CN112270502B - Environment emergency task cooperative disposal platform based on artificial intelligence technology - Google Patents
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Abstract
The invention provides an environment emergency task cooperative disposal platform based on an artificial intelligence technology, which comprises: the emergency task receiving module is used for receiving an emergency task; the collaborative scheme making module is used for making a collaborative scheme based on the emergency task; the task issuing and executing module is used for analyzing the collaborative scheme, obtaining a plurality of collaborative tasks and issuing the collaborative tasks to the corresponding first responsible persons; the result feedback module is used for receiving feedback information of the first responsible person on the execution result of the cooperative task; and the report issuing module is used for generating and issuing the cooperative treatment report according to the feedback information. The environment emergency task cooperative disposal platform based on the artificial intelligence technology solves the problem that when an environment pollution accident occurs, related departments are difficult to quickly and cooperatively carry out emergency work.
Description
Technical Field
The invention relates to the technical field of emergency treatment, in particular to an environment emergency task cooperative disposal platform based on an artificial intelligence technology.
Background
In recent years, while social economy is rapidly developed, various sudden environmental pollution accidents (events) are in a rising situation year by year, so that the sudden environmental pollution accidents are effectively responded, the pollution influence of the accidents on the environment is reduced to the greatest extent, and the method is a higher requirement for the work of emergency management departments.
The existing emergency detection tasks are managed in a scattered manner, and each organization has a suitable service system which can manage daily management tasks. The processes of task receipt, assignment, execution, report generation, etc. may be managed.
However, there are the following problems:
firstly, a business system built by each detection mechanism lacks support for emergency detection tasks and lacks a corresponding rapid disposal process.
And secondly, after an emergency event occurs, a plurality of professional and multi-level inspection and detection mechanisms are often required to participate and complete, and for the service management, the system support is lacked, so that the cooperativity among a plurality of departments is limited.
Disclosure of Invention
One of the purposes of the invention is to provide an environment emergency task cooperative disposal platform based on an artificial intelligence technology, and solve the problem of great difficulty in quickly and cooperatively developing emergency work by related departments when an environment pollution accident occurs.
The embodiment of the invention provides an environment emergency task cooperative disposal platform based on an artificial intelligence technology, which comprises:
the emergency task receiving module is used for receiving an emergency task;
the collaborative scheme making module is used for making a collaborative scheme based on the emergency task;
the task issuing and executing module is used for analyzing the collaborative scheme, obtaining a plurality of collaborative tasks and issuing the collaborative tasks to the corresponding first responsible persons;
the result feedback module is used for receiving feedback information of the first responsible person on the execution result of the cooperative task;
and the report issuing module is used for generating and issuing the cooperative treatment report according to the feedback information.
Preferably, the emergency task receiving module performs the following operations:
receiving application information of cooperative disposal of an emergency task of a first user;
verifying the authority of the first user;
when the verification is passed, receiving event information of the emergency task; the event information includes: event code, event type, severity, event content;
and informing the corresponding general responsible person according to the severity degree.
Preferably, the collaborative scheme making module performs the following operations:
inputting the event type and the event content into a deep neural network model which is trained in advance based on historical event cases to determine a collaborative scheme;
the cooperation scheme comprises the following steps: checking the detection content, the first person in charge, the response time limit, the sampling place, the sampling time, the detection place, the detection mode, the detection tool and the constraint of other cooperative services.
Preferably, the task assigning and executing module performs the following operations:
dividing the collaborative scheme into a plurality of collaborative tasks according to the inspection and detection content;
and/or the presence of a gas in the gas,
dividing the collaboration scheme into a plurality of collaboration tasks according to the inspection content responsible by the first person in charge;
and sending the cooperative task to the corresponding first responsible person.
Preferably, the emergency task receiving module further performs the following operations:
when an abnormal picture uploaded by a second user is received, acquiring first positioning information of the second user;
acquiring other users in a preset area range with the first positioning information as the center;
acquiring historical positioning information of other users, screening the other users based on the historical positioning information, and acquiring a target user;
sending the abnormal picture to a target user, and receiving evaluation information of the target user on the abnormal picture;
verifying the authenticity of the abnormal picture based on the evaluation information;
and/or the presence of a gas in the gas,
when an abnormal picture uploaded by a second user is received, acquiring first positioning information of the second user;
verifying the authenticity of the abnormal picture based on the first positioning information;
when the verification passes, acquiring a normal picture corresponding to the abnormal picture; and comparing the normal picture with the abnormal picture, and determining the type and the severity of the event.
