CN115660577A - Intelligent decision-making case base construction and extraction method and system - Google Patents
Intelligent decision-making case base construction and extraction method and system Download PDFInfo
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Abstract
The invention provides an intelligent decision-making case base construction and extraction method and system, which comprises the following steps: step1, setting different classification numbers for different industries/different application scenes; step2, disassembling the historical decision cases to form a plurality of atomic cases; the method comprises the steps of segmenting an atom case to form a series of keywords to form different atom conditions, obtaining high-similarity atom cases through intelligent arrangement by utilizing the different atom conditions to form an atom case library, and classifying the atom case library according to classification numbers; step3, forming different decision requirements through complexity setting, matching corresponding atomic conditions, extracting high-similarity atomic cases in the atomic case library, triggering a decision checking mechanism, and generating intelligent decision cases. The method and the system can effectively solve the problems of complex definition and formulation of the decision case in the operation of the urban public infrastructure and low definition efficiency of the decision case in the operation of the urban public infrastructure.
Description
Technical Field
The invention relates to a method for extracting a public infrastructure decision case base, in particular to a method and a system for constructing and extracting an intelligent decision case base.
Background
The operation of the modern urban public infrastructure forms a set of closed-loop flow of monitoring, reporting and disposing of the sensors through the installation and construction of various internet of things sensors. Various data information and event information generated based on sensors are collected by hundred million-level Internet of things sensors at any time in the process, the information is reported to a data management platform, and the management platform generates a decision case after analyzing and calculating by using the uploaded data and brings the decision case into a decision case library. The decision case is generated based on various event data sources, historical cases, policy documents, laws and regulations and other conditions, is produced intelligently and is provided for relevant management departments and personnel for reference and application. This requires the data management platform to have more sophisticated decision case algorithm extraction rules and capabilities.
In the urban informatization and urban sign monitoring construction process, a great number of decision cases are needed for operation of urban public infrastructure to be designed, integrated and disposed, finally, a closed loop is achieved, so that various types of events occurring in a city can be timely, quickly and accurately reported and disposed, the urban management efficiency is improved, and the urban safety is guaranteed. The decision cases involving so many public infrastructure operations such as fire fighting, urban transportation, etc. require a great deal of manpower to define, standardize, and make with reference to policy regulations, industry standards, etc., the complexity and inefficiency of which are the more central pain points. Under the background, an intelligent decision case production mode is urgent, and the method is very critical to the improvement of the efficiency of urban informatization and physical sign construction, so that a set of intelligent production method is created aiming at an operation decision case library of urban public infrastructure.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an intelligent decision case library construction and extraction method and system to solve the problems of complex decision case definition and formulation in the operation of urban public infrastructure and low efficiency in the decision case definition in the operation of the urban public infrastructure.
The method for constructing and extracting the intelligent decision case library provided by the invention comprises the following steps:
step1, setting different classification numbers for different application scenes of different industries;
step2, disassembling the historical decision cases to form a plurality of atomic cases; segmenting the atomic cases to form a series of key words to form different atomic conditions, acquiring the atomic cases with high similarity by intelligently arranging the different atomic conditions to form an atomic case library, and classifying the atomic case library according to the classification number;
and Step3, forming different decision requirements through complexity setting, matching corresponding atomic conditions, extracting high-similarity atomic cases in the atomic case library, triggering a decision checking mechanism, and generating intelligent decision cases.
Preferably, the intelligent layout adopts a big data analysis algorithm, the preliminary decision cases formed by combining different atomic conditions are identified and counted according to the keywords, and the preliminary decision cases with high similarity to the atomic cases are combined to obtain the atomic cases with high similarity.
Preferably, the complexity setting is to set the complexity of the requirement by a single formula or a combined formula of the classification number and the keyword.
Preferably, the complexity setting is to set the complexity of the demand by presetting a complexity threshold.
Preferably, the decision checking mechanism compares and checks the high-similarity atomic case with the rule checking content, and if the rule is violated, the high-similarity atomic case is obtained again to continue checking; and if no violation occurs, producing an intelligent decision case through intelligent arrangement.
Preferably, the rule checking contents include, but are not limited to, contents of policy documents issued by countries and places, and contents of laws and regulations issued by countries and places.
Preferably, the method for continuously checking the reacquired atomic cases with high similarity comprises: and acquiring a check condition according to the violation condition, matching the check atom condition, and acquiring a new high-similarity atom case by combining the atom condition in Step 2.
