CN111369231A - AI-based project management method, computer storage medium, and electronic device - Google Patents
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Abstract
The invention discloses an AI-based project management method, a computer storage medium and an electronic device, wherein the AI-based project management method comprises the following steps: s1, obtaining an enterprise basic data network set according to the enterprise basic data; s2, obtaining a domain-based data set through classification; s3, obtaining a primary project data network set according to the primary project data information and the declaration condition; s4, obtaining a first training model through training; s5, obtaining an enterprise project data network set according to the enterprise project data information; s6, obtaining a secondary project data network set according to the secondary project data and the declaration condition; s7, obtaining a second training model through training; s8, inputting the basic information of the enterprise into the first training model, and judging whether the declaration condition is met; and S9, if the requirements are met, inputting the project data information of the enterprise into the second training model, and screening out secondary projects meeting the declaration conditions. According to the method provided by the embodiment of the invention, enterprise declaration projects can be assisted, and the intelligent degree is improved.
Description
Technical Field
The invention belongs to the technical fields of software engineering, big data, distributed storage and calculation and the like, and particularly relates to an AI project management method, a computer storage medium and electronic equipment.
Background
The medium and small-sized enterprises play an important role in the economic and social development process of China, and the development of the medium and small-sized enterprises is supported by issuing and implementing various research, development, assistance and innovation projects every year by the country. With the progress of science and technology and the coming of the information age, governments usually establish a public service platform and related websites through information technology means, and release subsidy projects related to small and medium-sized enterprises on the platform and the websites. However, after acquiring project information issued by the government, the current small and medium-sized enterprises usually adopt manual collection of various information of the enterprises and manual judgment of whether various declaration conditions are met, which is time-consuming, labor-consuming and easy to miss information. However, at present, enterprises can only match with large-class projects of the enterprises after inputting enterprise-related data into the system, and then manually judge whether the reporting conditions of the small-class projects are met according to the large-class projects of the enterprises, so that the incomplete matching of the projects still exists, the enterprises cannot quickly and clearly judge whether the enterprises have qualifications to report specific projects, and the maintenance and management of the enterprises on the project information are not facilitated.
Disclosure of Invention
In view of this, the present invention provides an AI-based project management method, a computer storage medium, and an electronic device, which can facilitate and fast project management for an enterprise, and can accurately match related project information of the enterprise.
In order to solve the above technical problem, in one aspect, the present invention provides an AI-based project management method, including the following steps: s1, acquiring enterprise basic data information, and processing the enterprise basic data information to obtain an enterprise basic data network set; s2, classifying the enterprise basic data network set according to the entity nodes and/or the vertical fields of the node relation to obtain a plurality of domain-divided data sets of different vertical fields; s3, acquiring primary project data information and declaration conditions thereof, and processing the primary project data information to obtain a primary project data network set; s4, placing the plurality of domain data sets and the primary project data network set in a network for training to obtain a first training model; s5, acquiring enterprise project data information, and processing the enterprise project data information to obtain an enterprise project data network set; s6, acquiring secondary project data information and declaration conditions thereof, and processing the secondary project data information to obtain a secondary project data network set; s7, placing the enterprise project data network set and the secondary project data network set in a network for training to obtain a second training model; s8, acquiring enterprise basic information, inputting the enterprise basic information into the first training model, feeding back whether an enterprise meets the declaration condition of the primary project data network set or not by the first training model according to the enterprise basic data information, if not, sending out first feedback information, and if so, executing the step S9; and S9, acquiring the enterprise project data information according to the enterprise basic data information, inputting the enterprise project data information into the second training model, and screening the secondary projects meeting the secondary project data network set declaration conditions by the second training model according to the enterprise project data information.
According to the AI-based project management method provided by the embodiment of the invention, the enterprise basic data information, the primary project data information and the declaration condition are respectively processed to obtain a first training model, and then the enterprise project data information, the secondary project data information and the declaration condition are respectively processed to obtain a second training model. And when determining which items can be declared according to the enterprise basic data information, inputting the enterprise basic data information into the obtained first training model, and determining whether the declaration conditions of the primary item data network set are met according to the feedback result. And when the declaration condition of the primary project data network set is met, acquiring enterprise project data information according to the enterprise basic data information, inputting the enterprise project data information into a second training model, and screening out secondary projects meeting the declaration condition of the secondary project data network set through the second training model. According to the AI-based project management method provided by the embodiment of the invention, whether the large-class project declaration condition is met or not can be intelligently and automatically judged, whether the small-class project declaration condition is met or not can be intelligently and automatically judged, project matching can be rapidly and comprehensively carried out, and the maintenance and management of enterprises on project information per se are facilitated.
