CN113496335A - Method, system and equipment for recording decision-making behaviors - Google Patents
Method, system and equipment for recording decision-making behaviors Download PDFInfo
- Publication number
- CN113496335A CN113496335A CN202010266520.2A CN202010266520A CN113496335A CN 113496335 A CN113496335 A CN 113496335A CN 202010266520 A CN202010266520 A CN 202010266520A CN 113496335 A CN113496335 A CN 113496335A
- Authority
- CN
- China
- Prior art keywords
- decision
- recording
- model
- models
- record
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 51
- 230000006399 behavior Effects 0.000 title claims abstract description 44
- 230000008030 elimination Effects 0.000 claims abstract description 19
- 238000003379 elimination reaction Methods 0.000 claims abstract description 19
- 238000011156 evaluation Methods 0.000 claims description 33
- 230000001419 dependent effect Effects 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000013480 data collection Methods 0.000 claims description 3
- 238000007493 shaping process Methods 0.000 claims description 3
- 238000006467 substitution reaction Methods 0.000 claims description 3
- 238000010521 absorption reaction Methods 0.000 claims description 2
- 238000013441 quality evaluation Methods 0.000 claims description 2
- 230000007246 mechanism Effects 0.000 abstract description 6
- 238000011161 development Methods 0.000 abstract description 4
- 230000006872 improvement Effects 0.000 abstract description 2
- 239000000463 material Substances 0.000 description 11
- 230000014509 gene expression Effects 0.000 description 8
- 230000007115 recruitment Effects 0.000 description 7
- 238000003860 storage Methods 0.000 description 7
- 238000009825 accumulation Methods 0.000 description 6
- 238000012163 sequencing technique Methods 0.000 description 6
- 238000007726 management method Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 230000004044 response Effects 0.000 description 4
- 230000004083 survival effect Effects 0.000 description 4
- 230000002860 competitive effect Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000012546 transfer Methods 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 239000013589 supplement Substances 0.000 description 2
- 238000012384 transportation and delivery Methods 0.000 description 2
- 238000004590 computer program Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000013439 planning Methods 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 238000013468 resource allocation Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/02—Banking, e.g. interest calculation or account maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/03—Credit; Loans; Processing thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Primary Health Care (AREA)
- Development Economics (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Technology (AREA)
- Medical Informatics (AREA)
- Epidemiology (AREA)
- Biomedical Technology (AREA)
- Public Health (AREA)
- Technology Law (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention provides a method, a system and equipment for recording decision behaviors, the embodiment of the invention provides a method for recording decision behaviors, and the method comprises the following steps: constructing a decision recording model cluster, wherein the decision recording model cluster comprises different decision recording models; the different decision recording models independently learn the decision event data sets and jointly grow in set time; and (4) evaluating the decision recording model cluster, and updating the decision recording model according to a set elimination rule. The technical scheme provided by the embodiment of the invention is convenient for recording, accumulating, optimizing and inheriting decision events in groups, and the invention adopts a cluster growing common growth mechanism and gives consideration to the personalized development and the improvement of the commonality value of a decision record model. The method can be applied to wide application scenes such as service industry, regional management, social security, government department decision and the like, has wide applicability and high practicability, and provides a method for constructing a large-scale comprehensive intelligent decision scene for various scenes.
Description
Technical Field
The present invention relates to the field of decision management technologies, and in particular, to a method, a system, and a device for recording decision behaviors.
Background
Generally, the process flow of a production line or equipment in the industrial field is relatively solidified, and the data dimension of the basic working condition, the data dimension of the operation of a worker and the evaluation data dimension of the evaluation operation result, which are used for the decision-making action or the operation action, are definite. However, in some decision events, such as community service, social security, government policy and other scenarios, the same or the same type of decision event generally has the same decision purpose, evaluation data dimension and evaluation standard, but the data dimension on which different people base in decision making may be different, and the corresponding decision behaviors (i.e. operation data dimension) are also various, and although each person can summarize experience trainings, the comparability between experiences is poor, and the accumulation and the inheritance of experiences are difficult, so the problem of accumulation of decision experiences and transfer of decision experiences between people, people and systems, and systems and people is urgently needed to be solved.
Disclosure of Invention
The embodiment of the invention provides a method, a system and equipment for recording decision behaviors, and the core is to obtain the method for recording the decision behaviors, so that the decision behaviors of some special scenes can be effectively recorded, and the difficulty in accumulation and inheritance of decision experiences is reduced.
In a first aspect, an embodiment of the present invention provides a method for recording a decision behavior, where the method includes:
constructing a decision recording model cluster, wherein the decision recording model cluster comprises different decision recording models;
the different decision recording models independently learn the decision event data sets and jointly grow in set time;
and (4) evaluating the decision recording model cluster, and managing the decision recording model according to a set elimination rule.
