CN102880802B - A kind of assay method for the major hazard source towards industrial and mining establishment's safety production cloud service platform system - Google Patents

A kind of assay method for the major hazard source towards industrial and mining establishment's safety production cloud service platform system Download PDF

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CN102880802B
CN102880802B CN201210370810.7A CN201210370810A CN102880802B CN 102880802 B CN102880802 B CN 102880802B CN 201210370810 A CN201210370810 A CN 201210370810A CN 102880802 B CN102880802 B CN 102880802B
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accident
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童金虎
胡卫强
陈金华
张凯
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Zhejiang Iopinfo Technology Co Ltd
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Abstract

A kind of assay method for the major hazard source towards industrial and mining establishment's safety production cloud service platform system, described major hazard source evaluation is evaluated two aspects from natrual danger evaluation and real danger and is carried out, add external environment condition and offset the factor, evaluation model is proposed, select the numbering of dangerous matter sources, then the related data of data base is searched according to this numbering, it is analyzed after obtaining data, judge the species number of the material comprised in these dangerous matter sources data, whether toxic, it is exactly the scope that this dangerous matter sources affects according to the border circular areas that formula calculates, and come to dangerous matter sources classification by maximum dead radius.

Description

A kind of assay method for the major hazard source towards industrial and mining establishment's safety production cloud service platform system
Technical field
The invention belongs to industrial and mineral system automation information gathering and control field, relate to power system real-time data acquisition, process and control system, be based especially on industrial and mineral real time data integrated processing system and the method for designing of cloud computing.
Background technology
Being widely used cloud in the Internet, mainly included type three kinds different, namely software service SaaS, and namely platform services PaaS, and namely architecture services IaaS.Wherein PaaS provides the application development of the complete or part that user can access, and SaaS then provides the complete application program that can directly use, and manages ERM as by Internet.And the cloud in IaaS is the most key with Mass Data Management technology, mass data distribution memory technology, Intel Virtualization Technology, cloud computing platform management technique.Wherein Mass Data Management technology and distributed storage technology are the important component parts that data process, cloud computing needs distribution, magnanimity data are processed, analyzed, therefore, data management technique allows for managing substantial amounts of data efficiently, additionally needs the mode of redundant storage to ensure the reliability of data.In cloud computing system widely used data-storage system be Google GFS and Hadoop team exploitation increasing income of GFS realize HDFS.
The safety in production of industrial and mining establishment is in actual production run, mass data (such as various safety in production standards, the data of monitoring, educational training knowledge etc.) is necessarily had to need to process, cloud service platform adopts Distributed Calculation storage mode, by parallel processing in distribution of computation tasks to multiple stage machine, improve arithmetic speed with this.The data message of safety in production enterprise practical productive accumulation is carried out unified management and storage by cloud platform, for providing based on functions such as the prediction of data, abnormality detection in production process, it is achieved the safety in production of enterprise.
Data acquisition function and the concrete logic judging function of tradition industrial and mining establishment are tightly combined, generally completed by comparatively single hardware device, therefore, completing different functions and be accomplished by differently configured equipment or system, the equipment of these difference in functionalitys or system have each independent data acquisition unit.Each data acquisition also exists that overlapping collection, utilization rate be not high, data and the problem such as information content is inconsistent, time disunity, define, horizontal system many with longitudinal level and mostly be " information island " of principal character, constrain merging further and application of information, the repeated acquisition of data and information and repetition transmission process and certainly will cause the waste of various resource.Instrument in most of industrial and mining establishment is divided into three classes: protection class, observing and controlling class, measures class, and they have the data acquisition and processing (DAP) unit of oneself, can be connected with monitoring and display system, measure for underlying parameter.But there is also techniques below problem:
(1) precision of instrument is different, causes data inconsistent, also uneconomical on cost.
(2) it is also different in different instrument that instrument to collect frequency is different, algorithm difference causes same value, data redundancy and also chaotic.
(3) system and equipment, communication difficulties between equipment and equipment, lack unified standard.
(4) process data disappearance, in the process of sampling, creating substantial amounts of process data, these data are all valuable for accident analysis or the operation expanding in later stage, but we obtain from instrument all through n times computing " secondary data ", " simplification value ".This separate configurations, independent calculate, problem that the device of standalone feature brings is that information sharing is poor, utilization rate is low with hardware and software problem of resource waste.
For solving above-mentioned problem; especially data gather respectively, data process respectively with calculate, data are difficult to key issues such as sharing; it is necessary to set up a set of new framework and mechanism; the data realizing same type equipment once gather, and cloud computing technology can be utilized to realize the multiple application functions such as observing and controlling, protection, phasor measurement, metering under a whole set of unified software application platform.
Summary of the invention
It is an object of the invention to provide the safety production cloud service platform system of a kind of industrial and mining establishment; solving equipment separate configurations, independent calculate, information sharing that the device of standalone feature brings is poor, utilization rate is low and the problem of the soft and hardware wasting of resources; realize the multiple application such as the once collection of same type device data, and observing and controlling, protection, phasor measurement, metering.
The safety production cloud service platform system of described industrial and mining establishment, including:
1) safety production cloud service platform towards industrial and mining establishment is set up, this platform intergration supports the functions such as the mass data processing of safety in production service, production safety management business cooperation and system administration, provides important technical support for safety in production and the government regulation of industrial and mining establishment.
2) based on security incident event Analysis of Multidimensional Association Rule technology, frequent factor and the potential rule of the accident of may result in and near miss incidents generation can be had by analysis mining from the creation data of magnanimity, set up the dynamic monitoring early warning analysis method of safety in production standard effectiveness and shortage of standard situation, formulate, for government and supervision department, the foundation that science is provided based on operation or work standard risk, rational.