According to an embodiment of the present invention, in step S1, the enterprise basic data information includes: the name of the enterprise, the establishment time information of the enterprise, the registered fund information, the address information, the intellectual property information, the main operation range information and the legal information.
According to an embodiment of the present invention, in step S2, the enterprise primary data network set is divided into a talent field data set, an innovation field data set, a post-subsidy field data set, and an industrialization field data set, and in step S3, the primary project data network set includes: talent project data sets, innovation project data sets, post-subsidy project data sets, and industrialization project data sets.
According to one embodiment of the present invention, step S4 includes: s41, respectively randomly selecting enterprises according to different vertical fields, and obtaining corresponding enterprise basic data information; s42, splitting the basic data information of each enterprise respectively to obtain basic units of each enterprise; s43, matching each enterprise basic unit with the declaration condition of the primary project data network set; s44, screening out the enterprise basic units meeting the declaration conditions of the primary project data network set; and S45, classifying the enterprise basic units meeting the declaration conditions of the primary project data network set, and then finishing training to obtain the first training model.
According to one embodiment of the invention, the enterprise project data information includes: enterprise declared project data information, enterprise applied patent type and quantity.
According to one embodiment of the present invention, step S7 includes: s71, acquiring the enterprise basic data information which accords with the declaration condition of the primary project data network set in the step S45, and acquiring the enterprise project data information according to the enterprise basic data information; s72, splitting the enterprise project data information to obtain each enterprise project unit; s73, matching each enterprise project unit with the declaration condition of the secondary project data network set; s74, screening out the enterprise project units meeting the declaration conditions of the secondary project data network set; and S75, classifying the enterprise project units meeting the declaration conditions of the secondary project data network set, and then finishing training to obtain the second training model.
According to an embodiment of the present invention, step S7 further includes: s76, clustering the enterprise basic units and the enterprise project units classified in the steps S45 and S75 respectively to form trigger units; in step S8, after acquiring the enterprise basic data information and inputting the first training model, the enterprise basic data information is first matched with the trigger unit; in step S9, after the enterprise project data information is input into the second training model, the enterprise project data information is first matched with the triggering unit.
According to an embodiment of the invention, the method further comprises: and S10, generating an enterprise project management report according to the secondary projects which are fed back by the second training model and meet the secondary project data network set declaration conditions.
In a second aspect, embodiments of the present invention provide a computer storage medium comprising one or more computer instructions that, when executed, implement any of the methods described above.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, where the memory is configured to store one or more computer instructions, and the processor is configured to call and execute the one or more computer instructions, so as to implement the method described in any one of the above.
The AI-based project management method according to the embodiment of the invention has at least the following beneficial effects:
(1) while the internet of things technology is used for collecting basic data information of an enterprise and project data information of the enterprise, an artificial intelligence technology is introduced, projects which can be declared by the enterprise can be rapidly matched and screened, and the project management efficiency can be effectively improved;
(2) the secondary modeling comparison is carried out through the system, the comprehensiveness of project matching is improved, and therefore an enterprise can rapidly and clearly judge whether the enterprise has the qualification to declare a specific project.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a flowchart illustrating an AI-based project management method according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of an electronic device according to an embodiment of the invention.
Reference numerals:
an electronic device 300;
a memory 310; an operating system 311; an application 312;
a processor 320; a network interface 330; an input device 340; a hard disk 350; a display device 360.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be considered limiting of the invention. Furthermore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Hereinafter, an AI-based project management method according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.
As shown in fig. 1, an AI-based project management method according to an embodiment of the present invention includes the steps of: and S1, acquiring the enterprise basic data information, and processing the enterprise basic data information to obtain an enterprise basic data network set.
And S2, classifying the enterprise basic data network set according to the entity nodes and/or the vertical fields of the node relation to obtain a plurality of domain-divided data sets of different vertical fields.
