WO2024066903A1 - 识别待识别医药行业目标对象的方法、设备和介质 - Google Patents

识别待识别医药行业目标对象的方法、设备和介质 Download PDF

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WO2024066903A1
WO2024066903A1 PCT/CN2023/116180 CN2023116180W WO2024066903A1 WO 2024066903 A1 WO2024066903 A1 WO 2024066903A1 CN 2023116180 W CN2023116180 W CN 2023116180W WO 2024066903 A1 WO2024066903 A1 WO 2024066903A1
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identified
word segmentation
words
original data
name
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PCT/CN2023/116180
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French (fr)
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姜金陆
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上海寰通商务科技有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/205—Parsing
    • G06F40/216—Parsing using statistical methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • Embodiments of the present disclosure generally relate to the field of data identification, and more particularly to a method, a computing device, and a computer storage medium for identifying a target object to be identified in the pharmaceutical industry.
  • Target objects in the pharmaceutical industry to be identified are, for example but not limited to, institutions in the pharmaceutical distribution field
  • Traditional methods for identifying target objects in the pharmaceutical industry to be identified generally include: purely manual identification of unknown target objects in the pharmaceutical industry to be identified; and simple word segmentation technology based on natural language processing to identify target objects in the pharmaceutical industry to be identified.
  • the recognition efficiency is not high, and there are differences in the recognition results due to the experience differences of the recognition subject. Therefore, it is difficult to adapt to the accurate and rapid recognition of large amounts of pharmaceutical industry target objects to be identified, and further difficult to adapt to the recognition needs of pharmaceutical industry service platforms for pharmaceutical industry target objects.
  • the recognition method based on simple word segmentation technology given that the raw data of pharmaceutical industry target objects are not expressed in a standardized manner and usually have obvious differences in content and structure, and the pharmaceutical industry does not have ready-made word segmentation and matching logic, the recognition accuracy of target objects is relatively low.
  • the traditional method for identifying the target object in the pharmaceutical industry is insufficient in that it is difficult to identify the target object in the pharmaceutical industry quickly and accurately.
  • the present disclosure provides a method, a computing device and a computer storage medium for identifying a target object in the pharmaceutical industry to be identified, which can quickly and accurately identify the target object in the pharmaceutical industry.
  • a method for identifying a target object in the pharmaceutical industry to be identified comprising: obtaining original data to be identified that indicates the target object in the pharmaceutical industry; identifying administrative division information and channel type information in the original data to be identified; based on the administrative division information, the channel type information, and at least one of a noise vocabulary, a semantically equivalent vocabulary, and a fixed vocabulary, performing noise removal and word segmentation on the original data to be identified so as to generate a word segmentation result, wherein the word segmentation result includes a plurality of keywords; performing hash calculation on the plurality of keywords included in the word segmentation result so as to confirm whether the word segmentation result matches a reference name; and in response to confirming that the word segmentation result does not match the reference name, performing semantic similarity analysis on the reference name and preprocessed data combined based on the word segmentation result so as to identify the target object in the pharmaceutical industry to be identified based on the result of the similarity analysis.
  • a computing device comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method of the first aspect of the present disclosure.
  • a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the method of the first aspect of the present disclosure.
  • performing a hash calculation on multiple keywords included in the word segmentation result to confirm whether the word segmentation result matches the reference name includes: calculating the sum of the hash values of the multiple keywords included in the word segmentation result to generate the sum of the word segmentation result hash values; calculating the sum of the hash values of the multiple keywords included in the reference name to generate the sum of the reference name hash values; confirming whether the sum of the word segmentation result hash value and the sum of the reference name hash values are equal; and in response to confirming that the sum of the word segmentation result hash value and the sum of the reference name hash values are equal, determining that the word segmentation result matches the reference name.
  • the method for identifying a target object in the pharmaceutical industry to be identified further includes: in response to confirming that the word segmentation result matches the reference name, identifying the target object in the pharmaceutical industry to be identified as a target object associated with the reference name.
  • generating word segmentation results includes: based on the identified administrative division information, obtaining non-administrative division data excluding the administrative division information in the original data to be identified; removing noise words and replacing them with equivalent words for the non-administrative division data; and based on a fixed vocabulary, segmenting the data after noise words are removed and replaced with equivalent words, so as to generate a word segmentation result corresponding to the original data to be identified, the word segmentation result including multiple keywords and multiple predetermined identifiers indicating segmentation positions.
  • the method for identifying a target object in the pharmaceutical industry to be identified also includes: the method for identifying a target object in the pharmaceutical industry to be identified identifies numeric words in the original data to be identified; normalizes the identified numeric words so as to segment out keywords in the form of numeric words in the original data to be identified; and combines multiple keywords included in the word segmentation results into pre-processed data without geographic information for matching with a reference name.
  • normalization processing is performed on the identified numeric words so as to segment out the keywords in the form of numeric words in the original data to be identified, including: converting uppercase Chinese numerals and/or lowercase Chinese numerals in the original data to be identified into Arabic numerals; determining whether the number of digits of the converted Arabic numerals is greater than or equal to a predetermined digit threshold; in response to determining that the number of digits of the converted Arabic numerals is greater than or equal to the predetermined digit threshold, removing the converted Arabic numerals; in response to determining that the number of digits of the converted Arabic numerals is less than the predetermined digit threshold, determining whether the converted Arabic numerals are located at the starting position or the ending position of the original data to be identified; in response to determining that the converted Arabic numerals are located at the starting position or the ending position of the original data to be identified, determining whether the data adjacent to the Arabic numerals located at the starting position or the ending position indicates a predetermined channel type
  • the channel type information includes: a channel type sub-category name, a channel type category name, and a channel type category serial number.
  • identifying administrative division information and channel type information in the original data to be identified includes: determining multiple keyword sets associated with different priority orders, each keyword set including multiple predetermined keywords; determining a target keyword set in which the predetermined keywords included in the original data to be identified are located among the multiple keyword sets; determining a channel type sub-classification name that matches the original data to be identified based on the priority order associated with the target keyword set; and determining a channel type classification name and a channel type classification serial number that match the original data to be identified based on the determined channel type sub-classification name.
  • noise removal and word segmentation for the original data to be identified include: determining multiple groups of associated words, each group of associated words includes original words and equivalent words, the original words and the equivalent words have consistent semantics when indicating target objects in the pharmaceutical industry; determining an associated sequence number and a belonging category for each group of associated words, the sequence number indicating the priority of each group of associated words; and based on the determined associated sequence number, using equivalent words to replace and segment the original data to be identified, so that the data replaced and segmented by the equivalent words includes the equivalent words and a predetermined identifier, and the predetermined identifier indicates the segmentation position.
  • performing noise removal and word segmentation on the original data to be identified includes: determining the overlapping part of the preprocessed data and the reference name; deleting the overlapping part in the preprocessed data to obtain the remaining part; in response to determining that the first predetermined credibility condition is satisfied, determining that the matching credibility level between the original data to be identified and the reference name is the first level, and the matching credibility level being the first level indicates that the original data to be identified and the reference name match, and the first predetermined condition includes any of the following: determining that the number of words included in the remaining part is less than or equal to the first word count threshold; determining that the number of words included in the remaining part is greater than the second word count threshold and the remaining part and the overlapping part are associated with the same channel type information, and the second word count threshold is greater than the first word count threshold; the number of words included in the remaining part is greater than the first word count threshold and less than the second word count threshold and the remaining part contains a pair of brackets; the remaining part contains "original" or brackets
  • performing semantic similarity analysis on the word segmentation results and the reference name also includes: in response to determining that a second predetermined credibility condition is satisfied, determining that the matching credibility level between the original data to be identified and the reference name is a second level, the second predetermined credibility condition including: determining that the word segmentation results of the preprocessed data and the reference name have overlapping parts after structural reorganization, and the channel type classification information of the preprocessed data and the reference name is the same; in response to determining that a third predetermined credibility condition is satisfied, determining a mismatch between the original data to be identified and the reference name, the third predetermined credibility condition including: the word segmentation results of the preprocessed data and the reference name have overlapping parts after structural reorganization, and the channel type classification information of the preprocessed data and the reference name is different.