3) mass data processing method in industrial and mining establishment's safety in production is proposed, specifically include the safety in production abnormality detection technology based on support vector machine, knowledge discovering technologies based on generalization rule reasoning, the mass data produced can be analyzed and knowledge excavation, thus helping enterprise to promote accident prevention early warning and emergency disposal ability, improve enterprise safety operation level.
4) exploitation production safety management tool set, facilitates safety management that industrial and mineral industrial security produces, supervision, configuration etc., it is achieved quickly the investigating of enterprise, quickly respond.
The safety production cloud service platform towards industrial and mining establishment wherein developed, is integrated with the functions such as the support safety in production mass data processing of service cloud, production safety management business cooperation and system administration, it is achieved monitoring and the early warning in real time to enterprise's production safety.
Specifically, a kind of assay method for the major hazard source towards industrial and mining establishment's safety production cloud service platform system, it is characterized in that: the described safety production cloud service platform system towards industrial and mining establishment, including secure cloud service platform portal sub-system, system administration develops subsystem with related tool collection, application service layer subsystem, virtual resource layer subsystem, platform service supporting layer subsystem, access and adaptation layer subsystem, security service resource subsystem, infrastructure services supporting layer subsystem;In described platform service supporting layer subsystem, major hazard source evaluation is evaluated two aspects from natrual danger evaluation and real danger and is carried out, add external environment condition and offset the factor, following evaluation model is proposed: select the numbering of dangerous matter sources, then the related data of data base is searched according to this numbering, it is analyzed after obtaining data, judge the species number of the material comprised in these dangerous matter sources data, if toxic, according to
A = { Σ i = 1 n Σ j = 1 n ( B 111 ) i W ij ( B 112 ) j } B 12 ( 1 - B 2 k )
Result of calculation determine dead radius, and then determine a border circular areas, the physical hazard evaluation of estimate of i-th kind of material of (B111) i in formula;(B112) the process dangerous evaluation of estimate of j jth kind material;The correlation coefficient of Wij jth item technique and i-th kind of physical hazard;The serious evaluation of estimate of B12 accident;The factor is offset in B21 process equipment container building structure;B22 peopleware offsets the factor;The factor is offset in B23 safety management;N is natural number;Described border circular areas is exactly the scope of this dangerous matter sources impact, and is come to dangerous matter sources classification by maximum dead radius.
Accompanying drawing explanation
The safety production cloud service platform system of Fig. 1 industrial and mining establishment
Fig. 2 industrial and mining establishment real time data integrated processing system
Fig. 3 wireless data collection device cluster
Fig. 4 wireless data collection device structure chart
Fig. 5 cloud storage system structure chart
Fig. 6 cloud storage data center structure chart
Fig. 7 device resource monitor in real time model
Fig. 8 equipment performance monitor in real time model
Fig. 9 SVM training and prediction flow chart
Figure 10 Immune Evolutionary Algorithm flow process
Figure 11 Association Rule Analysis flow process
Figure 12 Petri network mining model
Detailed description of the invention
Fig. 1 show the cloud service platform of a kind of safety in production towards industrial and mining establishment, it is integrated with the functions such as the support safety in production real time data processing of service cloud, production safety management business cooperation and system administration, it is achieved monitoring and the early warning in real time to enterprise's production safety.
Specifically, the described safety production cloud service platform system towards industrial and mining establishment, including secure cloud service platform portal sub-system, system administration develops subsystem, application service layer subsystem, virtual resource layer subsystem with related tool collection, platform service supporting layer subsystem, access and adaptation layer subsystem, security service resource subsystem, infrastructure services supporting layer subsystem.
Described secure cloud service platform portal sub-system is responsible for outside is realized information inquiry and management based on web2.0 technology, platform is supervised including government, enterprise's application platform, trade supervision platform, described government supervision platform is responsible for government and is supervised device data and the property tax data of enterprise operation in real time, described enterprise application system is responsible for the monitoring and the feedback that externally provide value-added service and application query, described trade supervision platform to be used for product quality.
Described system administration and related tool collection are developed subsystem and are included the exploitation of cloud service instrument, deployment, monitoring, safety management, log management and configuration.
Described application service layer, including mass data processing system and business cooperation operating system, wherein said mass data processing system is responsible for, to data acquisition, Data Integration, data management and provide standardized service interface for secure cloud service platform door provide data stream, and described business cooperation operating system is responsible for collaborative task and cross-domain task are built mathematical model, it is specially and business datum is acquired, the business datum of the service content of cloud platform offer with collection is integrated, the development of prediction follow-up business, described cross-domain task specifically includes management and the monitoring in different business field, work between the different departments of enterprise is realized task coordinate.
Described virtual resource layer, the digital resource after virtualization is provided principally for Cloud Server, specifically include knowledge services resource pool, produce Service Source pond, data message resource pool, described virtual resource layer includes these original provided knowledge services, produces the data message resource of service, pass through Intel Virtualization Technology, this part data content is incorporated in the middle of application service layer, at any time can by described mass data processing unit and business cooperation cell call.
Described access and adaptation layer subsystem, mainly from relevant criterion and verifying test system, obtain data resource, additionally include security service resource, specifically include production device data, standardization fundamental norms, laws and regulations, safety management system, accident source historical data, educational training knowledge, safety in production put into, organization be responsible for, hidden danger check information from third party's service adapter access.