In a second aspect, an embodiment of the present invention provides a system for recording decision-making behavior, where the system includes:
a model building module configured for inputting or building a decision recording model to form a decision recording model cluster;
a data collection module configured to collect or input a decision event data set for learning by a decision record model;
a model analysis module configured to input a decision event to evaluate the decision recording model cluster and eliminate the inferior decision recording model;
a prediction module configured to input a decision event to output an operation decision and a result prediction.
With this system at least the method of recording decision-making behaviour of the first aspect can be implemented
In a third aspect, an embodiment of the present invention provides an apparatus for recording a decision-making behavior, where the apparatus includes: a memory, a processor; wherein the memory has stored thereon executable code which, when executed by the processor, causes the processor to implement at least the method of recording decision-making behaviour of the first aspect.
An embodiment of the present invention further provides a non-transitory machine-readable storage medium, on which executable code is stored, and when the executable code is executed by a processor of an electronic device, the processor is enabled to implement at least the method for recording decision behavior in the first aspect.
In the embodiment of the invention, a method for recording decision behaviors is provided, the method facilitates the recording, accumulation, optimization and inheritance of decision events in a group, and a cluster growing common growth mechanism is adopted, so that the personalized development and the common value improvement of a decision recording model are considered. The method can be applied to wide application scenes such as service industry, regional management, social security, government department decision and the like, has wide applicability and high practicability, and provides a method for constructing a large-scale comprehensive intelligent decision scene for various scenes.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a flowchart of a method for recording decision-making behavior according to an embodiment of the present invention;
FIG. 2 is a block diagram of a system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a medium according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an apparatus according to an embodiment of the present invention;
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and "a plurality" typically includes at least two.
The words "if", as used herein, may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
In some decision events, such as community services, social security, government policies and other scenarios, the same or the same type of decision event generally has the same decision purpose, evaluation data dimension and evaluation standard, but the data dimension according to which different people make decisions may be different, and the corresponding decision behaviors (i.e. operation data dimension) are also various, and although each person can summarize experience teaching, the comparability between experiences is poor, and the accumulation and inheritance of experiences are difficult, so the problem of accumulation of decision experiences and transfer of decision experiences between people, people and systems, and systems and people is urgently needed to be solved.
In view of the above problems, the present invention provides a method, system and device for recording decision-making behavior. According to the technical scheme provided by the invention, a plurality of decision recording models grow together, and then inferior decision recording models are eliminated, so that updating is realized, decision recording models conforming to decision events are reserved, the experience of the decision events is learned and accumulated, and prediction is made for the later decision events. The implementation principles of the methods, systems, media and devices are similar and will not be described herein again.
Having described the general principles of the invention, various non-limiting embodiments of the invention are described in detail below.
The embodiment of the invention can be applied to various scenes and various equipment types and is used for recording the decision behavior of the decision event. It should be noted that the examples provided by the present invention are only for convenience of understanding the spirit and principle of the present invention, and the embodiments of the present invention are not limited in any way in this respect. Rather, embodiments of the present invention may be applied to any scenario where applicable.
An embodiment of the present invention provides a method for recording a decision behavior, as shown in fig. 1, the method includes:
s101, constructing a decision recording model cluster, wherein the decision recording model cluster comprises different decision recording models;
s102, independently learning the decision event data sets by the different decision recording models, and jointly growing in set time;
s103, evaluating the decision recording model cluster, managing the decision recording model according to a set elimination rule, and further updating the decision recording model cluster;
in other embodiments, the method further includes step S104 of predicting the decision strategy and the strategy result of the decision event through the decision recording model cluster.
In this embodiment, the decision event data set includes decision events including dependency data dimensions, operational data dimensions, and evaluation data dimensions, and in particular,
the dependent data dimension includes a current fact on which the decision event depends, a data dimension of the transaction condition, denoted as X, X ═ X } ═ X1，X2，X3...，XaIn which XiI is more than or equal to 1 and less than or equal to a, and a is the dimension number of the dependent data;
the operation data dimension comprises decision content of a decision event and a data dimension of a behavior after decision, and is marked as Y, Y is { Y } ═ Y1，Y2，Y3...，YbIn which Y isiI is more than or equal to 1 and less than or equal to b, and b is the dimension number of the operation data;
the evaluation data dimension comprises a quality evaluation index, a mode and a formula of a decision result of the decision event, and is marked as Z, Z is { Z } { Z ═ Z1，Z2，Z3...，ZcIn which Z isiI is more than or equal to 1 and less than or equal to c, and c is the dimension number of the evaluation data.
And after the data are discretized, the decision recording model records X, Y and Z.
It should be noted that the evaluation data dimensions of the decision events in the same scenario are the same, i.e. the evaluation manner of the decision result is good or bad, or the expressions are the same, i.e. a group of decision recording models with the same evaluation data Z constitutes a decision recording model cluster.
Because the decision record model cluster comprises a plurality of decision record models and can eliminate inferior decision record models through a competitive survival mechanism, the decision record model cluster has a fault-tolerant mechanism, and when the decision record models are constructed, namely in S101, users or user groups can construct the decision record models according to the summary of habits or actual decision experiences of the users or the user groups without requiring that the decision record models are reasonable and effective.
