CN115203281A

CN115203281A - Information searching method and device, electronic equipment and storage medium

Info

Publication number: CN115203281A
Application number: CN202210729187.3A
Authority: CN
Inventors: 李俊辉
Original assignee: Guangdong Power Grid Co Ltd; Dongguan Power Supply Bureau of Guangdong Power Grid Co Ltd
Current assignee: Guangdong Power Grid Co Ltd; Dongguan Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority date: 2022-06-24
Filing date: 2022-06-24
Publication date: 2022-10-18

Abstract

The invention discloses an information searching method, an information searching device, electronic equipment and a storage medium. The information searching method comprises the following steps: determining at least one type of equipment characteristic keyword according to a national standard electrical equipment model catalog; determining a fuzzy model according to various equipment characteristic keywords and a preset fuzzy model generation rule; and acquiring matched target information at an information source based on the fuzzy model. According to the embodiment of the invention, the equipment keywords are processed into the fuzzy models through the preset fuzzy model generation rule, the accuracy of fuzzy model matching is increased, the information is inquired according to the fuzzy models, the fault tolerance rate of model searching is improved, the influence on information inquiry caused by the fact that the inquiry models are not filled in a standard manner is reduced, the rationality of target information inquiry can be improved, and the use experience of a user is enhanced.

Description

Information searching method and device, electronic equipment and storage medium

Technical Field

The present invention relates to the field of computer application technologies, and in particular, to an information searching method and apparatus, an electronic device, and a storage medium.

Background

At present, information data of equipment is required to be called in electric power grid calculation and planning scheme drawing, an information database of electric power grid calculation can store model information data of the equipment, and when the equipment information data are extracted, accurate equipment models need to be inquired in the information database.

When equipment information data required by electrical computing is called in the existing mode, the target data can be generally obtained by accurately matching a target data field of a grid Geographic Information System (GIS) ledger with an equipment model in an information database.

However, because the data content of some device models in the power grid resource center is relatively incomplete, and when the model in the GIS ledger is not standardized, the device information data required by calculation cannot be correctly matched, so that a method capable of reasonably and accurately searching device target data becomes an urgent problem to be solved.

Disclosure of Invention

The invention provides an information searching method, an information searching device, electronic equipment and a storage medium, which are used for realizing fuzzy matching of equipment models and improving the accuracy of extracting target information.

According to an aspect of the present invention, an information searching method is provided, wherein the method includes:

determining at least one type of equipment characteristic keyword according to a national standard electrical equipment model catalog;

determining a fuzzy model according to various equipment characteristic keywords and a preset fuzzy model generation rule;

and acquiring matched target information at the information source based on the fuzzy model.

According to another aspect of the present invention, there is provided an information search apparatus, wherein the apparatus comprises:

the keyword acquisition module is used for determining at least one type of equipment characteristic keyword according to the national standard electrical equipment model catalog;

the model determining module is used for determining fuzzy models according to various equipment characteristic keywords and preset fuzzy model generating rules;

and the information searching module is used for acquiring matched target information from the information source based on the fuzzy model.

According to another aspect of the present invention, there is provided an electronic apparatus, wherein the electronic apparatus includes:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the information lookup method of any of the embodiments of the invention.

According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the information search method of any one of the embodiments of the present invention when the computer instructions are executed.

According to the technical scheme of the embodiment of the invention, the characteristic keywords of various types of equipment are determined through the national standard electrical equipment model catalog, the equipment keywords are processed into the fuzzy models through the preset fuzzy model generation rule, the accuracy of fuzzy model generation is ensured, and the matched target information is acquired in the information source according to the fuzzy models, so that the fault tolerance rate of model searching can be improved, and the use experience of a user is enhanced.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a flowchart of an information searching method according to an embodiment of the present invention;

fig. 2 is a flowchart of an information searching method according to a second embodiment of the present invention;

fig. 3 is a flowchart of an information searching method according to a third embodiment of the present invention;

FIG. 4 is a flowchart of exemplary parameter matching using fuzzy matching according to a third embodiment of the present invention;

FIG. 5 is a flow chart of comprehensive unit price matching using fuzzy matching according to a third embodiment of the present invention;

fig. 6 is a schematic structural diagram of an information search apparatus according to a fourth embodiment of the present invention;

FIG. 7 is a block diagram of an information searching function according to a fourth embodiment of the present invention;

fig. 8 is a schematic structural diagram of an electronic device implementing the information searching method according to the embodiment of the present invention.

