CN112308376B - Emergency management method and system based on crowdsourcing preprocessing - Google Patents
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Abstract
The invention discloses an emergency management method based on crowdsourcing preprocessing, which comprises the steps of acquiring a monitored unknown abnormal condition; analyzing abnormal states and extracting characteristic information; establishing an emergency processing task and distributing the emergency processing task to a crowdsourcing platform; receiving emergency processing tasks and distributing the tasks by a crowdsourcing platform; after the crowdsourcing platform finishes the emergency treatment task, an emergency treatment answer is returned; receiving emergency processing answers returned by the crowdsourcing platform, and executing an abnormal state solution corresponding to the answers; and carrying out system self-checking operation and saving the abnormal state and the corresponding solution. According to the invention, the consumption of labor cost is reduced by combining the Internet of things system with the crowdsourcing technology, so that the system can perform iterative learning at lower cost, the key grades of tasks are divided by calculating abnormal state risks, and crowdsourcing modes of different grades are adopted aiming at tasks with different risks, so that the tasks threatening the system can be more reliably treated, and the emergency management system can run more safely and stably.
Description
Technical Field
The invention relates to the technical field of emergency management, in particular to an emergency management method and system based on crowdsourcing preprocessing.
Background
The smart city fully applies the latest information technology to various industries in the city so as to improve the urbanization quality, realize the fine and dynamic management and achieve the effects of improving the city management and improving the quality of life of citizens. In the process of building smart cities, a plurality of systems are responsible for managing or transporting city 'life lines' (water, electricity and gas), and whether the systems are safe or not is closely related to the safety of cities and urban residents. The intelligent systems can monitor articles managed in the system (for example, a valve well monitoring terminal can monitor information such as gas concentration, accumulated water, pipeline pressure, valve opening and closing conditions and the like in a valve well in real time, and alarm management personnel at the first time when the monitoring information is abnormal), but the monitoring function of the equipment can also be abnormal. The emergency management system is a unified management system which receives the functions of monitoring abnormal states of the management systems in the smart city and detecting whether the management systems operate normally or not.
At present, management and treatment of abnormal states of management systems in smart cities are basically completed by specially-assigned persons. If the handling manner and the result in the emergency system are analyzed by big data so that the emergency system can identify and handle more abnormal conditions, a large amount of information of the abnormal state, the handling process and the result needs to be collected. The information is difficult to predict or predict before the system is formally arranged, the emergency situation of the system can cause a plurality of abnormal situations to occur simultaneously, and even two same devices can have distinct abnormal states due to different environments. This makes it difficult to prepare a complete plan in advance for the abnormal situation of the emergency system, and a more flexible and intelligent determination method is required when the abnormal situation occurs in the system.
In recent years, with the rapid development of internet technology, people who can access the internet can utilize network resources, and people can obtain resources from the internet and bring lots of resources to others in the internet, so that in recent years, the task of labeling data in machine learning is also achieved in a crowdsourcing mode. The mode similar to the method can be adopted, crowdsourcing technology is combined, crowdsourcing platform interfaces are embedded into emergency platform disposal and big data learning processes, when unknown abnormal states occur to the system, abnormal states sent by the system are crowdsourced to crowdsourcing workers with corresponding technologies, appropriate disposal modes are selected through a certain method, big data analysis is carried out on disposal processes and results, and emergency disposal capacity is improved. It is anticipated that over a period of iterative learning the system will have the ability to handle more contingencies and abnormal conditions.
The concept of crowdsourcing, which was proposed by Jeff Howe in 2006, corresponds to the concept of contracting, and is a way to outsource work tasks that have been done by specific employees or institutions to unspecified public networks in a free-voluntary manner in the past. The objects in the crowdsourcing can be divided into requesters, tasks and workers, the requesters generally design and publish the tasks which are difficult to process by computers, the tasks are published by the crowdsourcing platform, the workers can voluntarily receive the tasks and solve the tasks, and finally the workers submit answers and return the answers to the task requesters.
Nowadays, the crowdsourcing work at home and abroad has certain development, and the most known crowdsourcing platform at home and abroad is as follows: amazon turkish robot (Amazon Mechanical Turks), the platform has over one hundred thousand workers and tens of thousands of requesters, and many abroad people get a return for completing crowdsourcing tasks every day as an economic source. The following also appear in China: the crowdsourcing platform of the mental force storehouse, the Zhu Bajie and the like is used for carrying out the following steps: software development, product design and the like. On the platforms, users can log in the platforms at any time and any place to issue tasks, and merchants can also receive the tasks at any time and any place to participate in completing crowdsourcing tasks. This greatly increases the speed of completion of the task compared to past contract patterns.
