CN117973806B - Method and system for generating electricity-retaining DRS (data processing system) plan - Google Patents
Method and system for generating electricity-retaining DRS (data processing system) plan Download PDFInfo
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Abstract
The invention discloses a method and a system for generating a power-preserving DRS plan, comprising the following steps: acquiring requirements through a user interaction interface; constructing constraint conditions according to the requirements of users; establishing a core electricity-retaining target of an electricity-retaining DRS plan, and confirming a final target function through interactive selection of a user; and generating a power-preserving DRS plan through the objective function and the constraint condition. The resources of manpower and material resources are intelligently scheduled by utilizing an advanced algorithm, so that the resources can be flexibly allocated under different conditions. The method is beneficial to reducing unnecessary resource waste and operation cost, and particularly relates to human resource management. The introduced stability evaluation algorithm ensures stable execution of the power-conserving DRS plan under various constraint conditions, enhancing the stability and reliability of the system in the face of demand fluctuations.
Description
Technical Field
The invention relates to the technical field of electricity-retaining DRS plan generation, in particular to an electricity-retaining DRS plan generation-oriented method and system.
Background
In the prior art, the generation of power conservation DRS plans has relied primarily on traditional power demand management methods, which generally include rule-based scheduling, fixed power distribution schemes, and the like. These methods have limitations in dealing with large-scale, dynamically changing power demands, especially during emergency situations or peak hours. In addition, conventional methods often fail to adequately account for efficient use of human resources, cost control, and overall stability of the system.
Conventional approaches lack flexibility in handling emergency or irregular demands. The scheduling and allocation of schedulable resources, such as human resources, cannot be maximized. Higher operating costs may result in terms of human resource management and energy allocation. During periods of high demand, systems may face stability and reliability challenges.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: the existing electricity-retaining DRS plan generation method has large power consumption loss, and cannot completely meet the actual requirements of users.
In order to solve the technical problems, the invention provides the following technical scheme: a method for generating a power-preserving oriented DRS plan comprises the following steps:
acquiring requirements through a user interaction interface;
constructing constraint conditions according to the requirements of users;
Establishing a core electricity-retaining target of an electricity-retaining DRS plan, and confirming a final target function through interactive selection of a user;
And generating a power-preserving DRS plan through the objective function and the constraint condition.
As a preferable scheme of the electricity-retaining DRS plan generation-oriented method, the invention comprises the following steps: the obtaining of the requirement through the user interaction interface comprises the steps of inputting requirement information of a user in the interaction interface and extracting key information of the requirement information;
The user imports the generation requirement of the electricity-retaining DRS plan into a system, and the system obtains the requirement information by identifying text content;
The requirement information comprises a venue task data table and a power-on period hierarchical command implementation rule document;
the venue task data table comprises a start time, an end time, venue operation tasks and venue operation places;
the power-saving period hierarchical command implementation rule document comprises working content, streamline reporting requirements and electric-saving field patrol requirements which are input by a user and aim at a power-saving DRS plan.
As a preferable scheme of the electricity-retaining DRS plan generation-oriented method, the invention comprises the following steps: the step of extracting the key information of the demand information comprises the step of extracting entity content and association among entities in a text through an identification algorithm;
the recognition algorithm comprises the steps of converting an original text into a format suitable for machine processing, improving the accuracy of entity recognition by combining context information, and finally constructing a relation diagram between entities, wherein the interaction between the entities is understood and expressed as follows:
，
Wherein X represents the original text and BERT-Embed represents the use of BERT model to convert the text into an embedded vector; biLSTM denotes a bi-directional long and short term memory network for understanding the semantics of the entity in context; Model parameters representing a bidirectional long-short-term memory network; g represents a graph attention network for learning complex relationships between entities; e represents edges between entities; Model parameters representing the attention network of the graph;
outputting the content of each verb entity and other related entities as a limiting set according to the association among the entities to obtain n limiting sets;
The limiting set comprises a verb entity and p other entity contents related to the verb entity, wherein the verb entity is taken as a constraint object, and the other entity contents are taken as constraints on the verb entity; wherein, p is more than or equal to 0.