Detailed Description

In order to make those skilled in the art better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example one

Fig. 1 is a flowchart of an information searching method according to an embodiment of the present invention, where the embodiment is applicable to the case of searching information, the method may be executed by an information searching apparatus, the information searching apparatus may be implemented in a form of hardware and/or software, and the information searching apparatus may be configured in a terminal device, such as a server, a mobile phone, a computer, and the like. As shown in fig. 1, the method includes:

and S110, determining at least one type of equipment characteristic keyword according to the national standard electrical equipment model catalog.

The national standard electrical equipment model catalog can be a catalog form formed by all national standard electrical equipment models, the national standard electrical equipment model catalog can comprise information of national standard electrical equipment parameters such as equipment models and equipment specification parameters, and the model parameters of any electrical equipment can be inquired through the national standard electrical equipment model catalog; the device feature key may be a feature key that distinguishes different devices, each device may have one or more device features, each device feature may generate one or more keys, for example, a device feature may include a device model, a device specification, and the like. The device characteristic keywords can be obtained by extracting sub-character strings with specific meanings from device characteristic character strings in a national standard electrical device model catalog, and the character strings with specific meanings can comprise character strings corresponding to device models, character strings corresponding to device specification parameters and the like.

Specifically, the method for determining the device feature keyword may be various, and specifically, the device feature keyword may be determined according to the national standard electrical device model directory by means of manual carding or computer extraction. The national standard electrical equipment model directory contains various equipment characteristic keywords, the extracted characteristic keywords can be one type or multiple types, the equipment characteristic keywords can comprise various keywords such as equipment model keywords and equipment specification parameter keywords, and the character strings corresponding to the equipment characteristic parameters such as equipment models and equipment specification parameters in the national standard electrical equipment model directory can be extracted to determine the character strings as the equipment characteristic keywords.

And S120, determining the fuzzy model according to the characteristic keywords of various devices and the preset fuzzy model generation rule.

The fuzzy model generation rule may be a specified method rule for generating a fuzzy model, and the fuzzy model rule may be a rule code existing in a configuration file or a specific plug-in including a model generation rule, and the like. The fuzzy model generation rule can be preset, corresponding fuzzy models can be generated according to the fuzzy model generation rule, one or more fuzzy model generation rules can be provided, when the fuzzy model generation rule is one, the fuzzy model generation rule can comprise that different types of equipment characteristic keywords are spliced to generate fuzzy models, or any type of equipment characteristic keywords can independently generate different fuzzy models; when the fuzzy model generation rule is multiple, the multiple fuzzy model generation rules can be used in combination. The fuzzy model can be a model generated by automatically carrying out fuzzy retrieval according to synonyms of keywords input by a user according to a fuzzy matching mode so as to obtain more fuzzy model retrieval results.

Specifically, the sub-character strings with specific meanings can be extracted from the national standard electrical equipment model directory character strings to obtain the equipment characteristic keywords, the character strings with specific meanings can include equipment model information, specification parameter information and the like, and the corresponding equipment characteristic keywords can be obtained by extracting the character strings with specific meanings. The device feature key may include a variety of keys, such as a device model key, a specification parameter key, and the like. The fuzzy model can be determined according to a preset fuzzy model generation rule. For example, the fuzzy model may be generated by concatenating the acquired device feature keywords in the form of text.

And S130, acquiring matched target information at an information source based on the fuzzy model.

The information source may refer to a data source matched with a fuzzy model, and may include an information database, a service, and the like, where the information source database may specifically include equipment information included in an electrical manual, a product directory of equipment of each manufacturer, and the like; the target information may be one or more types of information that the user needs to obtain, and the target information may include information such as a device model, a device integrated unit price, and device parameters.