Most of the current crowdsourcing platforms only have graphical interfaces, and only a few platforms are as follows: amazon Mechanical Turks has an API interface that can be embedded in a system. The former is more convenient for a task requester to issue a task, and the latter can be embedded into the system to create an issuing task when the system runs to a corresponding branch, and set a format returned by a worker, and continue the system operation after receiving the answer of the worker.
The crowdsourcing tasks in mainstream commercial platforms can be divided into complex tasks and simple tasks. The difficulty of completing complex tasks is high, the tasks are usually difficult to complete by one person, and the period for completing the tasks may be long, such as writing and developing software and designing a piece of clothes; simple tasks are often tasks that are easier to perform, such as tagging a picture, identifying the model of a vehicle. It is believed that platforms are more receptive to simple tasks, which makes it easier for the applicant of the task to price, crowd-sourced workers can also spend a small number of events to complete the task (more flexible). The mode of distributing tasks by the crowdsourcing platform mainly comprises the following steps: 1. the system distributes the designated tasks to designated crowdsourcing workers, and the workers decide whether to receive the tasks; 2. the system distributes the tasks to all people, and then the workers select the tasks to receive.
The problems faced by the current crowdsourcing mode are mainly the following: 1. how to assign the appropriate price to the task; 2. how to assign crowdsourcing tasks may enable crowdsourcing tasks to be assigned to workers with relevant knowledge; 3. how to balance task costs and the quality of the results obtained. These problems are also faced by crowd-sourced pre-processing emergency management systems. Too high a task price can drive up system costs, while too low a task price can drive up crowdsourcing workers. The assignment of crowdsourcing tasks to workers with relevant knowledge skills can effectively improve the quality of results. Distributing crowdsourcing tasks to more workers yields results that are inherently more reliable, but also increases system cost, and conversely may not yield reliable results.
The discovery speed and the disposal efficiency of the emergency event are related to the influence of the emergency event, and after the general emergency system discovers the abnormal condition existing in the system through the internet of things, the emergency event can be solved by manually identifying and adopting a corresponding preset disposal scheme or dispatching a specially-assigned person. The abnormal conditions occurring in the emergency system are contingency and special, it is difficult to include all the emergency conditions in a preset disposal plan, the mapping process of the abnormal conditions and the emergency response means is difficult to be completely completed by a computer, and the traditional emergency system detection method needs to arrange personnel for a long time to identify and process the abnormal conditions sent out in the emergency system, but the labor consumption and the system cost are increased by doing so. Therefore, the existing partial emergency system can carry out big data analysis on the abnormal conditions of the system and the corresponding emergency disposal process and result thereof, so that the system can identify more abnormal conditions and process more emergency events. However, abnormal conditions occurring in the emergency system are difficult to predict or exhaust before the system is formally arranged, and if the emergency system is subjected to learning iteration after the system is arranged, unnecessary loss may be caused to a company, or optimal handling time for handling emergency events may be missed; it is still difficult to achieve the goal of cost reduction if specialized personnel are still arranged to manage it.
The existing emergency management system mostly completes the detection and processing mode of the emergency event through manpower, or performs big data analysis on the abnormal condition, the handling mode and the result afterwards to perfect the system, so that the system can identify and process more abnormal events. However, data collection and analysis are performed after the emergency system is on line, a specially-assigned person needs to be arranged for detection and disposal, manpower is consumed, and the cost is raised, so that the big data analysis of the emergency system is difficult to perform.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an emergency management method and system based on crowdsourcing preprocessing.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
in a first aspect, the invention provides an emergency management method based on crowdsourcing preprocessing, which comprises the following steps:
s1, acquiring the monitored unknown abnormal condition;
s2, analyzing the content of the abnormal state, and extracting the characteristic information of the abnormal state;
s3, creating an emergency processing task according to the abnormal state and the characteristic information thereof, and distributing the emergency processing task to a crowdsourcing platform;
s4, receiving emergency treatment tasks by the crowdsourcing platform and distributing the tasks;
s5, the crowdsourcing platform returns an emergency treatment answer after completing the emergency treatment task;
s6, receiving an emergency treatment answer returned by the crowdsourcing platform, and executing an abnormal state solution corresponding to the answer;
s7, performing system self-checking operation, and judging whether an abnormal state still exists; if yes, return to step S2; otherwise, the abnormal state and the corresponding solution are saved.
