CN114357015A - Smart park information pushing method and device - Google Patents
Smart park information pushing method and device Download PDFInfo
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- CN114357015A CN114357015A CN202011091391.4A CN202011091391A CN114357015A CN 114357015 A CN114357015 A CN 114357015A CN 202011091391 A CN202011091391 A CN 202011091391A CN 114357015 A CN114357015 A CN 114357015A
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Abstract
The invention relates to a method and a device for pushing intelligent park information, which can provide more accurate intelligent recommendation for the intelligent park entrance requirement of a user by acquiring requirement information input by the user, extracting characteristic words in the requirement information, acquiring the intelligent park information corresponding to the characteristic words from a server, weighting and summing the scale, the goodness of appraisal and the policy support strength of each intelligent park to obtain the recommendation index of each intelligent park, recommending the intelligent park to the user according to the recommendation index of each intelligent park, calculating the recommendation index by combining the scale, the goodness of appraisal and the policy support strength information in the intelligent park information, and recommending the intelligent park to the user according to the recommendation index of each intelligent park.
Description
Technical Field
The invention relates to the technical field of intelligent parks, in particular to an intelligent park information pushing method and device.
Background
The intelligent park refers to a standard building or building group which is generally planned and constructed by the government, has complete water supply, power supply, gas supply, communication, roads, storage and other supporting facilities and reasonable layout and can meet the production and scientific experiment needs of a certain specific industry.
With the more and more enterprises selecting to enter the intelligent park, however, the information of the existing intelligent park is complicated, and the enterprises are difficult to obtain the accurate target intelligent park.
Disclosure of Invention
The embodiment of the application provides a smart park information pushing method and device, which can provide more accurate intelligent recommendation for smart park entrance requirements of users. The technical scheme is as follows:
in a first aspect, an embodiment of the present application provides an intelligent park information pushing method, including the following steps:
acquiring demand information input by a user;
extracting feature words in the demand information;
acquiring intelligent park information corresponding to the feature words from a server; the intelligent park information comprises the scale, the goodness rate and the policy support strength of each intelligent park;
carrying out weighted summation on the scale, the favorable rating and the policy support strength of each intelligent park to obtain a recommendation index of each intelligent park;
and recommending the intelligent park to the user according to the recommendation index of each intelligent park.
Optionally, the step of extracting the feature words in the requirement information includes:
segmenting the demand information to obtain a keyword set containing a plurality of keywords;
acquiring the weight value of each keyword in the keyword set by using a word frequency-reverse file frequency algorithm;
sorting the weighted values from large to small to obtain a weighted value sorting table;
taking the first N bits in the weighted value sorting table as feature words in the demand information; wherein N is a natural number.
The step of segmenting the demand information includes:
and utilizing a jieba word segmentation tool to segment the words of the demand information.
Optionally, before the step of performing word segmentation on the demand information, the method further includes:
and cleaning the text of the demand information, and deleting the symbols and the tone words in the demand information.
Optionally, the step of obtaining the weight value of each keyword in the keyword set by using a word frequency-reverse file frequency algorithm includes:
calculating the word frequency of the ith keyword according to the following mode:
wherein, TFiWord frequency, N, for the ith keywordiThe number of times of the ith keyword appearing in the keyword set is shown, and N is the total number of words in the keyword set;
acquiring a plurality of documents comprising each keyword from the server as a text set;
obtaining the reverse file frequency of the ith keyword according to the following mode:
wherein, IDFiIs the reverse file frequency of the ith keyword, Y is the number of the files of the text set, Y is the reverse file frequency of the ith keywordiThe number of documents containing i keywords;
acquiring a weight value of the ith keyword in the keyword set according to the following mode:
TFIDFi＝TFi*IDFi
wherein, TFIDFiIs the weighted value of the ith keyword.
Optionally, the step of performing weighted summation on the scale, the goodness of appreciation and the policy support strength of each smart park to obtain the recommendation index of each smart park includes:
obtaining the scale of each intelligent park and the grade of the policy support strength according to a preset grading rule;
obtaining recommendation indexes of the intelligent parks according to the following modes:
SSI＝G×ω1+P×ω2+Z×ω3
wherein SSI is a recommendation index, G is a scale score, P is a good rating, Z is a policy support force score, and omega1、ω2And ω3The scale, the goodness, and the weight of the policy support are the values, respectively.
