CN115204693B - Intelligent park management method based on artificial intelligence and cloud platform - Google Patents
Intelligent park management method based on artificial intelligence and cloud platform Download PDFInfo
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Abstract
The application discloses an intelligent park management method and a cloud platform based on artificial intelligence. Therefore, the target park management system can be more accurately selected for the management system interest points of the target new online park service to perform subsequent service function item configuration, and further the target park management system to be issued, which possibly needs the target new online park service, is determined, so that the matching issuing for the new online park service and the target park management system is realized.
Description
Technical Field
The application relates to the technical field of intelligent park service development, in particular to an intelligent park management method based on artificial intelligence and a cloud platform.
Background
Through the construction of wisdom garden, can help the garden to establish unified organization management coordination framework, business management platform and to interior external service operation platform in the aspect of informatization. For this, for related software services (such as medical services and e-commerce services) of the intelligent park, a new online park service is generated in the service iteration updating process, and how to realize matching and issuing of the new online park service and the target park management system in an artificial intelligence environment, and more precisely select a management system interest point of the target new online park service for subsequent service function item configuration aiming at the target park management system is a technical problem to be solved.
Disclosure of Invention
The application provides an intelligent park management method based on artificial intelligence and a cloud platform.
In a first aspect, an embodiment of the present application provides an artificial intelligence-based intelligent park management method, which is executed based on an intelligent park management cloud platform, and includes:
determining a target park management system sequence from an intelligent park management system sequence, issuing target new online park services to a plurality of target park management systems in the target park management system sequence, and determining a measurement value of a target service function configuration tendency of the target park management system for configuring the target new online park services; the number of campus management systems in the target campus management system sequence is smaller than the number of the whole intelligent campus management system sequence;
Determining interest influence factors of interest points of a management system of a target park management system on the target service function configuration tendency respectively based on the measurement value of the target service function configuration tendency of the target new online park service;
based on interest influence factors of the plurality of the management system interest points on the target service function configuration tendency, obtaining target system interest points of which the interest influence factors are matched with preset conditions from the management system interest points;
based on the target system interest points, obtaining a target system interest point matching preset interest characteristic condition from the intelligent park management system sequence to be issued; and issuing and configuring the target new online park service to the target park management system to be issued.
In a second aspect, an embodiment of the present application provides a smart park management cloud platform, including:
a processor;
and a memory storing a computer program which when executed implements the artificial intelligence based intelligent campus management method of the first aspect.
As described above, the present application determines, by issuing target new online park services to a target park management system sequence formed by a part of target park management systems, target system points of interest having obvious trends in selecting and using the target new online park services for the target park management systems from among different management system points of interest of the target park management systems, determines a target park management system to be issued based on the target system points of interest, and issues the target new online park services to the target park management system to be issued. Therefore, the target park management system can be more accurately selected for the management system interest points of the target new online park service to perform subsequent service function item configuration, and further the target park management system to be issued, which possibly needs the target new online park service, is determined, so that the matching issuing for the new online park service and the target park management system is realized.
Drawings
FIG. 1 is a schematic flow chart of steps of an intelligent park management method based on artificial intelligence according to an embodiment of the application;
fig. 2 is a schematic block diagram of a smart park management cloud platform for performing the artificial intelligence-based smart park management method of fig. 1 according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are some, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art, in light of the embodiments of the present application without undue burden are within the scope of the present application.
Step S200, determining a target park management system sequence from an intelligent park management system sequence, issuing target new online park services to a plurality of target park management systems in the target park management system sequence, and determining a measurement value of target service function configuration tendency of the target park management system for configuring the target new online park services; the number of campus management systems in the target sequence of campus management systems is less than the number of campus management systems in the entire sequence of intelligent campus management systems.
In an exemplary design concept, the target new online campus service may be a new online campus service such as a campus medical service, a campus e-commerce service, etc.
The target service function configuration trend of the target new online campus service is used to express whether the target campus management system is prone to configure the target new online campus service.