  • identifying administrative division information and channel type information in the original data to be identified includes: identifying administrative division information in the name of the institution to be identified based on the full name, abbreviation, former name and exclusion words of the province, city, district and county, the administrative division information including province information, city information and district and county information; in response to confirming that the identified district and county information or city information does not indicate a unique district and county or city, using the subordinate administrative division information of the identified district and county information or city information, or the administrative division information of the associated target object of the target object to be identified to identify the administrative division information in the original data to be identified.
  • FIG. 1 shows a schematic diagram of a system for implementing a method for identifying a target object to be identified in the pharmaceutical industry according to an embodiment of the present invention.
  • FIG. 2 shows a flow chart of a method for identifying a target object to be identified in the pharmaceutical industry according to an embodiment of the present disclosure.
  • FIG3 shows a flowchart of a method for identifying administrative division information and channel type information in raw data to be identified according to an embodiment of the present disclosure.
  • FIG. 4 shows a flow chart of a method for segmenting keywords in the form of numeric words from raw data to be identified according to an embodiment of the present disclosure.
  • FIG5 shows a flow chart of a method for performing semantic similarity analysis on a word segmentation result and a reference name according to an embodiment of the present disclosure.
  • FIG. 6 shows a flowchart of a method for generating word segmentation results according to an embodiment of the present disclosure.
  • FIG. 7 shows a block diagram of an electronic device according to an embodiment of the present disclosure.
  • the term “including” and its variations mean open inclusion, i.e., “including but not limited to”. Unless otherwise stated, the term “or” means “and/or”. The term “based on” means “based at least in part on”. The terms “an example embodiment” and “an embodiment” mean “at least one example embodiment”. The term “another embodiment” means “at least one additional embodiment”. The terms “first”, “second”, etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.
  • the traditional recognition method based on pure manual work has low recognition efficiency and there are differences in recognition results due to differences in the experience of the recognition subject. Therefore, it is difficult to adapt to the accurate and rapid recognition of target objects in the pharmaceutical industry to be identified with large amounts of data, and thus cannot meet the recognition needs of the pharmaceutical industry service platform for target objects in the pharmaceutical industry.
  • the traditional recognition method based on simple word segmentation technology lacks word segmentation and pattern logic in the pharmaceutical industry, so the recognition accuracy rate for the target object is relatively low. Therefore, the traditional method for identifying target objects in the pharmaceutical industry to be identified has the disadvantage that it is difficult to quickly and accurately identify target objects in the pharmaceutical industry. For example, the traditional method for identifying target objects in the pharmaceutical industry to be identified is difficult to quickly and accurately identify "Sanmen Pharmaceutical Co., Ltd.” and "China Resources Sanmenxia Pharmaceutical Co., Ltd.”
  • an example embodiment of the present disclosure proposes a scheme for identifying a target object in the pharmaceutical industry to be identified.
  • administrative division information and channel type information are identified for the acquired original data to be identified for indicating the target object in the pharmaceutical industry, so that noise removal and word segmentation are performed on the original data to be identified based on the identified administrative division information, channel type information, and at least one of a noise vocabulary, a semantically equivalent vocabulary and a fixed vocabulary to generate a word segmentation result.
  • the present disclosure can make the word segmentation result a word segmentation result after noise removal and standardization with semantically equivalent words and/or fixed words, and assist in judging with the channel type information, thereby overcoming the problems of differences in the original data structure, irregular expressions and easy confusion of the target object in the pharmaceutical industry.
  • the present disclosure utilizes hash calculation for multiple keywords included in the word segmentation result to confirm whether the word segmentation result matches the reference name; and if it is confirmed that the word segmentation result does not match the reference name, a semantic similarity analysis is performed on the preprocessed data and the reference name generated by the word segmentation result, so as to identify the target object of the pharmaceutical industry to be identified based on the results of the similarity analysis.
  • the present disclosure can accurately identify the matching relationship between the word segmentation result and the reference name through hash calculation based on the standardized word segmentation result, and use the semantic similarity analysis results to identify the target object of the pharmaceutical industry to be identified on the basis of the failure to match. Therefore, the present disclosure can identify the target object of the pharmaceutical industry to be identified more quickly and accurately.
  • Fig. 1 shows a schematic diagram of a system 100 for implementing a method for identifying a target object in the pharmaceutical industry to be identified according to an embodiment of the present invention.
  • the system 100 includes a computing device 110, a server 130, and a network 140.
  • the computing device 110 and the server 130 can perform data exchange through the network 140 (e.g., the Internet).
  • the network 140 e.g., the Internet
  • the server 130 may send raw data to be identified and used to indicate a target object in the pharmaceutical industry to the computing device 110 .
  • the computing device 110 it is used, for example, to obtain the raw data to be identified provided by the server 130 for indicating the target object of the pharmaceutical industry; and to identify the administrative division information and channel type information in the raw data to be identified.
  • the computing device 110 can also remove noise and segment the raw data to be identified based on the administrative division information, the channel type information, and at least one of the noise word library, the semantic equivalent word library and the fixed word library, so as to generate a segmentation result; perform hash calculation on multiple keywords included in the segmentation result to confirm whether the segmentation result matches the reference name; and if it is confirmed that the segmentation result does not match the reference name, perform semantic similarity analysis on the segmentation result and the reference name, so as to identify the target object of the pharmaceutical industry to be identified based on the result of the similarity analysis.
  • the computing device 110 can have one or more processing units, including dedicated processing units such as GPU, FPGA and ASIC, and general processing units such as CPU. In addition, one or more virtual machines can also be running on each computing device 110. In some embodiments, the computing device 110 and the medical imaging device 110 can be integrated together or separately set. In some embodiments, the computing device 110 includes, for example, a raw data acquisition unit 112 for identification, an administrative division and channel type information identification unit 114, a word segmentation result generation unit 116, a hash calculation unit 118, and a pharmaceutical industry target object identification unit 120 for identification.
  • the to-be-identified original data acquisition unit 112 is used to acquire the to-be-identified original data indicating the target object of the pharmaceutical industry.
  • the administrative division and channel type information identification unit 114 is used to identify the administrative division information and channel type information in the original data to be identified.
  • the word segmentation result generating unit 116 it is used to perform noise removal and word segmentation on the original data to be identified based on administrative division information, channel type information, and at least one of a noise word library, a semantically equivalent word library and a fixed word library, so as to generate a word segmentation result, and the word segmentation result includes multiple keywords.
  • hash calculation is performed based on a plurality of keywords included in the word segmentation result to confirm whether the word segmentation result matches the reference name.
  • the pharmaceutical industry target object identification unit 120 if it is confirmed that the word segmentation result does not match the reference name, it is used to perform semantic similarity analysis on the reference name and the preprocessed data combined based on the word segmentation result, so as to identify the pharmaceutical industry target object to be identified based on the result of the similarity analysis.
  • FIG. 2 shows a flow chart of a method 200 for identifying a target object in the pharmaceutical industry to be identified according to an embodiment of the present disclosure.