Optionally, the step of recommending the smart campus to the user according to the recommendation index of each smart campus specifically includes:
sorting the recommendation indexes of the intelligent parks from high to low, acquiring and pushing information of the first N intelligent parks; wherein N is a natural number.
In a second aspect, an embodiment of the present application provides an intelligent park information pushing device, including:
the information acquisition module is used for acquiring the demand information input by the user;
the characteristic word acquisition module is used for extracting the characteristic words in the demand information;
the intelligent park information acquisition module is used for acquiring intelligent park information corresponding to the characteristic words from the server; the intelligent park information comprises the scale, the goodness rate and the policy support strength of each intelligent park;
the recommendation index calculation module is used for weighting and summing the scale, the goodness of appreciation and the policy support strength of each intelligent park to obtain the recommendation index of each intelligent park;
and the pushing module is used for recommending the intelligent park to the user according to the recommendation index of each intelligent park.
In a third aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the intelligent park information pushing method according to any one of the above-mentioned methods.
In a fourth aspect, an embodiment of the present application provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable by the processor, where the processor executes the computer program to implement the steps of the intelligent park information pushing method according to any one of the above items.
In this application embodiment, through the demand information who obtains user's input, extract the characteristic word in the demand information and follow the server and obtain with the wisdom garden information that the characteristic word corresponds, through carrying out the weighted sum to the scale of each wisdom garden, good appraisal rate and policy support dynamics, obtain the recommendation index of each wisdom garden, recommend the wisdom garden to the user with the recommendation index of each wisdom garden, calculate the recommendation index through combining the scale in the wisdom garden information, good appraisal rate and policy support dynamics information, and recommend the wisdom garden to the user according to the recommendation index of each wisdom garden, can provide more accurate intelligent recommendation for the wisdom garden of user's wisdom garden advancing parking demand.
For a better understanding and practice, the invention is described in detail below with reference to the accompanying drawings.
Drawings
Fig. 1 is a schematic view of an application scenario of an intelligent park information pushing method according to an exemplary embodiment of the present invention;
FIG. 2 is a flowchart of a method for pushing information on an intelligent campus according to an exemplary embodiment of the present invention;
fig. 3 is a schematic structural diagram of an intelligent park information pushing device according to an exemplary embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
It should be understood that the embodiments described are only some embodiments of the present application, and not all embodiments. All other examples, which can be obtained by a person skilled in the art without making any inventive step based on the embodiments in the present application, belong to the scope of protection of the embodiments in the present application.
The terminology used in the embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the embodiments of the present application. As used in the examples of this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the application, as detailed in the appended claims. In the description of the present application, it is to be understood that the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not necessarily used to describe a particular order or sequence, nor are they to be construed as indicating or implying relative importance. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
In addition, in the description of the present application, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
As shown in fig. 1, which is an application environment diagram of the intelligent campus information pushing method according to the present invention, a client 10 and a server 30 are communicatively connected through a network 20, and the steps of the intelligent campus information pushing method provided in the present application may be used to generate an intelligent campus recommendation message of the client 10.
The client 10 is used for providing an interactive interface for the user, so as to facilitate the user to input the required information. The client 10 may be a personal computer, a notebook computer, a mobile phone, a tablet computer, or other devices that can be connected to the server 30.
The server 30 is a computer cluster composed of a plurality of servers. The server 30 provides cloud storage and cloud computing functions, and is used for generating demand information input by a user, processing the demand information and generating a recommendation message of the smart park.
As shown in fig. 2, the present invention provides an intelligent park information pushing method, which includes the following steps:
step S1: and acquiring the requirement information input by the user.
The requirement information is the condition of the target smart park input by the user, for example, the requirement information may include the geographical location, the scale of the target smart park, or policy support.
In one example, the demand information may include the size of the intelligent campus, goodness, and policy support.