In an exemplary design concept, the target campus management system sequence may be a subset of the smart campus management system sequence, wherein the number of campus management systems in the target campus management system sequence should be much smaller than the number of campus management systems of the entire smart campus management system sequence. That is, in an exemplary design concept, a large number of target campus management systems to be issued corresponding to the target new online campus service may be determined through testing a small number of target campus management systems, and issued.
Step S300, based on the measurement value of the target service function configuration trend of the target new online park service, determining interest influence factors of the management system interest points of the target park management system on the target service function configuration trend respectively.
In an exemplary design concept, the management system points of interest are used to express the points of interest of the target campus management system in configuring the new online campus service.
Based on the measurement values of the target service function configuration trends corresponding to different target park management systems and the management system interest points of each target park management system, the support degree of the different management system interest points on the target service function configuration trends, namely the interest influence factors in the embodiment, can be determined. The interest influence factor expresses the support degree of the interest point of the management system on the target service function configuration trend, and the greater the interest influence factor is, the greater the support degree of the interest point of the management system on the target service function configuration trend is, and the smaller the interest influence factor is, the smaller the support degree of the interest point of the management system on the target service function configuration trend is. For each management system interest point, an interest impact factor of the management system interest point on the target service function configuration trend can be determined based on the number distribution situation of the park management systems corresponding to different interest attribute values in the first reference target park management system sequence. When the distribution of the number of park management systems corresponding to different interest attribute values of the interest points of the management system in the first reference target park management system sequence tends to be even, the interest influence factor of the interest points of the management system on the target service function configuration trend is smaller, otherwise, the interest influence factor is larger.
Step S400, based on interest influence factors of a plurality of the management system interest points on the target service function configuration trend, obtaining target system interest points of which the interest influence factors are matched with preset conditions from the management system interest points.
In an exemplary design concept, based on interest impact factors of each of the management system interest points on the target service function configuration trend, target system interest points that have a significant trend toward the target service function configuration trend, that is, system interest points that determine whether a target campus management system will have a significant impact on configuring the target new online campus service, may be determined.
And step S500, based on the target system interest points, obtaining a target system interest point matched with a preset interest characteristic condition from the intelligent park management system sequence, and issuing and configuring the target new online park service to the target park management system.
In an exemplary design concept, after determining the target system interest point based on the target park management system sequence, determining whether a non-target park management system is a target park management system to be issued matching a preset interest feature condition based on a specific metric value of the target system interest point of each target park management system in other target park management systems, and issuing and configuring the target new online park service for the determined target park management system to be issued.
Based on the steps, the target new online park service is issued to the target park management system sequence formed by part of the target park management systems, the target system interest points with obvious tendency to select the target new online park service for use by the target park management systems among different management system interest points of the target park management systems are determined, the target park management system to be issued is determined based on the target system interest points, and the target new online park service is issued to the target park management system to be issued. Therefore, the target park management system can be more accurately selected for the management system interest points of the target new online park service to perform subsequent service function item configuration, and further the target park management system to be issued, which possibly needs the target new online park service, is determined, so that the matching issuing for the new online park service and the target park management system is realized.
In an exemplary design concept, metric values 1 and 0 may be used to express whether a target campus management system in the target campus management system sequence configures the target new online campus service after being issued the target new online campus service, and statistics of the configured and non-configured campus management system duty ratio of the target new online campus service is used as the target service function configuration trend.
In another exemplary design concept, 0 to 100 may be used to express the tendency of the target campus management system in the target campus management system sequence to configure the target new online campus service after the target new online campus service is issued, and then a feature average of the tendency of each target campus management system is calculated as the target service function configuration tendency.
In an exemplary design concept, before step S200, the method may further include the steps of:
step S110, obtaining service application node sequences corresponding to each new online park service respectively, wherein the service application node sequences comprise each park service application node of the new online park service.
In an exemplary design concept, the campus service application node may include each application function that a new online campus service has.
Step S120, based on the service application node sequences corresponding to the new online park services respectively, outputting park service application networks corresponding to the park service application nodes respectively; the campus service application network expresses the application relationship of each new online campus service under the corresponding campus service application node.
For example, the campus service application network is a graph composed of the campus service function objects corresponding to each new online campus service, and the function session information between each of the campus service function objects.