Preferably, verifying the authenticity of the abnormal picture based on the evaluation information includes:
grouping the evaluation information based on the historical positioning information to obtain a plurality of groups of evaluation data;
calculating a sum of first similarities of the historical positioning information corresponding to the evaluation information in the evaluation data and the historical positioning information corresponding to other evaluation information, and taking the historical positioning information with the maximum sum of the first similarities as standard positioning information representing the evaluation data;
acquiring a first coefficient configured to other users corresponding to evaluation information in evaluation data;
querying a preset second coefficient database based on the standard positioning information to determine a second coefficient;
calculating the weight of the evaluation data based on the first coefficient and the second coefficient, wherein the calculation formula is as follows:
wherein, muiWeight, α, representing the i-th set of evaluation data1,iIs a preset first relation ratio, alpha, corresponding to the first coefficient2,iThe second relation ratio is a preset second relation ratio corresponding to the second coefficient; b isiA second coefficient determined for the standard positioning information of the ith set of evaluation data; a. thei,jA first coefficient of other users corresponding to the jth evaluation information in the ith group of evaluation data; n isiThe number of evaluation data for the ith group;
acquiring a pre-established evaluation information identification database;
identifying evaluation information based on the evaluation information identification database to obtain a rating value corresponding to the evaluation information;
and calculating the evaluation value of the evaluation data based on the evaluation value of each piece of evaluation information in the evaluation data, wherein the calculation formula is as follows:
wherein, PiAn evaluation value for the ith group of evaluation data;pi,jthe value of the credit of the jth evaluation information in the ith group of evaluation data;
and calculating the truth of the abnormal picture based on the evaluation value and the weight of each evaluation data, wherein the calculation formula is as follows:
wherein Z represents the truth of the abnormal picture, and m is the group number of the evaluation data; p is an average value of evaluation values of each group of evaluation data; beta is an adjustment coefficient;
and when the authenticity is greater than the preset verification value, the verification is passed, otherwise, the verification is not passed.
Preferably, the verifying the authenticity of the abnormal picture based on the first positioning information includes:
acquiring a plurality of pictures to be screened corresponding to first positioning information from a picture library corresponding to pre-made positioning information and pictures based on the first positioning information;
and calculating a second similarity between the abnormal picture and the picture to be screened, when the second similarity is greater than a preset similarity threshold, passing the verification, and taking the picture to be screened with the maximum second similarity as a normal picture, otherwise, failing to pass the verification.
Preferably, the task assigning and executing module performs the following operations:
analyzing the cooperative scheme to obtain a plurality of cooperative tasks and the relationship among the cooperative tasks;
grading the collaborative tasks based on the relationship between the collaborative tasks; when the cooperative task does not have a preposed task, dividing the cooperative task into a first-level task; when the front task of the cooperative task is a first-level task, dividing the cooperative task into a second-level task; by analogy, when the front task of the cooperative task is the (N-1) th level task and the cooperative task has no subsequent task, dividing the cooperative task into the Nth level task;
assigning a preset initial task priority value to each level of tasks;
determining the priority value of each cooperative task based on the preset priority value transmission rule and the relationship between the cooperative tasks, wherein the calculation formula is as follows:
wherein, Yk,lRepresenting the priority value of the ith cooperative task in the kth level task; dkThe initial priority value of the cooperative task in the kth level task is configured; y ishThe priority value of the h-th cooperative task in the subsequent tasks related to the l-th cooperative task in the k-th level task is obtained; gamma raykThe transfer coefficient of the preset k-th-level task is obtained; n is the number of the subsequent tasks of the ith cooperative task in the kth level task;
obtaining an evaluation information table of each task team, wherein the evaluation information table comprises: one or more of task completion time, task on-time completion rate, team cooperation times, team cooperation evaluation, task completion times and task delay completion maximum delay time are combined;
the evaluation information is recorded in a preset evaluation model, and the evaluation value of each task team is obtained;
based on the score values, sorting the task teams to construct a first team list to be distributed;
and performing simulation execution based on the grading situation of the cooperative task and the first team to be distributed list.