The beneficial effect of this scheme is: the consumption of labor cost is reduced by combining an internet of things system with a crowdsourcing technology, the system is enabled to perform iterative learning with lower cost, task key levels are divided by calculating abnormal state risks, crowdsourcing modes of different levels are adopted for tasks with different risks, so that tasks threatening the system can be treated more reliably, and the emergency management system can operate more safely and stably.
Further, the step S1 specifically includes:
s101, monitoring abnormal states by utilizing subsystems in an Internet of things system, and uploading the abnormal states when the abnormal states are monitored;
s102, judging whether an abnormal state is received or not according to a set period; if yes, go to step S2; otherwise, continuing monitoring.
The beneficial effects of the further scheme are as follows: according to the invention, each subsystem in the Internet of things is self-monitored by a sensor and the like, the input/output and online state of each subsystem are monitored by the abnormality management system, whether the system is in normal operation or not and whether the system is abnormal or not are judged, and the labor cost consumption is reduced.
Further, the analyzing the content of the abnormal state in the step S2 specifically includes:
judging whether the acquired abnormal state has a corresponding known solution or not; if the solution exists, scheduling the Internet of things system to execute the solution according to the known solution; otherwise, the process proceeds to step S3.
The beneficial effects of the further scheme are as follows: the invention judges whether the abnormal condition has a known solution or not by establishing a historical abnormal information base when the abnormal condition of the system occurs each time; after the subsequent emergency processing steps are carried out, whether the solution can effectively solve the abnormal state or not can be repeatedly confirmed, and after a period of abnormal information is collected, the information base can also be used as original data for carrying out big data analysis on the abnormal state by the system.
Further, the allocating emergency processing tasks by the crowdsourcing platform in step S4 specifically includes:
s401, traversing abnormal states submitted in the emergency processing task, reading information characteristics of the abnormal states in the emergency processing task and a preset risk threshold value, and obtaining an abnormal condition matrix;
s402, calculating a risk value of the current emergency processing task, and judging whether the risk value is greater than or equal to a preset risk threshold value; if yes, go to step S403; otherwise, go to step S405;
s403, setting the current emergency processing task as a key emergency task and corresponding first cut-off time, first basic remuneration and the number of first crowdsourcing workers needing to answer;
s404, judging whether the crowdsourcing platform meets the number of first crowdsourcing workers needing to be answered and corresponding to the key emergency task; if yes, go to step S406; otherwise, go to step S408;
s405, setting the current emergency processing task as a common emergency task and a corresponding second deadline, a second basic reward and the number of second crowdsourcing workers needing to answer;
s406, distributing the emergency treatment tasks to crowdsourcing workers;
s407, judging whether the crowdsourcing platform receives the tasks by the crowdsourcing worker number required in the task setting within the set deadline time; if so, ending the task allocation flow; otherwise, go to step S408;
and S408, delivering the key emergency tasks to system maintenance personnel for processing, and ending the task allocation process.
The beneficial effects of the further scheme are as follows: according to the invention, the task key levels are divided according to the calculated abnormal state risk, and crowdsourcing modes with different levels are adopted according to the severity of the influence of the abnormality on the system, so that the task with high threat degree on the system can be solved more reliably.
Further, the calculation formula of the risk value of the current emergency processing task is as follows:
wherein W is the risk value of the current emergency processing task, gammatkFor the kth exception occurring in the tth exception stateEvent, diA risk coefficient indicating an abnormal state.
The beneficial effects of the further scheme are as follows: according to the method and the system, the risk coefficient is preset for different parts of the system, the system can intelligently acquire the threat degree of the current abnormity to the system, and different grades of solutions are adopted according to the risk, so that the risk and the cost of the system are balanced.
Further, the emergency processing answer returned by the crowdsourcing platform in the step S5 adopts a preset format when the task is issued, and specifically includes: the system comprises an emergency treatment plan preset by the system, operation formed by splicing basic operation of the system, and delivery of key emergency tasks to system maintenance personnel for processing.
The beneficial effects of the further scheme are as follows: according to the invention, emergency treatment can be completed under the condition that the system is not damaged by malicious operation through a mode of plan and splicing operation, and if crowdsourcing workers judge that the mode can not solve the problem according to professional knowledge and experience, the mode of processing by maintenance personnel can ensure the system safety to the maximum extent.