In an exemplary design concept, each campus service application node corresponds to one campus service application network, and weights of functional session information between two same campus service functional objects are the same or different in the corresponding campus service application networks of different campus service application nodes.
And step S130, grouping is carried out on the park service application networks corresponding to each park service application node respectively, so that park service groupings corresponding to each park service application node respectively are obtained, and each park service grouping comprises park service application labels divided according to the corresponding park service application nodes.
In an exemplary design concept, each campus service application tag contains at least two new online campus services, indicating that the new online campus services in the campus service application tag have an application relationship under the corresponding campus service application node.
And step S140, acquiring the tag characteristic attribute of each park service application tag based on the park service application node of the new online park service in each park service application tag.
Optionally, the tag characteristic attribute of the campus service application tag indicates a probability that a new online campus service in the campus service application tag is a target new online campus service.
And step S150, acquiring member tag characteristic attributes of the new online park services based on the tag characteristic attributes of the application tags of the park services.
And step S160, mining out target new online park services from the new online park services based on the member tag characteristic attribute of the new online park services.
Therefore, the campus service application labels of the new online campus services are respectively divided according to the application nodes of the campus services of different categories, the label characteristic attribute of the application labels of the new online campus services is determined according to the application nodes of the campus services of the new online campus services in each of the application labels of the new online campus services, the label characteristic attribute of the application labels of the new online campus services is determined according to the label characteristic attribute of the application labels of the new online campus services, and then target new online campus service identification is performed based on the label characteristic attribute of the new online campus services.
Based on the above steps, a more matched new online campus service can be determined as a target new online campus service, and then a suitable target campus management system is obtained through steps S100 to S500, and the target new online campus service is issued to the suitable target campus management system.
In an exemplary design concept, in step S120, based on the service application node sequences corresponding to the new online campus services respectively, outputting a campus service application network corresponding to each of the campus service application nodes respectively may include the following substeps:
step S121, outputting a service application relationship map corresponding to a second campus service application node based on application node scene characteristics of the second campus service application node in the service application node sequences corresponding to the new online campus services, where the second campus service application node is any one of the service application nodes of each campus; the service application relation map corresponding to the second park service application node comprises park service function objects corresponding to the new online park services respectively, service application function items corresponding to the scene characteristics of various application nodes of the second park service application node and function session information between the park service function objects and the service application function items; and the new online park service corresponding to the functional session information expression between the park service functional object and the service application functional item has corresponding application node scene characteristics.
In an exemplary design concept, each of the campus service function objects corresponding to each of the new online campus services may form a new online campus service sequence, and the service application function items corresponding to the scene features of each of the application nodes of the second campus service application node may form an application node scene feature sequence, where the new online campus service sequence and the application node scene feature sequence are obtained by reading the service application node sequence.
The new online park service sequence is a sequence including service function objects of each park, the application node scene features may be parameters corresponding to service application nodes of a certain kind, the application node scene feature sequence may be determined according to the application node scene feature kind, and the application node scene feature sequence corresponding to service application nodes of each kind may include application node scene features of service application nodes of a corresponding kind.
Step S122, acquiring the support degree of each service application function item based on the number of the connected service application function items of each service application function item; the connected service application function items of the service application function items are park service function objects connected with the service application function items through function session information;
Step S123, acquiring the support degree of each park service function object based on the support degree of the communication service application function item of each park service function object; the communication service application function items of the park service function objects are service application function items connected with the park service function objects through function session information;
step S124, outputting the first relation support degree sequence based on the support degree of each service application function item and the support degree of each park service function object; the first relation support degree sequence expresses the confidence degree of the relevance scheduling of each park service function object to each service application function item;
step S125, outputting the second relation support degree sequence based on the number of the connected service application function items of the service application function items; the second relation support degree sequence expresses the confidence degree of the service application function items to be associated and scheduled to the park service function objects respectively;
step S126, fusing the first relation support degree sequence and the second relation support degree sequence to obtain a third relation support degree sequence, wherein the third relation support degree sequence expresses the association confidence degree between the park service function objects;
Step S127, performing a cyclic traversal between the campus service function objects based on the third relationship support degree sequence, to obtain a relationship support degree distribution between the campus service function objects;
and step S128, the relation support degree distribution among the park service function objects is fused to the support degree of the function session information among the park service function objects, and the park service application network corresponding to the second park service application node is obtained.