Based on the intelligent decision case base construction and extraction method, the invention also provides an intelligent decision case base construction and extraction system, which comprises a basic data acquisition module, a decision case generation module, a manual review module and an execution module;
the basic data acquisition module acquires basic data of different devices related to different industries;
the decision case generation module is used for constructing an extraction method based on the intelligent decision case library, analyzing and integrating data of historical decision cases, and generating intelligent decision cases by combining basic data of the basic data acquisition module;
the manual review module is used for manually reviewing the intelligent decision-making cases to obtain operable intelligent decision-making cases;
the implementation module carries out actual situational operation on the operable intelligent decision-making cases.
Preferably, the basic data includes mass basic data and related technical parameters of mass devices in different industries.
Preferably, the manual review is to generate an operational intelligent decision case by examining whether the intelligent decision case meets the actual situation and has feasibility, and if the examination passes, the intelligent decision case is marked as an approval passing state.
Compared with the prior art, the invention has the following beneficial effects:
1. the method comprises the steps of gathering data obtained after various data sources, taking basic data as a base, gathering generated historical decision cases according to industries, analyzing and calculating by using a statistical method, generating atomic conditions required by a plurality of cases, intelligently arranging and checking the cases while arranging the cases, comparing the cases with the content of a policy file of a national government and a local government and the content of a legal regulation file, generating new decision cases under the condition of meeting the policy file and the legal regulation, and displaying the new decision cases to related industries and departments for manual review, adoption and formal operation.
2. The invention can effectively improve the efficiency of generating the public facility operation decision case of each subdivision scene in each large industry.
3. The invention can effectively improve the efficiency of each responsibility department in disposing the events generated during the operation of the public facilities.
4. The method can effectively improve the working efficiency of monitoring the whole city signs and enable the smart city.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a schematic logic flow diagram of an intelligent decision case library construction and extraction method according to the present invention;
FIG. 2 is a logic flow diagram of the verification mechanism of the present invention;
fig. 3 is a schematic logic flow diagram of the intelligent decision case generation according to the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
The invention provides an intelligent decision-making case base construction and extraction method, which comprises the following steps:
step1 decision case classification: setting different classification numbers for different application scenes of different industries;
specifically, the decision cases are classified according to different industries such as fire fighting and traffic and different application scenes such as communities, airports and shopping malls, and classification numbers of different classes are obtained.
Step2, disassembling the historical decision cases to form a plurality of atomic cases; the method comprises the steps of segmenting an atom case to form a series of keywords to form different atom conditions, obtaining high-similarity atom cases through intelligent arrangement by utilizing the different atom conditions to form an atom case library, and classifying the atom case library according to classification numbers;
the intelligent arrangement adopts a big data analysis algorithm, identifies and counts the preliminary decision cases formed by combining different atom conditions according to the keywords, and combines the preliminary decision cases with high similarity to the atom cases to obtain the atom cases with high similarity. In particular, the so-called high similarity applies to values of similarity up to 95%.
Specifically, the historical decision-making case is disassembled according to the disposal requirements and the disposal process and is split into item data of a single atomic case forming the whole case; meanwhile, the atomic cases are segmented by using a segmentation technology, the keywords after the segmentation are identified are independently stored in an elastic search database, and then the atomic cases with the similarity of more than 95% are identified and counted according to the keywords by using a big data analysis algorithm to be combined. And finally, forming an atomic case library, and classifying and combining the atomic case library according to large classification (such as fire protection) and fine scene (such as shopping mall).
Step3, forming different decision requirements through complexity setting, matching corresponding atomic conditions, extracting high-similarity atomic cases in the atomic case library, triggering a decision checking mechanism, and generating intelligent decision cases.
The complexity setting can set the requirement complexity in a single or combined mode of classification numbers and keywords; specifically, the keyword may be formed by combining different classification numbers, or by cross-combining classification numbers and keywords, or in a single keyword manner; the complexity of the demand can also be set by presetting a complexity threshold. In practical application, the complexity setting can also set the complexity of the requirement through the mixed setting of classification numbers, keywords and preset complexity thresholds.