Described platform service supporting layer is to above-mentioned application service layer, virtual resource layer, access Adaptation Layer Service, cloud service management is provided and supports engine, transaction collaborative logic$$$$ engine, knowledge accumulating and classification engine, described cloud service management provides cloud service registration with supporting engine for described application service layer, issue, nullifies, cloud service search, scheduling, combination, cloud service performs and monitoring;Described transaction collaborative logic$$$$ engine provides process management for business cooperation, cost accounting, credit evaluation, described knowledge accumulating provides the dispersion knowledge acquisition of industry multiple resource with classification engine for business cooperation, domain knowledge models, domain knowledge assembles classification, and for other responsible operation management in described platform service supporting layer, operation management, terminal software is developed, the module of platform development instrument is described virtual resource layer and access and Adaptation Layer Service, many tenants service is responsible in wherein said operation management, order management, delivery management, payment management, user manages, integration managing, safety management is responsible in described operation management, performance management and optimization, system configures, mass data is fault-tolerant to be managed with credibility, described terminal software is developed, fusion including heat transfer agent manages, Service Source graphic interface and pervasive man-machine interaction instrument, and platform development instrument, described platform service supporting layer is all passed through cloud computing by infrastructure services supporting layer, cloud network, cloud storage is unified to be supported.
Industrial and mining establishment's real-time data processing system based on cloud computing, as shown in Figure 2, it is made up of data acquisition equipment, Data Integration equipment, data management apparatus, standardized service interface equipment and high speed Networks of Fiber Communications five major part reliably, constitute the industrial and mining establishment's real time data integrated processing system based on cloud computing
The network structure of this data integrated system shown in data acquisition equipment: Fig. 2, wireless senser cluster is responsible for gathering the data that industrial and mineral is on-the-spot, and transfer data to industrial and mineral Data Integration equipment, on described Data Integration equipment, standard configuration HTTP Proxy module proxy server and cloud management server, both of which uses ubuntu server blade plate, as the node accepting management, every node server is assembled with node manager, the major function of this manager is to realize the management to KVM virtual machine, including: the control instruction of (1) reception cloud manager carries out the deployment of KVM virtual machine, start and stop etc. operate;(2) availability of the local KVM virtual machine of monitoring, when local KVM virtual machine is made mistakes unavailable, attempts starting the KVM virtual machine instance that another one is identical;(3) behaviour in service of monitor in real time each KVM resources of virtual machine local, and the real time resources service condition of KVM virtual machine is sent to cloud manager;(4) extended operation of KVM virtual machine is automatically performed according to the resource service condition that virtual machine is real-time;(5) receive the virtual machine (vm) migration instruction from cloud manager, meanwhile, every node server all also will be disposed multiple KVM virtual machine, and dispose node server in each virtual machine.All above-mentioned node servers all share same network data store.And locally stored of each node server is used for each virtual machine self-operating data of buffer memory, and the image file of virtual machine and embodiment all can be buffered in shared network data storage, so can more easily support the migration operation of high availability and virtual machine.Additionally, the data of industrial and mineral data acquisition, can be influenced by the control of cloud control server and proxy server and be randomly distributed in network memory, and worked in coordination with and cross-domain task cooperation by the application software finishing service on each node server.The wireless sensor data that is provided with shown in Fig. 3 gathers cluster, described wireless sensor data harvester is according to application component function needs in system, data or analog interface is passed through with by industrial and mineral data equipment, wired or wireless interface connects, and frequency acquisition is set to function need highest frequency and precision carry out data sampling, the data buffer storage gathered is in the memorizer of self, and data compression is transmitted into remote monitoring center through wireless transmit/receive units after becoming consolidation form.Wherein can also carry out communication either synchronously or asynchronously between each wireless sensor devices, constitute data acquisition cluster.The fundamental diagram of wireless data collection device shown in Fig. 4, including;Core processing unit, touch screen, photographic head, microphone, clock, memorizer, power management, interface circuit, sensor circuit form;Described core processing unit includes embedded controller, audio interface circuit, video interface circuit, touch screen interface circuit, multi-path serial interface, hi-speed USB interface, wireless communication module, debugging interface and sensor interface circuitry;Described touch screen interface circuit, memorizer, audio interface circuit, multi-path serial interface, wireless communication module, debugging interface and hi-speed USB interface are bi-directionally connected with embedded controller respectively;Described touch screen and touch screen interface circuit are bi-directionally connected;Described microphone and photographic head connect the input of audio interface circuit and video interface circuit respectively;Described multi-path serial interface and hi-speed USB interface connect industrial and mineral data equipment respectively;The respective input of the output termination embedded controller of described video interface circuit;Embedded controller is connected with the Internet;Power supply management device is used for providing electric energy, and power supply adopts photovoltaic solar panel and the combined form of accumulator battery, both can guarantee that the long-term work that equipment can be effectively stable, also can avoid laying power line;Wherein use the charging-discharging controller that MC3063 chip is constituted, to provide stable voltage, sensor unit is provided with acceleration, pressure, temperature, optical sensor and load signal sensor, realize the calculating to displacement by acceleration signal and load signal, be monitored by pressure and temperature sensors towards ambient.Core processing unit is used for collection computing and the storage of sensor signal, and wireless communication module realizes reception and the transmission of wireless signal;Embedded controller can adopt CC2530ZigBee chip, and by the transmission conveniently realizing the 2.4GHzISM band signal based on ZigBee and reception, for instance when being used on trepan, trepan includes motor, quadric chain, walking beam, support, pumping rod;Motor drives walking beam motion by quadric chain, and walking beam drives and moves up and down with pumping rod, and acceleration signal sensor and load signal sensor on data acquisition unit just can be arranged in pumping rod, in order to gather the kinematic parameter of pumping rod.In addition, shown data acquisition unit can also be arranged on industrial and mineral electrical appliance equipment, for real-time video, audio frequency, digital monitoring running parameter, obtain duty, and the data of collection are uploaded onto the server by multi-protocol gateway, the working condition of relevant device is analyzed by server according to the data gathered, in order to draw its running status.