In an exemplary design concept, before step S121, the method may further include:
step S1201, for the second campus service application nodes with relevance in the feature range of the corresponding application node scene feature, aggregating each service application function item through a preset aggregation algorithm;
step S1202, for the second campus service application nodes whose corresponding application node scene features are preset floating features in the feature range, aggregating each service application function item based on a preset aggregation algorithm.
In an exemplary design concept, step S160, mining a target new online campus service from the new online campus services based on the member tag feature attribute of the new online campus services, may include the following sub-steps:
Step S161, determining a new online park service with a corresponding member tag feature attribute greater than a target feature attribute value in the new online park services as the target new online park service; or alternatively
Step S162, determining the new online park service with the corresponding member label characteristic attribute larger than the target characteristic average value in the new online park services as the target new online park service; and the target characteristic mean value is the characteristic mean value of the member tag characteristic attribute of each new online park service.
In an exemplary design concept, in step S300, the act of determining interest impact factors of interest points of a management system of a target campus management system on the target service function configuration tendency of the target new online campus service based on the measurement value of the target service function configuration tendency of the target new online campus service may include the following substeps:
step S310, obtaining a metric value of a target service function configuration tendency of each target new online campus service in each target campus management system sequence before the target new online campus service is issued to the target campus management system sequence, and obtaining a metric value of a target service function configuration tendency of each target new online campus service in each target campus management system sequence after the target new online campus service is issued to the target campus management system sequence.
Step S320, determining a floating value of the metric value of the target service function configuration tendency.
In an exemplary design concept, a floating value of a metric of a target service function configuration tendency of each of the target new online campus services in the target campus management system sequence may be obtained before and after the target new online campus service is issued to the target campus management system sequence. The float value may express a degree of willingness change of the target campus management system after being delivered to the target new online campus service.
Step S330, determining a first reference target campus management system sequence in the target campus management system sequence based on the floating value of the metric value of the target service function configuration tendency, where the first reference target campus management system sequence is a sequence of target campus management systems with increased metric values of corresponding target service function configuration tendency.
In an exemplary design concept, the floating value is an increased target campus management system is a target campus management system with an increased propensity to configure the target new online campus service after being delivered. These tendencies-increasing target campus management systems may express where the management system points of interest they have may have in agreement with the target new online campus service, thus further performing step S340.
Step S340, counting the number of campus management systems corresponding to each interest attribute value in the first reference target campus management system sequence, for each interest attribute value of each management system interest point.
Step S350, determining a degree of inverse association of the management system interest point to the target service function configuration tendency based on the number of campus management systems corresponding to each interest attribute value in the first reference target campus management system sequence.
Step S360, determining an interest influence factor of the management system interest point on the target service function configuration trend based on the reverse association degree, wherein the interest influence factor of the management system interest point on the target service function configuration trend is inversely related to the reverse association degree.
In an exemplary design concept, the management system point of interest includes a management system point of interest of a system service tag, and an interest attribute value of the management system point of interest of the system service tag corresponds to at least two interest tag components.
In step S340, for each interest attribute value of each management system interest point, the act of counting the number of campus management systems corresponding to each interest attribute value in the first reference target campus management system sequence may include the following substeps:
Step S341, counting the number of park management systems corresponding to each interest tag component in a plurality of interest tag components of the management system interest point of the system service tag according to the management system interest point of the system service tag.
Step S342, determining the number of campus management systems corresponding to each interest tag component in the first reference target campus management system sequence based on the number of the campus management systems of the first reference target campus management system sequence and the number of the campus management systems corresponding to each interest tag component.
In another exemplary design concept, the management system points of interest include management system points of interest of a metric tag. For example, when the management system point of interest is annual revenue for a target campus management system, the interest attribute value corresponds to an annual revenue metric value for a different target campus management system.