The competitive survival mechanism requires that the decision record models need to grow together for a period of time, during which one decision event can be matched with a plurality of decision record models by learning a decision event data set, and the decision record models independently record and learn the decision event to perfect the models themselves.
And (5) after the decision-making recording models grow together for a period of time, the evaluation is needed, and the inferior decision-making recording models are eliminated, namely S103 is entered.
In the assessment process, respective X values of the same decision events are input into respective independent decision recording models of the decision recording model cluster, respective Y values and corresponding Z values which are optimal in history or calculated are output, and corresponding elimination rules are established.
In the assessment, the decision record model is processed according to elimination rules, and the elimination rules are not limited to the following:
sorting according to the average value of the Z values in a period of time, and eliminating the last place of the decision record model;
eliminating inferior decision recording models among the decision recording models with inclusion relation in an absorption or substitution mode;
generating a new decision recording model by combining the decision recording models;
and generating a simpler decision record model through model body shaping.
Specifically, the method comprises the following steps:
the average value of the prediction results of a decision record model is the average value of the expression values of Z recorded by each existing decision event of the decision record model.
In a decision recording model cluster, a decision recording model a comprises a decision recording model b, and when the decision recording model a is obviously superior to the decision recording model b, the decision recording model b is deleted, namely the decision recording model a absorbs the decision recording model b; when the decision recording model b is obviously superior to the decision recording model a, deleting the decision recording model a, namely replacing the decision recording model a with the decision recording model b;
let decision logging model a and decision logging model b be two decision logging models in one decision logging model cluster, say decision logging model c is the sum of a and b, if and only if { X }c＝{X}a∪{X}b，{Y}c＝{Y}a∪{Y}b，{Z}c＝{Z}a＝{Z}b；
The step of shaping the decision record model refers to deleting a plurality of data dimensions in the { X } of one decision record model or deleting a plurality of data dimensions in the { Y } to generate a new simpler decision record model.
The following is a judgment method of superiority and inferiority among decision-making record models:
the decision logging model a and the decision logging model b are two decision logging models in one decision logging model cluster, v being the mean of Z of a-the mean of Z of b,
when v >0 and the directionality of Z is greater and better, we say that a is better than b or b is worse than a;
when v >0 and the directivity of Z is smaller and better, b is said to be better than a or a is said to be worse than b;
when v > the preset significance >0 and the directionality of Z is greater and better, it is said that a is significantly better than b or b is significantly worse than a;
b is said to be significantly better than a or a is significantly worse than b when v > a preset significance >0 and the directionality of Z is smaller and better.
Obviously, in the decision record model cluster, more decision event records can be accumulated by the symbiotic growth of each independent decision record model in a period of time, and meanwhile, the invention provides a competitive survival mechanism for the decision behavior decision record model cluster, so that the decision record models in the decision record model cluster can realize the aims of elimination of inferior people and growth, survival and better evolution of superior people.
In other embodiments, after a part of the decision recording models are eliminated, new decision recording models can be continuously added to keep the continuous evolution and updating of the decision recording model cluster.
Having described the method of an exemplary embodiment of the present invention, the above-described embodiments are next applied in a specific context.
Scene one: enterprise petty loan delivery decision event
The specific implementation of the above method is as follows:
s101: generating a decision recording model cluster and generating a decision recording model;
specifically, the evaluation data dimension { Z } ═ { number of remaining days, direction: the smaller the size, the better.
The enterprise shares 200 employees, and the employees configure decision-dependent data dimension { X } and common operation data { Y } - { loan amount, loan term }, according to personal experiences.
S102: and different decision recording models independently learn the decision event data sets and jointly grow in set time. And triggering the matched decision record model to generate a decision behavior record every time of loan and repayment business.
S103: and (4) performing regular assessment, namely performing final elimination on the decision recording models in a one-year period, wherein the number of the decision recording models is maintained to be more than or equal to 3.
Scene two: community service decision
S101: generating a decision recording model cluster and generating a decision recording model;
specifically, { Z }, an expression thereof is service object ratio ═ 25+ the number of people who receive the service,/(service object ratio) × 20+ the average of service area total population) × + the service object satisfaction degree + the community management leader score; the direction is as follows: the higher the better.
Community service provision community configurations { X }, { Y } for the current year:
{ X } comprises at least: the service area, the general population of the service area, the service object type, the service object proportion and the annual per capita charge of the service object;
{ Y } comprises at least: the purpose of the service, the form of the service, and the content of the service.
S102: and different decision recording models independently learn the decision event data sets and jointly grow in set time. And triggering the matched decision record model to generate a decision behavior record each time of community service.
S103: and (4) performing regular evaluation, calculating the mean value of the evaluation data of each decision recording model, eliminating the evaluation data mean value lower than a set value, sequencing the evaluation data mean values of the decision recording models from top to bottom, and determining the priority of purchasing service in the second year.