And S3, acquiring the primary project data information and the declaration conditions thereof, and processing the primary project data information to obtain a primary project data network set.
And S4, placing the multiple domain data sets and the primary project data network set in a network for training to obtain a first training model.
And S5, acquiring the enterprise project data information, and processing the enterprise project data information to obtain an enterprise project data network set.
And S6, acquiring the secondary project data information and the declaration conditions thereof, and processing the secondary project data information to obtain a secondary project data network set.
And S7, placing the enterprise project data network set and the secondary project data network set in a network for training to obtain a second training model.
And S8, acquiring the enterprise basic data information, inputting the enterprise basic data information into a first training model, feeding back whether the enterprise meets the declaration conditions of the primary project data network set or not by the first training model according to the enterprise basic data information, if not, sending out first feedback information, and if so, executing the step S9.
And S9, acquiring enterprise project data information according to the enterprise basic data information, inputting the project data information into a second training model, and screening secondary projects meeting the secondary project data network set declaration conditions by the second training model according to the enterprise project data information.
In other words, the AI-based project management method according to an embodiment of the present invention mainly employs the following steps:
in a first step, a first training model is obtained. The method comprises the following specific steps: (1) and acquiring enterprise basic data information, and processing the enterprise basic data information to obtain an enterprise basic data network set. (2) And classifying the enterprise basic data network set to obtain a plurality of domain-divided data sets of different vertical domains, wherein the classification can be carried out according to the vertical domains of the nodes and/or the node relation. (3) And acquiring the primary project data information and the declaration conditions thereof, and processing the primary project data information and the declaration conditions thereof to obtain a primary project data network set. (4) And placing the domain data set and the primary project data network set in a network for training to obtain a first training model.
And secondly, acquiring a second training model. The method comprises the following specific steps: (1) and processing the enterprise project data information to obtain an enterprise project data network set. (2) And processing the secondary project data information and the declaration condition to obtain a secondary project data network set. (3) And placing the enterprise project data network set and the secondary project data network set in the network for training to obtain a second training model.
And thirdly, inputting the basic data information of the enterprise into the first training model obtained by the first-step operation, judging whether the enterprise meets the declaration condition of the first-level project data network set, and executing the next-step operation according to the judgment result. And if the judgment result is that the declaration condition of the primary project data network set is not met, sending first feedback information. And if the declaration condition of the primary project data network set is met, executing the next operation.
And fourthly, when the judgment result in the operation of the third step is that the declaration condition of the primary project data network set is met, inputting enterprise project data information obtained according to enterprise basic data information into a second training model obtained by the operation of the second step, and screening secondary projects meeting the declaration condition of the secondary project data network set by the second training model according to the enterprise project data information, namely, 0, one or more than one secondary projects meeting the declaration condition can be screened by the second training model, so that the enterprise can quickly and clearly judge which specific projects can be declared by the enterprise.
It should be noted that the first step operation and the second step operation may be performed simultaneously or alternatively, and may be selected according to specific data processors and requirements.
Therefore, according to the AI-based project management method provided by the embodiment of the invention, the enterprise basic data information, the primary project data information and the declaration condition are respectively processed to obtain a first training model, and the enterprise project data information, the secondary project data information and the declaration condition are respectively processed to obtain a second training model. And when determining which projects can be declared according to the basic information of the enterprise, inputting basic data information of the enterprise into the obtained first training model, and determining whether the declaration conditions of the primary project data network set are met according to the feedback result. And when the declaration condition of the primary project data network set is met, acquiring enterprise project data information according to the enterprise basic data information, inputting the enterprise project data information into a second training model, and screening out secondary projects meeting the declaration condition of the secondary project data network set through the second training model. According to the AI-based project management method provided by the embodiment of the invention, whether the large-class project declaration condition is met or not can be intelligently and automatically judged, whether the small-class project declaration condition is met or not can be further intelligently and automatically judged, project matching can be rapidly and comprehensively carried out, and the maintenance and management of enterprises on project information per se are facilitated.