  • the method 200 may be executed by the computing device 110 shown in FIG. 1, or may be executed at the electronic device 700 shown in FIG. 7. It should be understood that the method 200 may also include additional blocks not shown and/or may omit the blocks shown, and the scope of the present disclosure is not limited in this respect.
  • the computing device 110 obtains raw data to be identified for indicating a target object in the pharmaceutical industry. For example, the computing device 110 obtains raw data to be identified about an unknown organization in the pharmaceutical distribution field from the server 130 .
  • the pharmaceutical industry target object to be identified it is, for example, but not limited to, an unknown organization in the pharmaceutical distribution field.
  • the computing device 110 needs to identify which standard organization name an unknown company organization name in a pharmaceutical distribution field represents.
  • the same pharmaceutical industry target object for example, but not limited to, the same pharmaceutical store
  • the name or name of this pharmaceutical industry target object may be inconsistent in different pharmaceutical organizations (such as distributors).
  • step 204 the computing device 110 identifies administrative division information and channel type information in the original data to be identified.
  • administrative division information includes, for example, information on the affiliation of administrative agencies at the three levels of province, city, district and county.
  • a method for identifying administrative division information in original data to be identified includes: the computing device 110 identifies administrative division information in the name of an institution to be identified based on the full name, abbreviation, former name and exclusion words of provinces, cities, districts and counties, the administrative division information including province information, city information and district and county information; if it is confirmed that the identified district and county information or city information does not indicate a unique district and county or city, the administrative division information of the subordinate administrative divisions of the identified district and county information or city information, or the administrative division information of an associated target object of the target object to be identified, is used to identify the administrative division information in the original data to be identified.
  • the computing device 110 determines that the province information contained in the original data to be identified includes the full name, abbreviation, regional capital city or the former name of the provincial capital city, and does not include the excluded name of the province or the provincial capital city, then the province information is determined to be identified; if it is determined that the city information contained in the original data to be identified includes the full name, abbreviation or former name of the city, and does not include the excluded name of the city, then the city information is determined to be identified; and if it is determined that the district and county information contained in the original data to be identified includes the full name, abbreviation or former name of the district and county, and does not include the excluded name of the district and county, then the district and county information is determined to be identified; if the computing device 110 determines that any of the following items are met, then the administrative division information is determined to be identified: confirming the identification of province information, city information and district and county information; confirming the identification of administrative division information and district and county information, confirming the identification of city information and
  • the computing device 110 determines that the name of the institution to be identified contains three-level administrative agencies at the provincial, municipal, district and county levels, two-level administrative agencies at the provincial and district and county levels (for example, province + county/second-level city/district), and two-level administrative agencies at the municipal and district and county levels (for example, prefecture-level + district and county level), there is no need for subsequent detection and the administrative division information to which the name of the institution to be identified belongs can be directly identified, that is, it is considered that the province, city and county to which the name of the institution to be identified belongs has been accurately found.
  • the computing device 110 determines that the name of the organization to be identified includes the full name of a district or county or the full name of a city, and the full name of the district or county or the full name of the city is unique, then the administrative division information to which the name of the organization to be identified belongs is determined to be identified. It should be understood that cities and counties across the country are unique. If the name of the organization to be identified contains the full name or abbreviation or former name of a unique city/county, it is considered that the province, city, or county to which the name of the organization to be identified belongs can be uniquely identified.
  • the administrative division information of the lower-level administrative division information of the identified district and county information or city information, or the administrative division information of the associated target object of the target object to be identified is used to identify the administrative division information in the original data to be identified. For example, “Guoyuan Village Health Center, Yongshun Town, Tongzhou District” and “Herbal Medicine Store, Jinsha Town, Tongzhou District”, where Tongzhou District does not indicate a unique district and county, for example, Beijing includes Tongzhou, and Jiangsu province also includes Tongzhou.
  • the administrative division information in the name of the institution to be identified can be identified with the help of lower-level administrative division information (for example, the relationship between towns and districts and counties).
  • lower-level administrative division information for example, the relationship between towns and districts and counties.
  • the unique administrative division relationship "Beijing + Tongzhou + Yongshun” can be found through “Tongzhou” + “Yongshun”, and then the Tongzhou District of Beijing will be located at this time; similarly, the unique administrative division relationship "Jiangsu province + Nantong City + Tongzhou District” can be found through “Tongzhou” + “Jinsha”.
  • the name of the target object to be identified is, for example, "Beigou Health Center”. It is impossible to find the geographical information or administrative division information of the province, city, district, or county to which it belongs from "Beigou Health Center”.
  • the computing device 110 can identify that the associated target object (e.g., the seller organization "China Resources Yantai Pharmaceutical Co., Ltd.") is in Yantai, Shandong.
  • the computing device 110 can find out whether there is "Beigou” in the downstream towns in the Yantai area, and finally find that there is a Beigou town under the only "Penglai District”.
  • the computing device 110 identifies the administrative division information in the name of the institution to be identified based on the full name, abbreviation, former name and exclusion words of the province, city, district and county, and the administrative division information includes province information, city information and district and county information.
  • the following Table 2 exemplarily shows the full name, abbreviation, former name and exclusion words of the city, district and county. In Table 2, the full name, abbreviation, former name and exclusion words of the province are not shown.
  • the computing device 110 can assist in identifying administrative division information in other institution names based on exclusion words about provinces, cities, districts and counties. For example, Sanmen County, whose abbreviation is Sanmen, excludes words including: Sanmenxia, Sanmen City, the third gate, and Sanmen Store.
  • Sanmen County whose abbreviation is Sanmen, excludes words including: Sanmenxia, Sanmen City, the third gate, and Sanmen Store.
  • channel type information includes, for example: channel type sub-classification name, channel type sub-classification name and channel type classification serial number.
  • channel type information includes, for example: channel type sub-classification name, channel type sub-classification name and channel type classification serial number.
  • the institutional data of the pharmaceutical distribution industry is divided into three categories: distributors, medical terminals, and retail terminals. Each category has sub-classifications.
  • retail terminals are divided into single drug stores and chain drug store branches.
  • the institution name usually contains attribute information such as channel type information. These attribute information helps to improve the recognition accuracy of the target object in the pharmaceutical industry to be identified.
  • the retail terminal cannot identify the medical terminal. Therefore, by identifying the channel type information in the original data to be identified, it is helpful to improve the recognition accuracy of the target object in the pharmaceutical industry to be identified.
  • the computing device 110 determines a plurality of keyword sets respectively associated with different priority orders, each keyword set including a plurality of predetermined keywords; among the plurality of keyword sets, determines a target keyword set in which the predetermined keywords included in the original data to be identified are located; based on the priority order associated with the target keyword set, determines a channel type sub-classification name that matches the original data to be identified; and based on the determined channel type sub-classification name, determines a channel type classification name and a channel type classification serial number that match the original data to be identified.
  • step 206 the computing device 110 performs noise removal and word segmentation on the original data to be identified based on the administrative division information, the channel type information, and at least one of the noise word library, the semantic equivalent word library, and the fixed word library to generate a word segmentation result, which includes multiple keywords.
  • the method for performing noise removal and word segmentation on the original data to be identified includes, for example: confirming whether the preprocessed data after noise removal and normalization matches at least one of the full name, alias and former name of the reference name; if it is confirmed that the preprocessed data after noise removal and normalization does not match the full name, alias and former name of the reference name, word segmentation is performed on the preprocessed data to generate a word segmentation result.
  • the computing device 110 determines that the original data to be identified matches the reference name, and word segmentation is not required for the preprocessed data.