Scene three: decision event for urban emergency material release
S101: generating a decision recording model cluster and generating a decision recording model;
specifically, setting an evaluation data dimension { Z } ═ { meeting the required emergency material scheduling duration }, direction: the smaller the size, the better.
Relevant provincial, municipal government emergency departments and agencies, such as emergency management office configurations { X }, { Y }:
{ X } comprises at least: distribution of reserve points, reserve amount of emergency materials, transportation time of the emergency materials, material demand amount of the emergency points, limiting period, emergency degree/emergency event level;
{ Y } comprises at least: the emergency material releasing scheme comprises emergency material releasing types, emergency material releasing quantity, storage point selection and releasing point selection.
S102: and different decision recording models independently learn the decision event data sets and jointly grow in set time. And triggering the matched decision record model to generate a decision behavior record in each emergency event and emergency material requirement.
S103: and (4) performing regular assessment, calculating the evaluation data of each decision record model, sequencing the evaluation data from low to high, and determining the priority of the next emergency material delivery scheme selection model.
Scene four: educational resource allocation decisions
S101: generating a decision recording model cluster and generating a decision recording model;
specifically, an evaluation data dimension { Z } ═ { enrollment growth rate }, a direction: the higher the better.
Each college configures { X }, { Y }:
{ X } comprises at least: teaching places, teacher's materials;
{ Y } comprises at least: and (5) course establishment schemes.
S102: and different decision recording models independently learn the decision event data sets and jointly grow in set time. And (4) setting requirements for each course in the school period, and triggering the matched decision record model to generate a decision behavior record.
S103: and (4) performing regular assessment, sequencing according to the current Z value, and performing final elimination on the decision record model.
Scene five: medical resource investment decision
S101: generating a decision recording model cluster and generating a decision recording model;
specifically, an evaluation data dimension { Z } ═ medical instrument return on investment }, a direction: the higher the better.
A hospital department purchasing decision-making person configures { X }, { Y }:
{ X } comprises at least: the number of various medical instruments and patients at present;
{ Y } comprises at least: type of medical instruments introduced, number of medical instruments introduced, charge criteria.
S102: and different decision recording models independently learn the decision event data sets and jointly grow in set time. And triggering the matched decision record model to generate a decision behavior record according to the investment requirement of the medical instrument every time.
S103: and (4) performing regular assessment, sequencing according to the average value of the Z values in the current period, and performing final elimination on the decision record model.
Scene six: enterprise investment decision events
The specific implementation of the above method is as follows:
s101: generating a decision recording model cluster and generating a decision recording model;
specifically, setting an evaluation data dimension { Z } ═ return on investment }, direction: the higher the better.
The enterprise shares 200 persons, and the configuration decision depends on the data dimension { X } and the configuration operation data { Y } according to personal experience by the staff:
{ Y } comprises at least: the amount of investment.
S102: and different decision recording models independently learn the decision event data sets and jointly grow in set time. And triggering the matched decision record model to generate a decision behavior record each time investment decision is made.
S103: and (4) performing regular assessment, sequencing according to the average value of the Z values in the current period, and eliminating the last place of the decision record model.
Scene seven: urban street parking space increasing decision event
S101: generating a decision recording model cluster and generating a decision recording model;
specifically, set up evaluation data dimension { Z } ═ district vehicle number, parking stall income }, its expression is parking stall income/district vehicle number, direction: the higher the better.
Local street staff configure decision-dependent data dimension { X } and operation data { Y } according to charging of different sections and different time lengths:
y comprises at least: and (4) charging standard.
S102: and different decision recording models independently learn the decision event data sets and jointly grow in set time. And triggering the matched decision record model to generate a decision behavior record every time the parking space is charged.
S103: and (4) performing regular assessment, sequencing according to the current Z value, and eliminating the last place of the decision record model.
And eighth scene: college enrollment policy decision
S101: generating a decision recording model cluster and generating a decision recording model;
specifically, { Z }, an expression thereof is region actual recruiting number/region reference period recruiting number 100 + 5+ (region reference period recruiting number-region recruiting number average cost) + 20+ region actual recruiting number/region Source size 100; the direction is as follows: the higher the better.
The method comprises the following steps of dividing the enrollment areas according to cities and counties, taking the previous year implemented by the decision mode as a reference period, adopting different enrollment strategies for each area, and providing area configurations { X } and { Y } for each enrollment season:
{ X } comprises at least: the area of enrollment, the number of enrolled people in the area reference period, the per-capita cost of enrollment in the area reference period, the professional set of enrollment, the economic scale of the area and the source scale of the area;
{ Y } comprises at least: the channel of the student recruitment, the introduction focus of the colleges, the professional introduction order and the professional introduction focus.
S102: and different decision recording models independently learn the decision event data sets and jointly grow in set time. And triggering the matched decision recording model to generate a decision behavior record in each enrollment season of each enrollment area.