Specifically, the information which needs to be queried by the user can be obtained in the information source through matching of the fuzzy model. The number of the information sources can be multiple, the information types included in the information sources can include information such as equipment types, equipment comprehensive unit prices, equipment parameters and the like, target information can be searched by matching corresponding character strings in the information sources through the fuzzy model character strings, the same characters in the information sources and undetermined fuzzy models can be found, and the fuzzy models containing the same substrings are subjected to character string matching to inquire target information needed by a user.

In the embodiment of the invention, at least one type of equipment characteristic key word can be determined through the national standard electrical equipment model catalog, various equipment models are processed into fuzzy models according to the preset fuzzy model generation rule, the matched target information is obtained in the information source based on the fuzzy models, the fault tolerance rate of signal query is improved, the matching rate of similar parameter models is increased, the influence of irregular filling of query models on information query is reduced, and the rationality of searching the target information is improved.

Example two

Fig. 2 is a flowchart of an information searching method according to a second embodiment of the present invention, and this embodiment is a further refinement of the information searching method in the foregoing embodiment technology. As shown in fig. 2, the method includes:

and S210, respectively extracting model parameters and specification parameters from a national standard electrical equipment model catalog.

Specifically, the national standard electrical equipment model directory may include various equipment parameters, such as model parameters, specification parameters, and the like. The extraction of the model parameters and the specification parameters can be realized by combing a national standard electrical equipment model catalog manually or by a computer, and extracting the model parameters and the sub-character strings corresponding to the specification parameters from character strings of the national standard electrical equipment model catalog to obtain the model parameters and the specification parameters.

And S220, storing the model parameters and the specification parameters into a keyword database as equipment characteristic keywords.

The keyword database may be a database for storing device characteristic parameters, the keyword database may include one or more device characteristic parameter data lists, and when the keyword database is one device characteristic parameter data list, different fields in the list may correspond to different device characteristic parameters; when the keyword database is a plurality of device characteristic parameter data lists, different lists may correspond to different device characteristic parameters. The device characteristic parameters may include model parameters, specification parameters, and the like.

Specifically, the model parameters and the specification parameters may be stored in a keyword database, and the model parameters and the specification parameters may be stored in different device characteristic parameter data lists, respectively, where each device characteristic parameter data list corresponds to one model parameter; or, the model parameters and the specification parameters may be stored in the same device feature parameter data list, different fields in the device feature parameter data list may correspond to the corresponding model parameters and specification parameters, and the keyword database storing the model parameters and the specification parameters is used as the device feature keyword.

And S230, selecting one equipment characteristic keyword from various equipment characteristic keywords respectively and splicing the selected equipment characteristic keywords into the undetermined fuzzy model.

The splicing mode of the feature device keywords may include that various device feature keywords are spliced in a one-to-one correspondence manner, and character strings corresponding to the device keywords may be directly added and connected or character strings corresponding to the device keywords may be sequentially spliced.

Specifically, the various device feature keywords may include a device model keyword and a device specification parameter keyword, the device model keyword and the device specification parameter keyword each include different sub-type keywords, one device feature keyword may be selected from the various device feature keywords, and the corresponding device feature keyword is extracted by extracting a corresponding device feature keyword character string. The selected characteristic keyword character strings can be directly added to form a new character string as an undetermined fuzzy model, or the selected characteristic keywords can be spliced according to an appointed sequence to form the undetermined fuzzy model, and the selected characteristic keywords can be spliced according to the sequence of model characteristic keywords + equipment specification keywords or equipment rule keywords + model characteristic keywords to form the undetermined fuzzy model.

S240, determining whether information which accords with the character string matching rule with the undetermined fuzzy model exists in the information source, if so, taking the undetermined fuzzy model as the fuzzy model, and if not, deleting the undetermined fuzzy model.

Specifically, it may be determined whether information matching the undetermined fuzzy model exists in the information source, the information matching the undetermined fuzzy model may be information conforming to a string matching rule, and the string matching rule may be a target string and a pattern string defined in advance. The information source can be defined as a target string, the model to be determined is defined as a mode string, the first character of the target string is matched with the first character of the mode string, and if the first character of the target string is equal to the first character of the mode string, the second character of the target string is continuously compared with the second character of the mode string; and if not, comparing the second character of the target string with the first character of the pattern string, and sequentially comparing until a final matching result is obtained. If the corresponding matching result can be found, the information matched with the undetermined fuzzy model is considered to exist in the information source; if the corresponding matching result cannot be found, the information matched with the undetermined fuzzy model does not exist in the information source. If the undetermined fuzzy model has the information of the character string matching rule, taking the undetermined fuzzy model as a fuzzy model; and if the undetermined fuzzy model does not have the information of the character string matching rule, deleting the undetermined fuzzy model.