As shown in fig. 2, the decision checking mechanism compares and checks the high-similarity atomic case with the rule checking content, and if the rule is violated, the high-similarity atomic case is obtained again to continue checking; if no violation is caused, case arrangement is realized through intelligent arrangement to generate an intelligent decision case; further, the method for reacquiring the atomic case with high similarity to continue checking comprises the following steps: and acquiring a check condition according to the violation condition, matching the check atom condition, and acquiring a new high-similarity atom case by combining the atom condition in Step 2.
The rule checking content includes, but is not limited to, content of policy documents issued by countries and places, content of laws and regulations issued by countries and places, and the like.
In practical application, when a new intelligent decision case of a relevant scene of a relevant industry is generated as required, for example, a market fire-fighting relevant decision case is required to be referenced, the method can be used for generating the relevant new intelligent decision case, the decision atom case and the content of the policy documents issued by the country and the place are compared and verified in the generation process, meanwhile, the comparison and verification are carried out with the content of laws and regulations issued by the country and the place, and the final arrangement of the decision case is carried out if no violation occurs, and a new intelligent decision case is generated.
Based on the above method for constructing and extracting the intelligent decision case base, the embodiment also provides an intelligent decision case base constructing and extracting system, which comprises a basic data acquisition module, a decision case generation module, a manual review module and an execution module;
the basic data acquisition module acquires basic data of different devices related to different industries; specifically, mass basic data and related technical parameters of mass equipment in different industries are accessed to a set of internet of things platform for convergence and management. Such as fire fighting, traffic, etc., various sensor devices such as smoke alarms, ultrasonic sensors, etc.
The decision case generation module is used for constructing and extracting a method based on the intelligent decision case library, analyzing and integrating data of historical decision cases, and generating intelligent decision cases by combining basic data of the basic data acquisition module;
the manual review module is used for manually reviewing the intelligent decision case to obtain an operable intelligent decision case; specifically, after the decision case is generated, the responsible department involved in the decision case performs manual review on the decision case, and checks whether the intelligently generated decision case meets the actual situation and has feasibility. The approval pass may mark the decision case as approved.
The implementation module carries out actual situational operation on the operable intelligent decision-making cases. Specifically, when the decision case is in an approval passing state, the responsibility department can train and inform each related unit of formally operating the case in a later period. So far, the decision case of intelligent production is formally started to take effect.
Specifically, in practical application, as shown in fig. 3, an application scenario is selected by selecting an industry, a demand direction is determined, and a case, that is, an intelligent decision case, is generated by adjusting complexity (generally set to 50 to 100) and by the above intelligent decision case library construction and extraction method. In particular, the generation of the intelligent decision case can be a batch generation of a series of intelligent decision cases to form an intelligent decision case library, or a single intelligent decision case can be generated. Then, manually reviewing the intelligent decision case to determine whether the actual situation is met, and if so, passing; if not, the intelligent decision case is abandoned, and a new intelligent decision case is generated according to the situation.
Further, when the existing decision cases of the system are not covered completely and the decision cases do not accord with the actual demand conditions of the responsibility departments, new intelligent decision cases can be generated through the regeneration of the intelligent decision cases to be used for the reference or operation of the responsibility departments.
On the other hand, when the responsibility department has a large demand on the decision cases and has a high requirement on the complexity of the decision cases, the complexity level can be increased through the production of the intelligent decision cases, and the decision cases with high complexity can be produced in batches for the responsibility department to refer to or operate.
The working principle of the invention is as follows:
as shown in fig. 1, firstly, the intelligent production of the decision case needs to depend on the following conditions: 1. the method comprises the following steps that multiple data source channels are adopted, basic data monitored by various internet of things direct-connected sensors and cascade data provided by third-party manufacturers are obtained; 2. historical cases, wherein the mature and operated historical decision cases are manually arranged; 3. policy documents and decision cases for intelligent production need to meet the policy documents issued by the state and various parties. 4. Legal rules and decision cases for intelligent production need to meet the legal rules issued by the state.
1. The basic data acquisition module is used for realizing acquisition management of the basic data:
and accessing mass basic data and related technical parameters of mass equipment in different industries to a set of Internet of things platform for convergence and management. Such as fire fighting, traffic, etc., various sensor devices such as smoke alarms, ultrasonic sensors, etc.