In step S340, for each interest attribute value of each management system interest point, the act of counting the number of campus management systems corresponding to each interest attribute value in the first reference target campus management system sequence may include the following substeps:
step S343, aiming at the management system interest points of the measurement value labels, determining a plurality of interval nodes based on the interest attribute value intervals of the management system interest points of the measurement value labels;
Step S344, for each interval node, dividing the interest attribute value interval of the management system interest point of the metric label into two metric intervals;
step S345, determining the number of campus management systems corresponding to each metric interval in the first reference target campus management system sequence based on the number of the first reference target campus management systems and the number of the first reference target campus management systems.
Based on the above, the step S350 of determining the inverse relevance of the interest point of the management system to the target service function configuration tendency based on the number of campus management systems corresponding to each interest attribute value in the first reference target campus management system sequence may include the following substeps:
step S351, for each interval node, determining a first reverse association degree of the management system interest point of the metric label on the target service function configuration tendency according to the interval node splitting based on the number of campus management systems corresponding to each metric interval corresponding to the interval node, so as to obtain a plurality of first reverse association degrees.
And step S352, determining the minimum value in the first reverse association degrees as the reverse association degree of the management system interest point of the metric label on the target service function configuration trend.
In other words, in an exemplary design idea, a split party with the least reverse association degree can be determined in various interval nodes through the statistical method, and then the reverse association degree of the target service function configuration tendency for the management system interest point of the metric label is determined.
In an exemplary design concept, the act of obtaining, in step S400, a target system interest point with an interest influence factor matching a preset condition from the management system interest points based on interest influence factors of a plurality of management system interest points on the target service function configuration tendency may include the following substeps:
step S410, determining a target management system interest point with the largest interest influence factor from the management system interest points based on interest influence factors of the plurality of management system interest points on the target service function configuration trends.
In an exemplary design concept, based on the reverse association degree of each management system interest point on the target service function configuration trend, the management system interest point with the maximum measurement value of the reverse association degree can be obtained as the obtained target management system interest point.
Step S420, for each interest attribute value of the target management system interest point, determining, based on the number of campus management systems corresponding to each interest attribute value in the first reference target campus management system sequence, a target system interest point value with the number of the campus management systems greater than a target number threshold in each interest attribute value.
Step S430, based on analysis of the management system interest points of the target park management systems in the first reference target park management system sequence, determining interest influence factors of a plurality of non-target management system interest points on the target service function configuration tendency respectively, wherein the target park management system is a target park management system with the interest attribute value of the target management system interest points in the first reference target park management system sequence as the target system interest point value, and the plurality of non-target management system interest points are management system interest points except for the target management system interest points in the plurality of management system interest points.
In an exemplary design concept, when determining the interest influence factor of the non-target management system interest point on the target service function configuration trend, the server may count, for each non-target management system interest point, the number of campus management systems corresponding to each interest attribute value in the target campus management system of the first reference target campus management system sequence based on each interest attribute value of the non-target management system interest point, and determine the inverse relevance of the non-target management system interest point on the target service function configuration trend based on the number of campus management systems corresponding to each interest attribute value in the target campus management system.
Step S440, based on a plurality of the non-target management system points of interest, repeating the target management system point of interest determining step and the target management system point of interest value determining step until a preset number of target management system points of interest are obtained, and taking the preset number of target management system points of interest as the target system points of interest.
In an exemplary design concept, for each non-target management system interest point, the execution process of determining the target management system interest point and the target system interest point value of the target management system interest point can be repeatedly executed, one target management system interest point is obtained again, and the target park management system is determined based on the obtained target management system interest point; and taking the preset interest number target management system interest points as target system interest points until the preset interest number target management system interest points are obtained.
In another exemplary design concept, the number of campus management systems corresponding to different interest attribute values may be combined to screen the target campus management systems to be delivered that meet the target interest value. Based on the target system interest point, the act of obtaining, from the intelligent campus management system sequence, a target campus management system to be issued whose target system interest point matches a preset interest feature condition in step S500 may include the following steps:
Step S510, based on each interest attribute value of the target system interest points, obtaining, from each interest attribute value, a target system interest point value of which the number of the corresponding target campus management systems is greater than a target number threshold in the first reference target campus management system sequence.
