 and a computer program stored in the memory and executable on the processor, wherein the processor performs the big data based campus intelligent task allocation method. In this embodiment, the terminal device communicates with the user terminal, and transmits the acquired detection information to the corresponding user terminal, so as to implement transmission of the detection information on hardware. The method is based on network implementation aiming at the information sending mode, and an association relation between the user terminal and the terminal equipment is required to be established before the terminal equipment is applied, and the association between the terminal equipment and the user terminal can be realized through a registration mode. The terminal device can be aimed at a plurality of user terminals or one user terminal, and the user terminal communicates with the terminal device through passwords and other encryption modes.
In this embodiment, the memory, the processor and each element of the communication unit are electrically connected directly or indirectly to each other, so as to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory is used for storing specific information and programs, and the communication unit is used for sending the processed information to the corresponding user side.
In this embodiment, the storage module is divided into two storage areas, where one storage area is a program storage unit and the other storage area is a data storage unit. The program storage unit is equivalent to a firmware area, the read-write authority of the area is set to be in a read-only mode, and the data stored in the area can not be erased and changed. And the data in the data storage unit can be erased or read and written, and when the capacity of the data storage area is full, the newly written data can cover the earliest historical data.
The memory may be, but is not limited to, random access memory (Random Access Memory, RAM), read Only Memory (ROM), programmable read only memory (Programmable Read-only memory, PROM), erasable read only memory (Erasable Programmable Read-only memory, EPROM), electrically erasable read only memory (ele ultrasound ric Erasable Programmable Read-only memory, EEPROM), etc.
The processor may be an integrated circuit chip having signal processing capabilities. The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also Digital Signal Processors (DSPs)), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It is to be understood that the terminology which is not explained by terms of nouns in the foregoing description is not intended to be limiting, as those skilled in the art can make any arbitrary deduction from the foregoing disclosure.
The person skilled in the art can undoubtedly determine technical features/terms of some preset, reference, predetermined, set and preference labels, such as threshold values, threshold value intervals, threshold value ranges, etc., from the above disclosure. For some technical feature terms which are not explained, a person skilled in the art can reasonably and unambiguously derive based on the logical relation of the context, so that the technical scheme can be clearly and completely implemented. The prefixes of technical feature terms, such as "first", "second", "example", "target", etc., which are not explained, can be unambiguously deduced and determined from the context. Suffixes of technical feature terms, such as "set", "list", etc., which are not explained, can also be deduced and determined unambiguously from the context.
The foregoing disclosure of embodiments of the present application will be apparent to and complete in light of the foregoing disclosure to those skilled in the art. It should be appreciated that the development and analysis of technical terms not explained based on the above disclosure by those skilled in the art is based on the description of the present application, and thus the above is not an inventive judgment of the overall scheme.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements and adaptations of the application may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within the present disclosure, and therefore, such modifications, improvements, and adaptations are intended to be within the spirit and scope of the exemplary embodiments of the present disclosure.
Meanwhile, the present application uses specific terms to describe embodiments of the present application. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the application. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various portions of this specification are not necessarily all referring to the same embodiment. Furthermore, certain features, structures, or characteristics of at least one embodiment of the present application may be combined as suitable.
In addition, those skilled in the art will appreciate that the various aspects of the application are illustrated and described in the context of a number of patentable categories or conditions, including any novel and useful processes, machines, products, or materials, or any novel and useful improvements thereof. Accordingly, aspects of the application may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.) or by a combination of hardware and software. The above hardware or software may be referred to as a "unit," component, "or" system. Furthermore, aspects of the application may be embodied as a computer product in at least one computer-readable medium, the product comprising computer-readable program code.
The computer readable signal medium may comprise a propagated data signal with computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take on a variety of forms, including electro-magnetic, optical, etc., or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer readable signal medium may be propagated through any suitable medium including radio, electrical, fiber optic, RF, or the like, or any combination of the foregoing.