When the first training model judges that the enterprise does not meet the declaration conditions of the primary project data network set, the first training model can identify the directory information which does not meet the declaration conditions, so that a client can quickly and clearly know which conditions of the enterprise do not meet the declaration conditions, and the enterprise can check defects and repair leakage in time. When the second training model screens the secondary projects, information catalogues which are not satisfied by other projects can be identified, and enterprises can clearly know the difference between self conditions and declared conditions.
In order to further improve the intelligent management level of the enterprise project, the method according to the embodiment of the invention further comprises the following steps:
(1) and acquiring the directory data information obtained by the first training model and/or the second training model according to the identification, and processing the directory data information to obtain a directory data network set.
(2) And acquiring the correlation degree of the identification of each vertex in the directory data network set and different declaration conditions in different declaration information, and dividing the directory data network set into a primary classification data set and a secondary classification data set according to the correlation degree.
(3) And classifying the primary classification data set and the secondary classification data set according to enterprise registered capital, enterprise personnel number, enterprise social security payment personnel total number, enterprise personnel academic calendar, enterprise reward and punishment conditions, enterprise independent intellectual property rights and the like to obtain a plurality of fine classification data sets.
(4) And placing a plurality of fine classification data sets of the primary classification data set and the secondary classification data set in a network for training respectively to obtain a third training model.
(5) And acquiring the catalogue information of the first training model and the second training model, and inputting the catalogue information into the third training model.
(6) The third training model gives out corresponding prompt information according to the directory information, the prompt information may include preset information capable of meeting the declaration condition and associated third parties, for example, when the autonomous intellectual property condition of the enterprise does not meet the declaration condition, the third training model gives out information such as the number of patents which should be met, whether the patents need to be in an authorization stage or a substantial review stage, or associates the third parties, and determines whether the third parties need to be contacted according to feedback information of the third training model. The third party may include the national intellectual property office, a provincial intellectual property agency, a provincial project declaration agency, a patent office, etc.
According to an embodiment of the present invention, in step S1, the enterprise basic data information includes a name of the enterprise, establishment time information of the enterprise, registered capital information, address information, intellectual property information, business home range information, legal information, and the like, and the type and content of the enterprise basic data information may be limited or screened according to the primary project data information and its declaration condition, and the enterprise basic data network set may be obtained by processing the enterprise basic data information.
Alternatively, the enterprise basic data information may include enterprise scale information, and the enterprise scale may be divided into large-scale enterprises, medium-scale enterprises, small-scale enterprises in the initial period, scientific and technological institutions, and the like.
In some embodiments of the present invention, in step S2, the enterprise primary data network set is divided into a talent domain data set, an innovation domain data set, a post-subsidy domain data set, and an industrialization domain data set, and in step S3, the primary project data network set comprises: talent project data sets, innovation project data sets, post-subsidy project data sets, and industrialization project data sets. That is to say, the enterprise data network set is divided into a plurality of large classes in advance, so that data classification and processing are facilitated, and the processing efficiency of the enterprise basic data network set is improved.
After step S2, obtaining a plurality of domain-divided data sets of different vertical domains, according to an embodiment of the present invention, step S4 includes: s41, respectively and randomly selecting enterprises according to different vertical fields, and obtaining corresponding enterprise basic data information, wherein the vertical fields can include but are not limited to a talent field data set, an innovation field data set, a post-subsidy field data set and an industrialization field data set; s42, splitting the enterprise basic data information obtained in step S41 to obtain each enterprise basic unit, for example: splitting the obtained basic information such as the name of the enterprise, establishment time information of the enterprise, registered fund information, address information, intellectual property information, main and business scope information, legal information and the like to obtain basic units of each enterprise; s43, matching each enterprise basic unit obtained in the step S42 with declaration conditions of the primary project data network set, for example: the declaration conditions of the primary project data network set can include that the establishment time of an enterprise is not shorter than n years, the total number of enterprise personnel and other data information, and when the declaration conditions of the primary project data network set are matched, the relevant data information in the enterprise basic unit obtained in the step (2), such as the establishment time of the enterprise is 2 years, the information of 500 personnel and the like, can be matched with the declaration conditions of the primary project data network set; s44, screening out the enterprise basic units meeting the declaration conditions of the primary project data network set; and S45, classifying the enterprise basic units meeting the declaration conditions of the primary project data network set, and then finishing training to obtain a first training model.