Step S520, based on the target system interest point value of the target system interest point, obtaining, from the intelligent campus management system sequence, a target campus management system to be delivered, wherein the interest attribute value of the target system interest point is the target system interest point value.
In another exemplary design concept, when the number of target system points of interest is multiple, a target campus management system sequence may be screened based on the multiple target system points of interest. The target park management system to be issued comprises a first target park management system to be issued and a second target park management system to be issued.
Based on the target system interest point, the act of obtaining, from the intelligent campus management system sequence, a target campus management system to be issued whose target system interest point matches a preset interest feature condition in step S500 may include the following steps:
Step S530, for a first target system interest point obtained for the first time from the preset interest number of target system interest points, obtaining a first target park management system to be issued, where the interest attribute value of the first target system interest point is the value of the first target system interest point, from the smart park management system sequence.
Step S540, for the second target system interest points except the first target system interest point in the preset interest number, obtaining a second target park management system to be delivered, where the interest attribute value of the second target system interest point is the value of the second target system interest point, from the target park management systems to be delivered screened last time.
In accordance with the same inventive concept, an embodiment of the present application further provides a smart campus management cloud platform, referring to fig. 2, fig. 2 is a schematic diagram of the smart campus management cloud platform 100 according to the embodiment of the present application, where the smart campus management cloud platform 100 may have a relatively large difference due to different configurations or performances, and may include one or more central processing units 112 (Central Processing Units, CPU) (e.g., one or more processors) and a memory 111. Wherein the memory 111 may be a transient storage or a persistent storage. The program stored in the memory 111 may include one or more modules, each of which may include a series of instruction operations on the intelligent campus management cloud platform 100. Still further, the central processor 112 may be configured to communicate with the memory 111 to perform a series of instruction operations in the memory 111 on the smart campus management cloud platform 100.
The smart campus management cloud platform 100 may also include one or more power supplies, one or more communication units 113, one or more delivery to output interfaces, and/or one or more operating systems, such as Windows ServerTM, mac OS XTM, unixTM, linuxTM, freeBSDTM, etc.
The steps performed by the intelligent campus management cloud platform in the above embodiments may be according to the intelligent campus management cloud platform structure shown in fig. 2.
In addition, the embodiment of the application also provides a machine-readable medium for storing a computer program for executing the method provided in the above embodiment.
The embodiments of the present application also provide a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method provided by the above embodiments.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
Claims (9)
1. An artificial intelligence-based intelligent park management method is executed based on an intelligent park management cloud platform and is characterized by comprising the following steps:
determining a target park management system sequence from an intelligent park management system sequence, issuing target new online park services to a plurality of target park management systems in the target park management system sequence, and determining a measurement value of a target service function configuration tendency of the target park management system for configuring the target new online park services; the number of campus management systems in the target campus management system sequence is smaller than the number of the whole intelligent campus management system sequence;
determining interest influence factors of interest points of a management system of a target park management system on the target service function configuration tendency respectively based on the measurement value of the target service function configuration tendency of the target new online park service;
Based on interest influence factors of the plurality of the management system interest points on the target service function configuration tendency, obtaining target system interest points of which the interest influence factors are matched with preset conditions from the management system interest points;
based on the target system interest points, obtaining a target system interest point matching preset interest characteristic condition from the intelligent park management system sequence to be issued; issuing and configuring the target new online park service to the target park management system to be issued;
the step of determining interest influence factors of interest points of a management system of a target campus management system on the target service function configuration tendency based on the measurement value of the target service function configuration tendency of the target new online campus service comprises the following steps:
obtaining a measurement value of a target service function configuration tendency of each target new online campus service in each target campus management system sequence before issuing the target new online campus service to the target campus management system sequence, and obtaining a measurement value of a target service function configuration tendency of each target new online campus service in each target campus management system sequence after issuing the target new online campus service to the target campus management system sequence;
Determining a floating value of a metric value of the target service function configuration tendency;
determining a first reference target campus management system sequence in the target campus management system sequences based on the floating value of the measurement value of the target service function configuration tendency, wherein the first reference target campus management system sequence is a sequence of target campus management systems with increased measurement values of the corresponding target service function configuration tendency;
counting the number of park management systems corresponding to each interest attribute value in the first reference target park management system sequence aiming at each interest attribute value of each management system interest point;
determining the reverse association degree of the interest points of the management system to the target service function configuration tendency based on the number of park management systems corresponding to each interest attribute value in the first reference target park management system sequence;
and determining an interest influence factor of the management system interest point on the target service function configuration trend based on the reverse association degree, wherein the interest influence factor of the management system interest point on the target service function configuration trend is inversely related to the reverse association degree.