As a preferable scheme of the electricity-retaining DRS plan generation-oriented method, the invention comprises the following steps: the constraint condition comprises generating a constraint by using each constraint set;
，
Wherein R represents a lower-limit coefficient, and if there is no constraint lower limit on Y, r=0; u represents an upper limit coefficient, and if there is no constraint upper limit on Y, u=0; y represents a constrained object; ymin represents a constraint lower limit; ymax represents the constraint upper limit;
summarizing all generated constraints to generate a constraint set;
And matching each constraint condition in the constraint set with an operation task in the electricity-retaining DRS plan, and outputting the obtained matching result to be used as a task constraint for the electricity-retaining DRS plan.
As a preferable scheme of the electricity-retaining DRS plan generation-oriented method, the invention comprises the following steps: the constraint conditions further comprise that if the constraint conditions in the constraint set have conflict, errors are reported when the power-saving DRS plan is generated, and text contents corresponding to the constraint conditions causing the errors are marked; if the constraint conditions in the constraint set have no conflict, generating a power-saving DRS plan by using an objective function;
The objective functions include a least lossy objective function, a maximum and minimum schedulable resource objective function, and an equalized objective function.
As a preferable scheme of the electricity-retaining DRS plan generation-oriented method, the invention comprises the following steps: the objective function of the minimum loss includes,，
，
Wherein L represents the minimum total cost,Representing the total cost; Human resources hours representing the ith task; representing the efficiency of the ith task; representing the workload of the ith task; representing an ith human resource cost coefficient; representing a work efficiency adjustment coefficient;
The objective function of the maximum schedulable resource includes, ，
The objective function of the minimum schedulable resource includes,，
，
Wherein R represents the total amount of maximum schedulability; representing the total amount of schedulability; a skill diversity index representing employee j; A task fitness index representing employee j; A schedulability weight coefficient representing employee j;
The balanced objective function includes a balanced objective in cost, efficiency, and schedulability of human resources:
，
Wherein, Representing the degree of equalization; the equalization weight coefficient of the task k is represented; parameters indicating the importance of adjusting the equalization; n, m, l all represent the number of tasks.
As a preferable scheme of the electricity-retaining DRS plan generation-oriented method, the invention comprises the following steps: performing stability evaluation on the objective function, and for each constraint conditionDefining its minimum valueAnd maximum value；
Intermediate position of constraint conditionThe definition is as follows:
，
for each objective function Is calculated according to the calculation result of (2)Determining the corresponding constraint conditionIs a position in the middle; stability scoringFor objective functionsIn the constraint conditionIs defined as:
，
and preferentially outputting a generating result of the objective function with the highest stability score on the interactive page of the user, and if the user negatively selects the objective function, generating a power-saving DRS plan according to the newly selected objective function.
A system for generating a power-preserving oriented DRS plan using the method according to the present invention is characterized in that:
The acquisition module is used for acquiring requirements through a user interaction interface;
The constraint module is used for constructing constraint conditions according to the requirements of users;
The generation module establishes a core electricity-retaining target of the electricity-retaining DRS plan, and confirms a final target function through interactive selection of a user; and generating a power-preserving DRS plan through the objective function and the constraint condition.
A computer device, comprising: a memory and a processor; the memory stores a computer program characterized in that: the processor, when executing the computer program, implements the steps of the method of any of the present invention.
A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program, when executed by a processor, implements the steps of the method of any of the present invention.
The invention has the beneficial effects that: the method for generating the electricity-retaining DRS-oriented plan provided by the invention utilizes an advanced algorithm to intelligently schedule the manpower and material resources, and can flexibly allocate the resources under different conditions. The method is beneficial to reducing unnecessary resource waste and operation cost, and particularly relates to human resource management. The introduced stability evaluation algorithm ensures stable execution of the power-conserving DRS plan under various constraint conditions, enhancing the stability and reliability of the system in the face of demand fluctuations.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is an overall flowchart of a method for generating a power-preserving oriented DRS plan according to a first embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Embodiment one referring to fig. 1, for one embodiment of the present invention, a method for generating a power-preserving oriented DRS plan is provided, including:
s1: and obtaining the requirement through a user interaction interface.