  • the method for generating word segmentation results includes, for example: based on the identified administrative division information, obtaining non-administrative division data other than administrative division information in the original data to be identified; removing noise words and replacing equivalent words for the non-administrative division data; and segmenting the data after noise word removal and equivalent word replacement based on a fixed vocabulary, so as to generate a word segmentation result corresponding to the original data to be identified, the word segmentation result includes multiple keywords and multiple predetermined identifiers indicating segmentation positions; identifying numeric words in the original data to be identified; normalizing the identified numeric words, so as to segment the keywords in the form of numeric words in the original data to be identified; and combining the multiple keywords included in the word segmentation result into pre-processed data without geographic information for matching with the reference name.
  • the method for performing semantic similarity analysis on the word segmentation results and the reference name will be described in detail below in conjunction with FIG. 6, and will not be repeated here.
  • the computing device 110 converts the uppercase Chinese numerals and/or lowercase Chinese numerals in the original data to be identified into Arabic numerals; determines whether the number of digits of the converted Arabic numerals is greater than or equal to a predetermined number of digits threshold; in response to determining that the number of digits of the converted Arabic numerals is greater than or equal to the predetermined number of digits threshold, removes the converted Arabic numerals; in response to determining that the number of digits of the converted Arabic numerals is less than the predetermined number of digits threshold, determines whether the converted Arabic numerals are located at the starting position or the ending position of the original data to be identified; in response to determining that the converted Arabic numerals are located at the starting position or the ending position of the original data to be identified, determines whether the data adjacent to the Arabic numerals located at the starting position or the ending position indicates
  • the computing device 110 determines multiple groups of associated words, each group of associated words includes original words and equivalent words, and the original words and the equivalent words have consistent semantics when indicating target objects in the pharmaceutical industry; determines an associated sequence number and a belonging category for each group of associated words, the sequence number indicates the priority of each group of associated words; and based on the determined associated sequence number, uses equivalent words to replace and split the original data to be identified, so that the data replaced and split by the equivalent words includes equivalent words and predetermined identifiers, and the predetermined identifier indicates the split position.
  • step 208 the computing device 110 performs hash calculation on a plurality of keywords included in the word segmentation result to confirm whether the word segmentation result matches the reference name.
  • the computing device 110 calculates the sum of the hash values of the multiple keywords included in the word segmentation result to generate the sum of the hash values of the word segmentation result; calculates the sum of the hash values of the multiple keywords included in the reference name to generate the sum of the hash values of the reference name; confirms whether the sum of the hash value of the word segmentation result and the sum of the hash value of the reference name are equal; and in response to confirming that the sum of the hash value of the word segmentation result and the sum of the hash value of the reference name are equal, determines that the word segmentation result matches the reference name.
  • the computing device 110 calculates the sum of the hash values of the multiple keywords included in the word segmentation result to generate the sum of the hash values of the word segmentation result; calculates the sum of the hash values of the multiple keywords included in the reference name to generate the sum of the hash values of the reference name; confirms whether the sum of the hash value of the word segmentation result and the sum of the hash value of the reference name are
  • the following formula (1) schematically shows an algorithm for confirming whether the word segmentation result matches the reference name.
  • ora_hash(key reference i) represents the hash value calculated for the i-th keyword included in the word segmentation result of the reference data.
  • i represents the sequence number of the keyword. represents the sum of the hash values of the reference name.
  • n represents the total number of keywords.
  • the total number of keywords n is 19.
  • ora_hash(key original i) represents the hash value calculated for the i-th keyword included in the word segmentation result of the original data to be identified. represents the sum of the hash values of the word segmentation results.
  • Table 3 schematically shows the word segmentation results of the reference name.
  • the reference name is, for example, "Fuyang Yansheng Pharmacy Retail Chain Co., Ltd. Menglian Branch", and the reference name is, for example, divided into 19 keywords from keyword 1 to keyword 19 in Table 3.
  • Table 3 schematically shows only nine of the keywords.
  • Table 4 schematically shows the word segmentation results of the original data to be identified.
  • the original data to be identified is, for example, "Fuyang Yansheng Pharmacy Retail Chain Company (Menglian)", and the original data to be identified is, for example, divided into 19 keywords from keyword 1 to keyword 19 in Table 4.
  • Table 4 schematically shows only nine of the keywords.
  • the above raw data to be identified "Fuyang Yansheng Pharmacy Retail Chain Company (Menglian)" is de-noised and segmented, the word order is broken, and the upper and lower case are normalized to generate 19 keywords from keyword 1 to keyword 19 in Table 4.
  • the sum of the hash values of all keywords from keyword 1 to keyword 19 of the segmentation results of the raw data to be identified i.e., the sum of the hash values of the segmentation results
  • the reference name refers to the standard target object name
  • the following is an exemplary program code for implementing an algorithm for confirming whether a word segmentation result matches a reference name.
  • o.orgname as stdorgname, 2as status, 2as gradelevel, length(o.orgname)asorglen,
  • step 210 if the computing device 110 confirms that the word segmentation result does not match the reference name, a semantic similarity analysis is performed on the reference name and the preprocessed data combined based on the word segmentation result, so as to identify the target object of the pharmaceutical industry to be identified based on the result of the similarity analysis. For example, if the computing device 110 confirms that the word segmentation result matches the reference name, the target object of the pharmaceutical industry to be identified is identified as a target object associated with the reference name.
  • the method for performing semantic similarity analysis on word segmentation results and reference names includes, for example: determining the overlapping part of preprocessed data and reference names; deleting the overlapping part in the preprocessed data to obtain the remaining part; in response to determining that a first predetermined credibility condition is satisfied, determining that the matching credibility level between the original data to be identified and the reference name is the first level, and the matching credibility level being the first level indicates that the original data to be identified and the reference name match each other, and the first predetermined condition includes any of the following: determining that the number of words included in the remaining part is less than or equal to a first word count threshold; determining that the number of words included in the remaining part is greater than a second word count threshold and the remaining part and the overlapping part are associated with the same channel type information, and the second word count threshold is greater than the first word count threshold; the number of words included in the remaining part is greater than the first word count threshold and less than the second word count threshold and the remaining part contains a pair of brackets; the remaining part
  • the computing device 110 can adjust the weight of the semantic similarity analysis of the word segmentation result and the reference name based on the channel type information, so as to perform a semantic similarity analysis on the word segmentation result and the reference name based on the adjusted weight.
  • administrative division information and channel type information are identified for the original data to be identified that are obtained and used to indicate the target object of the pharmaceutical industry, so that noise removal and word segmentation are performed on the original data to be identified based on the identified administrative division information, channel type information, and at least one of the noise word library, semantic equivalent word library and fixed word library to generate a word segmentation result.
  • the present disclosure can make the word segmentation result a word segmentation result after noise removal and standardization through semantic equivalent words and/or fixed words, and assist in judging the channel type information, thereby overcoming the problems of differences in the original data structure, irregular expressions and easy confusion of the target objects of the pharmaceutical industry.
  • the present disclosure utilizes hash calculation for multiple keywords included in the word segmentation result to confirm whether the word segmentation result matches the reference name; and if it is confirmed that the word segmentation result does not match the reference name, a semantic similarity analysis is performed on the preprocessed data and the reference name generated by the word segmentation result, so as to identify the target object of the pharmaceutical industry to be identified based on the results of the similarity analysis.
  • the present disclosure can accurately identify the matching relationship between the word segmentation result and the reference name through hash calculation based on the standardized word segmentation result, and use the semantic similarity analysis results to identify the target object of the pharmaceutical industry to be identified on the basis of the failure to match. Therefore, the present disclosure can identify the target object of the pharmaceutical industry to be identified more quickly and accurately.
  • FIG3 shows a flow chart of a method 300 for identifying administrative division information and channel type information in raw data to be identified according to an embodiment of the present disclosure.