S103: performing regular assessment, namely performing final elimination on decision recording models by taking one enrollment season as a period, and maintaining the number of the decision recording models to be more than or equal to 2; after the decision-making recording models of one area are eliminated, the remaining decision-making recording models can be selected or new decision-making recording models can be created in the next enrollment season.
Scene nine: economic future planning trend decision
The specific implementation of the above method is as follows:
s101: generating a decision recording model cluster and generating a decision recording model;
specifically, setting an evaluation data dimension { Z }, namely { region GDP, energy consumption per percentage point of GDP, pollution index }, wherein an expression of the evaluation data dimension is average GDP of each region 10000-energy consumption per percentage point of GDP (million kilowatt-hour) -pollution index 50; the direction is as follows: the larger the size, the more preferable the size.
The method comprises the following steps that 13 counties are arranged in a district, an economic development decision recording model is established in each county according to conditions, and the counties are configured with { X }, { Y }:
{ X } comprises at least: regional population, regional area, regional annual GDP, regional economic scale;
{ Y } comprises at least: the system comprises an economic development plan, a key supporting industry, a supporting scheme, a supporting capital total amount, energy-saving emission-reduction measures, a business recruitment and investment introduction measure and a talent introduction measure.
S102: and different decision recording models independently learn the decision event data sets and jointly grow in set time. And triggering the matched decision record model to generate a decision behavior record every quarter.
S103: and (4) performing regular assessment, namely performing final elimination on the decision recording models in a one-year period, wherein the number of the decision recording models is maintained to be more than or equal to 3. And after the decision recording models of one city and county are eliminated, the remaining decision recording models are preferentially selected or new decision recording models are created in the next year.
Scene ten: military resource scheduling layout decision
The specific implementation of the above method is as follows:
s101: generating a decision recording model cluster and generating a decision recording model;
specifically, an evaluation data dimension Z is set as { target damage level, whether one party is exposed, consumption cost level and supplement difficulty level }, and an expression thereof is set as target damage level (low damage: 0, loss war force: 20, war force halved: 50, war force not subtracted: 100) + whether one party is exposed (yes: 20, no: 0), consumption cost level + supplement difficulty level; the direction is as follows: the smaller the size, the better.
The staff for staff to attend configures the { X }, { Y } of the decision record model according to each tactical element:
{ X } comprises at least: battlefield situation, enemy target type, strategy \ battle target, deployable military force, military and ammunition cardinality, maneuverability and war zone replenishment capability;
{ Y } comprises at least: the forces of the troops, the used troops, the quantity of the troops, the maneuvering path, the transfer scheme after the war and the replenishment strategy are used.
S102: and different decision recording models independently learn the decision event data sets and jointly grow in set time. And triggering the matched decision record model to generate a decision behavior record every time of exercise or actual combat.
S103: and (4) performing regular assessment, namely performing final elimination on the decision recording models in a one-year period, wherein the number of the decision recording models is maintained to be more than or equal to 3.
Scene eleven: recruitment decision event for human administration department
The specific implementation of the above method is as follows:
s101: generating a decision recording model cluster and generating a decision recording model;
specifically, an evaluation data dimension { Z }, i.e., { landscape score, identity recognition score, belief score, ability score, behavior score, knowledge score }, whose expression ═ 30% landscape score, + 20% identity recognition score, + 20% belief score, + 10% behavior score, + 10% knowledge score, }; the direction is as follows: the higher the better.
The enterprise human administration department arranges { X } and { Y } according to the elements:
and configuring common operation data (Y) (recruitment).
S102: and different decision recording models independently learn the decision event data sets and jointly grow in set time. And triggering the matched decision record model to generate a decision behavior record every time of recruitment.
S103: and (4) performing regular assessment, performing assessment on employees who enter the employment in the last year in a one-year period, and performing final elimination on the recruitment model according to assessment results of the employees.
Scene twelve: market solicitation decision event
The specific implementation of the above method is as follows:
s101: generating a decision recording model cluster and generating a decision recording model;
specifically, setting an evaluation data dimension { Z } - { market area renting rate }; the direction is as follows: the higher the better.
The staff of the department of market recruitment arranges { X }, { Y }:
{ X } comprises at least: a consumer group;
{ Y } comprises at least: advertising channels, solicitation strategies, pricing strategies.
S102: and different decision recording models independently learn the decision event data sets and jointly grow in set time. And triggering the matched decision record model to generate a decision behavior record each time the solicitation demands.
S103: and (4) performing regular assessment, and performing final elimination on the decision recording model by taking a year as a period.
Having described the method of an exemplary embodiment of the present invention, it follows that the present invention provides a system for an exemplary implementation.