Further, inputting requirement information of a user in an interactive interface, and extracting key information from the requirement information; the user imports the generation requirement of the electricity-retaining DRS plan into a system, and the system obtains the requirement information by identifying text content.
The requirement information comprises a venue task data table and a power-on period hierarchical command implementation rule document.
The venue task data table comprises a start time, an end time, venue operation tasks and venue operation places; the power-saving period hierarchical command implementation rule document comprises working content, streamline reporting requirements and electric-saving field patrol requirements which are input by a user and aim at a power-saving DRS plan.
The method is characterized in that the power-preserving period hierarchical command implementation rule document: defining the division standard of different electricity-keeping time periods according to the rules of the stadium operation task; and the working content, the streamline reporting requirement and the patrol requirement of the electric field protection period of each stage are defined through the rules. The output result is: a level of power up time period; the on-site command part operates tasks; the station command part operates tasks; the team command part runs tasks; the division standard of the power-on period is completed by setting the division standard of different power-on periods. Comprising the following steps: special power-up period:
the start-to-end time of the open-close curtain of the major event is pointed, and the time from 1 hour before the formal start to half hour after the formal start of the activity and literature performance with particularly significant impact and specific scale is pointed.
First-level electricity-keeping period:
Special stadium: refers to the fact that there is a particularly significant impact and a specific scale of activity, from 2 hours before the formal start of the literature performance to 1 hour after the end. The method comprises the steps of starting a formal match, a awarding period and a news bulletin period from the first 1 hour to the second half hour;
first-level stadium: refers to the formal start to end of the event activity, and has significant impact and a larger scale of the event activity from 1 hour before the formal start to 0.5 hour after the end;
Second-level stadium: the time of the event, the period of awarding the prize and the news release will be from 1 hour before the start to 0.5 hour after the end;
Non-competitive venues: a Main Operation Center (MOC), a media center (MMC): refers to the normal running period (8:00-24:00) of daily events;
Subfortune village: refers to a period of significant activity by subfortune village tissue.
Secondary power-up period:
Special stadium: the start-to-end time of the open-close curtain of the major event is pointed, and the time from 3 hours before the formal start to 1.5 hours after the end of the formal start of the activity and literature performance with particularly significant impact and specific scale is pointed. The method comprises the steps of starting a formal match, a awarding period and a news bulletin period from 2 hours before starting to 1 hour after finishing;
first-level stadium: refers to the formal beginning to the end of the event activity, and has significant impact and a larger scale of the event activity from 2 hours before the formal beginning to 1 hour after the end;
second-level stadium: refers to the period from 2 hours before starting to 1 hour after finishing the event activity;
Non-competitive venues: a Main Operation Center (MOC), a media center (MMC): two hours (6:00-800) before the normal start of the daily event;
subfortune village: refers to daily athlete's normal activity daily life (6:00-23:00).
General power retention period:
Special stadium: the rest time periods except the special-stage, the first-stage and the second-stage power-preserving time periods;
First-level stadium: the rest time periods except the primary and secondary power-keeping time periods;
Second-level stadium: the rest time periods except the primary and secondary power-keeping time periods;
Non-competitive venues: a Main Operation Center (MOC), a media center (MMC): refers to the daily holiday period (24:00-6:00);
subfortune village: refers to the normal rest time of a daily athlete (23:00 a day 6:00);
the power-saving period division of the training venues and the service places basically requires inspection according to the general normal power-saving period.
Identifying (venue operation task) content in a venue task data table through an algorithm, and identifying main keywords; and (3) carrying out similarity algorithm matching on the division standards of different electricity-keeping time periods and keywords of (venue operation tasks) content, and determining the level of the electricity-keeping time periods. If the recognition (stadium operation task) is carried out through the similarity algorithm and is special and primary, the system judges that the system is in a higher level; the level of the power-keeping period is as follows from high to low: special power-up period, primary power-up period, secondary power-up period and general power-up period. The system supports the level adjustment of the power up period based on user subjectivity.