  • the method 300 may be executed by the computing device 110 shown in FIG1 , or may be executed at the electronic device 700 shown in FIG7 . It should be understood that the method 300 may also include additional boxes not shown and/or the boxes shown may be omitted, and the scope of the present disclosure is not limited in this respect.
  • step 302 the computing device 110 determines a plurality of keyword sets respectively associated with different priority orders, each keyword set including a plurality of predetermined keywords.
  • the keyword set used to identify the channel type classification includes, for example, a keyword set for identifying chain drug stores, a keyword set for identifying single drug stores, a keyword set for identifying chain companies, a keyword set for identifying hospitals, and a keyword set for identifying health inspection institutes.
  • Table 3 exemplifies a keyword set for identifying chain drug stores and a keyword set for identifying single drug stores.
  • the computing device 110 determines the target keyword set where the predetermined keyword included in the original data to be identified is located from the plurality of keyword sets. For example, if the computing device 110 determines that the original data to be identified includes the predetermined keyword "% retail center %", and does not include "% chain % store %", then the target keyword set where the predetermined keyword is located is the keyword set in the second row of Table 5.
  • the computing device 110 determines the channel type sub-category name that matches the original data to be identified based on the priority order associated with the target keyword set. For example, the priority order associated with the keyword set in the second row of Table 5 is 18, and the computing device 110 determines that the channel type sub-category name that matches the original data to be identified is "single-store pharmacy" based on the priority order 18. It should be understood that when identifying the channel type, based on the priority order associated with the target keyword set, the one that meets the conditions is given priority, that is, it is considered that "the channel type sub-category name that matches the original data to be identified is located.”
  • the computing device 110 determines the channel type classification name and channel type classification serial number that match the original data to be identified based on the determined channel type sub-classification name. For example, the computing device 110 determines that the channel type classification name that matches the original data to be identified is "terminal drugstore" and the channel type classification serial number is "114" based on the determined channel type sub-classification name "monomer drugstore".
  • the present disclosure can accurately determine the channel type to which the original data to be identified belongs, which is conducive to improving the accuracy of identifying target objects in the pharmaceutical industry based on the accurate channel type.
  • FIG. 4 shows a flow chart of a method 400 for segmenting out keywords in the form of digital words in raw data to be identified according to an embodiment of the present disclosure.
  • the method 400 may be executed by the computing device 110 shown in FIG. 1, or may be executed at the electronic device 700 shown in FIG. 7. It should be understood that the method 400 may also include additional blocks not shown and/or may omit the blocks shown, and the scope of the present disclosure is not limited in this respect.
  • the computing device 110 converts the uppercase Chinese numerals and/or lowercase Chinese numerals in the raw data to be identified into Arabic numerals.
  • the raw data to be identified may contain uppercase and lowercase numerals, telephone numbers, and zip codes. These numerals may appear in front of, behind, or in the middle of the name of the target object to be identified.
  • the computing device 110 can convert all uppercase and lowercase Chinese numerals that appear in the raw data to be identified into Arabic numerals, for example, "one hundred and fifty-one", “one hundred and fifty-one", or "one hundred and fifty-one” are ultimately converted into the Arabic numeral "151".
  • the computing device 110 determines whether the number of digits of the converted Arabic numeral is greater than or equal to a predetermined digit threshold.
  • the predetermined number of digits threshold it is, for example but not limited to, a number of 6 or more.
  • the converted Arabic numeral is removed in step 406. For example, if it is determined that the number of digits of the converted Arabic numeral is greater than or equal to 6 (or 6 or more), the converted Arabic numeral is directly removed regardless of where it appears. This is because the converted Arabic numeral may be information such as a telephone number or a zip code.
  • step 408 if the computing device 110 determines that the number of digits of the converted Arabic numeral is less than a predetermined threshold, it is determined whether the converted Arabic numeral is located at the start position or the end position of the original data to be recognized.
  • step 410 if the computing device 110 determines that the converted Arabic numeral is located at the starting position or the ending position of the original data to be identified, it is determined whether the data adjacent to the Arabic numeral located at the starting position or the ending position indicates a predetermined channel type. For example, if the computing device 110 determines that the converted Arabic numeral is less than a predetermined threshold and appears at the starting position of the name of the target object to be identified, the converted Arabic numeral may also be removed because the converted Arabic numeral is likely to be a serial number accidentally added when providing the name of the target object.
  • the process jumps to step 406 to remove the converted Arabic numeral.
  • step 412 if the computing device 110 determines that the data adjacent to the Arabic numeral located at the start position or the end position indicates a predetermined channel type, the converted Arabic numeral is not removed.
  • the computing device 110 determines that the converted Arabic numerals are located at the starting position or the ending position of the original data to be identified, and the converted Arabic numerals are not followed by the drugstore type or the medical institution type (the channel type is not a drugstore), the Arabic numerals can be removed; if the computing device 110 determines that the Arabic numerals located at the ending position appear at the ending position, and the converted Arabic numerals are followed by the drugstore type or the medical institution type and the converted Arabic numerals are less than or equal to the predetermined digital threshold, the Arabic numerals cannot be removed.
  • the example “56 Store Wang Zhiheng” in Table 6 below where the Arabic numeral "56" is located at the starting position of the original data to be identified, and the converted Arabic numeral "56” is followed by the drugstore type, and at the same time, the Arabic numeral "56" is less than the predetermined digital threshold associated with the municipal pharmaceutical company. At this time, the computing device 110 determines that the Arabic numeral "56" cannot be removed.
  • the Arabic numeral "56” is located at the starting position of the original data to be identified, and the converted Arabic numeral "56” is followed by the drugstore type. At the same time, the Arabic numeral "56" is less than the predetermined digital threshold associated with the municipal pharmaceutical company. At this time, the computing device 110 determines that the Arabic numeral "56" cannot be removed.
  • the Arabic numeral "50" or “1” is located at the end position of the original data to be identified, and the converted Arabic numeral “50” or “1” is followed by the type of medical institution. Assuming that the Arabic numeral "50” is greater than the predetermined digital threshold associated with the township health center, at this time, the computing device 110 determines to remove the Arabic numeral "50”; and the Arabic numeral "1" is less than the predetermined digital threshold associated with the township health center. At this time, the computing device 110 determines that the Arabic numeral "1" cannot be removed.
  • the present disclosure can accurately identify and remove digital noise in the original data to be identified, and is conducive to accurately segmenting digital keywords that are conducive to identifying the target object.
  • FIG5 shows a flow chart of a method 500 for performing semantic similarity analysis on a word segmentation result and a reference name according to an embodiment of the present disclosure.
  • the method 500 may be executed by the computing device 110 shown in FIG1 , or may be executed at the electronic device 700 shown in FIG7 . It should be understood that the method 500 may also include additional blocks not shown and/or may omit the blocks shown, and the scope of the present disclosure is not limited in this respect.
  • computing device 110 determines the overlap between the pre-processed data and the reference name.
  • step 504 the computing device 110 removes the overlapping parts from the pre-processed data to obtain a remaining part.
  • the preprocessed data is "Xuanhua District Tian Zhidong Clinic 1" and the reference name is "Xuanhua District Tian Zhidong Clinic”.
  • the overlapped part of the preprocessed data and the reference name is "Xuanhua District Tian Zhidong Clinic”.