Preferably, the simulation execution is performed based on the hierarchical condition of the collaborative task and the first team to be distributed list, and includes:
establishing a time axis based on the start time of the collaborative scheme;
when the number of task teams is greater than the number of first level tasks,
distributing a corresponding task team for the cooperative task of the first-level task according to the descending order of the priority values of the cooperative tasks of the first-level task and the order of the task teams in the first team list to be distributed, and deleting the task team from the first team list to be distributed after the task team is distributed with the cooperative task;
placing the first-level task of the assigned task team at the starting time point of the time axis to start simulation execution;
after the task team executes the cooperative task, adding the task team into the tail end of the first team list to be distributed;
pulling the time axis backwards, and configuring a task team positioned at the first position in the current first team list to be allocated for the second-level task from the current first team list to be allocated after the front tasks of the second-level task are executed; repeating the steps until all the Nth-level tasks are executed;
constructing simulation parameters of the cooperative task according to the simulation execution conditions from the first-level task to the Nth-level task;
the simulation parameters are synchronously sent when the collaborative task is assigned to the first person in charge of the task team.
Preferably, the task assigning and executing module further performs the following operations:
determining a cooperative task corresponding to each task team according to the simulation execution conditions from the first-level task to the Nth-level task;
acquiring a preset cooperative task demand table, and inquiring the cooperative task demand table to acquire the demand of a cooperative task;
determining each preparation item of the task team based on the corresponding relation between the task team and the collaborative task;
acquiring a preparation item responsible table of a task team;
based on the preparation item responsibility table, the preparation item is sent to the corresponding item responsibility person.
The environment emergency task cooperative disposal platform based on the artificial intelligence technology has the following beneficial effects:
the platform specially used for processing the emergency inspection detection task fully considers the urgency of the emergency task in process setting, has various strong reminding functions and artificial intelligence auxiliary decision making functions, and is suitable for disposal of the emergency task.
And secondly, the task cooperation among multiple departments is fully considered, and cooperative business processes are set in multiple processes of scheme formulation, task execution, result feedback and report release, so that the problem of department cooperation among multiple specialties and multiple levels can be effectively solved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic diagram of an environment emergency task co-processing platform based on an artificial intelligence technology in an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
An embodiment of the present invention provides an environment emergency task cooperative disposition platform based on an artificial intelligence technology, as shown in fig. 1, including:
the emergency task receiving module 1 is used for receiving an emergency task;
the collaborative scheme making module 2 is used for making a collaborative scheme based on the emergency task;
the task issuing and executing module 3 is used for analyzing the collaborative scheme, obtaining a plurality of collaborative tasks and issuing the collaborative tasks to the corresponding first responsible persons;
the result feedback module 4 is used for receiving feedback information of the first responsible person on the execution result of the collaborative task;
and the report issuing module 5 is used for generating and issuing the cooperative treatment report according to the feedback information.
The working principle and the beneficial effects of the technical scheme are as follows:
a main user of the environment emergency task cooperative disposal platform based on the artificial intelligence technology is a service inspection and detection department related to an emergency event, when the emergency event occurs, the platform can acquire a task through an emergency monitoring and command center interface and automatically register detailed information of the task, wherein the event information comprises an event code, an event type, a severity degree, an event content and the like. After receiving the emergency task and completing automatic registration, the platform can rapidly notify corresponding responsible persons through short messages, telephones and the like according to the severity level of the event, and the responsible persons organize a team after receiving event information, so that rapid response is realized, and support is provided for disposal decision of the emergency event. The collaborative scheme making module is used for making a collaborative scheme based on the emergency task, for example, feature extraction is carried out on time information of the emergency task, a preset collaborative scheme library is inquired based on the extracted features, and the collaborative scheme is determined; the task issuing and executing module is used for analyzing the collaborative scheme, obtaining a plurality of collaborative tasks and issuing the collaborative tasks to the corresponding first responsible persons; the task issuing and executing module is mainly used for disassembling the cooperative tasks in the cooperative scheme and sending the cooperative tasks to corresponding responsible persons; for example: one emergency task comprises a plurality of detection items, and the detection items can be independently disassembled and sent to corresponding collection and detection responsible persons or responsibility teams; a result feedback module, configured to receive feedback information of the result of the execution of the collaborative task by the first person in charge, for example: after the person responsible for sampling and detecting finishes the work, the information sampled and detected is fed back to the platform in time, the platform supports automatic acquisition of instrument and equipment data and manual information input, and in the process of manually inputting the information, an artificial intelligence tool can judge errors and non-normative parts in a data format, a data range and semantic expression according to historical information, so that the accuracy and the conciseness of the fed-back information are guaranteed.
A report issuing module, configured to generate and issue a co-treatment report according to the feedback information, for example: after the detection data are acquired and the relevant responsible persons verify the data without errors, the platform can automatically collect the detection results of multiple departments to generate a report, and the report can be transmitted to other emergency management platforms through the platform interface to provide support for decision-making personnel.