Still further, entity content and associations between entities in the text are extracted by an identification algorithm.
Advanced pre-processing methods are used to convert the original text into a format more suitable for machine processing,
P (x) =bert-embedded (x), where x is the original text and BERT-embedded (x) is the conversion of text into embedded vectors using BERT model.
The context information is combined to improve accuracy of entity identification,Wherein, the method comprises the steps of, wherein,Is a candidate for an entity to embed the representation,Is a bidirectional long and short term memory network used for understanding the semantics of the entities in the context, constructing a relationship diagram between the entities and better understanding the interaction between the entities.
Where v denotes entity nodes, E denotes edges between entities,Is a graph attention network for learning complex relationships between entities.
The recognition algorithm comprises the steps of converting an original text into a format suitable for machine processing, improving the accuracy of entity recognition by combining context information, and finally constructing a relation diagram between entities, wherein the interaction between the entities is understood and expressed as follows:
Wherein X represents the original text and BERT-Embed represents the use of the BERT model to convert the text into an embedded vector; biLSTM denotes a bi-directional long and short term memory network for understanding the semantics of the entity in context; Model parameters representing a bidirectional long-short-term memory network; g represents a graph attention network for learning complex relationships between entities; e represents edges between entities; model parameters representing the attention network are shown.
And outputting the content of each verb entity and other related entities as a limit set according to the association among the entities to obtain n limit sets.
The limiting set comprises a verb entity and p other entity contents related to the verb entity, wherein the verb entity is taken as a constraint object, and the other entity contents are taken as constraints on the verb entity; wherein, p is more than or equal to 0.
It is known that outputting each verb entity and other entity contents related to the verb entity as a restriction set, and taking the verb entity as a constraint object, wherein the other entity contents are taken as constraints on the verb entity, and the main purpose is to create a structured and highly organized data model, so that the efficiency and the accuracy of generating the electricity-retaining DRS plan are improved. By combining verb entities with related entity content to form a restricted set, user needs can be more accurately understood and handled. The method is helpful for accurately identifying and analyzing complex demand scenes, and ensures that the generated DRS plan meets actual demands better. In such designs, verb entities typically represent certain operations or requirements, while other entity content associated therewith provides specific conditions or constraints for performing those operations. Such a structure makes constraints more explicit and concrete, facilitating efficient application and management in the plan generation process. Through analysis of verb entities and the content of the related entities, the properties and requirements of tasks can be better understood, so that the allocation and scheduling of resources are optimized. For example, human and material resources may be intelligently allocated based on factors such as the urgency of the task, the type of resources required, and the like. The method supports automatic conversion of complex user demands into a structured data model, and helps to promote the automation level of electricity-retaining DRS plan generation. The automation processing of complex data not only improves efficiency, but also reduces the possibility of human error. In the power conservation DRS plan, a variety of factors and conditions may need to be considered. By combining verb entities with related entity content, the formed constraint set provides necessary information support for complex decisions, which is helpful for making a more comprehensive and reasonable plan. This approach allows the system to flexibly handle a variety of different needs and situations, improving the adaptability of the system. In the face of changing requirements and conditions, the system can be quickly adjusted to generate a DRS plan which is suitable for the current situation.
S2: and constructing constraint conditions according to the requirements of the user.
Further, the constraint conditions include, with each constraint set, generating constraints (with key information, constituting constraints on power-conserving DRS plans, such as constraints on power-conserving inspection frequency of the scene, constraints on power-conserving period, etc.),Wherein R represents a lower-limit coefficient, and if there is no constraint lower limit on Y, r=0; u represents an upper limit coefficient, and if there is no constraint upper limit on Y, u=0; y represents a constrained object; ymin represents a constraint lower limit; ymax represents the constraint upper limit.