The declaration condition of the primary project data network set according to the embodiment of the present invention is described in detail below with reference to specific embodiments.
(1) The preset declaration conditions of the talent items are as follows: mainly aims at high scholars of enterprises, such as more masters scholars and ages less than 55 years old, and the high scholars have not less than 1 effective intellectual property right and the like.
(2) Presetting declaration conditions on innovation class items: not less than 1 patent with effective invention; a small test or pilot test stage for processing the target product; the production, study and research are cooperated with colleges and universities; enterprises have self-raising research and development expenses of not less than 1: 1; enterprises establish related research and development platforms and the like.
(3) Preset declaration conditions for post-subsidy projects: and performing post-subsidy according to the amount of the last-year investment of the enterprise in the aspects of research and development, production equipment, infrastructure, enterprise intelligence, informatization management, energy-saving transformation, system authentication and the like in a certain proportion. For example, according to the fact that an enterprise invests more than 500 thousands of equipment in the last year, invests more than 200 thousands of information, and carries out 10% post-assistance on the basis of more than 500 thousands of energy-saving reconstruction.
(4) The preset declaration conditions of the industrialization project are as follows: the method mainly means that project investment is carried out on enterprise popularization capacity, the total project investment is not less than 1 hundred million, the project newly increased investment is not less than 4000-5000 ten thousand, and the yield value is about 2 hundred million-3 hundred million; the product industry direction accords with 10 industry fields supported by the state.
According to one embodiment of the invention, the enterprise project data information includes: the enterprise declared project data information and the enterprise applied patent types and the number are obtained through the basic data information of the enterprise, data irrelevant to the secondary project data set can be removed quickly, the data processing range is narrowed, and the data processing speed is improved.
In some embodiments of the invention, step S7 includes: s71, acquiring enterprise basic data information which accords with the declaration condition of the primary project data network set in the step S45, and acquiring enterprise project data information according to the enterprise basic data information, namely, the step S71 is to acquire the enterprise basic data information screened in the step S45, so that the data processing amount can be reduced, and the data processing speed is improved; s72, splitting the data information of each enterprise project respectively to obtain each enterprise project unit; s73, matching each enterprise project unit with the declaration conditions of the secondary project data network set; s74, screening the enterprise project units meeting the declaration conditions of the secondary project data network set; and S75, classifying the enterprise project units meeting the declaration conditions of the secondary project data network set, and finishing training to obtain a second training model.
Further, step S7 further includes: s76, clustering the enterprise basic units and the enterprise project units classified in the steps S45 and S75 respectively to form trigger units; in step S8, after the enterprise basic data information is obtained and the first training model is input, the enterprise basic data information is first matched with the trigger unit, so that the data processing efficiency can be improved; in step S9, after the enterprise project data information is input into the second training model, the enterprise project data information is first matched with the triggering unit.
In other words, according to the AI-based project management method of the embodiment of the present invention, the enterprise base units and the enterprise project units classified in steps S45 and S75 may be clustered to form trigger units, after the enterprise base units and the enterprise project units are clustered, after the enterprise base data information is obtained in step S8, the enterprise base data information may be first matched with the trigger units, if the enterprise base data information can be directly matched, it may be directly determined that the enterprise meets the requirements of the first-level project, if the enterprise base data information cannot be matched, the enterprise base data information may be matched with other enterprise base units, which may speed up the matching efficiency of data, and similarly, the AI-based project management method of the embodiment of the present invention may also be applied in step S9.
According to an embodiment of the invention, the method according to an embodiment of the invention further comprises the steps of: and S10, generating an enterprise project management report according to the secondary projects which are fed back by the second training model and meet the secondary project data network set declaration conditions, so that project managers of enterprises can read and file conveniently.
In summary, the AI-based project management method according to an embodiment of the present invention has at least the following advantages: (1) while the internet of things technology is used for collecting basic data information of an enterprise and project data information of the enterprise, an artificial intelligence technology is introduced, projects which can be declared by the enterprise can be rapidly matched and screened, and the project management efficiency can be effectively improved; (2) the secondary modeling comparison is carried out through the system, the comprehensiveness of project matching is improved, and therefore an enterprise can rapidly and clearly judge whether the enterprise has the qualification to declare a specific project.