The environment emergency task cooperative disposal platform based on the artificial intelligence technology solves the problem that when an environment pollution accident occurs, related departments are difficult to quickly and cooperatively carry out emergency work.
In one embodiment, the emergency task receiving module performs the following operations:
receiving application information of cooperative disposal of an emergency task of a first user;
verifying the authority of the first user;
when the verification is passed, receiving event information of the emergency task; the event information includes: event code, event type, severity, event content;
and informing the corresponding general responsible person according to the severity degree.
The working principle and the beneficial effects of the technical scheme are as follows:
the first user is an employee of a service inspection detection department related to the emergency event, and firstly, the authority of the first user is verified, namely, each employee cannot have the authority to apply for cooperative disposal of the emergency task, so that the legality and validity of the application rights and interests are guaranteed; the input of the event information can be manually input, and can also be automatically input by scanning a pre-filled event information table; the event information mainly includes: event codes, event types, severity degrees, event contents and the like, and a corresponding general responsible person is notified according to the severity degree of the event; for example, when the event is slight, the general responsible person is the office principal; when the event is serious, the general responsible person is the chief or subordinate person of the service inspection and detection department.
In one embodiment, the collaborative project formulation module performs the following operations:
inputting the event type and the event content into a deep neural network model which is trained in advance based on historical event cases to determine a collaborative scheme;
the cooperation scheme comprises the following steps: checking the detection content, the first person in charge, the response time limit, the sampling place, the sampling time, the detection place, the detection mode, the detection tool and the constraint of other cooperative services.
The working principle and the beneficial effects of the technical scheme are as follows:
in the process of making a scheme, the platform supports making of a collaborative scheme among longitudinal departments of provinces, cities and counties, horizontal departments of environments and the like. According to the event type, the event content and the historical event case in the platform, the platform adopts an artificial intelligence technology to automatically give out a plurality of disposal schemes, the scheme contents comprise inspection and detection contents, responsible persons, response time limit, sampling places, sampling time, detection places (field inspection and local inspection), detection modes, detection tools, constraints of other collaborative services and the like, and the task responsible persons can select and adjust the reference scheme given out by the platform according to actual conditions.
In one embodiment, the task assignment and execution module performs the following operations:
dividing the collaborative scheme into a plurality of collaborative tasks according to the inspection and detection content;
and/or the presence of a gas in the gas,
dividing the collaboration scheme into a plurality of collaboration tasks according to the inspection content responsible by the first person in charge;
and sending the cooperative task to the corresponding first responsible person.
The working principle and the beneficial effects of the technical scheme are as follows:
after the scheme is formulated, the task responsible person issues the task, the related responsible persons receive the task, each responsible person is ensured to complete the task response, and each responsible person performs sampling and detection according to the time, the place and the specific detection requirements in the scheme; the platform can track the execution condition of the task, when the deviation of time, place and inspection process occurs in the task execution, the artificial intelligence tool can judge the severity level of the deviation, the information can be timely fed back to the responsible person of the corresponding level, the responsible person can timely make the adjustment of the scheme, the information closed loop is guaranteed, and the control in the matter is achieved.
In one embodiment, the emergency task receiving module further performs the following operations:
when an abnormal picture uploaded by a second user is received, acquiring first positioning information of the second user;
acquiring other users in a preset area range with the first positioning information as the center;
acquiring historical positioning information of other users, screening the other users based on the historical positioning information, and acquiring a target user;
sending the abnormal picture to a target user, and receiving evaluation information of the target user on the abnormal picture;
verifying the authenticity of the abnormal picture based on the evaluation information;
and/or the presence of a gas in the gas,
when an abnormal picture uploaded by a second user is received, acquiring first positioning information of the second user;
verifying the authenticity of the abnormal picture based on the first positioning information;
when the verification passes, acquiring a normal picture corresponding to the abnormal picture; and comparing the normal picture with the abnormal picture, and determining the type and the severity of the event.