Referring to fig. 2, the present invention provides a system for recording decision-making behavior, which can implement the method for recording decision-making behavior in the exemplary embodiment of the present invention corresponding to fig. 1. The system comprises: the model building module, the data acquisition module, the model analysis module, the prediction module, it is specific:
a model building module configured for inputting or building a decision recording model to form a decision recording model cluster;
a data collection module configured to collect or input a decision event data set for learning by a decision record model;
a model analysis module configured to input a decision event to evaluate the decision recording model cluster and eliminate the inferior decision recording model;
a prediction module configured to input a decision event to output an operation decision and a result prediction.
The system of the embodiment has an implementation principle similar to the technical solution of the method, and is not described herein again.
Having described the method and apparatus of the exemplary embodiments of this invention, and referring next to FIG. 3, the present invention provides an exemplary medium having stored thereon computer-executable instructions operable to cause the computer to perform the method of the corresponding exemplary embodiments of this invention of FIG. 1.
Having described the method, system, and media of exemplary embodiments of the present invention, next, referring to fig. 4, an exemplary device 40 provided by the present invention is described, the device 40 comprising a processing unit 401, a Memory 402, a bus 403, an external device 404, an I/O interface 405, and a network adapter 406, the Memory 402 comprising a Random Access Memory (RAM) 4021, a cache Memory 4022, a Read-Only Memory (ROM) 4023, and a storage unit array 4025 made up of at least one storage unit 4024. The memory 402 is used for storing programs or instructions executed by the processing unit 401; the processing unit 401 is configured to execute the method according to the present invention example corresponding to fig. 1 according to the program or the instructions stored in the memory 402; the I/O interface 405 is used for receiving or transmitting data under the control of the processing unit 401.
Here, the exemplary device 40 includes, but is not limited to, a user device, a network device, or a device formed by integrating a network device with a user device through a network; the user equipment includes but is not limited to any electronic product capable of performing man-machine interaction with a user through a keyboard, a remote controller, a touch panel or voice control equipment, such as a computer, a smart phone, a common mobile phone, a tablet computer and the like; the network device includes, but is not limited to, a computer, a network host, a single network server, a plurality of network server sets, or a cloud of multiple servers.
The above-described apparatus embodiments are merely illustrative, wherein the various modules illustrated as separate components may or may not be physically separate. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by adding a necessary general hardware platform, and of course, can also be implemented by a combination of hardware and software. With this understanding in mind, the above-described aspects and portions of the present technology which contribute substantially or in part to the prior art may be embodied in the form of a computer program product, which may be embodied on one or more computer-usable storage media having computer-usable program code embodied therein, including without limitation disk storage, CD-ROM, optical storage, and the like.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method of recording decision-making behavior, comprising:
constructing a decision recording model cluster, wherein the decision recording model cluster comprises different decision recording models;
the different decision recording models independently learn the decision event data sets and jointly grow in set time;
and (4) evaluating the decision recording model cluster, and managing the decision recording model according to a set elimination rule.
2. The method of claim 1, wherein the decision event dataset comprises decision events comprising dependency data dimensions, operational data dimensions, and evaluation data dimensions; the evaluation data dimensions of the same decision recording model cluster are the same.
3. The method of claim 2, wherein the dependent data dimension comprises a current fact on which a decision event depends, a data dimension of a transaction condition, denoted X; the operation data dimension comprises decision content of a decision event and a data dimension of a behavior after decision and is marked as Y; the evaluation data dimension comprises a decision result quality evaluation index and a decision result mode of a decision event, and is marked as Z; and the X, the Y and the Z form a decision recording model.
4. The method of claim 1, wherein the qualifying comprises inputting respective X values for the same decision event in respective independent decision recording models of a decision recording model cluster, outputting respective historically optimal or calculated Y values and corresponding Z values, and establishing corresponding culling rules.
5. The method of claim 4, wherein the culling rules include, but are not limited to:
sorting according to the average value of the Z values in a period of time, and eliminating the last place of the decision record model;
eliminating inferior decision recording models among the decision recording models with inclusion relation in an absorption or substitution mode;
generating a new decision recording model by combining the decision recording models;
and generating a simpler decision record model through model body shaping.
6. The elimination rule of claim 5, wherein:
let the decision logging model a and the decision logging model b be two decision logging models in a cluster of decision logging models, v being the mean of Z of a-the mean of Z of b,
when v >0 and the directionality of Z is greater and better, we say that a is better than b or b is worse than a;
when v >0 and the directivity of Z is smaller and better, b is said to be better than a or a is said to be worse than b;
when v > the preset significance >0 and the directionality of Z is greater and better, it is said that a is significantly better than b or b is significantly worse than a;
b is said to be significantly better than a or a is significantly worse than b when v > a preset significance >0 and the directionality of Z is smaller and better.
7. The elimination rule of claim 5, wherein the merged decision record model generating a new decision record model refers to: combining the decision record model a and the decision record model b to generate a decision record model c, { X }c＝{X}a∪{X}b，{Y}c＝{Y}a∪{Y}b，{Z}c＝{Z}a＝{Z}b。
8. The elimination rule of claim 5, wherein the decision record model getting into shape means deleting a number of data dimensions in { X } or deleting a number of data dimensions in { Y } of a decision record model to generate a new simpler decision record model.