2. The artificial intelligence based intelligent campus management method of claim 1, further comprising, prior to the step of delivering a target new online campus service to a plurality of target campus management systems in the sequence of target campus management systems:
Acquiring service application node sequences corresponding to each new online park service respectively, wherein the service application node sequences comprise each park service application node of the new online park service;
based on the service application node sequences corresponding to the new online park services respectively, outputting park service application networks corresponding to the park service application nodes respectively; the campus service application network expresses the application relation of each new online campus service under the corresponding campus service application node;
grouping the park service application networks respectively corresponding to each park service application node to obtain park service groups respectively corresponding to each park service application node, wherein each park service group comprises park service application labels divided according to the corresponding park service application node;
acquiring tag characteristic attributes of the various park service application tags based on the park service application nodes of the new online park service in the various park service application tags;
acquiring member tag characteristic attributes of the new online park service based on the tag characteristic attributes of the application tags of the park service;
And mining out target new online park services from the new online park services based on the member tag characteristic attribute of the new online park services.
3. The intelligent campus management method based on artificial intelligence according to claim 2, wherein the step of outputting the respective corresponding campus service application network for each of the respective campus service application nodes based on the respective corresponding service application node sequence for each of the respective new online campus services comprises:
based on application node scene characteristics of second park service application nodes in service application node sequences corresponding to the new online park services respectively, outputting a service application relation map corresponding to the second park service application nodes, wherein the second park service application nodes are any one of the park service application nodes; the service application relation map corresponding to the second park service application node comprises park service function objects corresponding to the new online park services respectively, service application function items corresponding to the scene characteristics of various application nodes of the second park service application node and function session information between the park service function objects and the service application function items; the new online park service corresponding to the functional session information expression between the park service functional object and the service application functional item has corresponding application node scene characteristics;
Acquiring the support degree of each service application function item based on the number of connected service application function items of each service application function item; the connected service application function items of the service application function items are park service function objects connected with the service application function items through function session information;
acquiring the support degree of each park service function object based on the support degree of the connected service application function item of each park service function object; the communication service application function items of the park service function objects are service application function items connected with the park service function objects through function session information;
outputting a first relation support degree sequence based on the support degree of each service application function item and the support degree of each park service function object; the first relation support degree sequence expresses the confidence degree of the relevance scheduling of each park service function object to each service application function item;
outputting a second relation support degree sequence based on the number of connected service application function items of the service application function items; the second relation support degree sequence expresses the confidence degree of the service application function items to be associated and scheduled to the park service function objects respectively;
Fusing the first relation support degree sequence and the second relation support degree sequence to obtain a third relation support degree sequence, wherein the third relation support degree sequence expresses the association confidence degree between the park service function objects;
performing cyclic traversal among the park service function objects based on the third relation support degree sequence to obtain relation support degree distribution among the park service function objects; and fusing the relation support degree distribution among the park service function objects to the support degree of the function session information among the park service function objects to obtain the park service application network corresponding to the second park service application node.
4. The intelligent campus management method based on artificial intelligence according to claim 3, further comprising, before outputting a campus service application network corresponding to the second campus service application node based on a service application relationship graph corresponding to the second campus service application node:
aiming at the second park service application nodes with relevance in the feature range of the corresponding application node scene features, aggregating each service application function item through a preset aggregation algorithm; and aiming at the second park service application nodes with the corresponding application node scene characteristics in the characteristic range and with preset floating characteristics, aggregating each service application function item based on a preset aggregation algorithm.