The working principle and the beneficial effects of the technical scheme are as follows:
if the user of the platform is only a service inspection and detection department related to an emergency event, all environment abnormalities in the jurisdiction cannot be accurately controlled; in this embodiment, the second user who is proposed as an employee of the service inspection detection department related to the non-emergency event may be a registered user of the platform, and certainly, when the second user applies for the application, the authenticity of the information provided by the second user needs to be verified, and the verification method mainly includes: firstly, inquiring other nearby users based on the positioning information to form group testification; directly verifying authenticity based on the positioning information, wherein verification is mainly to verify whether the uploading location of the information is matched with the positioning information; when the group evidence verification method is adopted, other users need to be screened according to historical positioning information of the other users, and whether the other users are workers at the place or not is mainly judged according to the historical positioning information, for example, the time of the historical positioning information in the area represented by the first positioning information meets the condition of workers, and the condition that the historical positioning information passes through the area represented by the first positioning information at fixed time and fixed point every day; other users of these situations need to be proposed to ensure the accuracy of group demonstration. After the verification is passed, acquiring a normal picture corresponding to the abnormal picture; comparing the normal picture with the abnormal picture, determining the type and the severity of the event, calculating the similarity between the abnormal picture and the abnormal judgment picture by adopting the abnormal judgment picture stored corresponding to the normal picture, and determining the type and the severity of the event represented by the abnormal picture; or respectively extracting the characteristic values of the normal picture and the abnormal picture, calculating the characteristic value difference, and substituting the characteristic value difference into a pre-established neural network model for judgment to judge the type and the severity of the event.
In one embodiment, verifying the authenticity of the abnormal picture based on the evaluation information includes:
grouping the evaluation information based on the historical positioning information to obtain a plurality of groups of evaluation data;
calculating a sum of first similarities of the historical positioning information corresponding to the evaluation information in the evaluation data and the historical positioning information corresponding to other evaluation information, and taking the historical positioning information with the maximum sum of the first similarities as standard positioning information representing the evaluation data;
acquiring a first coefficient configured to other users corresponding to evaluation information in evaluation data;
querying a preset second coefficient database based on the standard positioning information to determine a second coefficient;
calculating the weight of the evaluation data based on the first coefficient and the second coefficient, wherein the calculation formula is as follows:
wherein, muiWeight, α, representing the i-th set of evaluation data1,iIs a preset first relation ratio, alpha, corresponding to the first coefficient2,iThe second relation ratio is a preset second relation ratio corresponding to the second coefficient; b isiSecond system for standard positioning information determination of i-th group evaluation dataCounting; a. thei,jA first coefficient of other users corresponding to the jth evaluation information in the ith group of evaluation data; n isiThe number of evaluation data for the ith group;
acquiring a pre-established evaluation information identification database;
identifying evaluation information based on the evaluation information identification database to obtain a rating value corresponding to the evaluation information;
and calculating the evaluation value of the evaluation data based on the evaluation value of each piece of evaluation information in the evaluation data, wherein the calculation formula is as follows:
wherein, PiAn evaluation value for the ith group of evaluation data; p is a radical ofi,jThe value of the credit of the jth evaluation information in the ith group of evaluation data;
and calculating the truth of the abnormal picture based on the evaluation value and the weight of each evaluation data, wherein the calculation formula is as follows:
wherein Z represents the truth of the abnormal picture, and m is the group number of the evaluation data; p is an average value of evaluation values of each group of evaluation data; beta is an adjustment coefficient;
and when the authenticity is greater than the preset verification value, the verification is passed, otherwise, the verification is not passed.
The working principle and the beneficial effects of the technical scheme are as follows:
grouping the evaluation information based on the historical positioning information, ensuring the multi-channel property of the evaluation information for verifying the authenticity, and preventing the evaluation information of a single channel source from being too much to form the error of the final authenticity verification result; in addition, in the weight calculation process, the same group represents users with the same and similar historical positioning information, which can be classified as a type of person, and the standard positioning information of each group is determined firstly; then determining a second coefficient based on the standard positioning information, wherein the second coefficient represents the difference between groups, and the first coefficient represents the individual difference; the differences among groups and the differences among individuals are integrated, so that the determined weight is more accurate; when the trueness is determined based on the evaluation values and the weights, the trueness is more accurate by adjusting based on the difference between the evaluation values and the average value of each group.
In one embodiment, verifying the authenticity of the anomalous picture based on the first positioning information comprises:
acquiring a plurality of pictures to be screened corresponding to first positioning information from a picture library corresponding to pre-made positioning information and pictures based on the first positioning information;
and calculating a second similarity between the abnormal picture and the picture to be screened, when the second similarity is greater than a preset similarity threshold, passing the verification, and taking the picture to be screened with the maximum second similarity as a normal picture, otherwise, failing to pass the verification.
The working principle and the beneficial effects of the technical scheme are as follows:
when the authenticity of the picture is verified by adopting the positioning information, the picture library is mainly inquired by adopting the first positioning information, and the picture corresponding to the positioning information stored in the picture library is obtained; verifying the authenticity of the abnormal picture based on the obtained picture; for example: and no river exists near the positioning information position, and the abnormal picture uploaded by the second user is a picture of the river, so that the verification is not passed.