Computer program code required for carrying out aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming such as Java, scala, smalltalk, eiffel, JADE, emerald, C ++, c#, vb net, python, etc., or similar conventional programming languages such as the "C" programming language, visual basic, fortran2003, perl, COBOL 2002, php, abap, dynamic programming languages such as python, ruby and groovy or other programming languages. The programming code may execute entirely on the user's computer, or as a stand-alone software package, or partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any form of network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as software as a service (SaaS).
Furthermore, the order in which the processing elements and sequences are described, the use of numerical letters, or other designations are used is not intended to limit the order in which the processes and methods of the application are performed unless specifically recited in the claims. While in the foregoing disclosure there has been discussed, by way of various examples, some embodiments of the application which are presently considered to be useful, it is to be understood that this detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments of the application. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
It should also be appreciated that in the foregoing description of at least one embodiment of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of at least one embodiment of the application. This method of disclosure does not imply that the subject application requires more features than are set forth in the claims. Indeed, less than all of the features of a single embodiment disclosed above.
Claims (4)
1. The intelligent campus task allocation method based on big data is characterized by being applied to a server, wherein a first storage unit, a second storage unit, a third storage unit and a task allocation unit are deployed in an edge service area where the server is located, and the method comprises the following steps:
Determining a plurality of corresponding hardware devices based on task instructions issued by a task allocation unit, and acquiring a plurality of real-time data corresponding to the plurality of hardware devices;
Determining first data and second data according to the real-time data of the hardware equipment, wherein the first data is associated with the real-time data of the hardware equipment, the second data is associated with the real-time data of the hardware equipment, the first data is used for representing information of the hardware equipment, and the second data is used for representing behavior characteristics of the real-time data;
Determining an abnormality detection model based on the first data, detecting the second data based on the abnormality detection model, and determining the hardware equipment corresponding to the second data as abnormal equipment when the second data is abnormal;
removing the abnormal equipment and issuing the task instruction to any other equipment;
Determining first data and second data according to the real-time data of the hardware device, wherein the determining comprises the following steps:
Determining a mapping model based on the first data, and mapping the real-time data based on the mapping model to obtain first data and second data;
The mapping model comprises a first mapping model and a second mapping model, and the second mapping model comprises a plurality of second mapping sub-models;
Determining that the real-time data is mapped based on the mapping model to obtain first data and second data, wherein the mapping process comprises the following steps:
The real-time data is processed through the first mapping model to obtain first data, and the second data is obtained through a corresponding second mapping sub-model in the second mapping model;
Obtaining the first data by the real-time data through the first mapping model, including:
acquiring at least part of first data in the real-time data;
Mapping at least part of the first data to a vector space through the first mapping model to obtain first vector data, wherein the first vector data comprises a multi-dimensional first vector;
Performing dimension reduction compression on the first vector data to obtain second vector data, wherein the second vector data is the first data;
obtaining the second data through a corresponding second mapping sub-model in the second mapping model comprises the following steps:
acquiring at least part of second data in the real-time data;
Mapping at least part of the second data to a vector space through a corresponding second mapping sub-model to obtain third vector data, wherein the third vector data comprises a multidimensional third vector;
Performing dimension reduction compression on the third vector data to obtain fourth vector data, wherein the fourth vector data is the second data;
The second mapping model comprises a plurality of mapping sub-models which are configured independently corresponding to a plurality of second data types, and the second data types comprise three data types, so that the second mapping model comprises corresponding second mapping sub-models; determining corresponding equipment information through the first data, determining what type of data is acquired by the second data based on the equipment information, determining a corresponding second mapping sub-model based on the determined second data type, and performing mapping processing on the second data through the second mapping sub-model to obtain second data of a data structure which is processed later;
The mapping model comprises a preprocessing mapping model and an encryption mapping model, so the second mapping sub-model comprises a second preprocessing mapping sub-model and a second encryption mapping sub-model;