Further, the step S6 specifically includes:
s601, starting an interface to asynchronously receive emergency processing answers returned by the crowdsourcing platform;
s602, carrying out logic verification on operation answers spliced by basic system operations;
s603, judging whether the answer passes the logic check; if yes, go to step S604; otherwise, the step S7 is carried out, the abnormal situation is read again, the task is issued, and the step S2 is returned;
s604, judging whether the current emergency processing task is a key emergency task; if yes, go to step S605; otherwise, go to step S606;
s605, selecting a final executed solution for the answer passing the logic verification in a weighted voting mode;
and S606, directly adopting the answer passing the logic verification as an executed solution.
The beneficial effects of the further scheme are as follows: the invention can preliminarily judge the feasibility of the answer through logic verification, and after the answer which is not feasible in logic is eliminated, the rest answers are voted by taking the weight as the credibility to obtain the most probable correct solution, thereby more effectively solving the system abnormity.
Further, the calculation formula of the weighted vote is as follows:
wherein H represents the weighted voting value of the answer, c represents the answer category, uiIs the answer hiK is the set number of the first crowdsourcing workers, and j is the kind number of the answer.
The beneficial effects of the further scheme are as follows: the method takes the coverage rate of the skill set held by crowdsourcing workers and the skill set required by the task as the answer weight to represent the answer credibility; the most probable solution is selected from the answer set by a relatively majority voting method, so that the system abnormity can be solved more effectively and reliably.
Further, the step S7 specifically includes:
s701, performing system self-checking operation;
s702, judging whether an abnormal state still exists or not; if yes, return to step S2; otherwise, entering step S703;
s703, paying the basic remuneration and the additional remuneration to crowdsourcing workers, and saving the abnormal state and the corresponding solution.
The beneficial effects of the further scheme are as follows: the invention judges whether the emergency state should be relieved or not through self-checking, and whether to pay additional reward is effectively selected or not according to the answer provided by the worker, thereby being beneficial to mobilizing the enthusiasm of crowdsourcing workers for seriously solving the problem and eliminating the cheater in the crowdsourcing platform, and leading the operation of the system to be more efficient.
In a second aspect, the invention provides an emergency management system applying the emergency management method, which comprises an internet of things system, an emergency system and a crowdsourcing system;
the Internet of things system is used for monitoring abnormal states by utilizing all emergency subsystems in the Internet of things system and uploading the abnormal states when the abnormal states are monitored;
the emergency system is used for intelligently scheduling a solution when an abnormal state is received, scheduling the Internet of things system to execute the solution according to a known solution when the corresponding known solution exists in the obtained abnormal state, creating an emergency processing task when the corresponding known solution does not exist in the obtained abnormal state, issuing the emergency processing task to the crowdsourcing platform, receiving an emergency processing answer returned by the crowdsourcing platform and executing the solution of the abnormal state corresponding to the answer;
the crowdsourcing system is used for receiving the emergency treatment tasks, distributing the emergency treatment tasks to crowdsourcing workers for treatment, and returning emergency treatment answers to the emergency system after the emergency treatment tasks are treated.
The beneficial effect of this scheme is: the labor cost consumption is reduced by combining a crowdsourcing technology in the abnormal state disposal process of the Internet of things system, the system is enabled to perform iterative learning with lower cost, the task key levels are divided by calculating abnormal state risks, crowdsourcing modes of different levels are adopted for tasks of different risks, so that the tasks threatening the system can be disposed in a more reliable manner, and the emergency management system can operate more safely and stably.
Drawings
FIG. 1 is a schematic flow chart of an emergency management method based on crowdsourcing preprocessing according to the present invention;
FIG. 2 is a schematic diagram illustrating an emergency task allocation process according to the present invention;
FIG. 3 is a schematic view of an answer receiving process according to the present invention;
fig. 4 is a schematic structural diagram of an emergency management system based on crowdsourcing preprocessing according to the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Example 1
As shown in fig. 1, an embodiment of the present invention provides an emergency management method based on crowdsourcing preprocessing, including the following steps S1 to S7:
s1, acquiring the monitored unknown abnormal condition;
in this embodiment, step S1 specifically includes:
s101, monitoring abnormal states by utilizing subsystems in an Internet of things system, and uploading the abnormal states when the abnormal states are monitored;
s102, judging whether an abnormal state is received or not according to a set period; if yes, go to step S2; otherwise, continuing monitoring.
The invention monitors corresponding monitoring content by utilizing each subsystem in the Internet of things, integrates abnormal conditions monitored by each subsystem, and informs an emergency management system when the subsystem monitors abnormal states, such as: when the valve well monitoring terminal monitors that the pipeline PRESSURE is too high, the system receives that the heartbeat signal of the LINE _ PRESSURE is 0x00F6 or the valve well monitoring terminal in the internet of things is unexpected, and the heartbeat signal is not received within 2 to 3 times of the appointed time, then the system receives the MISS _ HBP _ ERROR is 0x0AD 4.