Step S2: and extracting the characteristic words in the demand information.
The characteristic words are keywords in the requirement information. In one embodiment, the feature words may be extracted from the requirement information by using a general keyword extraction algorithm such as TF-IDF word frequency-inverse file frequency algorithm, LDA algorithm, TextRank algorithm, and the like.
Step S3: and acquiring the intelligent park information corresponding to the feature words from the server.
Specifically, the intelligent park including the feature words in the intelligent park information is captured from the server.
Wherein the intelligent park information includes, but is not limited to, scale, goodness, and policy support strength of each intelligent park.
Step S4: carrying out weighted summation on the scale, the favorable rating and the policy support strength of each intelligent park to obtain a recommendation index of each intelligent park;
the scale of wisdom garden can include the specific information in wisdom gardens such as the office area of this wisdom garden, supporting service facility. For example, the intelligent park may be classified as being suitable for large-scale enterprises, medium-scale enterprises, or micro-scale enterprises according to the office area of the intelligent park.
The supporting service facilities are supporting facilities for work and life in the intelligent park. Such as canteens, drug stores, mini-supermarkets, basketball courts, gyms, restaurants, and the like.
The policy support strength refers to that the intelligent park obtains government support policies, including but not limited to rent subsidies, financing policies and other government support policies.
Specifically, the scale and the policy support degree of each smart campus can be graded according to a preset grading rule, then, a weight value is given to the scale, the rating rate and the policy support degree of each smart campus according to the actual demand of a user, and the scale, the rating rate and the policy support degree of each smart campus are weighted and summed to obtain a recommendation index of the smart campus.
Step S5: and recommending the intelligent park to the user according to the recommendation index of each intelligent park.
When the intelligent park is recommended, the intelligent parks with the highest recommendation indexes can be displayed at the mobile terminal, and the recommendation indexes of the intelligent parks can be sorted from high to low and displayed at the mobile terminal.
In this application embodiment, through the demand information who obtains user's input, extract the characteristic word in the demand information and follow the server and obtain with the wisdom garden information that the characteristic word corresponds, through carrying out the weighted sum to the scale of each wisdom garden, good appraisal rate and policy support dynamics, obtain the recommendation index of each wisdom garden, recommend the wisdom garden to the user with the recommendation index of each wisdom garden, calculate the recommendation index through combining the scale in the wisdom garden information, good appraisal rate and policy support dynamics information, and recommend the wisdom garden to the user according to the recommendation index of each wisdom garden, can provide more accurate intelligent recommendation for the wisdom garden of user's wisdom garden advancing parking demand.
In an exemplary embodiment, the step of extracting the feature words in the requirement information includes:
segmenting the demand information to obtain a keyword set containing a plurality of keywords;
acquiring the weight value of each keyword in the keyword set by using a word frequency-reverse file frequency algorithm;
sorting the weighted values from large to small to obtain a weighted value sorting table;
taking the first N bits in the weighted value sorting table as feature words in the demand information; wherein N is a natural number.
The segmentation is used for dividing a large text segment in the demand information into a plurality of keywords.
In one embodiment, the requirement information is segmented by a jieba segmentation tool. Jieba is a common Chinese word segmentation tool and can segment Chinese texts. In other embodiments, the demand information may also be segmented by using other commonly used segmentation tools such as SnowNLP, pkuserg, theelac, HanLP, and the like.
Preferably, before the step of segmenting the demand information, the method further includes:
and cleaning the text of the demand information, and deleting the symbols and the tone words in the demand information.
Text cleaning can be used for removing useless information such as symbols and tone words in the text, so that the data volume of the required text is reduced, and the information processing efficiency is improved.
The word frequency-reverse file frequency algorithm is a keyword extraction method for judging the importance of a word according to the frequency of the word appearing in a file and the frequency of the word appearing in a corpus so as to obtain keywords in the file.