For example smoke alarm technical parameters:
* LoRaWAN Wireless backhaul
* The battery is powered and the service life of the battery is standby for more than 3 years
* Smoke concentration measurement (value)
* Battery capacity measurement, low voltage alarm, fault alarm, alarm sensitivity 0.13-0.19dBm
* Obtaining the inspection report of the national fire-fighting electronic product quality supervision inspection center
Technical parameters of the ultrasonic sensor:
* Detection range: 20-200mm
* Detection angle: less than or equal to 50 °
* Scanning frequency: defaults to 10 minutes, can be customized
Application scenario definition: when the smoke reaches a certain concentration and the concentration continuously rises, a fire alarm is triggered and the urban brain is informed through a wireless network
The application scope is defined as follows: the fire alarm device is mainly suitable for the fields of fire alarm, safety detection and the like. Is suitable for household, business and closed underground space
Mounting points: the wireless smoke detector is installed at a public position in a corridor of a building with a building wood structure, the coverage radius of wireless smoke detection is 7.5 meters, only two users of one ladder are installed, and the smoke detection positions and the smoke detection quantity of multiple users of one ladder are set according to specific conditions.
2. The decision case generation module is used for constructing and extracting a method based on the intelligent decision case library, analyzing and integrating data of historical decision cases, and generating intelligent decision cases by combining basic data of the basic data acquisition module. Take the following historical case as an example:
the case of the monitoring and alarming decision for the fire smoke of the residential corridor is as follows:
and (4) responsibility departments: district fire-fighting team, street (town), rights and belongings unit (property)
The treatment requirement is as follows:
1) After receiving the alarm, the property checks the authenticity of the alarm, and confirms that the property arrives at the site in 5 minutes in the large residential area and confirms that the property arrives at the site in 3 minutes in the small residential area.
2) After the fire alarm is confirmed, the district fire branch sends a police department to deal with the fire by directly dialing 119 an alarm telephone.
3) The district fire control teams handle the fire alarm, and the street (town), district and property have well maintained stability and personnel evacuation work.
A treatment process:
1) After receiving the alarm, the brain of the city pushes information to the district city transportation center, the street (town), the district fire-fighting branch and the property. For cases which automatically solve within five minutes, the case which does not receive the order is regarded as false alarm, and the case which receives the order is regarded as manual disposal (the number of alarms is listed).
2) After the property checks the authenticity of the fire alarm, the property can be confirmed by directly dialing 119 an alarm phone and then pressing an independent manual fire alarm button.
3) The field maintenance and evacuation of streets (towns), districts and properties are well done;
4) And the regional fire control teams are dispatched to a police department and attend to the site for treatment. And (5) feeding back the fire fighting branch in the area and settling a case.
5) And if false alarm is given, feeding back the fire fighting team in the area.
The treatment basis is as follows:
1) Chapter i, eighteenth, thirtieth, forty-th, etc. of the fire Law of the people's republic of China;
2) Ninth article, eighteenth article and so on in the implementation of the fire safety responsibility system (made by the ministry of our offices [ 2017 ] 87);
3) The third, second, fourth and second chapters of "Shanghai City housing Property fire management practice" (municipal administration, no. 55 Committee).
The decision cases as the above example form a massive historical case library, and the above intelligent decision case library construction and extraction method is applied to the cases in the historical case library to intelligently produce new intelligent decision cases.
Step1, setting different classification numbers for different industries/different application scenes to realize historical decision case classification: and classifying the decision cases according to different industries such as fire fighting and traffic and different application scenes such as communities, airports, markets and the like.
Step2, the historical decision-making cases are disassembled according to the disposal requirements and the disposal flow, and the historical decision-making cases are split into single atomic case entry data forming the whole case. Meanwhile, the atomic cases are segmented by using a segmentation technology, the keywords after the segmentation are identified are independently stored in an elastic search database, and then the atomic cases with the similarity of more than 95% are identified and counted according to the keywords by using a big data analysis algorithm to be combined. And finally, forming an atomic case library, and classifying and combining the atomic case library according to large classification (such as fire protection) and fine scene (such as shopping mall).
Step3, generating a new decision case of a relevant industry relevant scene according to the requirement, for example, a market fire-fighting relevant decision case is needed to be referenced, using a system intelligent decision case generation tool to generate a relevant new decision case by one key, comparing and checking the decision atom case and the contents of policy documents issued by the country and the place in the generation process, and simultaneously comparing and checking the decision atom case and the contents of laws and regulations issued by the country and the place, and finally arranging the decision case and generating a new decision case if no violation occurs.