Data storage layer: be provided with cloud data storage pool, for in industrial and mining establishment from the mass data in various different pieces of information sources, the subject data of the unified interface corresponding with different business is generated after it is processed, and these subject datas are stored in distributed file system, when there being task requests, according to different task requests, the data of storage in described distributed file system are carried out multinode, the parallel computation of multitask and analysis, represents accordingly according to different application analyzing result.As it is shown in figure 5, the cloud storage pond that data provided by the invention process, drawing together at least one cloud storage management node, at least one cloud storage space and at least one virtual unit, cloud storage management node, cloud storage space constitute privately owned cloud with virtual unit.In embodiment as shown in Figure 4, this cloud storage framework includes two cloud storage nodes, three cloud storage spaces and multiple virtual units, and three cloud storage spaces constitute a privately owned cloud with multiple virtual units.Cloud storage management node a is connected with each other with cloud storage management node b, mutually take between two cloud storage management nodes, between two cloud storages management nodes can equally loaded and wherein a cloud storage management node take over each other when breaking down, to ensure unfailing performance when running based on the system of this cloud storage framework.Wherein, three cloud storage spaces and multiple virtual unit can be managed by two cloud storage management nodes respectively, and its management includes newly-built, deletion and configuration that three cloud storage spaces and multiple virtual units are carried out, to carry out system backup, recovery and dilatation.Having data directory in each cloud storage management node, data directory is for recording the relevant information of cloud storage space and virtual unit, and cloud storage management node finds corresponding cloud storage space and virtual unit by the related data in its internal data directory.It addition, be both provided with unified application program access entrance on each cloud storage management node, this application program access entrance is application programming interfaces, and application program/service accesses cloud storage space by calling this application program access entrance.Each virtual unit in Fig. 4 all can be mapped as a raw device in operating system, memory field, file system or memory file system etc. the disk array of virtual management physical memory, built-in disk and various interface, agreement.Additionally, owing to multiple virtual unit c1, virtual unit c2 ..., virtual unit cn are the virtual unit that characteristic is identical, so, in the present embodiment, virtual unit c1, virtual unit c2 ..., virtual unit cn may be constructed a virtual unit group b, simplify the management to multiple virtual units by this virtual unit group b.Multiple virtual units can distinguish a memory space in a corresponding physical storage device or a physical storage device.In the present embodiment, virtual unit a1, virtual unit a2 ..., virtual unit an are connected with physical storage device a1, physical storage device a2 ..., physical storage device an respectively, thus by from the data information memory of industrial and mineral collection in worksite in above-mentioned physical storage device.Outside application program/service a, b manages node a, b with the cloud storage in cloud storage framework respectively and is connected.Being provided with unified application program access entrance owing to managing in cloud storage on node, therefore, application program/service is by calling the interface function of application program access entrance thus accessing corresponding cloud storage space.Application program/service can indicate access by application program access entrance or accesses the corresponding information in any one cloud storage space or cloud storage space and virtual unit are managed.Application program/service connects upper cloud storage management node by calling application program access entrance, then application program/service can indicate the cloud storage space of access, filename, side-play amount, accessing operation etc. by API, finally operation is assigned on one or more concrete physical storage device and completes accessing operation according to information connecting inner data dictionary incoming for these API by cloud storage management node, manages node finally by cloud storage and returns access results.
Data computation layer: calculate platform corresponding to cloud data, these cloud data calculate platform for call in cloud data storage pool the Real-time Collection of storage to data calculate its characteristic according to industrial and mineral system business public relation respectively, set up general data computational analysis model, such as numerical value such as computing equipment performance, load capacity, work efficiency, safe coefficient, ambient parameters, the data computation module of the virtual system on Node Controller can realize adding flexibly and arranging, update data analysis computing module, obtain corresponding value of calculation.The structure of data center is as shown in Figure 6.Including: cloud controls the period of service by network adapter and proxy server and data network, internet interconnection, monitor and control multiple memory node and virtual machine thereon, the task control module of master controller is responsible for downstream node controller is carried out unified allocation of resources management, including adding, delete and the operation such as the phisical drive of any amount of readable data of Migration Control System and storage medium, administrative model is responsible for being analyzed the load collected and resource using information processing, and then transfers to controller to be controlled.Computing node comprises any number of virtual machine, each intra-node comprises the resources of virtual machine within a Node Controller responsible node and controls, the distribution management by synchronization of resource is responsible for by coordinated management engine, application resource is run, if resource monitor, Network Performance Monitor, early warning monitoring device are at interior multiple Network Performance Monitors in virtual machine.Data statistics flow process under Network Performance Monitor introduction, as shown in Figure 7, real time load and monitoring resource model, according to sampled data type, select to preset device type, and then call the service behaviour data of the statistical model analyzing and processing sampling for this equipment, generation equipment ideal Distribution data, model calculating is carried out in conjunction with actual samples data again through controller, the data of output actual allocated, judgment device resource service condition and load level thereof, and then instruct the distribution of main controlled node control system equipment resource, and for load monitoring module, as shown in Figure 8, in system is run, according to collected data message, the curve that monitor in real time generates, observe whether curve has catastrophe point or do not meet the exceptional value appearance of matched curve.If existing, then illustrate that Rush Hour occurs in this application of current data center, it was demonstrated that system resource requirement needs to carry out bigger variation.Now need quickly system resource behaviour in service to be analyzed, whether can meet resource requirement when system resource reaches heap(ed) capacity.Because being outlier, current obtained parameter can not represent the relational model between the load of entirety, performance and resource, if but not in time process the performance of system can be had a great impact, so needing in time outlier to be analyzed process.If system resource Pooled resources meets current demand, then directly give cloud controller to process, the current outlier of quick solution, if resource is unsatisfactory for current demand, just start alternate device resource, period uses the simple linear regression model (LRM) prediction live load of next 5 minutes, and simple linear regression model (LRM) can effectively catch live load and change over rule, even increasingly complex historical data can also conclude its load of prediction easily.The live load of prediction assess existing workload as the input of model needed for device resource requirements and the performance that can reach of system.The factor of many complexity all can affect the performance of application program, the such as change etc. of ambient parameter, operator's quantity, at this moment KCCA algorithm and remote related algorithm can be adopted to realize multi-variate statistical analysis modeling, analyze the impact that systematic function is brought by multiple influence factor simultaneously, adjust model parameter in real time, generate desirable device resource allocation data.The distribution data calculated can pass through the standardized query interface on main controlled node or communication interface, user actively carry out query manipulation or be passively pushed to user.Wherein need the data message of real-time collecting for those, it is necessary to upgrade in time data, the service quality of guarantee data center.