And S250, if the fuzzy model does not exist, taking the preset default model as the fuzzy model.

The default model can be a default device parameter model which is configured in an information source in advance by a user, the default model can be a model parameter which is used as a fuzzy model when the undetermined fuzzy model does not accord with the information of the character string matching rule, and the default model can be stored in the local system or can be stored in a cloud server and other positions. Specifically, when the fuzzy model does not exist, the system may read default model parameter information stored locally in the system or stored in the cloud server as the fuzzy model. For example, the default model parameter may be a preset parameter, the parameter may be placed in a configuration file, for example, the SBH15-M-400/10, the SCB11-630/10 may all be the default model configured in advance, and the parameter of the default model may be extracted as the fuzzy model.

And S260, extracting field information of the model field in the information source.

Specifically, the field information of the model field in the information source may be extracted by inputting a text string including the model field, or the field information of the model field in the information source may be extracted by inputting a specified number of characters from the left side of the model field. The field information of the model field can be stored in the information source, and the information in the information source can be extracted by extracting the character string corresponding to the model field.

And S270, judging whether the field information is matched with the fuzzy model through the character string, if so, identifying the field information as target information, and if not, continuously extracting the field information from the model field of the signal source to perform character string matching until new field information cannot be acquired.

Specifically, whether the field information and the fuzzy model are matched with the character string or not is judged, and a target string and a mode string can be defined firstly. The fuzzy model number can be defined as a target string, the field information is defined as a mode string, a first character of the target string is matched with a first character of the mode string, and if the fuzzy model number and the field information are equal, a second character of the target string and a second character of the mode string are continuously compared; if not, comparing the second character of the target string with the first character of the mode string, sequentially comparing until the final target result field information is obtained, identifying the result field information as the target information, and stopping searching; if the matching is not successful, the field information is continuously extracted from the model field of the signal source for character string matching until new field information cannot be acquired.

And S280, fusing all target information and storing the fused target information into a preset information table.

The preset information table may be a data list storing the target information. Specifically, the acquired target information is fused and stored in a preset information table, the target information may be one or more, and multiple target information may be stored in the same preset information table. When the target information is one type, the target can be directly stored in a preset information table, when the target information is multiple types, different target information can be fused and simultaneously stored in the same preset information table, different target information can be stored in different columns or different rows in the preset information table, and information parameters of the same type can be stored in the same row or the same column in the target information table.

In the embodiment of the invention, the extracted model parameters and specification parameters are stored in a keyword database to be used as equipment characteristic keywords, one equipment characteristic keyword is respectively selected from various equipment characteristic keywords, the equipment characteristic keywords are spliced into the undetermined fuzzy model according to the characteristic keyword generation rule, the accuracy of the undetermined fuzzy model is judged, if no fuzzy model exists, the preset default model is used as the fuzzy model, the condition that the wrong undetermined fuzzy model cannot extract the target information is reduced, the fuzzy model query can extract the corresponding target information, and the use experience of a user is enhanced.

Further, determining whether information which accords with the character string matching rule with the undetermined fuzzy model exists in the information source comprises the following steps:

determining each undetermined fuzzy model to search substrings, and dividing each undetermined fuzzy model containing the same substring into the same groups; and sequentially carrying out character string matching in the signal source according to the sequence of the character string lengths from small to large aiming at the undetermined fuzzy models of the same group.

The substring search may refer to an operation of finding a substring that matches a pattern in a text given a text with a length of N and a pattern string with a length of M. The method can be understood that the information source is a section of text with the length of N, the undetermined fuzzy model is a mode character string with the length of M, and a sub character string which is consistent with the undetermined fuzzy model can be found in the information source text.