Summarizing all generated constraints to generate a constraint set; and matching each constraint condition in the constraint set with an operation task in the electricity-retaining DRS plan, and outputting the obtained matching result to be used as a task constraint for the electricity-retaining DRS plan.
If the constraint conditions in the constraint set have conflict, sending out error reporting when the power-saving DRS plan is generated, and marking text content corresponding to the constraint condition causing the error reporting; and if the constraint conditions in the constraint set have no conflict, generating a power-saving DRS plan by using an objective function.
It is noted that by translating the set of constraints into specific constraints (e.g., power up patrol frequency, power up period, etc.), it is ensured that the power up DRS plan complies with all necessary specifications and standards. This helps avoid legal and safety issues that may arise in the implementation of the program. By analysis and application of constraints, resources such as manpower, equipment, and time can be more efficiently allocated and scheduled. This optimization helps to improve overall efficiency and reduce resource waste. By taking into account different constraints, the power conservation DRS plan can be more flexible to accommodate various situations, such as emergency situations or special events. This flexibility is critical to ensuring stable operation of the power system.
And in the plan generation process, conflicts between constraint conditions are detected, and errors are sent out when the conflicts are found, so that the potential problems can be recognized and solved in time. This not only improves the reliability of the plan, but also reduces the risk in the implementation. By automatically matching constraint conditions and running tasks, and automatically detecting conflicts, the design improves the automation and intelligence level of the plan generation process, and reduces the need for human intervention. Thereby improving the quality and effectiveness of the overall plan.
S3: establishing a core electricity-retaining target of an electricity-retaining DRS plan, and confirming a final target function through interactive selection of a user; and generating a power-preserving DRS plan through the objective function and the constraint condition.
Further, the objective functions include a least lossy objective function, a maximum and minimum schedulable resource objective function, and an equalized objective function.
Still further, the objective function of the minimum loss includes,，
，
Wherein L represents the minimum total cost,Representing the total cost; Human resources hours representing the ith task; representing the efficiency of the ith task; representing the workload of the ith task; representing an ith human resource cost coefficient; representing a work efficiency adjustment coefficient; representing the efficiency of each task; representing the workload of each task; Is the number of hours of human resources for each task or station.
The objective function of the maximum schedulable resource includes; The objective function of the minimum schedulable resource includes,
,
Wherein R represents the total amount of maximum schedulability; representing the total amount of schedulability; a skill diversity index representing employee j; A task fitness index representing employee j; A schedulability weight coefficient representing employee j; Is the skill diversity index of each employee; is the task fitness index of each employee.
The balanced objective function includes a balanced objective in cost, efficiency, and schedulability of human resources:
，
Wherein, Representing the degree of equalization; the equalization weight coefficient of the task k is represented; parameters indicating the importance of adjusting the equalization; n, m, l all represent the number of tasks.
It is to be noted that the stability evaluation is performed on the objective function, for each constraintDefining its minimum valueAnd maximum value。
Intermediate position of constraint conditionThe definition is as follows:
，
for each objective function Is calculated according to the calculation result of (2)Determining the corresponding constraint conditionIs a position in the middle; stability scoringFor objective functionsIn the constraint conditionIs defined as:
，
and preferentially outputting a generating result of the objective function with the highest stability score on the interactive page of the user, and if the user negatively selects the objective function, generating a power-saving DRS plan according to the newly selected objective function.
It is noted that by providing different objective functions (minimum loss, maximum and minimum schedulable resources, balance objectives), the system is able to meet the diversified needs of different users. The multi-objective method enables the system to be more flexible and can adapt to different operation and management scenes. And a stability scoring mechanism is introduced, so that the stability of the generated electricity-retaining DRS plan under the constraint condition can be ensured. This not only improves the reliability of the plan, but also reduces the risk in the implementation. Through the user interaction interface, the system preferentially displays the objective function result with the highest stability score, so that the suggestion of the optimal solution is provided. Meanwhile, the user can select other objective functions according to own judgment and demand, so that the user friendliness and flexibility of the system are improved. By comprehensively considering cost, efficiency, schedulability, and stability, the system is able to generate a more comprehensive and balanced power conservation DRS plan.