In one embodiment, the task assignment and execution module performs the following operations:
analyzing the cooperative scheme to obtain a plurality of cooperative tasks and the relationship among the cooperative tasks;
grading the collaborative tasks based on the relationship between the collaborative tasks; when the cooperative task does not have a preposed task, dividing the cooperative task into a first-level task; when the front task of the cooperative task is a first-level task, dividing the cooperative task into a second-level task; by analogy, when the front task of the cooperative task is the (N-1) th level task and the cooperative task has no subsequent task, dividing the cooperative task into the Nth level task;
assigning a preset initial task priority value to each level of tasks;
determining the priority value of each cooperative task based on the preset priority value transmission rule and the relationship between the cooperative tasks, wherein the calculation formula is as follows:
wherein, Yk,lRepresenting the priority value of the ith cooperative task in the kth level task; dkThe initial priority value of the cooperative task in the kth level task is configured; y ishThe priority value of the h-th cooperative task in the subsequent tasks related to the l-th cooperative task in the k-th level task is obtained; gamma raykThe transfer coefficient of the preset k-th-level task is obtained; n is the number of the subsequent tasks of the ith cooperative task in the kth level task;
obtaining an evaluation information table of each task team, wherein the evaluation information table comprises: one or more of task completion time, task on-time completion rate, team cooperation times, team cooperation evaluation, task completion times and task delay completion maximum delay time are combined;
the evaluation information is recorded in a preset evaluation model, and the evaluation value of each task team is obtained;
based on the score values, sorting the task teams to construct a first team list to be distributed;
and performing simulation execution based on the grading situation of the cooperative task and the first team to be distributed list.
The working principle and the beneficial effects of the technical scheme are as follows:
when the collaborative scheme is split into a plurality of collaborative tasks, the collaborative tasks are graded based on the relationship among the collaborative tasks; calculating the priority value of each cooperative task after grading, wherein the priority value represents the priority processing requirement and the importance degree of the cooperative task; in addition, the evaluation value of each task team is determined according to the evaluation information table of each task team, simulation execution is realized according to the evaluation value and the priority value, and the distribution scheme and the execution effect of the cooperative task are determined through the simulation execution process. The evaluation information table is brought into a preset evaluation model to obtain the evaluation value of each task team, and the evaluation value can be multiplied by the corresponding weight value according to the indexes of each evaluation index.
In one embodiment, the simulation execution is performed based on the hierarchical condition of the collaborative task and the first to-be-assigned team list, and comprises the following steps:
establishing a time axis based on the start time of the collaborative scheme;
when the number of task teams is greater than the number of first level tasks,
distributing a corresponding task team for the cooperative task of the first-level task according to the descending order of the priority values of the cooperative tasks of the first-level task and the order of the task teams in the first team list to be distributed, and deleting the task team from the first team list to be distributed after the task team is distributed with the cooperative task;
placing the first-level task of the assigned task team at the starting time point of the time axis to start simulation execution;
after the task team executes the cooperative task, adding the task team into the tail end of the first team list to be distributed;
pulling the time axis backwards, and configuring a task team positioned at the first position in the current first team list to be allocated for the second-level task from the current first team list to be allocated after the front tasks of the second-level task are executed; repeating the steps until all the Nth-level tasks are executed;
constructing simulation parameters of the cooperative task according to the simulation execution conditions from the first-level task to the Nth-level task;
the simulation parameters are synchronously sent when the collaborative task is assigned to the first person in charge of the task team.
The working principle and the beneficial effects of the technical scheme are as follows:
during simulation execution, firstly establishing a time axis, executing a first-stage task at the starting point of the time axis according to a grading condition, starting to execute a second-stage task when all the tasks in front of the second-stage task are executed, and so on, wherein when the first-stage task is distributed, the higher the score value of a task team is, the higher the priority value of the distributed first-stage task is, and the important task is guaranteed to be processed by an excellent team; in addition, when the number of task teams is smaller than that of the first-level tasks, the number of the started first-level tasks can be selected according to the number of the task teams, and the rest first-level tasks are processed together with or before the second-level tasks.