The present invention periodically monitors whether each subsystem has an abnormal condition according to a set period, and executes step S2 when an abnormal condition is monitored.
S2, analyzing the content of the abnormal state, and extracting the characteristic information of the abnormal state;
in this embodiment, the analyzing the content of the abnormal state specifically includes:
judging whether the acquired abnormal state has a corresponding known solution or not; if the solution exists, scheduling the Internet of things system to execute the solution according to the known solution; otherwise, the process proceeds to step S3.
S3, creating an emergency processing task according to the abnormal state and the characteristic information thereof, and distributing the emergency processing task to a crowdsourcing platform;
in the embodiment, when the task is created, the task requirement, the task description and the task reward of the emergency processing task are set, wherein error codes of abnormal conditions and specifications and related description documents of the system need to be submitted to crowdsourcing workers in the task description to serve as judgment bases.
In particular, the abnormal condition of the system may be represented by the abnormal condition of multiple subsystems, and when the abnormal condition is submitted, the related information of all the abnormal conditions of the current system needs to be submitted together.
S4, receiving emergency treatment tasks by the crowdsourcing platform and distributing the tasks;
in this embodiment, the task allocation method for the emergency processing task is set based on task risk and worker skill.
When the system sends out an emergency condition, a plurality of subsystems may have abnormal conditions at the same time, assuming that the abnormal events monitored by each subsystem in the emergency management system are a, the abnormal events monitored by each subsystem in the emergency management system are b, in order to distinguish the risk of the abnormal conditions, d is adoptedi∈[0,1](i-1, 2, …, a + b) represents the risk factor for various abnormal situations. At the same time, a plurality of abnormal conditions may occur in the system, and if m abnormal conditions exist in the system at a certain time, let n be a + b, and γ betk(t-1, …, m; k-1, …, n) represents an abnormal event occurring in an abnormal situation, γtkIs 0 or 1, gammatk0 means that the kth abnormal event in the tth abnormal condition does not occur, and vice versa means that the abnormal event occurs in the abnormal condition.
The current abnormal-condition matrix is then expressed as:
R＝[γtk]m×n
the risk value calculation formula of the current abnormal state is as follows:
w is the total risk value of the task at this time.
For emergency systems to be disposed ofIn an emergency event, a worker is required to have corresponding professional skills, Y is set as a skill set required by the current abnormal condition, A is a crowd-sourced worker set on line at the current moment, and X is set asiFor worker wiThe set of skills possessed, B | (Y ≧ X |)i) I/| Y | is worker wiA ratio of the number of tasks-related skills possessed to the number of tasks required.
As shown in fig. 2, the emergency processing task allocation performed by the crowdsourcing platform of the present invention specifically includes:
s401, traversing abnormal states submitted in the emergency processing task, reading information characteristics of the abnormal states in the emergency processing task and a preset risk threshold value g, and obtaining an abnormal condition matrix R;
s402, calculating a risk value W of the current emergency processing task, and judging whether the risk value W is greater than or equal to a preset risk threshold value g; if yes, go to step S403; otherwise, go to step S405;
s403, setting the current emergency processing task as a key emergency task, a corresponding first deadline, a first basic reward and the number of first crowdsourcing workers needing to answer;
here, the first deadtime deadline should be set to a more urgent time, the first basic consideration should be set to a higher basic consideration, and the number p of the first crowdsourcing workers who need to answer is set;
s404, judging whether the crowdsourcing platform meets the number of first crowdsourcing workers needing to be answered and corresponding to the key emergency task; if yes, go to step S406; otherwise, go to step S408;
crowdsourcing workers required for critical emergency tasks require a skill set that is highly coincident with the skill required for the task, i.e., B ═ X (Y ═ Xi) And | V Y | is more than epsilon, wherein epsilon is the coincidence rate of the skill of the worker in the key task acceptable by the system and the skill required by the task. If the crowdsourcing platform has more than p workers who have the ratio B of the number of skills related to the task to the number of skills required by the task, which is greater than the coincidence rate C of the skills of the workers in the key task acceptable by the system and the skills required by the task, the step S406 is executed; otherwise, go to step S408;
s405, setting the current emergency processing task as a common emergency task and a corresponding second deadline, a second basic reward and the number of second crowdsourcing workers needing to answer; here, the second deadline profile should be set to a looser time, and the second basic consideration should be set to a normal basic consideration;
s406, distributing the emergency treatment tasks to crowdsourcing workers;
s407, judging whether the crowdsourcing platform receives the tasks by the crowdsourcing worker number required in the task setting within the set deadline time; if so, ending the task allocation flow; otherwise, go to step S408;
the critical emergency task means that the system may have dangerous abnormal conditions which easily cause loss, and if workers without enough related skills make wrong judgment or delay the disposal time, greater loss may be caused; and no matter the key emergency task or the common emergency task has the optimal disposal time, in order to not miss the optimal disposal time, the invention does not have enough crowdsourcing workers in a crowdsourcing system or directly informs system maintenance personnel to go to the solution when the deadline task is not received beyond the set cut-off time.