This helps to improve the quality of the decision making, ensuring the effectiveness and practicality of the plan. By optimizing the allocation and scheduling of human resources, the objective function with the lowest loss is beneficial to reducing unnecessary resource waste and reducing operation cost. By considering different constraint conditions and objective functions, the system can adapt to various complex and changing requirements, and the adaptability and flexibility under different conditions are improved.
On the other hand, the embodiment also provides a system for generating a power-preserving oriented DRS plan, which comprises: and a system for generating a power-preserving DRS plan.
And the acquisition module is used for acquiring the requirements through a user interaction interface.
And the constraint module is used for constructing constraint conditions according to the requirements of users.
The generation module establishes a core electricity-retaining target of the electricity-retaining DRS plan, and confirms a final target function through interactive selection of a user; and generating a power-preserving DRS plan through the objective function and the constraint condition.
The above functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-only memory (ROM), a random access memory (RAM, randomAccessMemory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Example 2: the following provides a method for generating a power-preserving DRS plan, and in order to verify the beneficial effects of the invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.
Comparing the invention with the traditional method, test is carried out through a simulation experiment to obtain a table 1:
as can be seen from table 1, the present invention significantly reduces the time from receiving demand to generating a plan through automated and intelligent data processing. The optimized resource scheduling algorithm enables the resource allocation to be more efficient and reduces waste. Accurate demand analysis and resource allocation reduces unnecessary expense and overall cost. Customized services and quick response promote overall user satisfaction. The stability assessment algorithm ensures stable execution of the plan under various constraints. The design of multiple objective functions supports more comprehensive decision making.
Table 1 provides an intuitive way to demonstrate the advantages of the present invention over conventional approaches, particularly in terms of key aspects such as response time, resource utilization efficiency, cost efficiency, and user satisfaction.
The test results of the present invention are shown in table 2 by testing under different environments:
Standard environment: conditions are as follows: normal power demand, normal human resources;
high demand environment: conditions are as follows: peak power demand, human resources are intense;
Low demand environment: conditions are as follows: the power demand and the manpower resources are low and sufficient;
Emergency situation: conditions are as follows: sudden changes in power demand caused by an emergency;
changing environment: conditions are as follows: the power demand and human resources are constantly changing;
As can be seen from table 2, the response time of the system varies slightly under different circumstances, but remains generally within reasonable limits. In high demand and emergency situations, resource utilization efficiency is reduced, but performs well in standard and low demand environments. Cost effectiveness remains relatively stable in different environments, reflecting the advantages of the present invention in terms of cost control. User satisfaction remains high in all test environments, especially in standard and low demand environments. The planned stability performs well in various circumstances, albeit with a slight drop in emergency situations.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (4)
1. The method for generating the electricity-retaining DRS-oriented plan is characterized by comprising the following steps of:
acquiring requirements through a user interaction interface;
Constructing constraint conditions according to the requirements of users; extracting entity contents and associations among entities in a text through an identification algorithm, outputting each verb entity and other entity contents related to each verb entity as a limiting set according to the associations among the entities to obtain n limiting sets, and generating a constraint by utilizing each limiting set;
Establishing a core electricity-retaining target of an electricity-retaining DRS plan, and confirming a final target function through interactive selection of a user;
generating a power-saving DRS plan through the objective function and the constraint condition;
The obtaining of the requirement through the user interaction interface comprises the steps of inputting requirement information of a user in the interaction interface and extracting key information of the requirement information;
The user imports the generation requirement of the electricity-retaining DRS plan into a system, and the system obtains the requirement information by identifying text content;
The requirement information comprises a venue task data table and a power-on period hierarchical command implementation rule document;
the venue task data table comprises a start time, an end time, venue operation tasks and venue operation places;
The power-saving period hierarchical command implementation rule document comprises working content, streamline reporting requirements and electric-saving field patrol requirements which are input by a user and aim at a power-saving DRS plan;