In one embodiment, the task assignment and execution module further performs the following operations:
determining a cooperative task corresponding to each task team according to the simulation execution conditions from the first-level task to the Nth-level task;
acquiring a preset cooperative task demand table, and inquiring the cooperative task demand table to acquire the demand of a cooperative task;
determining each preparation item of the task team based on the corresponding relation between the task team and the collaborative task;
acquiring a preparation item responsible table of a task team;
based on the preparation item responsibility table, the preparation item is sent to the corresponding item responsibility person.
The working principle and the beneficial effects of the technical scheme are as follows:
the preparation items are refined to the item responsible persons, the coordination operation among all the item responsible persons in the task team is improved, and the task implementation efficiency is improved.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (8)
1. An environmental emergency task co-processing platform based on artificial intelligence technology, comprising:
the emergency task receiving module is used for receiving an emergency task;
the collaborative scheme making module is used for making a collaborative scheme based on the emergency task;
the task issuing and executing module is used for analyzing the collaborative scheme, obtaining a plurality of collaborative tasks and issuing the collaborative tasks to corresponding first responsible persons;
the result feedback module is used for receiving feedback information of the first responsible person on the execution result of the collaborative task;
the report issuing module is used for generating and issuing a cooperative disposal report according to the feedback information;
the task issuing and executing module executes the following operations:
analyzing the cooperative scheme to obtain a plurality of cooperative tasks and the relationship among the cooperative tasks;
ranking the collaborative tasks based on relationships between the collaborative tasks; when the cooperative task does not have a preposed task, dividing the cooperative task into a first-level task; when the front task of the cooperative task is a first-level task, dividing the cooperative task into a second-level task; by analogy, when the front task of the cooperative task is the (N-1) th level task and the cooperative task has no subsequent task, dividing the cooperative task into the (N) th level task;
assigning a preset initial task priority value to each level of tasks;
determining the priority value of each cooperative task based on a preset priority value transmission rule and the relationship between the cooperative tasks, wherein the calculation formula is as follows:
wherein the content of the first and second substances,
is shown as
First in a level task
A priority value of each of the collaborative tasks;
to be configured to
An initial priority value of the collaborative task in a level task;
is the first
First in a level task
The first of the subsequent tasks associated with the collaborative task
A priority value of the individual collaborative task;
is a preset one
The transfer coefficient of the level task;
is as follows
First in a level task
A number of subsequent tasks of the collaborative task;
obtaining an evaluation information table of each task team, wherein the evaluation information table comprises: one or more of task completion time, task on-time completion rate, team cooperation times, team cooperation evaluation, task completion times and task delay completion maximum delay time are combined;
the evaluation information is recorded in a preset evaluation model, and the evaluation value of each task team is obtained;
based on the scoring value, sequencing the task teams to construct a first to-be-distributed team list;
and performing simulation execution based on the grading situation of the cooperative task and the first team list to be distributed.
2. The artificial intelligence technology-based environmental emergency task co-disposal platform of claim 1, wherein the emergency task receiving module performs the following operations:
receiving application information of cooperative disposal of an emergency task of a first user;
verifying the authority of the first user;
when the verification is passed, receiving event information of the emergency task; the event information includes: event code, event type, severity, event content;
and informing the corresponding general responsible person according to the severity.
3. The artificial intelligence technology-based environmental emergency task co-disposal platform of claim 2, wherein the collaboration schema formulation module performs the following operations:
inputting the event type and the event content into a deep neural network model trained in advance based on historical event cases to determine a collaborative scheme;
the collaborative scheme includes: checking the detection content, the first person in charge, the response time limit, the sampling place, the sampling time, the detection place, the detection mode, the detection tool and the constraint of other cooperative services.
4. The artificial intelligence technology-based environmental emergency task co-disposal platform of claim 3, wherein the task issuing and executing module performs the following operations:
dividing the collaborative scheme into a plurality of collaborative tasks according to the inspection and detection content;
and/or the presence of a gas in the gas,
dividing the collaborative scheme into a plurality of collaborative tasks according to the inspection content responsible by the first person in charge;
and sending the cooperative task to a corresponding first person in charge.