When there are enough crowdsourcing workers to receive the task within the set deadline, the task is handled by the crowdsourcing workers.
And S408, delivering the key emergency tasks to system maintenance personnel for processing, and ending the task allocation process.
After the step is finished, the task issuing and distributing link of the crowdsourcing system is finished.
S5, the crowdsourcing platform returns an emergency treatment answer after completing the emergency treatment task;
in this embodiment, the emergency processing answer returned by the crowdsourcing platform adopts a preset format when the task is issued, and specifically includes: the system comprises an emergency treatment plan preset by the system, operation formed by splicing basic operation of the system, and delivery of key emergency tasks to system maintenance personnel for processing.
The preset formats are specifically classified into three types:
a) the system processes the preset emergency disposal plan;
b) splicing basic system operations into operations which are operations without affecting the system safety and opening the operations and aim to repair the abnormal condition of the system;
c) the system has serious problems and is used for ensuring property and system safety to be in contact with system maintenance personnel for processing.
S6, receiving an emergency treatment answer returned by the crowdsourcing platform, and executing an abnormal state solution corresponding to the answer;
in this embodiment, the step S6 and the step S7 are combined as the process of answer reception handling, as shown in fig. 3, wherein the step S6 specifically includes:
s601, starting an interface to asynchronously receive emergency processing answers returned by the crowdsourcing platform;
the system adopts an embedded crowdsourcing API, after the task is issued, the system starts an interface to asynchronously wait for the crowdsourcing system to return an answer, and after the answer is received, the step S602 is carried out.
S602, carrying out logic verification on operation answers spliced by basic system operations;
the system performs logic verification on the emergency processing answer with the return format b), directly discards the answer if the answer has conditions of no authority operation, mutual exclusion operation and the like, and enters step S603 after the logic verification is performed.
S603, judging whether the answer passes the logic check; if yes, go to step S604; otherwise, the step S7 is carried out, the abnormal situation is read again, the task is issued, and the step S2 is returned;
after logical verification, judging whether answers are in an alternative set or not; if not, the step S7 is carried out, the abnormal situation is read again, the task is issued, and the step S2 is returned; otherwise, go to step S604;
s604, judging whether the current emergency processing task is a key emergency task; if yes, go to step S605; otherwise, go to step S606;
s605, selecting a final executed solution for the answer passing the logic verification in a weighted voting mode;
since the answers to the critical emergency tasks come from a plurality of crowdsourcing workers, the invention selects the final executed solution by means of weighted voting.
Suppose the resulting answer set is h1,h2,…,hkFrom workers { w } respectively1,w2,…,wkV answers, i.e. the answer category set is { c }1,…,cvThe final selected scheme is H, then the formula of weighted voting is:
wherein H represents the weighted voting value of the answer, c represents the answer category, uiFor task hiIs the weight of worker wiA ratio of the number of tasks-related skills possessed to the number of tasks required. If c isjC, if the answers in the class are weighted the most, executing cjA class handling method.
And S606, directly adopting the answer passing the logic verification as an executed solution.
The answer to the general emergency task is from a single worker, and if the answer passes the logical verification, the answer can be directly adopted, and the step S7 is entered after the disposal scheme is adopted.
S7, performing system self-checking operation, and judging whether an abnormal state still exists; if yes, return to step S2; otherwise, the abnormal state and the corresponding solution are saved.
In this embodiment, step S7 specifically includes:
s701, performing system self-checking operation;
s702, judging whether an abnormal state still exists in the system; if yes, reading the abnormal condition issue task again, and returning to the step S2; otherwise, entering step S703 when the abnormal state in the system disappears;
s703, paying the basic remuneration and the additional remuneration to crowdsourcing workers, and saving the abnormal state and the corresponding solution.