Data access layer: cloud service access modules is provided with cloud service access interface, for the triggering according to application component, finds Cloud Server by fiber optic network, quickly can calculate platform from cloud data in real time and obtain corresponding value of calculation.The interface service that data access layer provides includes the cloud service access interface that Various types of data server provides.
Above four layers are sequentially connected with by reliable and light switched communication network at a high speed, this communication looped network and power circuit tight association, and the fibre circuit of high-speed communication is applied to all intelligent cells along power circuit, provides powerful high speed information communication port for enterprise.Compare the data acquisition in tradition industrial and mineral system, the data handling system of the present invention can extend data storage and the computing function in high in the clouds, and the concrete logic judging function based on data model, completed the unified monitoring of different functions by differently configured hardware device or system, the equipment of these difference in functionalitys or system have each independent data processing unit.And traditional real-time data acquisition and processing unit are made up of the multiple systems isolated, Data duplication collection and also data are incomplete, observing and controlling, metering, protection, automatic safety device are to be realized by different hardware unit localities.It has the drawback that data acquisition repeats, and data are difficult to share, and system application function is worked in coordination with being difficult to.Observing and controlling in the present invention, protection, metering, automatic safety device application function be all realized by the clustered form of functional unit; by high-speed network communication technology; really realize data to be uniformly processed, calculate, share, be conducive to the reliability of raising system, the installation cost reducing system and maintenance cost.
The data message of safety in production enterprise practical productive accumulation is carried out unified management and storage by above-mentioned magnanimity information processing cloud platform, in addition, in order to production process providing based on functions such as the prediction of data, abnormality detection, realize the safety in production of enterprise, managers also want to find the abnormal conditions in producing in time, find out reason, and propose counter-measure in time, keep what produce to be normally carried out.The judgement of the condition of production is divided into normal and abnormal two kinds of situations, so it is classified as classification problem, adopt the good support vector machine of classifying quality to carry out the abnormal judgement of the condition of production so long.By the achievement data of safety in production in analytical database, product quality, composition, practical productivity is selected to pass judgment on, as index, the support the data whether condition of production is abnormal, to supporting that initial data carries out box traction substation analysis and correlation analysis, obtain influencing each other between data.Select data sample, owing between variable, magnitude gap is bigger, it is necessary first to carry out needing to be standardized variable data processing,
V i ′ = V i - μ σ , i = 1,2,3 , . . . . . . , n
Wherein, Vi is former variate-value, and μ is the meansigma methods of former variate-value, and σ is former variable standard deviation.
After standardization, training and the test of vector machine decision model can be supported, as shown in Figure 9, training sample set data are used to carry out model training, its training algorithm adopts SMO algorithm (SequentialMinimalOptimization, the minimum optimization of sequence), draw the support vector of its disaggregated model, according to supporting that vector calculates the parameter of decision function f (x).The modified model support vector machine obtained after training is not optimum, and this is owing to some parameters in initial reference template and algorithm arrange the result that can affect training.By selecting the design parameter being layered in core and software metrics being selected, it is possible to the model more optimized.Inputting the tree form data structure that the software module needing to be predicted is corresponding in the modified model support vector machine trained, obtain the output between [-1 ,+1], if output is more than 0, the product quality condition of production is absent from exception;Otherwise, output means that less than 0 the condition of production of product quality is abnormal.