In addition, an embodiment of the present invention further provides a computer storage medium, where the computer storage medium includes one or more computer instructions, and when executed, the one or more computer instructions implement any one of the AI-based project management methods described above.
That is, the computer storage medium stores a computer program that, when executed by a processor, causes the processor to execute any one of the AI-based item management methods described above.
As shown in fig. 2, an embodiment of the present invention provides an electronic device 300, which includes a memory 310 and a processor 320, where the memory 310 is configured to store one or more computer instructions, and the processor 320 is configured to call and execute the one or more computer instructions, so as to implement any one of the methods described above.
That is, the electronic device 300 includes: a processor 320 and a memory 310, in which memory 310 computer program instructions are stored, wherein the computer program instructions, when executed by the processor, cause the processor 320 to perform any of the methods described above.
Further, as shown in fig. 2, the electronic device 300 further includes a network interface 330, an input device 340, a hard disk 350, and a display device 360.
The various interfaces and devices described above may be interconnected by a bus architecture. The bus architecture may be any architecture that includes any number of interconnected buses and bridges. Various circuits of one or more Central Processing Units (CPUs), represented in particular by processor 320, and one or more memories, represented by memory 310, are coupled together. The bus architecture may also connect various other circuits such as peripherals, voltage regulators, power management circuits, and the like. It will be appreciated that a bus architecture is used to enable communications among the components. The bus architecture includes a power bus, a control bus, and a status signal bus, in addition to a data bus, all of which are well known in the art and therefore will not be described in detail herein.
The network interface 330 may be connected to a network (e.g., the internet, a local area network, etc.), obtain relevant data from the network, and store the relevant data in the hard disk 350.
The input device 340 may receive various commands input by an operator and send the commands to the processor 320 for execution. The input device 340 may include a keyboard or a pointing device (e.g., a mouse, trackball, touch pad, touch screen, etc.).
The display device 360 may display the result of the instructions executed by the processor 320.
The memory 310 is used for storing programs and data necessary for operating the operating system, and data such as intermediate results in the calculation process of the processor 320.
It will be appreciated that memory 310 in embodiments of the invention may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read Only Memory (EPROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), or a flash memory. Volatile memory can be Random Access Memory (RAM), which acts as external cache memory.
In some embodiments, memory 310 stores the following elements, executable modules or data structures, or a subset thereof, or an expanded set thereof: an operating system 311 and application programs 312.
The operating system 311 includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, and is used for implementing various basic services and processing hardware-based tasks. The application programs 312 include various application programs, such as a Browser (Browser), and are used for implementing various application services. A program implementing methods of embodiments of the present invention may be included in application 312.
The method disclosed by the above embodiment of the present invention can be applied to the processor 320, or implemented by the processor 320. Processor 320 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 320. The processor 320 may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof, and may implement or perform the methods, steps, and logic blocks disclosed in the embodiments of the present invention. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 310, and the processor 320 reads the information in the memory 310 and completes the steps of the method in combination with the hardware.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or any combination thereof. For a hardware implementation, the processing units may be implemented within one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof.
For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in memory 310 and executed by processor 320. The memory 310 may be implemented in the processor 320 or external to the processor 320.
In particular, the processor 320 is also configured to read the computer program and execute any of the methods described above.
In the several embodiments provided in the present application, it should be understood that the disclosed method and apparatus may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, each functional unit in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately provided, or two or more units may be integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) to execute some steps of the transceiving method according to various embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. An AI-based project management method, comprising the steps of:
s1, acquiring enterprise basic data information, and processing the enterprise basic data information to obtain an enterprise basic data network set;
s2, classifying the enterprise basic data network set according to the entity nodes and/or the vertical fields of the node relation to obtain a plurality of domain-divided data sets of different vertical fields;
s3, acquiring primary project data information and declaration conditions thereof, and processing the primary project data information to obtain a primary project data network set;
s4, placing the plurality of domain data sets and the primary project data network set in a network for training to obtain a first training model;
s5, acquiring enterprise project data information, and processing the enterprise project data information to obtain an enterprise project data network set;
s6, acquiring secondary project data information and declaration conditions thereof, and processing the secondary project data information to obtain a secondary project data network set;
s7, placing the enterprise project data network set and the secondary project data network set in a network for training to obtain a second training model;
s8, acquiring enterprise basic information, inputting the enterprise basic information into the first training model, feeding back whether an enterprise meets the declaration condition of the primary project data network set or not by the first training model according to the enterprise basic data information, if not, sending out first feedback information, and if so, executing the step S9;
and S9, acquiring the enterprise project data information according to the enterprise basic data information, inputting the enterprise project data information into the second training model, and screening the secondary projects meeting the secondary project data network set declaration conditions by the second training model according to the enterprise project data information.