  • the remaining part after deleting the overlapped part in the preprocessed data is "1"
  • the matching credibility level between the original data to be identified and the reference name is determined to be the first level, and the matching credibility level being the first level indicates that the original data to be identified and the reference name match each other, and the first predetermined condition includes any one of the following: determining that the number of words included in the remaining part is less than or equal to the first word count threshold; determining that the number of words included in the remaining part is greater than the second word count threshold and the remaining part and the overlapping part are associated with the same channel type information, and the second word count threshold is greater than the first word count threshold; the number of words included in the remaining part is greater than the first word count threshold and less than the second word count threshold and the remaining part contains a pair of brackets; the remaining part contains "original” or brackets and "original”; or the remaining part contains a pair of brackets and the number of words in the brackets is less than the third word count threshold, and the third word count threshold is greater than the first word count threshold
  • the first word count threshold it is, for example, but not limited to, 2.
  • the number of words included in the above-mentioned remaining part "1" is less than the first word count threshold, and the matching credibility level between the original data to be identified and the reference name is determined to be the first level, for example, the matching similarity is 100%, that is, the original data to be identified and the reference name match.
  • the second word count threshold it is, for example, but not limited to, 10.
  • the overlapping part between the pre-processed data "Qitaihe Yuanfu Pharmacy (Qitaihe Yuanhongfu Medical Equipment Store)" and the reference name “Qitaihe Yuanfu Pharmacy” is "Xuanhua District Tian Zhidong Clinic”.
  • the remaining part after deleting the overlapping part in the pre-processed data is "(Qitaihe Yuanhongfu Medical Equipment Store)”.
  • the matching credibility level between the original data to be identified and the reference name is determined to be the first level, for example, the matching similarity is 98%, that is, the original data to be identified and the reference name are highly similar, and thus match.
  • the overlapping part between the preprocessed data "Kangbei Village Health Center, Kangcun Town, Huojia County (formerly Kangbei Joint Health Center)" and the reference name “Kangbei Village Health Center, Kangcun Town” is "Kangbei Village Health Center, Kangcun Town”.
  • the remaining part after deleting the overlapping part in the preprocessed data is "Huojia County (formerly Kangbei Joint Health Center)”. If the remaining part contains "original” or "(original), it is determined that the matching credibility level between the original data to be identified and the reference name is the first level, for example, the matching similarity is 99%, that is, the original data to be identified and the reference name are highly similar, and therefore match.
  • the third word count threshold it is, for example but not limited to, 4.
  • the overlapped part between the preprocessed data "NE Zhongshan Sanxiang Town Xianghongtang Drug Retail Store (06)" and the reference name "Zhongshan Sanxiang Town Xianghongtang Drug Retail Store” is "Zhongshan Sanxiang Town Xianghongtang Drug Retail Store”.
  • the matching credibility level between the original data to be identified and the reference name is the first level, for example, the matching similarity is 96%, that is, the original data to be identified and the reference name are highly similar, and thus matched.
  • the overlapped part between the preprocessed data "Xiamen Huli Dingling Doctors' First Clinic Co., Ltd.” and the reference name "Xiamen Huli Dingling Doctors' First Clinic” is "Xiamen Huli Dingling Doctors' First Clinic”.
  • the two have completely overlapped parts and the two have the same channel sub-category name, then the matching credibility level between the original data to be identified and the reference name is determined to be the first level, for example, the matching similarity is 70%, that is, the original data to be identified and the reference name have a high similarity, and thus match.
  • the matching confidence level is a first level, which indicates, for example, that the matching similarity is between 70% and 100%.
  • step 508 if the computing device 110 determines that the second predetermined credibility condition is met, the matching credibility level between the original data to be identified and the reference name is determined to be the second level, and the second predetermined credibility condition includes: determining that the word segmentation results of the preprocessed data and the reference name have overlapping parts after structural reorganization, and that the channel type classification information of the preprocessed data and the reference name is the same.
  • the preprocessed data is "Luji Town Health Center (Chengbei Village Health Center) in Panji District, Huainan City", and there is an overlap between the reference name "Luji Town Chengbei Village Health Center”.
  • the word segmentation result of the preprocessed data "Luji Town Health Center (Chengbei Village Health Center) in Panji District, Huainan City” is "Luji Health (Chengbei Health)" after structural reorganization.
  • the word segmentation result "(Chengbei Health)" after structural reorganization is included in the reference name "Luji Chengbei Health", and the channel type classification information of the preprocessed data and the reference name is the same, and the matching credibility level between the original data to be identified and the reference name is determined to be the second level, for example, the matching similarity is 65%, that is, there is a certain similarity between the original data to be identified and the reference name.
  • a second level of match confidence may be considered as a match between the pre-processed data and the reference name.
  • step 510 if the computing device 110 determines that the third predetermined credibility condition is met, a mismatch between the original data to be identified and the reference name is determined, and the third predetermined credibility condition includes: the word segmentation results of the preprocessed data and the reference name have overlapping parts after structural reorganization, and the channel type classification information of the preprocessed data and the reference name is different.
  • the present disclosure can quickly and accurately identify whether the original data to be identified and the reference name match when there is a difference between the pre-processed data and the reference name.
  • FIG6 shows a flowchart of a method 600 for generating a word segmentation result according to an embodiment of the present disclosure.
  • the method 600 may be executed by the computing device 110 shown in FIG1 , or may be executed at the electronic device 700 shown in FIG7 . It should be understood that the method 600 may also include additional blocks not shown and/or may omit the blocks shown, and the scope of the present disclosure is not limited in this respect.
  • step 602 the computing device 110 obtains non-administrative division data excluding the administrative division information from the original data to be identified based on the identified administrative division information.
  • step 604 the computing device 110 removes noise words and replaces equivalent words for the non-administrative division data.
  • Equivalent words are words that can be regarded as equivalent in the medical industry, or words that are semantically equivalent. For example, “Qihe Township Lizhuang Health Center” and “Qihe Township Lizhuang Health Center” have only one difference in the word segmentation structure between "Health Center” and "Health Center”. In reality, the two names usually belong to the same type and can be regarded as equivalent in the medical industry, and belong to "equivalent words”.
  • the equivalent word library includes a large amount of equivalent words, which are classified through manual annotation or based on machine learning.
  • Table 6 schematically shows some equivalent words in the equivalent word library.
  • the computing device 110 determining multiple groups of associated words, each group of associated words includes original words and equivalent words, the original words and the equivalent words have consistent semantics when indicating target objects in the pharmaceutical industry; determining an associated sequence number and a belonging classification for each group of associated words, the sequence number indicating the priority of each group of associated words; and based on the determined associated sequence number, using equivalent words to replace and split the original data to be identified, so that the data replaced and split by the equivalent words includes the equivalent words and a predetermined identifier, and the predetermined identifier indicates the split position.
  • the predetermined identifier indicates that the corresponding position is a segmentation position.
  • the computing device 110 uses equivalent words to replace and segment the original data to be identified according to the "sequence number" (for example, as shown in Table 6, generally speaking, the original word with a large length has a high priority).
  • the original word "Shop Co., Ltd.” in “Suzhou Huiren Pharmaceutical Store Co., Ltd.” is replaced and segmented by the equivalent word %Company%.
  • the data replaced and segmented by the equivalent word is, for example, "Suzhou Huiren Pharmaceutical % Company”.
  • the computing device 110 is based on a fixed word library, and the data after noise word removal and equivalent word replacement is segmented to generate a segmentation result corresponding to the original data to be identified, and the segmentation result includes multiple keywords and multiple predetermined identifiers indicating the segmentation position.
  • fixed words includes, for example, at least: the full name and abbreviation of provinces, cities, districts and counties, and other conventional fixed phrases. For example, in the name of the institution "Beijing Normal University affiliated Middle School Health Station", the words Normal University, affiliated, middle school, and health station are fixed words and do not need to be split again.