After the system self-checks, the crowdsourcing worker is given positive feedback, i.e. additional consideration is paid in addition to the basic consideration (this will also be written in the introduction of the crowdsourcing task to encourage the crowdsourcing worker to work), and the abnormal condition and the disposal scheme of the time are recorded for the system to use in iterative learning. After this step, the answer receiving disposal process of the round of the system will end;
in a second aspect, the invention provides an emergency management system applying the emergency management method, which comprises an internet of things system, an emergency system and a crowdsourcing system;
the Internet of things system is used for monitoring abnormal states by utilizing all emergency subsystems in the Internet of things system and uploading the abnormal states when the abnormal states are monitored;
the emergency system is used for intelligently scheduling a solution when an abnormal state is received, scheduling the Internet of things system to execute the solution according to a known solution when the corresponding known solution exists in the obtained abnormal state, creating an emergency processing task when the corresponding known solution does not exist in the obtained abnormal state, issuing the emergency processing task to the crowdsourcing platform, receiving an emergency processing answer returned by the crowdsourcing platform and executing the solution of the abnormal state corresponding to the answer;
the crowdsourcing system is used for receiving the emergency treatment tasks, distributing the emergency treatment tasks to crowdsourcing workers for treatment, and returning emergency treatment answers to the emergency system after the emergency treatment tasks are treated.
As shown in fig. 4, the internet of things system of the present invention employs an example of a collection of a pipe rack pipeline management subsystem, a security management subsystem, a gas management subsystem, and an electric energy management subsystem. Each subsystem in the Internet of things monitors abnormal state information in the corresponding system, and reports the abnormal state information to the emergency management system when each subsystem monitors the abnormal state. The emergency abnormal state is uniformly scheduled and processed by the intelligent processing system in the emergency management system, and the intelligent processing system schedules the system for disposal according to a preset disposal mode when receiving the abnormal state with a precedent; and if the abnormal condition is not processed, the task is issued to the crowdsourcing system according to the task issuing flow. And after completing the task, crowdsourcing workers of the crowdsourcing system return the answers to the emergency management system according to the answer receiving and handling process, and when the intelligent processing system and the crowdsourcing system cannot handle abnormal conditions, the system requests system maintenance personnel to help, and the abnormal conditions of the Internet of things system are manually processed.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (9)
1. An emergency management method based on crowdsourcing preprocessing is characterized by comprising the following steps:
s1, acquiring the monitored unknown abnormal condition;
s2, analyzing the content of the abnormal state, and extracting the characteristic information of the abnormal state;
s3, creating an emergency processing task according to the abnormal state and the characteristic information thereof, and distributing the emergency processing task to a crowdsourcing platform;
s4, receiving emergency processing tasks by the crowdsourcing platform and distributing the tasks, wherein the tasks specifically comprise:
s401, traversing abnormal states submitted in the emergency processing task, reading information characteristics of the abnormal states in the emergency processing task and a preset risk threshold value, and obtaining an abnormal condition matrix;
s402, calculating a risk value of the current emergency processing task, and judging whether the risk value is greater than or equal to a preset risk threshold value; if yes, go to step S403; otherwise, go to step S405;
s403, setting the current emergency processing task as a key emergency task and corresponding first cut-off time, first basic remuneration and the number of first crowdsourcing workers needing to answer;
s404, judging whether the crowdsourcing platform meets the number of first crowdsourcing workers needing to be answered and corresponding to the key emergency task; if yes, go to step S406; otherwise, go to step S408;
s405, setting the current emergency processing task as a common emergency task and a corresponding second deadline, a second basic reward and the number of second crowdsourcing workers needing to answer;
s406, distributing the emergency treatment tasks to crowdsourcing workers;
s407, judging whether the crowdsourcing platform receives the tasks by the crowdsourcing worker number required in the task setting within the set deadline time; if so, ending the task allocation flow; otherwise, go to step S408;
s408, delivering the key emergency tasks to system maintenance personnel for processing, and ending the task allocation process;
s5, the crowdsourcing platform returns an emergency treatment answer after completing the emergency treatment task;
s6, receiving an emergency treatment answer returned by the crowdsourcing platform, and executing an abnormal state solution corresponding to the answer;
s7, performing system self-checking operation, and judging whether an abnormal state still exists; if yes, return to step S2; otherwise, the abnormal state and the corresponding solution are saved.
2. The emergency management method based on crowdsourcing preprocessing of claim 1, wherein the step S1 specifically comprises:
s101, monitoring abnormal states by utilizing subsystems in an Internet of things system, and uploading the abnormal states when the abnormal states are monitored;
s102, judging whether an abnormal state is received or not according to a set period; if yes, go to step S2; otherwise, continuing monitoring.