Also needing to extract and produce relevant standard, knowledge, data etc. between virtual resource layer and access and adaptation layer described in Fig. 1 help to produce and decision-making, this just requires that cloud platform can search useful knowledge in data base.For intension and the characteristic of knowledge, the purpose of Knowledge Conversion and requirement, Immunity Operator (Immuneerator) is also incorporated in standard Evolutionary Programming Algorithm by we.Knowledge, Knowledge Conversion being combined with theory of immunity, the immune programming algorithm of knowledge based transfer realizes knowledge-based reasoning and finds.In actual operating process, immune algorithm is a kind of global optimization approach grown up on genetic algorithm basis, the problem that most genetic algorithm can solve the problem that, immune algorithm can both effectively solve and efficiency is better than genetic algorithm, utilize optimizing ability that immune algorithm is good can access, at virtual resource layer and wireless sensor network, the selecting paths scheme that all nodes complete data collection and total energy consumption is minimum, finally achieve reduce wireless sensor network energy expenditure, reduce network system time delay, the problem improving virtual resource layer interactive efficiency.As described in Figure 10, first, according to optimized target and condition, the problem solved made a concrete analysis of and decomposes, extracting most basic characteristic information or feature set;Secondly, this characteristic information is processed, with a kind of scheme being translated under local environment or optimal constraint conditions Solve problems;Finally, this scheme is changed in a suitable form Immunity Operator and for producing new individuality.The process of knowledge based and Knowledge Conversion, on the basis of reasonable drawing immune vaccine, realizes immunoevolution by vaccination and immunoselection, effectively to treat the priori seeking problem, improves individual fitness.
For immunity collaborative fault diagnosis software workflow, in immunological investigation, mutually promoting and suppressing phenomenon to can be understood as a kind of distinctive coevolution form-immunity coevolution between various immunocytes, immunity coevolution mechanism can be used for reference, feature and collaborative diagnosis pattern is solved for excitation system troubleshooting issue, propose a kind of many diagnostic casts coevolution diagnosis policy, the main composition key element that immunity collaborative diagnosis calculates is each immunologic diagnosis cell colony, the diagnosis evolution algorithm of immunologic diagnosis cell and cell population regulatory mechanism etc., evolve and adopt basic immune algorithm, immunity collaborative diagnosis strategy can formalized description as follows: ICED=(CPD, CPDN, CEDA, CPCM), wherein: CPD: immunocyte diagnosis population, CPDN: immunocyte diagnosis population number, the immune collaborative diagnosis algorithm of diagnosis population,
CEDA = CED 1,1 CED 1,2 . . . CED 1 , CPN DA 1 CN 1 CED 2,1 CED 2,2 . . . CED 2 , CPN DA 2 CN 2 . . . . . . . . . . . . . . . . . . CED CPN , 1 CED CPN , 2 . . . CED CPN , CPN DA CPN CN CPN
Wherein CEDij={ PCMij, CEDPij, EDVijJj}, i, j=1,2, ..., CPDN: i-th diagnosis cell population is with the Cooperative Mode of jth diagnosis cell population, and PCMij represents synchronous task assessment, determining that this task has cooperated the need of with other intelligent bodies, CEDPij represents and synchronizes evolutionary operator, and EDVij represents and synchronizes to evaluate operator;DAi={ si, gi, pi, fi, di; i=1; 2 ..., CPN: the Immune Evolutionary Algorithm of i-th diagnosis cell population; si represents the selection strategy of i-th diagnosis cell population; gi represents the evolutional operation operator of i-th diagnosis cell population, and including cell clone, clone inhibition etc., pi represents the execution probability of the evolutional operation operator of i-th diagnosis cell population; fi represents the close fitness function of i-th diagnosis cell population, and di represents the concentration function of i-th diagnosis cell population;CNi: the quantity of the contained individuality of i-th diagnosis cell population;CPCM={CPO, CPD, CPA, CPE}: immunocyte Advanced group species control model, CPO represents population scale operator, and CPD represents that population concentration regulates, and CPA represents Evolution of Population object function;CPE represents population evaluation method.After adopting foregoing description mode, if: CPD (k)={ CPD1 k, CPD2 k..., CPDCPN kRepresent that kth is for immune cell population, then kth is expressed as immune cell population coevolution: CEDA{CPD (k) }=CEDA{{CPD1 k, CPD2 k..., CPDCPN k, then immunity collaborative diagnosis in from kth-1 generation evolve to kth generation can be expressed as: CPCM (k): CEDA{CPD (k-1) } → CEDA{CPD (k), k=1,2 ....Abnormality diagnosis process is roughly divided into six stages: 1. genI=1, and fault diagnosis territory is set to equipment fault set F={F1 ..., Fi ..., FM}, diagnostic cast set is FDM={FDM1 ..., FDMi, ..., FDMM}, M is equipment fault pattern number, and FDMi is i-th fault diagnosis model;2. for certain diagnostic task FDM_TASK, initialization failure diagnostic cast colony FDM, distribute each diagnostic cast weight w={w1 ..., wi ..., wM}, wi represents diagnostic cast FDMi diagnosis importance degree in tracing trouble task FDM_TASK;3. each fault model FDMi is to diagnostic task FDM_TASK, provides the sub-conclusion FDR={FDR1 of each self diagnosis ..., FDRi, ..., FDRM}, calculate each diagnostic cast individuality FDMi affinity degree and concentration, evaluating FDM_TASK fault diagnosis effect, if meeting fault diagnosis termination condition, then turning to 6.;4. genI=genI+1, to fault diagnosis model colony FDM, chooses colony of a new generation based on affinity degree and concentration value from previous generation colony;5. Immunity Operator (clone, sudden change, suppression) is applied in the individuality of colony, it is thus achieved that new fault diagnosis model colony FDM, and distributes new diagnostic cast weight w={w1 ..., wi ..., wM}, turn to 3.;6. fault diagnosis terminates.The failure diagnostic process in a certain moment, immunity collaborative fault diagnosis software module 5 will complete above-mentioned full diagnostics behavior, after the diagnostic result being comparatively satisfied with, just terminate this diagnostic process.