  • the computing device 110 removes noise words and replaces equivalent words, based on the ASCII code table, it is confirmed whether the ASCII value of each character in the data after noise word removal and equivalent word replacement is outside the first predetermined numerical range (for example, 48 to 57), so that all characters outside the first predetermined numerical range (for example, 48 to 57) are removed.
  • the main reason for adopting the above means is that a number of noise words can be removed by removing noise words and replacing them with equivalent words, but letters, hyphens and the like often appear in the names of institutions. China's medical retail institutions and medical terminals do not contain uppercase and lowercase letters or other English symbols. Therefore, except for Chinese and numbers, other symbols need to be removed. Therefore, through the above means, the present disclosure can further filter out noise words.
  • the method for segmenting the data after noise words are removed and replaced by equivalent words includes, for example: after the computing device 110 completes the replacement of noise words and equivalent words, it uses fixed words to "segment" the original data to be identified. Take “Suzhou Huiren Pharmaceutical Store Co., Ltd.” as an example, after equivalent word replacement and segmentation, it becomes “Suzhou Huiren Pharmaceutical% Company”; after being segmented through a fixed word library, it becomes “Suzhou% Huiren% Pharmaceutical% Company", where "Suzhou is geographic information, which will be intercepted separately and stored separately, and the remaining parts are separated one by one to generate the word segmentation results corresponding to the original data to be identified.
  • Table 7 illustrates the analysis results of the original data to be identified "Suzhou Huiren Pharmaceutical Store Co., Ltd.”
  • Table 8 illustrates the word segmentation results of the original data to be identified "Pharmaceutical Store Co., Ltd. (Suzhou Huiren)”.
  • Table 9 illustrates the word segmentation results of the original data to be identified "Suzhou Huiren Pharmaceutical Trading Co., Ltd.”
  • step 608 the computing device 110 identifies numeric words in the raw data to be identified.
  • step 610 the computing device 110 performs normalization processing on the recognized digital words to segment the digital word form keywords in the original data to be recognized.
  • the method for segmenting the digital word form keywords in the original data to be recognized has been described above in conjunction with FIG.
  • step 612 the computing device 110 combines the multiple keywords included in the word segmentation result into pre-processed data without geographic information for matching with the reference name.
  • the computing device 110 may combine the segmented multiple keywords into pre-processed data without geographic information for matching with the reference name. For example, taking “Suzhou Huiren Pharmaceutical Store Co., Ltd.” as an example, the pre-processed data without geographic information may be "Herien Pharmaceutical Company" for matching with the reference name.
  • FIG7 schematically shows a block diagram of an electronic device 700 suitable for implementing an embodiment of the present invention.
  • the electronic device 700 may be used to implement the methods 200 to 600 shown in FIG2 to FIG6.
  • the electronic device 700 includes a central processing unit (i.e., CPU 701), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (i.e., ROM 702) or computer program instructions loaded from a storage unit 708 to a random access memory (i.e., RAM 703).
  • RAM 703 various programs and data required for the operation of the electronic device 700 may also be stored.
  • the CPU 701, ROM 702, and RAM 703 are connected to each other via a bus 704.
  • An input/output interface i.e., I/O interface 705 is also connected to the bus 704.
  • the CPU 701 performs the various methods and processes described above, such as executing methods 200 to 600.
  • methods 200 to 600 may be implemented as a computer software program, which is stored in a machine-readable medium, such as a storage unit 708.
  • part or all of the computer program may be loaded and/or installed on the electronic device 700 via the ROM 702 and/or the communication unit 709.
  • the CPU 701 may be configured to perform one or more actions of the methods 200 to 600 by any other appropriate means (e.g., by means of firmware).
  • the present invention may be a method, an apparatus, a system and/or a computer program product.
  • the computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.
  • Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device.
  • Computer readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof.
  • Non-exhaustive list of computer readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination thereof.
  • RAM random access memory
  • ROM read-only memory
  • EPROM or flash memory erasable programmable read-only memory
  • SRAM static random access memory
  • CD-ROM compact disk read-only memory
  • DVD digital versatile disk
  • memory stick a floppy disk
  • mechanical encoding device for example, a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination thereof.
  • the computer readable storage medium used here is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (for example, a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.
  • the computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing/processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and/or a wireless network.
  • the network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and/or edge servers.
  • the network adapter card or network interface in each computing/processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing/processing device.