Specifically, the step of obtaining the weight value of each keyword in the keyword set by using a word frequency-reverse file frequency algorithm includes:
calculating the word frequency of the ith keyword according to the following mode:
wherein, TFiWord frequency, N, for the ith keywordiThe number of times of the ith keyword appearing in the keyword set is shown, and N is the total number of words in the keyword set;
acquiring a plurality of documents comprising each keyword from the server as a text set;
obtaining the reverse file frequency of the ith keyword according to the following mode:
wherein, IDFiIs the reverse file frequency of the ith keyword, Y is the number of the files of the text set, Y is the reverse file frequency of the ith keywordiThe number of documents containing i keywords;
acquiring a weight value of the ith keyword in the keyword set according to the following mode:
TFIDFi＝TFi*IDFi
wherein, TFIDFiIs the weighted value of the ith keyword.
In an exemplary embodiment, the step of weighting and summing the scale, goodness and policy support of each smart campus to obtain the recommendation index of each smart campus comprises:
obtaining the scale of each intelligent park and the grade of the policy support strength according to a preset grading rule;
obtaining recommendation indexes of the intelligent parks according to the following modes:
SSI＝G×ω1+P×ω2+Z×ω3
wherein SSI is a recommendation index, G is a scale score, P is a good rating, Z is a policy support force score, and omega1、ω2And ω3The scale, the goodness, and the weight of the policy support are the values, respectively.
The preset scoring rules are used for quantifying the scale and policy support of the intelligent park. For example, the more complete the supporting facilities, the higher the scale score of the intelligent campus; the more government support policies the smart campus receives, the higher the score of the policy support effort.
The scale, the rating and the weight value of the policy support strength can be set according to the actual requirements of the user.
The step of recommending the smart park to the user according to the recommendation index of each smart park specifically comprises the following steps:
sorting the recommendation indexes of the intelligent parks from high to low, acquiring and pushing information of the first N intelligent parks;
wherein, N is a natural number, and the specific numerical value of N can be set according to the actual requirement of the user.
The information of the former N intelligent parks can be pushed to mobile terminals of a mobile phone, a notebook computer or a tablet computer and the like of a user.
As shown in fig. 3, an embodiment of the present application further provides an intelligent park information pushing apparatus, including:
the information acquisition module 1 is used for acquiring demand information input by a user;
the characteristic word acquisition module 2 is used for extracting the characteristic words in the demand information;
the intelligent park information acquisition module 3 is used for acquiring intelligent park information corresponding to the characteristic words from the server; the intelligent park information comprises the scale, the goodness rate and the policy support strength of each intelligent park;
the recommendation index calculation module 4 is used for weighting and summing the scale, the goodness of appreciation and the policy support strength of each intelligent park to obtain the recommendation index of each intelligent park;
and the pushing module 5 is used for recommending the intelligent park to the user according to the recommendation index of each intelligent park.
It should be noted that, when the intelligent park information pushing apparatus provided in the foregoing embodiment executes the intelligent park information pushing method, only the division of the function modules is taken as an example, and in practical applications, the function distribution may be completed by different function modules according to needs, that is, the internal structure of the device is divided into different function modules, so as to complete all or part of the functions described above. In addition, the intelligent park information pushing device and the intelligent park information pushing method provided by the embodiment belong to the same concept, and specific implementation processes are described in the embodiment of the method, and are not described again.
The present invention also provides a computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the intelligent park information pushing method according to any one of the above-mentioned embodiments.
Embodiments of the present application may take the form of a computer program product embodied on one or more storage media including, but not limited to, disk storage, CD-ROM, optical storage, and the like, in which program code is embodied. Computer readable storage media, which include both non-transitory and non-transitory, removable and non-removable media, may implement any method or technology for storage of information. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of the storage medium of the computer include, but are not limited to: phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, may be used to store information that may be accessed by a computing device.
The embodiment of the present application further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor executes the computer program to implement the steps of the intelligent park information pushing method according to any one of the above items.
The present invention is not limited to the above-described embodiments, and various modifications and variations of the present invention are intended to be included within the scope of the claims and the equivalent technology of the present invention if they do not depart from the spirit and scope of the present invention.