9. A system for recording decision-making behavior, comprising:
a model building module configured for inputting or building a decision recording model to form a decision recording model cluster;
a data collection module configured to collect or input a decision event data set for learning by a decision record model;
a model analysis module configured to input a decision event to evaluate the decision recording model cluster and eliminate the inferior decision recording model;
a prediction module configured to input a decision event to output an operation decision and a result prediction.
10. An apparatus for recording decision-making behavior, comprising: a memory, a processor; the memory has stored thereon executable code which, when executed by the processor, causes the processor to perform a method of logging decision-making behaviour as claimed in any one of claims 1 to 8.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010266520.2A CN113496335A (en) | 2020-04-07 | 2020-04-07 | Method, system and equipment for recording decision-making behaviors |
| PCT/CN2020/086253 WO2021203489A1 (en) | 2020-04-07 | 2020-04-23 | Decision behavior recording method, system, and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010266520.2A CN113496335A (en) | 2020-04-07 | 2020-04-07 | Method, system and equipment for recording decision-making behaviors |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113496335A true CN113496335A (en) | 2021-10-12 |
Family
ID=77995650
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010266520.2A Pending CN113496335A (en) | 2020-04-07 | 2020-04-07 | Method, system and equipment for recording decision-making behaviors |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN113496335A (en) |
| WO (1) | WO2021203489A1 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116542831A (en) * | 2023-07-07 | 2023-08-04 | 杭州海亮优教教育科技有限公司 | Method and device for processing recruitment data, electronic equipment and storage medium |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH1173323A (en) * | 1997-08-29 | 1999-03-16 | Nippon Telegr & Teleph Corp <Ntt> | Method for generating decision tree and recording medium recording decision tree generating program |
| US20050096950A1 (en) * | 2003-10-29 | 2005-05-05 | Caplan Scott M. | Method and apparatus for creating and evaluating strategies |
| US20090187425A1 (en) * | 2007-09-17 | 2009-07-23 | Arthur Solomon Thompson | PDA software robots leveraging past history in seconds with software robots |
| US20100223211A1 (en) * | 2000-10-11 | 2010-09-02 | Johnson Gregory A | Decision service method and system |
| US20150286692A1 (en) * | 2014-04-04 | 2015-10-08 | Panasonic Intellectual Property Corporation Of America | Evaluation result display method, evaluation result display apparatus, and non-transitory computer-readable recording medium storing evaluation result display program |
| CN107480686A (en) * | 2016-06-08 | 2017-12-15 | 阿里巴巴集团控股有限公司 | A kind of method and apparatus of screening machine learning characteristic |
| CN107895501A (en) * | 2017-09-29 | 2018-04-10 | 大圣科技股份有限公司 | Unmanned car steering decision-making technique based on the training of magnanimity driving video data |
| CN109991987A (en) * | 2019-04-29 | 2019-07-09 | 北京智行者科技有限公司 | Automatic Pilot decision-making technique and device |
| CN110047006A (en) * | 2019-04-22 | 2019-07-23 | 陈硕坚 | The method and device of portfolio investment decision is carried out based on neural network and machine learning |
| CN110210718A (en) * | 2019-05-09 | 2019-09-06 | 厦门邑通软件科技有限公司 | A method of the promotion product qualification rate based on Multidimensional decision-making woodlot |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10572818B2 (en) * | 2015-06-02 | 2020-02-25 | International Business Machines Corporation | Horizontal decision tree learning from very high rate data streams with horizontal parallel conflict resolution |
| CN105447519A (en) * | 2015-11-20 | 2016-03-30 | 携程计算机技术（上海）有限公司 | Model detection method based on feature selection |
| CN107464025B (en) * | 2017-08-25 | 2021-06-11 | 智脑智能科技（苏州）有限公司 | Supply chain demand prediction and intelligent decision method based on big data |
| TWI650714B (en) * | 2018-01-19 | 2019-02-11 | 財團法人工業技術研究院 | Dynamic intelligent scheduling method and device |
| CN110399986B (en) * | 2019-06-24 | 2023-04-07 | 中水三立数据技术股份有限公司 | Generating method of pump station unit fault diagnosis system |
| CN110752942B (en) * | 2019-09-06 | 2021-09-17 | 平安科技（深圳）有限公司 | Alarm information decision method and device, computer equipment and storage medium |
-
2020
- 2020-04-07 CN CN202010266520.2A patent/CN113496335A/en active Pending
- 2020-04-23 WO PCT/CN2020/086253 patent/WO2021203489A1/en active Application Filing
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH1173323A (en) * | 1997-08-29 | 1999-03-16 | Nippon Telegr & Teleph Corp <Ntt> | Method for generating decision tree and recording medium recording decision tree generating program |
| US20100223211A1 (en) * | 2000-10-11 | 2010-09-02 | Johnson Gregory A | Decision service method and system |