3. And manually reviewing the intelligent decision case through a manual review module to obtain an operable intelligent decision case: through the steps, after a new decision case is generated, the responsible department related to the decision case can perform manual review on the new decision case, and whether the intelligently produced decision case meets the actual situation or not is examined, and feasibility is achieved. The examination passing can mark the decision case as an examination passing state, and the marked decision case is an operable intelligent decision case.
4. The implementation module is used for carrying out actual scene operation on the operable intelligent decision case: when the decision case is in an approval passing state, the responsibility department can train and inform each relevant unit of formally operating the case in a selected period. So far, the decision case of intelligent production is formally started to take effect.
Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, units provided by the present invention as pure computer readable program code, the system and its various devices, modules, units provided by the present invention can be fully implemented by logically programming method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units included in the system for realizing various functions can also be regarded as structures in the hardware component; means, modules, units for performing the various functions may also be regarded as structures within both software modules and hardware components for performing the method.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. An intelligent decision case library construction and extraction method is characterized by comprising the following steps:
step1: setting different classification numbers for different application scenes of different industries;
step2: disassembling the historical decision case to form a plurality of atomic cases; segmenting the atomic cases into words to form a series of keywords to form different atomic conditions, acquiring high-similarity atomic cases by intelligent arrangement by utilizing the different atomic conditions to form an atomic case library, and classifying the atomic case library according to the classification number;
step3: different decision requirements are formed through complexity setting, corresponding atomic conditions are matched, high-similarity atomic cases in the atomic case library are extracted, a decision checking mechanism is triggered, and intelligent decision cases are generated.
2. The method for constructing and extracting an intelligent decision case library according to claim 1, wherein the intelligent arrangement adopts a big data analysis algorithm, identifies and counts preliminary decision cases formed by combining different atomic conditions according to keywords, and combines the preliminary decision cases with high similarity to the atomic cases to obtain the atomic cases with high similarity.
3. The method for constructing and extracting an intelligent decision case base according to claim 1, wherein the complexity setting is to set the requirement complexity by a single formula or a combined formula of classification number and keyword.
4. The method for constructing and extracting an intelligent decision case base according to claim 1, wherein the complexity setting is to set the complexity of the requirement by presetting a complexity threshold.
5. The method for constructing and extracting an intelligent decision case base according to claim 1, wherein the decision checking mechanism compares and checks the high-similarity atomic cases with the rule checking content, and if the rule is violated, the high-similarity atomic cases are obtained again to continue checking; and if no violation is caused, producing an intelligent decision case through intelligent arrangement.
6. The method for constructing and extracting an intelligent decision case base as claimed in claim 1, wherein the rule checking contents include but are not limited to the contents of national and local issued policy documents and the contents of national and local issued laws and regulations.
7. The method for constructing and extracting an intelligent decision case library according to claim 4, wherein the method for reacquiring the atomic cases with high similarity and continuously checking comprises the following steps: and acquiring a check condition according to the violation condition, matching the check atom condition, and acquiring a new high-similarity atom case by combining the atom condition in Step 2.
8. An intelligent decision-making case base construction and extraction system is characterized in that the intelligent decision-making case base construction and extraction method of any one of claims 1 to 7 is adopted, and the system further comprises a basic data acquisition module, a decision-making case generation module, a manual review module and an execution module;
the basic data acquisition module acquires basic data of different devices related to different industries;
the decision case generation module is used for constructing an extraction method based on the intelligent decision case library, analyzing and integrating data of historical decision cases, and generating intelligent decision cases by combining basic data of the basic data acquisition module;
the manual review module is used for manually reviewing the intelligent decision-making cases to obtain operable intelligent decision-making cases;
the implementation module carries out actual situational operation on the operable intelligent decision-making cases.
9. The system for constructing and extracting an intelligent decision case library according to claim 8, wherein the basic data comprises mass basic data and related technical parameters of mass equipment in different industries.
10. The system for constructing and extracting an intelligent decision case base according to claim 8, wherein the manual review is to generate an operational intelligent decision case by examining whether the intelligent decision case meets the actual situation and has feasibility, and if the intelligent decision case passes the examination, the intelligent decision case is marked as a pass-by-examination state.
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| CN117350288A (en) * | 2023-12-01 | 2024-01-05 | 浙商银行股份有限公司 | Case matching-based network security operation auxiliary decision-making method, system and device |
| CN117350288B (en) * | 2023-12-01 | 2024-05-03 | 浙商银行股份有限公司 | Case matching-based network security operation auxiliary decision-making method, system and device |
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