2. The method according to claim 1, wherein in step S1, the enterprise essential data information includes: the name of the enterprise, the establishment time information of the enterprise, the registered fund information, the address information, the intellectual property information, the main operation range information and the legal information.
3. The method of claim 2, wherein in step S2, the set of enterprise primary data networks is divided into a talent domain data set, an innovation domain data set, a post-subsidy domain data set, and an industrialization domain data set, and wherein in step S3, the set of primary project data networks comprises: talent project data sets, innovation project data sets, post-subsidy project data sets, and industrialization project data sets.
4. The method according to claim 1, wherein step S4 includes:
s41, respectively randomly selecting enterprises according to different vertical fields, and obtaining corresponding enterprise basic data information;
s42, splitting the basic data information of each enterprise to obtain basic units of each enterprise;
s43, matching each enterprise basic unit with the declaration condition of the primary project data network set;
s44, screening out the enterprise basic units meeting the declaration conditions of the primary project data network set;
and S45, classifying the enterprise basic units meeting the declaration conditions of the primary project data network set, and then finishing training to obtain the first training model.
5. The method of claim 4, wherein the enterprise project data information comprises: enterprise declared project data information, enterprise applied patent type and quantity.
6. The method according to claim 4, wherein step S7 includes:
s71, acquiring the enterprise basic data information which accords with the declaration condition of the primary project data network set in the step S45, and acquiring the enterprise project data information according to the enterprise basic data information;
s72, splitting the enterprise project data information respectively to obtain each enterprise project unit;
s73, matching each enterprise project unit with the declaration condition of the secondary project data network set;
s74, screening out all enterprise project units meeting the declaration conditions of the secondary project data network set;
and S75, classifying all enterprise project units meeting the declaration conditions of the secondary project data network set, and finishing training to obtain the second training model.
7. The method according to claim 6, wherein step S7 further comprises:
s76, clustering the enterprise basic units and the enterprise project units classified in the steps S45 and S75 respectively to form trigger units;
in step S8, after acquiring the enterprise basic data information and inputting the first training model, the enterprise basic data information is first matched with the trigger unit;
in step S9, after the enterprise project data information is input into the second training model, the enterprise project data information is first matched with the triggering unit.
8. The method of claim 1, further comprising:
and S10, generating an enterprise project management report according to the secondary projects which are fed back by the second training model and meet the enterprise secondary project data network set declaration conditions.
9. A computer storage medium comprising one or more computer instructions which, when executed, implement the method of any one of claims 1-8.
10. An electronic device comprising a memory and a processor, wherein,
the memory is to store one or more computer instructions;
the processor is configured to invoke and execute the one or more computer instructions to implement the method of any one of claims 1-8.
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| CN104375998A (en) * | 2013-08-13 | 2015-02-25 | *** | Intelligentized project matching analysis tool and implementation method thereof |
| CN110264043A (en) * | 2019-05-29 | 2019-09-20 | 深圳市霏凡网络科技有限公司 | A kind of evaluation of S&T projects method, system and storage medium based on big data |
| CN110503337A (en) * | 2019-08-26 | 2019-11-26 | 付强 | A kind of business strategy small watersheds, method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104375998A (en) * | 2013-08-13 | 2015-02-25 | *** | Intelligentized project matching analysis tool and implementation method thereof |
| CN110264043A (en) * | 2019-05-29 | 2019-09-20 | 深圳市霏凡网络科技有限公司 | A kind of evaluation of S&T projects method, system and storage medium based on big data |
| CN110503337A (en) * | 2019-08-26 | 2019-11-26 | 付强 | A kind of business strategy small watersheds, method |
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