When the second data type is image data, preprocessing is performed on a preprocessing mapping sub-model corresponding to the image data, and a preprocessing mapping function is configured in the preprocessing mapping sub-model corresponding to the image data, wherein the function specifically is as follows:
；
Wherein c and gamma are constants, and the image data is processed based on the value range of gamma, specifically:
firstly, carrying out gray processing on image data to obtain gray image data, and then processing the gray image data based on the preprocessing mapping function to obtain a gray image with high definition;
When gamma > 1, the low gray scale range in the input image is compressed, and the high gray scale range is stretched; when gamma < 1, the high gray scale range in the input image is compressed, and the low gray scale range is stretched; the image data is processed through the image data preprocessing mapping sub-model, so that image data with higher definition and smaller data size are obtained;
the encryption method is realized based on the following encryption formula:
；
wherein mod represents a modulo operation; and represents a bitwise exclusive or operation; r 1,r2,r3 is the product of the chaotic sequence value and 255, the key for replacing transformation is provided by an encryption system corresponding to r 1,r2,r3, the encryption system can perform multiple transformations to search for better encryption effect, I (I, j) is the gray value of the image (I, j), and the gray value of I (I, j) at (I, j) after replacement is represented by the gray value of I (I, j) which is more than or equal to 1 and less than or equal to M and less than or equal to 1 and less than or equal to N;
And the decryption formula for the settings is:
；
When the second data type is digital data, the processing procedure for the digital data also comprises the process of encrypting the digital data after the data volume reduction processing, and specifically comprises the following steps:
The unstructured data are obtained through normalization processing, and the method is based on the following formula:
P_new= (P-MI)/(MA-MI), where P is the original data, MI is the minimum value in this attribute, and MA is the maximum value in this attribute; through the above processing, all values are limited between 0 and 1, so that data with a standard structure is obtained;
the standardized data is in a matrix structure, and the standardized data is used as an initial matrix to be processed to obtain a target matrix, and the method comprises the following steps:
Zero-averaging is carried out on the initial matrix C to obtain covariance matrixes of n characteristics, wherein a definition formula for covariance is as follows:
，
after zero-mean processing, the covariance is defined as:
；
the covariance matrix corresponding to the initial matrix C is:
，
wherein, Initial data values representing feature X and feature Y for n samples;
processing the covariance matrix to obtain n standard orthogonal eigenvectors of the covariance matrix, and arranging the n standard orthogonal eigenvectors according to the size to obtain a matrix P for diagonalizing the covariance matrix C, wherein the matrix P is obtained by the following formula:
，
Wherein PA is: Wherein/> Representing the value of the ith sample under the newly constructed feature;
The method comprises the steps of projecting an obtained diagonalized matrix P to an A direction to obtain new n eigenvalues, then obtaining variances based on the new n eigenvalues, reserving the eigenvalue with the largest variance corresponding to the obtained n eigenvalues, wherein the larger the variance is, the larger the data distribution degree is, and the larger the information content contained in the characteristics is, so that a group of data with larger variance is reserved, and the whole compression processing process is completed;
the compressed data is obtained according to the above process, and then encryption processing is carried out on the data, wherein the specific process is carried out based on an encryption mapping function, and the encryption mapping function is as follows: Selecting a key space/> Wherein/>And [ mu ] is the control parameter and is the initial value.
2. The big data based campus intelligent task allocation method according to claim 1, wherein determining an anomaly detection model based on the first data, detecting the second data based on the anomaly detection model to obtain anomaly data, comprises:
determining a data type to be detected based on the first data, and determining an anomaly detection model based on the data type to be detected, wherein the anomaly detection model is an anomaly state decision network meeting network convergence requirements;
and processing the second data based on the abnormal state decision network to obtain abnormal state activity characteristics of abnormal state activity in the second data, and determining abnormal data.
3. The big data based campus intelligent task allocation method according to claim 2, further comprising sending the abnormal data to a corresponding user terminal, storing the abnormal data in a first storage unit, and determining and storing a storage unit corresponding to the real-time data by the first data and the second data in non-abnormal data.
4. The big data based campus intelligent task allocation method according to claim 3, wherein determining and storing the first data and the second data in the non-abnormal data to the storage unit corresponding to the real-time data includes: acquiring the data quantity of the second data, assigning any one of the second data, and ranking based on the assigned second data; determining a data storage margin of the second storage unit; the first data and the second data to be stored to the second storage unit are determined based on the ranking and the data storage margin.
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