Specifically, in the process of determining whether information which accords with the character string matching rule with the undetermined fuzzy model exists in the information source, the undetermined fuzzy model can be subjected to substring search firstly, the same characters in the information source and the undetermined fuzzy model are found, the matched information is stored as a substring, each undetermined fuzzy model containing the same substring is divided into the same groups through a computer programming language, the undetermined fuzzy models of the same groups can be subjected to character string matching in the signal source, the character string matching can be performed on the fuzzy models according to the sequence of the character string length from small to large, and the accuracy of character string matching is improved.

EXAMPLE III

Fig. 3 is a flowchart of an information searching method according to a third embodiment of the present invention, and this embodiment is a specific embodiment of a typical parameter information searching method based on the above embodiments. By way of example, the ancient city that generates the fuzzy type number and matches the target information according to the fuzzy type number is explained by taking the feature root keyword of the manual combing signal as an example. As shown in fig. 3, the method comprises the steps of:

and S310, inputting a name list of national standard equipment products.

And S320, manually combing model feature keywords.

And S330, inputting keyword matching and splicing rules.

And S340, matching the target model name by using the feature keywords.

And S350, splicing the matched feature keywords to form a fuzzy model.

And S360, matching the target information through the fuzzy model.

In one embodiment, fuzzy matching of keywords based on equipment model information is achieved, firstly, feature extraction is carried out on model information of equipment such as transformers and wires, and model feature keywords can be obtained by extracting sub-character strings with specific business significance from equipment model character strings. Because the definitions of the model and the specification of the equipment are national standard, and the national standard is relatively stable in a long period and generally cannot be frequently modified, the feature extraction of the typical equipment model information can be manually sorted according to the relevant service standards to form a model feature keyword list aiming at the current model list. The model characteristic keyword list of the model information can be used in various planning tools only by being solidified in the system, and a list management module based on a rule form is required to be provided, so that the operations of increasing, deleting, modifying, checking and the like on the model characteristic keyword list are realized, and the subsequent parameter maintenance is facilitated.

In one embodiment, for extracting the model feature keywords, the model parameters of the power equipment generally consist of two parts, namely a series model and a specification parameter, and the specification parameters are more in variety, so that the model feature keywords are easy to extract quickly, the matching difficulty is reduced, the model feature keywords are not mixed with the specification parameters to be analyzed as much as possible, and the model feature keywords can be extracted respectively by being divided into two parts, namely the series model and the specification parameter. For example, for a transformer with the model number of "SBH15-M-400/10", the substring "SBH15" is a series model number, and can be generally analyzed by looking up the model name definition rules in the relevant national standard documents, and for a substring "400/10" which is a parameter relevant to the specification, the specific specification definitions and the enumeration ranges of the specifications of various types of equipment can be generally obtained by looking up the electrician manual or the name list of the equipment products of the manufacturer.

In one embodiment, the fuzzy model generation rule is utilized to analyze and match the model of the typical parameter maintained in the electric calculation tool, the fuzzy model generated by matching is fused into the typical parameter, and the subsequent typical parameter matching is based on the fuzzy model as a matching field. In the typical parameter processing stage of the equipment for electrical calculation, the same model feature keyword extraction rule and the fuzzy model generation rule are applied, the fuzzy models of the equipment within the calculation range are generated, and the generated fuzzy models are matched with the fuzzy models in the typical parameter library, so that the original accurate model matching is replaced by the fuzzy model matching, and the matching rate of the typical parameter models is improved. Similarly, the matching of the planning comprehensive unit price and the model of the project equipment is also carried out by fuzzy processing of the model to improve the matching rate of the comprehensive construction cost model and the model of the project scheme equipment.

In one embodiment, the generation rules of the fuzzy model number may include the following rules:

rule 1: the matching of keywords to the equipment name is to search the substrings of the original target model character strings through the sorted characteristic character strings (subdividing the subclass code and the specification type), but the characteristic character strings are observed to be overlapped to a certain extent (such as ZB and ZBW, 80 and 800) to bring great influence to the substring matching result.

Rule 2: according to different technical iterations, technical parameters of different iteration models of the same subdivision series are also different remarkably, so that in addition to the subdivision models shown in the table above, consideration needs to be given to also incorporating technical serial numbers into model feature keywords, such as "SCB9" and "SCB11".