In addition, very to the knowledge accumulating in platform service supporting layer and classification engine, production and construction along with safety in production enterprise, the value volume and range of product of data information increases accordingly, relation day also benefit complexity between data, security incident carry out traditional qualitative analysis inadaptable a large amount of and the needs of complex data, the feature and the rule that utilize the analytical tool of correlation rule to excavate security incident data are proposed, find the Strong association rule between accident occurrence type and " people-machine-environment-management " each factor, accident is carried out early warning.Collect the historical disaster data relevant with excavation and design prospecting data, obtain the data that construction accident is relevant.Construction accident data are carried out data scrubbing, it is ensured that the exact specification of data.Record imperfect in construction accident data, inconsistent, also vicious information etc., therefore, for ensureing with data effective in post analysis, this stage needs these type of data are cleared up, and mainly solves the personal error during data file is set up, and in data file, the bigger particular values of statistic analysis result impact is set up Multidimensional Data Model by some.Figure 11 gives a kind of data analysing method based on Association Rule Analysis flow process, its reason occurred according to the feature accident of construction accident, data attribute is set, from data base, extract casualty data and reconnoitre data composition accident sample data, its data cube is set up from accident pattern, traffic injury time, three dimensions of equipment parameters, and utilize the Aggregation Query of SQL language and connection statement that this data cube is operated, and complete frequent predicate set and the search procedure of strong rule. produce frequent predicate set.Specifically need to meet minimum support and min confidence, minimum support is set and is counted as 3, so that it is determined that there is no the predicate collection of Concept Hierarchies dimension, min confidence can collect the minimum support conditional probability that represents of counting with item expresses, thus utilizing the apriori traditional of improvement to produce frequent predicate set.Produce Strong association rule.Further can pass through to describe the real implication of the correlation rule meeting minimum support threshold value and minimal confidence threshold, the such as time in month, contingent accident rank is associated prediction by equipment parameters, will assist in the early warning of industrial and mineral accident, auxiliary enterprises science decision, in concrete mining process, each threshold value can by staff and domain expert's common setups, that is whether rule is correct, applicable, will depend on the setting of boundary condition.
For industrial and mining establishment's production safety management alternately frequently, the feature of close in cooperation, the type of service of combing enterprise, it is achieved support that the safety management of business collaboration is worked in coordination with, also specifically include following technology:
According to external appreciative standard, combine again the production technology level of China and the difference of each site surrounding, it is possible to adopt the method for step analysis and fuzzy mathematics to carry out overall merit, to judge that whether it is for major hazard source simultaneously.The assay of major hazard source is included the dangerous reason to various dangerous matter sources, accident occurrence probability, the coverage etc. of consequence.The evaluation of major hazard source is one of key measure controlling substantial industrial accident.Major hazard source evaluation should be evaluated two aspects from natrual danger evaluation and real danger and carry out.Natrual danger evaluation mainly reflects inside material inherent character, the feature of dangerous substance production process and dangerous unit, external ambient conditions.Real danger evaluation is the counteracting factor considering various danger on the former basis, reflects people's subjective initiative in the generation of control accident and control damage sequence expansion.Add external environment condition and offset the factor following evaluation model of proposition:
A = { Σ i = 1 n Σ j = 1 n ( B 111 ) i W ij ( B 112 ) j } B 12 ( 1 - B 2 k )
The physical hazard evaluation of estimate of i-th kind of material of (B111) i in formula;(B112) the process dangerous evaluation of estimate of j jth kind material;The correlation coefficient of Wij jth item technique and i-th kind of physical hazard;The serious evaluation of estimate of B12 accident;The factor is offset in B21 process equipment container building structure;B22 peopleware offsets the factor;The factor is offset in B23 safety management.
Generally adopting method during dangerous matter sources classification, determine a border circular areas by dead radius, this border circular areas is exactly the scope of this dangerous matter sources impact.The scope of this border circular areas is applicable for the impact of blast, but just inapplicable for some other situation, and ratio, such as relating to gas leakage, cannot be determined fully according to dead radius.Carry out dangerous matter sources judging accurately and classification, then pass through the result of its evaluation to take corresponding prevention, emergency treatment.The purpose of major hazard source Fast Evaluation classification, is on the data message basis that substantial risk source data is included, major hazard source carries out Fast Evaluation and classification, in order to major hazard source is carried out macroscopic view hierarchical monitoring and management by profit responsible departments of the government.The damage sequence that major hazard source mainly be may result in by dangerous matter sources fast appraisement method is evaluated, to predict that dead radius that accident occurs is for primary evaluation index, carries out the classification of major hazard source with the size of dead radius.First select the numbering of dangerous matter sources, then search the related data of data base according to this numbering, be analyzed after obtaining data, it is judged that the species number of the material comprised in these dangerous matter sources data, if toxic, thus point several approach process this dangerous matter sources.Most important of which part calculates noxious substance exactly, inflammable, the dead radius of the injury model of explosive material, comes to dangerous matter sources classification finally by maximum dead radius.