| US20050096950A1 (en) * | 2003-10-29 | 2005-05-05 | Caplan Scott M. | Method and apparatus for creating and evaluating strategies |
| US20090187425A1 (en) * | 2007-09-17 | 2009-07-23 | Arthur Solomon Thompson | PDA software robots leveraging past history in seconds with software robots |
| US20150286692A1 (en) * | 2014-04-04 | 2015-10-08 | Panasonic Intellectual Property Corporation Of America | Evaluation result display method, evaluation result display apparatus, and non-transitory computer-readable recording medium storing evaluation result display program |
| CN107480686A (en) * | 2016-06-08 | 2017-12-15 | 阿里巴巴集团控股有限公司 | A kind of method and apparatus of screening machine learning characteristic |
| CN107895501A (en) * | 2017-09-29 | 2018-04-10 | 大圣科技股份有限公司 | Unmanned car steering decision-making technique based on the training of magnanimity driving video data |
| CN110047006A (en) * | 2019-04-22 | 2019-07-23 | 陈硕坚 | The method and device of portfolio investment decision is carried out based on neural network and machine learning |
| CN109991987A (en) * | 2019-04-29 | 2019-07-09 | 北京智行者科技有限公司 | Automatic Pilot decision-making technique and device |
| CN110210718A (en) * | 2019-05-09 | 2019-09-06 | 厦门邑通软件科技有限公司 | A method of the promotion product qualification rate based on Multidimensional decision-making woodlot |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116542831A (en) * | 2023-07-07 | 2023-08-04 | 杭州海亮优教教育科技有限公司 | Method and device for processing recruitment data, electronic equipment and storage medium |
| CN116542831B (en) * | 2023-07-07 | 2023-10-03 | 杭州海亮优教教育科技有限公司 | Method and device for processing recruitment data, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2021203489A1 (en) | 2021-10-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Fairweather et al. | Experimental Methods for Social Policy Research: Pergamon International Library of Science, Technology, Engineering and Social Studies | |
| CN112418699A (en) | Resource allocation method, device, equipment and storage medium | |
| Bagaka | Fiscal decentralization in Kenya and the growth of government: The Constituency development fund | |
| CN113496335A (en) | Method, system and equipment for recording decision-making behaviors | |
| Bulut et al. | Decentralization and poverty reduction: Opportunities and challenges in Kenya | |
| Mayekiso et al. | A public participation model for enhanced local governance | |
| Lardinois | Simulation, gaming and training in a competitive, multimodal, multicompany, intercity passenger-transportation environment | |
| Nurdin et al. | Development Planning Deliberatif | |
| CN111311066A (en) | Performance distribution DApp system combined with intuitionistic fuzzy set | |
| Thomas | Developments in mathematical programming models and their impact on the spatial allocation of educational resources | |
| Peng | The paradox of technical governance: A public opinion survey’s political process and its results | |
| Ganpule | A Study of Household Expenditure on Higher education in Mumbai | |
| Manduna et al. | Local Government Participatory Budget System in Zimbabwe: The Case of Harare City Council, 1995-2013 | |
| Gänzle et al. | The ECOWAS Commission and the making of regional order in West Africa: Intersecting logics in international public administration 1 | |
| Das et al. | Indicative planning perspectives for development of Bloemfontein as a smart city in South Africa | |
| Newton | Improving Equity in Decision Support Systems | |
| Di Giacomo et al. | Standard needs of Italian primary schools | |
| Urban | Increasing Admitted Student Yield Using a Political Targeting Model and Discriminant Analysis: An Institutional Research-Admissions Partnership. AIR Professional File. | |
| Kearns et al. | School Traffic Trip Generation Calculator Evaluation and Data Collection | |
| Mapuwei | Integrating artificial neural networks, simulation and optimisation techniques in improving public emergency ambulance preparedness for heterogeneous regions under stochastic environments. | |
| Zhong | Dissecting Chinese county governmental authorities | |
| Diatmika et al. | Supervision of the Government in Strengthening the Influence of Community Participation of the Effectiveness of Village Funds in the District Buleleng | |
| Santorineou et al. | Spatial conflict management in urban planning | |
| Bayraktar | Essays on Education Market Design | |
| Karnani | AI EMPOWERMENT IN DEMOCRATIC GOVERNANCE: ENHANCING PARTICIPATION AND EFFICIENCY |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Address after: 361000 one of unit 702, No. 1, xishanwei Road, phase III Software Park, Xiamen Torch High tech Zone, Xiamen, Fujian Province Applicant after: Xiamen Yitong Intelligent Technology Group Co.,Ltd. Address before: 361000 no.0269, unit 109, 62 Chengyi North Street, phase III, software park, Xiamen City, Fujian Province Applicant before: XIAMEN ETOM SOFTWARE TECHNOLOGY Co.,Ltd. |
|
| CB02 | Change of applicant information |