Rule 3: the generation rule of the fuzzy model can be defined as: the fuzzy matching model is obtained by extracting and splicing the keywords such as SCB11-630/10 according to the keyword extraction and splicing rules, wherein the fuzzy model = model characteristic keywords (the distribution transformer contains a design serial number) + specification keywords, and the fuzzy matching model is SCB11-630.

Rule 4: the equipment classification required by the comprehensive unit price and investment estimation is rough, the classification code of the small molecule is not needed, and the fuzzy type generation rule is generated by adopting the main type classification plus the specification keyword.

And matching the character strings of the fuzzy model identification result of the typical model parameter and the fuzzy model identification result of the equipment in the calculation range according to the rule.

There are many types of transformers, and in order to distinguish between the various types of transformers, the model designations of the transformers are usually identified using letters or numbers. The types of transformers are usually composed of letters and numbers, and are used to indicate the contents of the phase number, cooling mode, voltage regulating mode, winding core material, winding connection mode, etc. of the transformers, the following table is a key card for device signal characteristics, and table 1 is the definition of the type name of the distribution transformer.

TABLE 1 model name definition of distribution transformers

Manually extracting feature keywords, for a three-phase power transformer, planning services are generally divided into two main types of oil transformer and dry transformer according to the cooling mode of the three-phase power transformer, the extraction of the corresponding model feature keywords is also divided into two main types of oil transformer and dry transformer firstly, then the main types are defined according to the model names in the table, and a product model catalog on a technical manual is referred, a plurality of sub-classifications can be derived according to different sub-classification model features through the main type classification, and the arrangement combination of the model features is obtained through manual arrangement and is shown in the following table:

TABLE 2 distribution transformer subdivision type List

TABLE 3 PRE-FITTED BOX TRANSFORMER SEPARATION MODE LIST

Table 4 distribution transformer design sequence number list

Specification type	Serial number
		Design serial number of transformer	7
Design serial number of transformer	8
		Design serial number of transformer	9
Design serial number of transformer	10
		Design serial number of transformer	11
Design serial number of transformer	12
		Design serial number of transformer	13
Design serial number of transformer	14
		Design serial number of transformer	15

TABLE 5 10kV distribution transformer capacity List

TABLE 6 list of medium voltage wire subdivision models

TABLE 7 10kV wire section List

Fig. 4 is a flowchart of exemplary parameter matching using fuzzy matching according to a third embodiment of the present invention, and this embodiment is a specific scenario application of the information search method based on the foregoing embodiments. The application scenario is exemplarily used for explaining a process adopting typical parameters of fuzzy matching. As shown in fig. 4, the method includes the steps of:

and S4010, inputting a national standard file electrician manual equipment model directory.

S4020, manually combing the model feature keywords.

And S4030, storing the characteristic type keywords which are manually carded in a warehouse.

S4040, model numbers in the typical parameter library are explained and analyzed.

S4050, inputting a fuzzy model generation and matching rule.

S4060, forming a fuzzy model matching result of the typical parameter library.

S4070, explaining and analyzing the model of the equipment in the electric calculation range to form a fuzzy model of the calculation equipment.

S4080, judging whether the fuzzy model is matched with the fuzzy model of the typical parameter library, and if so, entering S4100; if not, S4090 is entered.

And S4090, matching by adopting a default model.

S4100, acquiring a typical parameter model on matching.

S4110, fusing the standing book and the typical model parameters of the computing equipment, and ending the process.

In one embodiment, model feature keywords can be manually combed and input into a system, models in a typical parameter library are explained and searched, a fuzzy model matching result in the typical parameter library is formed according to fuzzy model generation and matching rules, and model numbers of equipment in an electrical calculation range are explained and analyzed to form a fuzzy model number of the computing equipment. After the fuzzy result is generated, judging whether the fuzzy model is matched with the fuzzy model of the typical parameter library, if so, acquiring the matched typical parameter model; and if not, matching by adopting a default model. And when the default model is used for matching, acquiring parameters of the default model, and finally fusing the standing book and the typical model parameters of the computing equipment to form a new data table so as to realize typical parameter matching by fuzzy matching.