The step of extracting the key information of the demand information comprises the step of extracting entity content and association among entities in a text through an identification algorithm;
the recognition algorithm includes converting the original text into a format suitable for machine processing and constructing a relationship graph between entities expressed as:
T(x，Θ，Φ)=G(BiLSTM(BERT-Embed(P(x))，Θ)，E，Φ)
wherein X represents the original text and BERT-Embed represents the use of BERT model to convert the text into an embedded vector; biLSTM denotes a bi-directional long and short term memory network for understanding the semantics of the entity in context; Θ represents model parameters of the bidirectional long-short-term memory network; g represents a graph attention network for learning complex relationships between entities; e represents edges between entities; phi represents model parameters of the graph annotation force network;
The limiting set comprises a verb entity and p other entity contents related to the verb entity, wherein the verb entity is taken as a constraint object, and the other entity contents are taken as constraints on the verb entity; wherein, p is more than or equal to 0;
the constraint condition comprises generating a constraint by using each constraint set;
R×Ymin≤Y≤U×Ymax
Wherein R represents a lower-limit coefficient, and if there is no constraint lower limit on Y, r=0; u represents an upper limit coefficient, and if there is no constraint upper limit on Y, u=0; y represents a constrained object; ymin represents a constraint lower limit; ymax represents the constraint upper limit;
summarizing all generated constraints to generate a constraint set;
Matching each constraint condition in the constraint set with an operation task in the electricity-retaining DRS plan, and outputting the obtained matching result to be used as a task constraint for the electricity-retaining DRS plan;
The constraint conditions further comprise that if the constraint conditions in the constraint set have conflict, errors are reported when the power-saving DRS plan is generated, and text contents corresponding to the constraint conditions causing the errors are marked; if the constraint conditions in the constraint set have no conflict, generating a power-saving DRS plan by using an objective function;
the objective functions comprise an objective function with the lowest loss, an objective function with the largest and smallest schedulable resources and an objective function with balance;
the objective function of the minimum loss includes, ；
；
Wherein,Representing a minimum total cost of the device,Representing the total cost; Human resources hours representing the ith task; representing the efficiency of the ith task; representing the workload of the ith task; representing an ith human resource cost coefficient; representing a work efficiency adjustment coefficient;
The objective function of the maximum schedulable resource includes, ；
The objective function of the minimum schedulable resource includes,；
；
Wherein,A total amount representing a maximum schedulability; representing the total amount of schedulability; a skill diversity index representing employee j; A task fitness index representing employee j; Representing staff Is a schedulability weight coefficient of (a);
The balanced objective function includes a balanced objective in cost, efficiency, and schedulability of human resources:
；
Wherein, Representing the degree of equalization; representing tasks Is used for balancing the weight coefficient of the (a); parameters indicating the importance of adjusting the equalization; n, m and l all represent the number of tasks;
Performing stability evaluation on the objective function, and for each constraint condition Defining its minimum valueAnd maximum value；
Intermediate position of constraint conditionThe definition is as follows:
；
for each objective function Is calculated according to the calculation result of (2)Determining the corresponding constraint conditionIs a position in the middle; stability scoringFor objective functionsIn the constraint conditionIs defined as:
；
and preferentially outputting a generating result of the objective function with the highest stability score on the interactive page of the user, and if the user negatively selects the objective function, generating a power-saving DRS plan according to the newly selected objective function.
2. A system for power conservation oriented DRS plan generation employing the method of claim 1, characterized by:
The acquisition module is used for acquiring requirements through a user interaction interface;
The constraint module is used for constructing constraint conditions according to the requirements of users;
The generation module establishes a core electricity-retaining target of the electricity-retaining DRS plan, and confirms a final target function through interactive selection of a user; and generating a power-preserving DRS plan through the objective function and the constraint condition.
3. A computer device, comprising: a memory and a processor; the memory stores a computer program characterized in that: the steps of a method for generating a power-up oriented DRS plan as claimed in claim 1 are implemented when the processor executes the computer program.
4. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program when executed by a processor implements the steps of a method for power-save oriented DRS plan generation as claimed in claim 1.
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