5. The intelligent campus management method according to claim 2, wherein the step of mining out the target new online campus service from the new online campus services based on the member tag feature attributes of the new online campus services comprises:
determining the new online park service with the corresponding member label characteristic attribute larger than the target characteristic attribute value in the new online park services as the target new online park service; or alternatively
Determining the new online park service with the corresponding member label characteristic attribute larger than the target characteristic average value in the new online park services as the target new online park service; and the target characteristic mean value is the characteristic mean value of the member tag characteristic attribute of each new online park service.
6. The intelligent campus management method according to claim 1, wherein the management system points of interest include management system points of interest of a system service tag, and the interest attribute values of the management system points of interest of the system service tag correspond to at least two interest tag components;
the step of counting the number of campus management systems corresponding to each interest attribute value in the first reference target campus management system sequence for each interest attribute value of each management system interest point includes:
Counting the number of park management systems corresponding to each interest tag component in a plurality of interest tag components of the management system interest points of the system service tags aiming at the management system interest points of the system service tags;
determining the number of park management systems corresponding to each interest tag component in the first reference target park management system sequence based on the number of park management systems of the first reference target park management system sequence and the number of park management systems corresponding to each interest tag component; the management system interest points further comprise management system interest points of the measurement value labels;
aiming at the management system interest points of the measurement value labels, determining a plurality of interval nodes based on the interest attribute value intervals of the management system interest points of the measurement value labels;
for each interval node, dividing an interest attribute value interval of the management system interest point of the metric value label into two metric value intervals;
determining the number of park management systems corresponding to each metric value interval in the first reference target park management system sequence based on the number of park management systems of the first reference target park management system sequence and the number of park management systems corresponding to each metric value interval;
The step of determining the reverse association degree of the management system interest points to the target service function configuration trend based on the number of park management systems corresponding to each interest attribute value in the first reference target park management system sequence comprises the following steps:
determining, for each interval node, a first reverse association degree of the management system interest point of the metric label on the basis of the number of park management systems corresponding to each metric interval corresponding to the interval node, wherein the first reverse association degree is configured to the target service function when the interval node is split, so as to obtain a plurality of first reverse association degrees;
and determining the minimum value in the first reverse association degrees as the reverse association degree of the management system interest point of the metric value label on the target service function configuration trend.
7. The intelligent park management method according to claim 1, wherein the step of obtaining target system interest points from the management system interest points based on interest influence factors of the plurality of the management system interest points on the target service function configuration tendency, the interest influence factors matching preset conditions, comprises:
Based on interest influence factors of the plurality of the management system interest points on the target service function configuration trend, determining a target management system interest point with the maximum interest influence factor from the management system interest points;
determining target system interest point values of which the number of park management systems is greater than a target number threshold value in each interest attribute value based on the number of park management systems corresponding to each interest attribute value in a first reference target park management system sequence aiming at each interest attribute value of the target management system interest point;
determining interest influence factors of a plurality of non-target management system interest points on the target service function configuration trend respectively based on management system interest point analysis of a target management system in the first reference target park management system sequence, wherein the target park management system is a target park management system with an interest attribute value of the target management system interest point in the first reference target park management system sequence as a target system interest point value, and the plurality of non-target management system interest points are management system interest points except for the target management system interest point in the plurality of management system interest points;
And repeating the target management system interest point determining step and the target system interest point value determining step of the target management system interest point based on the plurality of non-target management system interest points until a preset interest number of target management system interest points are obtained, and taking the preset interest number of target management system interest points as the target system interest points.
8. The intelligent park management method according to claim 1, wherein the step of obtaining a target park management system to be issued from the intelligent park management system sequence that matches a target system interest point with a preset interest feature condition based on the target system interest point comprises:
based on each interest attribute value of the target system interest points, obtaining target system interest point values of which the number of the corresponding target park management systems is greater than a target number threshold value in the first reference target park management system sequence from each interest attribute value;
and obtaining the interest attribute value of the target system interest point from the intelligent park management system sequence based on the target system interest point value of the target system interest point, wherein the interest attribute value of the target system interest point is the target system interest point value to be issued.
9. An intelligent park management cloud platform, comprising:
a processor;
a memory having stored therein a computer program which when executed implements the artificial intelligence based intelligent campus management method of any one of claims 1-8.
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