Fig. 5 is a flowchart of comprehensive unit price matching using fuzzy matching according to a third embodiment of the present invention, and this embodiment is a specific application scenario of the information search method based on the foregoing embodiment, and exemplarily describes a process of using an application scenario as comprehensive unit price matching using fuzzy matching. As shown in fig. 5, the method includes the steps of:

and S510, explaining and analyzing the model of the project plan engineering quantity statistics.

And S520, forming a fuzzy model matching result of the engineering quantity statistics.

And S530, fuzzy matching of the project statistics and the comprehensive unit price model information.

S540, judging whether the fuzzy model is matched with the comprehensive unit price model, and if so, entering S560; if not, the process proceeds to S550.

And S550, adopting a default model to perform unit price matching.

And S560, acquiring the comprehensive unit price of the equipment from the comprehensive unit price table.

And S570, estimating the project engineering investment.

In one embodiment, the model of project quantity statistics can be explained and analyzed by using manually combed feature keywords, fuzzy matching is carried out on the project statistics and comprehensive unit price model information according to the feature keywords, whether the fuzzy model is matched with the comprehensive unit price model or not needs to be judged after fuzzy matching, and if yes, the comprehensive unit price of the equipment is obtained from a comprehensive unit price table; and if not, adopting a default model to carry out unit price matching. And when the unit price matching is carried out by using the default model, acquiring the comprehensive unit price of the default model, and finally carrying out project engineering investment estimation to realize comprehensive unit price matching by adopting fuzzy matching.

Example four

Fig. 6 is a schematic structural diagram of an information searching apparatus according to a fourth embodiment of the present invention. As shown in fig. 6, the apparatus includes: a keyword acquisition module 61, a model determination module 62 and an information search module 63.

The keyword obtaining module 61 is configured to determine at least one type of device feature keyword according to the national standard electrical device model directory.

And the model determining module 62 is configured to determine the fuzzy model according to the various device feature keywords and the preset fuzzy model generation rule.

And the information searching module 63 is used for acquiring the matched target information from the information source based on the fuzzy model.

In the embodiment of the invention, the keyword acquisition module can determine at least one type of equipment characteristic keyword through a national standard electrician equipment model catalog, the model determination module processes various types of equipment into fuzzy models according to a preset fuzzy model generation rule, the information search module inquires information according to the fuzzy models, the fault tolerance rate of model search is improved, the influence on information search due to the fact that the inquired models are not filled in the specification is reduced, the rationality of target information search can be improved, and the use experience of a user is enhanced.

Further, on the basis of the above embodiment of the present invention, the keyword obtaining module 61 includes:

and the parameter extraction unit is used for respectively extracting the model number parameters and the specification parameters from the national standard electrical equipment model catalog.

A keyword storage unit for storing the model parameter and the specification parameter into a keyword database as an apparatus characteristic keyword.

Further, on the basis of the above-described embodiment of the present invention, the model determination module 62 includes:

and the fuzzy model production unit is used for selecting one equipment characteristic keyword from various equipment characteristic keywords respectively and splicing the selected equipment characteristic keywords into the undetermined fuzzy model.

And the fuzzy model confirming unit is used for determining whether information which accords with the character string matching rule with the undetermined fuzzy model exists in the information source, if so, the undetermined fuzzy model is used as the fuzzy model, and if not, the undetermined fuzzy model is deleted.

Further, on the basis of the above embodiment of the present invention, the fuzzy model confirmation unit further includes:

and the character string grouping unit is used for determining each undetermined fuzzy model to search substrings and dividing each undetermined fuzzy model containing the same substring into the same group.

And the character string arrangement unit is used for sequentially carrying out character string matching in the signal source according to the sequence from small to large of the character string length aiming at the undetermined fuzzy models of the same group.

Further, on the basis of the above embodiment of the present invention, the information search module 63 includes:

and the field information extraction unit is used for extracting the field information of the model field in the information source.

And the field information acquisition unit is used for judging whether the field information is matched with the fuzzy model through the character string, if so, identifying the field information as target information, and if not, continuously extracting the field information from the model field of the signal source to perform character string matching until new field information cannot be acquired.