5. The artificial intelligence technology-based environmental emergency task co-disposal platform of claim 1, wherein the emergency task receiving module further performs the following operations:
when an abnormal picture uploaded by a second user is received, acquiring first positioning information of the second user;
acquiring other users in a preset area range with the first positioning information as the center;
acquiring historical positioning information of other users, screening the other users based on the historical positioning information, and acquiring a target user;
sending the abnormal picture to the target user, and receiving evaluation information of the target user on the abnormal picture;
verifying the authenticity of the abnormal picture based on the evaluation information;
and/or the presence of a gas in the gas,
when an abnormal picture uploaded by a second user is received, acquiring first positioning information of the second user;
verifying the authenticity of the abnormal picture based on the first positioning information;
when the verification is passed, acquiring a normal picture corresponding to the abnormal picture; comparing the normal picture with the abnormal picture, and determining the type and the severity of the event;
wherein the verifying the authenticity of the abnormal picture based on the first positioning information comprises:
acquiring a plurality of pictures to be screened corresponding to the first positioning information from a picture library corresponding to pre-made positioning information and pictures based on the first positioning information;
and calculating a second similarity between the abnormal picture and the picture to be screened, when the second similarity is greater than a preset similarity threshold, passing the verification, and taking the picture to be screened with the maximum second similarity as a normal picture, otherwise, failing to pass the verification.
6. The artificial intelligence technology-based environmental emergency task co-disposal platform of claim 5, wherein the verifying the authenticity of the abnormal picture based on the evaluation information comprises:
grouping the evaluation information based on the historical positioning information to obtain multiple groups of evaluation data;
calculating a sum of first similarities between the historical positioning information corresponding to the evaluation information and the historical positioning information corresponding to the other evaluation information in the evaluation data, and using the historical positioning information with the largest sum of the first similarities as standard positioning information representing the evaluation data;
acquiring a first coefficient configured to other users corresponding to the evaluation information in the evaluation data;
querying a preset second coefficient database based on the standard positioning information to determine a second coefficient;
calculating a weight of the evaluation data based on the first coefficient and the second coefficient, the calculation formula being as follows:
wherein the content of the first and second substances,
is shown as
The weights of the evaluation data are grouped together,
for a preset first relation ratio corresponding to the first coefficient,
a preset second relation ratio corresponding to the second coefficient;
is as follows
The second coefficient determined by the standard positioning information of the evaluation data is combined;
is as follows
Group the first in the evaluation data
A first coefficient of other users corresponding to the evaluation information;
is the first
(ii) the number of sets of said assessment data;
acquiring a pre-established evaluation information identification database;
identifying the evaluation information based on the evaluation information identification database to obtain a score value corresponding to the evaluation information;
calculating an evaluation value of the evaluation data based on the score value of each piece of the evaluation information in the evaluation data, the calculation formula being as follows:
wherein the content of the first and second substances,
is as follows
An evaluation value for grouping the evaluation data;
is as follows
Group the first in the evaluation data
A value of credit for each of the rating information;
calculating the truth degree of the abnormal picture based on the evaluation value and the weight of each evaluation data, wherein the calculation formula is as follows:
wherein the content of the first and second substances,
representing the degree of realism of the exceptional picture,
the number of groups of the evaluation data;
an average value of evaluation values for each set of the evaluation data;
to adjust the coefficient;
and when the truth is larger than a preset verification value, the verification is passed, otherwise, the verification is not passed.
7. The artificial intelligence technology-based environmental emergency task co-disposal platform of claim 1, wherein the performing simulation based on the hierarchical situation of the collaborative task and the first to-be-assigned team list comprises:
establishing a time axis based on the start time of the collaborative scheme;
when the number of task teams is greater than the number of first level tasks,
distributing the corresponding task team to the collaborative tasks of the first-level task according to the descending order of the priority values of the collaborative tasks of the first-level task and the order of the task teams in the first team list to be distributed, and deleting the task team from the first team list to be distributed after the task team is distributed with the collaborative tasks;
placing the first-stage task assigned to the task team at the starting time point of the time axis to start simulation execution;
after the task team executes the collaborative task, adding the task team into the tail end of the first team list to be distributed;
pulling the time axis backwards, and configuring the task team positioned at the first position in the current first team list to be allocated for the second-level task from the current first team list to be allocated after all the tasks of the second-level task are executed; repeating the steps until all the Nth-level tasks are executed;
establishing simulation parameters of the cooperative task according to simulation execution conditions from the first-level task to the Nth-level task;
synchronously sending the simulation parameters when the collaborative task is assigned to the first person in charge of the task team.
8. The artificial intelligence technology based environmental emergency task co-disposal platform of claim 1,
determining the cooperative task corresponding to each task team according to the simulation execution conditions from the first-level task to the Nth-level task;
acquiring a preset cooperative task demand table, and inquiring the cooperative task demand table to acquire the demand of the cooperative task;
determining each preparation item of the task team based on the corresponding relation between the task team and the collaborative task;
acquiring a preparation item responsible table of the task team;
and sending the preparation items to corresponding item responsible persons based on the preparation item responsible table.
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