Additionally, enterprise can also pass through cloud platform strengthens the control of production scene safety management and production process.The hidden danger that production process and material, installations and facilities, equipment, passage, working environment etc. are existed, should be analyzed and control.The dangerous higher activity such as operation in fire operation, restricted clearance, temporary electricity operation, high-rise working is implemented work permit management, strictly fulfils examination and approval procedures.As shown in figure 12, based on the process model mining algorithm of Petri, the process model that non-Petri network models is converted to Petri network, solves to excavate from daily record to hide this open problem of task so that excavate the task node comprising not tape label in the Petri network obtained.Safety in production process logs is analyzed, the data of production process is estimated, stops hidden danger in time.May be used for supporting the workflow engine of a kind of composite service based on this Petr algorithm, including: the data base of interface layer, key-course, physical layer, accumulation layer and the flow instance for storage service;After described workflow engine is disposed, being received operation system or the business information of other interface systems transmission by described interface layer, other interface systems described include resource management system, service release management system, billing and accounting system;Described interface layer provides the interface of three kinds of modes, including api interface, Corba interface, web Service interface, it is simple to workflow engine is connected with operation system;Described business information includes the current link performance of the flow instance of business, flow instance;After described key-course receives the business information of described interface layer transmission, according to the flow process route control method supporting composite service, control the generation of the flow instance of business, scheduling, decomposition, merging, end;And determine that the flow instance of business is that automatic flow arrives next link, it is desired nonetheless to original place waits;The circulation result that described key-course calls the performance of current link of the method record flow instance that described physical layer provides and described flow process route control method is determined simultaneously, result of flow is returned by described interface layer;The method that described physical layer provides is: the operation increasing newly, revising, delete and inquire about to the management object described by workflow engine inside, described management object includes: the flow instance object of business, flow process routing object, the current link object of flow instance, flow tasks object;Wherein, the specific tasks that the link of the described each flow instance of flow tasks object factory performs;Described accumulation layer is by the flow instance information of the described data base described business of persistent preservation.Described flow operations process can also merge the verification method of a kind of Optimized Operation, comprise the concrete steps that:
(1) scheduling scheme generates: according to product marketing plan, purchasing of raw materials plan, equipment maintenance plan, product (intermediate, raw material) inventory information and equipment capacity, the resources of production take, consume, production cost etc. produces constraint information, the data file generating object module sets up the mathematical model of Optimized Operation, and according to the optimization aim (maximum productivity, maximum profit or meet sales order) that user sets, and model solver, calculate Optimized Operation scheme.
(2) abnormal by calculating the production that is likely to occur of precognition: to gather product marketing plan current in enterprise resource planning, purchasing of raw materials plan (including arrival situation), inventory information, Plant maintenance plan, energy supply plan etc., retraining (including Batch Process equipment and continuous producing apparatus), capacity-constrained, logistical balancing constraint and energy constraint by promiscuous device to be temporally tracked calculating, the production that precognition is likely to occur is abnormal.
(3) abnormal with the production being likely to occur for Optimized Operation scheme (data that step 1 obtains) (data that step 2 obtains) are combined, utilize the visualization ESCPetri-Nets network technology virtual production process of figure.With scheduling time for axis, dynamic graph display workshop equipment working condition, material balance (buying-stock-sale), simplation verification production scheduling situation.Centered by material, changing with scheduling time, graphically the variation tendency of certain material of Dynamic Announce consumes relevant equipment operation condition with certain moment to certain material production.

Claims (1)

1. the assay method for the major hazard source towards industrial and mining establishment's safety production cloud service platform system, it is characterized in that: the described safety production cloud service platform system towards industrial and mining establishment, including secure cloud service platform portal sub-system, system administration develops subsystem with related tool collection, application service layer subsystem, virtual resource layer subsystem, platform service supporting layer subsystem, access and adaptation layer subsystem, security service resource subsystem, infrastructure services supporting layer subsystem;In described platform service supporting layer subsystem, major hazard source evaluation is evaluated two aspects from natrual danger evaluation and real danger and is carried out, add external environment condition and offset the factor, following evaluation model is proposed: select the numbering of dangerous matter sources, then the related data of data base is searched according to this numbering, it is analyzed after obtaining data, judge the species number of the material comprised in these dangerous matter sources data, if toxic, according to
A = { Σ i = 1 n Σ j = 1 n ( B 111 ) i W i j ( B 112 ) j } B 12 ( 1 - B 2 k )
Result of calculation determine dead radius, and then determine a border circular areas, (B in formula111)iThe physical hazard evaluation of estimate of i-th kind of material;(B112)jThe process dangerous evaluation of estimate of jth kind material;WijThe correlation coefficient of jth item technique and i-th kind of physical hazard;B12The serious evaluation of estimate of accident;B2KIt is that the factor is offset in process equipment container building structure, peopleware offsets the factor, any one in the factor is offset in safety management;N is natural number;Described border circular areas is exactly the scope of this dangerous matter sources impact, and is come to dangerous matter sources classification by maximum dead radius;Additionally, for the knowledge accumulating in platform service supporting layer and classification engine, data are cleared up, solve the personal error during data file is set up, and the bigger particular values of statistic analysis result impact is set up Multidimensional Data Model by data file;Data analysing method based on Association Rule Analysis flow process, the reason that feature accident according to construction accident occurs, data attribute is set, from data base, extract casualty data and reconnoitre data composition accident sample data, data cube is set up from accident pattern, traffic injury time, three dimensions of equipment parameters, and utilize the Aggregation Query of SQL language and connection statement that this data cube is operated, complete frequent predicate set and the search procedure of strong rule;Produce frequent predicate set;Need to meet minimum support and min confidence, minimum support is set and is counted as 3, state so that it is determined that do not have the predicate collection min confidence of Concept Hierarchies dimension can collect the minimum support conditional probability that represents of counting with item, thus utilizing the apriori traditional of improvement to produce frequent predicate set;Produce Strong association rule, can pass through to describe the real implication of the correlation rule meeting minimum support threshold value and minimal confidence threshold, contingent accident rank is associated prediction by equipment parameters, it will help the early warning of industrial and mineral accident, auxiliary enterprises science decision.
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