Further, on the basis of the above embodiment of the present invention, the information search apparatus further includes:

and the information table storage module is used for fusing all the target information and storing the fused target information into a preset information table.

and the default model configuration unit is used for taking the default model which is configured in advance as the fuzzy model if the fuzzy model does not exist before the fuzzy model acquires the matched target information from the information source.

Fig. 7 is a block diagram of an information search function provided according to the fourth embodiment of the present invention, and in an exemplary implementation, as shown in fig. 7, optionally, an apparatus may include: the system comprises a fuzzy matching module of equipment calculation parameters, a project scheme investment estimation module, a fuzzy model matching module of typical parameter models, a fuzzy model matching module of calculation equipment models, a scheme drawing equipment model fuzzy model matching module, a comprehensive unit price fuzzy model matching module, a keyword matching and fuzzy signal generation algorithm module, a keyword extraction rule module and a characteristic keyword management module, and realizes the function of searching information by fuzzy matching.

The fuzzy matching module of the equipment calculation parameters can be used for fuzzy model matching of typical parameter models and fuzzy model matching of calculation equipment models; the project plan investment estimation module can be used for carrying out plan drawing equipment model fuzzy matching and comprehensive unit price fuzzy model matching. The keyword may be extracted by the keyword extraction rule module, and the feature keyword may be managed by the feature keyword management module. And the fuzzy model matching can be realized according to the key matching and the fuzzy model generation algorithm module.

The information searching device provided by the embodiment of the invention can execute the information searching method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the executing method.

EXAMPLE five

Fig. 8 is a schematic structural diagram of the electronic device 10 implementing the information search method according to the embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 8, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as the information lookup method.

In some embodiments, the information lookup method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the information lookup method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the information lookup method by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a 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 the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage 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. Alternatively, the computer readable storage medium may be a machine readable signal medium. 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 compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device 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 a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can 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, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end 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 back-end, 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), blockchain networks, and the internet.

The computing system may include clients and servers. A client and server are generally 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. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.

The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An information search method, comprising:

and acquiring matched target information at an information source based on the fuzzy model.

2. The method of claim 1, wherein determining at least one type of device feature key according to the national standard electrical device model directory comprises:

respectively extracting model number parameters and specification parameters from the national standard electrical equipment model catalog;

and storing the model parameters and the specification parameters into a keyword database as the equipment characteristic keywords.

3. The method of claim 1, wherein the determining the fuzzy model according to the various types of device feature keywords and preset fuzzy model generation rules comprises:

selecting one equipment characteristic keyword from various equipment characteristic keywords respectively and splicing the selected equipment characteristic keywords into a to-be-determined fuzzy model;

and determining whether information which accords with a character string matching rule with the undetermined fuzzy model exists in the information source, if so, taking the undetermined fuzzy model as the fuzzy model, and if not, deleting the undetermined fuzzy model.

4. The method of claim 3, wherein the determining whether information is present in the information source that complies with string matching rules with the pending fuzzy model comprises:

determining each undetermined fuzzy model to search substrings, and dividing each undetermined fuzzy model containing the same substring into the same group;

and sequentially carrying out character string matching in the signal source according to the sequence of the character string lengths from small to large aiming at the undetermined fuzzy models of the same group.

5. The method of claim 1, wherein the obtaining the matched target information at the information source based on the fuzzy model number comprises:

extracting field information of a model field in the information source;

and judging whether the field information is matched with the fuzzy model by the character string, if so, identifying the field information as the target information, and if not, continuously extracting the field information from the model field of the signal source to perform character string matching until new field information cannot be acquired.

6. The method of claim 1, further comprising:

and fusing all the target information and storing the fused target information into a preset information table.

7. The method of any of claims 1-6, wherein before the information source obtains the matching target information based on the fuzzy model number, the method further comprises:

and if the fuzzy model does not exist, taking a preset default model as the fuzzy model.

8. An information lookup apparatus, comprising:

and the information searching module is used for acquiring matched target information in an information source based on the fuzzy model.

9. An electronic device, characterized in that the electronic device comprises:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the information lookup method of any one of claims 1-7.

10. A computer-readable storage medium storing computer instructions for causing a processor to implement the information lookup method of any one of claims 1-7 when executed.