3. The emergency management method based on crowdsourcing preprocessing of claim 2, wherein the analyzing of the abnormal state content in step S2 specifically comprises:
judging whether the acquired abnormal state has a corresponding known solution or not; if the solution exists, scheduling the Internet of things system to execute the solution according to the known solution; otherwise, the process proceeds to step S3.
4. The emergency management method based on crowdsourcing preprocessing of claim 1, wherein the risk value of the current emergency treatment task is calculated by the formula:
wherein W is the risk value of the current emergency processing task, gammatkFor the kth exception event occurring in the t-th exception state, diA risk coefficient indicating an abnormal state.
5. The emergency management method based on crowdsourcing preprocessing of claim 4, wherein the emergency processing answer returned by the crowdsourcing platform in the step S5 is in a format preset when the task is issued, and specifically comprises: the system comprises an emergency treatment plan preset by the system, operation formed by splicing basic operation of the system, and delivery of key emergency tasks to system maintenance personnel for processing.
6. The emergency management method based on crowdsourcing preprocessing of claim 5, wherein the step S6 specifically comprises:
s601, starting an interface to asynchronously receive emergency processing answers returned by the crowdsourcing platform;
s602, carrying out logic verification on operation answers spliced by basic system operations;
s603, judging whether the answer passes the logic check; if yes, go to step S604; otherwise, the step S7 is carried out, the abnormal situation is read again, the task is issued, and the step S2 is returned;
s604, judging whether the current emergency processing task is a key emergency task; if yes, go to step S605; otherwise, go to step S606;
s605, selecting a final executed solution for the answer passing the logic verification in a weighted voting mode;
and S606, directly adopting the answer passing the logic verification as an executed solution.
7. The emergency management method based on crowdsourcing preprocessing of claim 6, wherein the weighted voting is calculated by the formula:
wherein H represents the weighted voting value of the answer, c represents the answer category, uiIs the answer hiK is the set number of the first crowdsourcing workers, and j is the kind number of the answer.
8. The emergency management method based on crowdsourcing preprocessing of claim 7, wherein the step S7 specifically comprises:
s701, performing system self-checking operation;
s702, judging whether an abnormal state still exists or not; if yes, return to step S2; otherwise, entering step S703;
s703, paying the basic remuneration and the additional remuneration to crowdsourcing workers, and saving the abnormal state and the corresponding solution.
9. An emergency management system applying the emergency management method according to any one of claims 1 to 8, comprising an internet of things system, an emergency system and a crowdsourcing system;
the Internet of things system is used for monitoring abnormal states by utilizing all emergency subsystems in the Internet of things system and uploading the abnormal states when the abnormal states are monitored;
the emergency system is used for intelligently scheduling a solution when an abnormal state is received, scheduling the Internet of things system to execute the solution according to a known solution when the corresponding known solution exists in the obtained abnormal state, creating an emergency processing task when the corresponding known solution does not exist in the obtained abnormal state, issuing the emergency processing task to the crowdsourcing platform, receiving an emergency processing answer returned by the crowdsourcing platform and executing the solution of the abnormal state corresponding to the answer;
the crowdsourcing system is used for receiving emergency treatment tasks and distributing the emergency treatment tasks to crowdsourcing workers for treatment, and specifically comprises:
a1, traversing abnormal states submitted in the emergency processing task, reading information characteristics of the abnormal states in the emergency processing task and a preset risk threshold value, and obtaining an abnormal condition matrix;
a2, calculating a risk value of the current emergency processing task, and judging whether the risk value is greater than or equal to a preset risk threshold value; if yes, go to step A3; otherwise go to step A5;
a3, setting the current emergency treatment task as a key emergency task and corresponding first cut-off time, first basic remuneration and the number of first crowdsourcing workers needing to answer;
a4, judging whether the crowdsourcing platform meets the number of first crowdsourcing workers needing to be answered and corresponding to the key emergency task; if yes, go to step A6; otherwise go to step A8;
a5, setting the current emergency treatment task as a common emergency task and a corresponding second deadline, a second basic reward and the number of second crowdsourcing workers needing to answer;
a6, distributing emergency treatment tasks to crowdsourcing workers;
a7, judging whether the crowdsourcing platform receives tasks by the number of crowdsourcing workers required in the task setting within the set deadline; if so, ending the task allocation flow; otherwise go to step A8;
a8, delivering the key emergency tasks to system maintenance personnel for processing, and ending the task allocation process;
and returning an emergency treatment answer to the emergency system after the emergency treatment task is processed.
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