Claims (10)
1. An intelligent park information pushing method is characterized by comprising the following steps:
acquiring demand information input by a user;
extracting feature words in the demand information;
acquiring intelligent park information corresponding to the feature words from a server; the intelligent park information comprises the scale, the goodness rate and the policy support strength of each intelligent park;
carrying out weighted summation on the scale, the favorable rating and the policy support strength of each intelligent park to obtain a recommendation index of each intelligent park;
and recommending the intelligent park to the user according to the recommendation index of each intelligent park.
2. The intelligent campus information pushing method according to claim 1, wherein the step of extracting the feature words in the requirement information includes:
segmenting the demand information to obtain a keyword set containing a plurality of keywords;
acquiring the weight value of each keyword in the keyword set by using a word frequency-reverse file frequency algorithm;
sorting the weighted values from large to small to obtain a weighted value sorting table;
taking the first N bits in the weighted value sorting table as feature words in the demand information; wherein N is a natural number.
3. The intelligent campus information pushing method of claim 2 wherein said step of segmenting said demand information includes:
and utilizing a jieba word segmentation tool to segment the words of the demand information.
4. The intelligent campus information pushing method of claim 2, wherein before the step of segmenting the requirement information, further comprising:
and cleaning the text of the demand information, and deleting the symbols and the tone words in the demand information.
5. The intelligent campus information pushing method according to claim 2, wherein the step of obtaining the weight value of each keyword in the keyword set by using a word frequency-inverse file frequency algorithm comprises:
calculating the word frequency of the ith keyword according to the following mode:
wherein, TFiWord frequency, N, for the ith keywordiThe number of times of the ith keyword appearing in the keyword set is shown, and N is the total number of words in the keyword set;
acquiring a plurality of documents comprising each keyword from the server as a text set;
obtaining the reverse file frequency of the ith keyword according to the following mode:
wherein, IDFiIs the reverse file frequency of the ith keyword, Y is the number of the files of the text set, Y is the reverse file frequency of the ith keywordiThe number of documents containing i keywords;
acquiring a weight value of the ith keyword in the keyword set according to the following mode:
TFIDFi＝TFi*IDFi
wherein, TFIDFiAs the ith keywordAnd (4) weighting values.
6. The method of claim 1, wherein the step of obtaining the recommendation index of each smart campus by weighted summation of the scale, goodness and policy support of each smart campus comprises:
obtaining the scale of each intelligent park and the grade of the policy support strength according to a preset grading rule;
obtaining recommendation indexes of the intelligent parks according to the following modes:
SSI＝G×ω1+P×ω2+Z×ω3
wherein SSI is a recommendation index, G is a scale score, P is a good rating, Z is a policy support force score, and omega1、ω2And ω3The scale, the goodness, and the weight of the policy support are the values, respectively.
7. The method as claimed in claim 1, wherein the step of recommending the smart campus to the user based on the recommendation index of each smart campus comprises:
sorting the recommendation indexes of the intelligent parks from high to low, acquiring and pushing information of the first N intelligent parks; wherein N is a natural number.
8. The utility model provides a wisdom garden information pusher which characterized in that includes:
the information acquisition module is used for acquiring the demand information input by the user;
the characteristic word acquisition module is used for extracting the characteristic words in the demand information;
the intelligent park information acquisition module is used for acquiring intelligent park information corresponding to the characteristic words from the server; the intelligent park information comprises the scale, the goodness rate and the policy support strength of each intelligent park;
the recommendation index calculation module is used for weighting and summing the scale, the goodness of appreciation and the policy support strength of each intelligent park to obtain the recommendation index of each intelligent park;
and the pushing module is used for recommending the intelligent park to the user according to the recommendation index of each intelligent park.
9. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program when executed by a processor implements the steps of the intelligent park information pushing method according to any one of claims 1 to 7.
10. A computer device, characterized by: comprising a memory, a processor and a computer program stored in said memory and executable by said processor, said processor implementing the steps of the intelligent park information push method according to any one of claims 1 to 7 when executing said computer program.
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| CN202011091391.4A CN114357015A (en) | 2020-10-13 | 2020-10-13 | Smart park information pushing method and device |
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| CN202011091391.4A CN114357015A (en) | 2020-10-13 | 2020-10-13 | Smart park information pushing method and device |
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