  • the computer program instructions for performing the operation of the present invention may be assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as "C" language or similar programming languages.
  • Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server.
  • the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet).
  • an electronic circuit such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present invention.
  • These computer-readable program instructions can be provided to a processor in a voice interaction device, a general-purpose computer, a special-purpose computer, or a processing unit of other programmable data processing devices, thereby producing a machine, so that when these instructions are executed by a processing unit of a computer or other programmable data processing device, a device that implements the functions/actions specified in one or more boxes in the flowchart and/or block diagram is generated.
  • These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions enable the computer, programmable data processing device, and/or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions/actions specified in one or more boxes in the flowchart and/or block diagram.
  • Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions/actions specified in one or more boxes in the flowchart and/or block diagram.
  • each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of this module, program segment or instruction includes one or more executable instructions for realizing the specified logical function.
  • the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved.
  • each square box in the block diagram and/or flow chart, and the combination of the square boxes in the block diagram and/or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

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Abstract

一种用于识别待识别医药行业目标对象的方法，该方法包括：获取用于指示医药行业目标对象的待识别原始数据；识别待识别原始数据中的行政区划信息和渠道类型信息；基于行政区划信息、渠道类型信息、以及噪音词库、语义等价词库和固定词库中的至少一个词库，针对待识别原始数据进行噪音去除和分词，以便生成分词结果；针对分词结果所包括的多个关键词进行哈希计算，以便确认分词结果与参考名称是否匹配；以及响应于确认分词结果与参考名称不匹配，针对参考名称和基于分词结果所组合的预处理数据进行语义相似性分析，以便识别待识别医药行业目标对象。由此，能够快速并且准确地针对医药行业目标对象进行识别。还公开了一种用于识别待识别医药行业目标对象的设备和介质。

Description

识别待识别医药行业目标对象的方法、设备和介质 技术领域
本公开的实施例总体涉及数据识别领域，并且更具体地涉及一种用于识别待识别医药行业目标对象的方法、计算设备和计算机存储介质。
 背景技术
传统的用于识别待识别医药行业目标对象(待识别医药行业目标对象例如而不限于是医药分销领域中的机构)的方法通常包括：基于纯粹人工对未知的待识别医药行业目标对象进行识别；以及基于自然语言处理的简单分词技术对待识别医药行业目标对象进行识别两种方法。
关于基于纯粹人工的识别方法，其虽然能够识别不规范的医药行业目标对象的原始数据，但是识别效率不高，并且存在因识别主体的经验差异而使得识别结果存在差异性，因此，难以适应大数据量的待识别医药行业目标对象的准确、快速识别，进而难以适应医药行业的服务平台对医药行业目标对象的识别需求。关于基于简单分词技术的识别方法，鉴于医药行业目标对象的原始数据表达得不规范，并且通常在内容和结构上存在明显的差异，加之医药行业没有现成的分词与匹配逻辑，因此使得目标对象的识别准确率相对较低。
综上，传统的用于识别待识别医药行业目标对象的方法存在的不足之处在于：难以快速并且准确地针对医药行业目标对象进行识别。
发明内容
针对上述问题，本公开提供了一种用于识别待识别医药行业目标对象的方法、计算设备和计算机存储介质，能够快速并且准确地针对医药行业目标对象进行识别。
根据本公开的第一方面，提供了一种用于识别待识别医药行业目标对象的方法，包括：获取用于指示医药行业目标对象的待识别原始数据；识别待识别原始数据中的行政区划信息和渠道类型信息；基于行政区划信息、渠道类型信息、以及噪音词库、语义等价词库和固定词库中的至少一个词库，针对待识别原始数据进行噪音去除和分词，以便生成分词结果，分词结果包括多个关键词；针对分词结果所包括的多个关键词进行哈希计算，以便确认分词结果与参考名称是否匹配；以及响应于确认分词结果与参考名称不匹配，针对参考名称和基于分词结果所组合的预处理数据进行语义相似性分析，以便基于相似性分析的结果识别待识别医药行业目标对象。
根据本公开的第二方面，提供了一种计算设备，包括：至少一个处理器；以及与至少一个处理器通信连接的存储器；存储器存储有可被至少一个处理器执行的指令，指令被至少一个处理器执行，以使至少一个处理器能够执行本公开的第一方面的方法。
在本公开的第三方面中，提供了一种存储有计算机指令的非瞬时计算机可读存储介质，其中计算机指令用于使计算机执行本公开的第一方面的方法。
在一些实施例中，针对分词结果所包括的多个关键词进行哈希计算，以便确认分词结果与参考名称是否匹配包括：计算分词结果所包括的多个关键词的哈希值之和，以便生成分词结果哈希值之和；计算参考名称所包括的多个关键词的哈希值之和，以便生成参考名称哈希值之和；确认分词结果哈希值之和参考名称哈希值之和是否相等；以及响应于确认分词结果哈希值之和参考名称哈希值之和相等，确定分词结果与参考名称匹配。
在一些实施例中，用于识别待识别医药行业目标对象的方法还包括：响应于确认分词结果与参考名称相匹配，将待识别医药行业目标对象识别为与参考名称相关联的目标对象。
在一些实施例中，生成分词结果包括：基于所识别出的行政区划信息，获取待识别原始数据中除去行政区划信息之外的非行政区划数据；针对非行政区划数据，进行噪音词去除和等价词替换；以及基于固定词库，针对经由噪音词去除和等价词替换后的数据进行分割，以便生成与待识别原始数据对应的分词结果，分词结果包括多个关键词和指示分割位的多个预定标识符。
在一些实施例中，用于识别待识别医药行业目标对象的方法还包括：用于识别待识别医药行业目标对象的方法识别待识别原始数据中的数字型的词；针对所识别的数字型的词进行归一化处理，以便分割出待识别原始数据中的数字词形式的关键词；以及把分词结果所包括的多个关键词组合成不含地理信息的预处理数据，以用于与参考名称的匹配。
在一些实施例中，针对所识别的数字型的词进行归一化处理，以便分割出待识别原始数据中的数字词形式的关键词包括：将待识别原始数据中的大写中文数字和/或小写中文数字转换为阿拉伯数字；确定经转换的阿拉伯数字的位数是否大于或者等于预定位数阈值；响应于确定经转换的阿拉伯数字的位数大于或者等于预定位数阈值，去除经转换的阿拉伯数字；响应于确定经转换的阿拉伯数字的位数小于预位数定阈值，确定经转换的阿拉伯数字是否位于待识别原始数据的起始位置或者终止位置；响应于确定经转换的阿拉伯数字位于待识别原始数据的起始位置或者终止位置，确定与位于起始位置或者终止位置的阿拉伯数字位相邻的数据是否指示预定渠道类型；以及响应于确定与位于起始位置或者终止位置的阿拉伯数字位相邻的数据未指示预定渠道类型，去除经转换的阿拉伯数字。
在一些实施例中，渠道类型信息包括：渠道类型子分类名称、渠道类型分类名称和渠道类型分类序号。
在一些实施例中，识别待识别原始数据中的行政区划信息和渠道类型信息包括：确定分别与不同优先级顺序关联的多个关键词集合，每个关键词集合包括多个预定关键词；在多个关键词集合中，确定待识别原始数据中所包括的预定关键词所在的目标关键词集合；基于目标关键词集合所关联的优先级顺序，确定与待识别原始数据匹配的渠道类型子分类名称；以及基于所确定的渠道类型子分类名称，确定与待识别原始数据匹配的渠道类型分类名称和渠道类型分类序号。
在一些实施例中，针对待识别原始数据进行噪音去除和分词包括：确定多组关联词，每组关联词包括原始词和等价词，原始词和等价词在指示医药行业目标对象时具有一致的语义；为每组关联词确定关联的顺序号和所属分类，顺序号指示每组关联词的优先级；以及基于所确定的关联的顺序号，使用等价词替换和分割待识别原始数据，使得经由等价词替换和分割的数据中包括等价词和预定标示符，预定标示符指示分割位。
在一些实施例中，针对待识别原始数据进行噪音去除和分词包括：确定预处理数据和参考名称的重合部分；在预处理数据中删除重合部分，以便获得剩余部分；响应于确定满足第一预定可信度条件，确定待识别原始数据和参考名称之间的匹配可信度等级为第一等级，匹配可信度等级为第一等级指示待识别原始数据和参考名称之间相匹配，第一预定条件包括以下任一项：确定剩余部分所包括的字数小于或者等于第一字数阈值；确定剩余部分所包括的字数大于第二字数阈值并且剩余部分和重合部分关联有相同的渠道类型信息，第二字数阈值大于第一字数阈值；剩余部分所包括的字数大于第一字数阈值并且小于第二字数阈值并且剩余部分包含一对括号；剩余部分包含“原”或括号和“原”；剩余部分包含一对括号并且括号的字数小于第三字数阈值，第三字数阈值大于第一字数阈值并且小于第二字数阈值；确定预处理数据和参考名称存在重合部分，并且预处理数据和参考名称具有相同的渠道类型子分类。
在一些实施例中，针对分词结果和参考名称进行语义相似性分析还包括：响应于确定满足第二预定可信度条件，确定待识别原始数据和参考名称之间的匹配可信度等级为第二等级，第二预定可信度条件包括：确定预处理数据和参考名称的分词结果经由结构重组后具有重合部分，并且预处理数据和参考名称的渠道类型分类信息相同；响应于确定满足第三预定可信度条件，确定待识别原始数据和参考名称之间的不匹配，第三预定可信度条件包括：预处理数据和参考名称的分词结果经由结构重组后具有重合部分，并且预处理数据和参考名称渠道类型分类信息不同。
在一些实施例中，识别待识别原始数据中的行政区划信息和渠道类型信息包括：基于关于省份、城市、区县的全称、简称、曾用名和排除词来识别待识别的机构名称中的行政区划信息，行政区划信息包括省份信息、城市信息和区县信息；响应于确认所识别的区县信息或者城市信息没有指示唯一的区县或者城市，利用所识别的区县信息或者城市信息的下级行政区划信息、或者待识别目标对象的关联目标对象的行政区划信息来识别待识别原始数据中的行政区划信息。
应当理解，本部分所描述的内容并非旨在标识本公开的实施例的关键或重要特征，也不用于限制本公开的范围。本公开的其它特征将通过以下的说明书而变得容易理解。
 附图说明
结合附图并参考以下详细说明，本公开各实施例的上述和其他特征、优点及方面将变得更加明显。在附图中，相同或相似的附图标注表示相同或相似的元素。
图1示出了用于实现根据本发明的实施例的用于识别待识别医药行业目标对象的方法的系统的示意图。
图2示出了根据本公开的实施例的用于识别待识别医药行业目标对象的方法的流程图。
图3示出了根据本公开的实施例的用于识别待识别原始数据中的行政区划信息和渠道类型信息的方法的流程图。
图4示出了根据本公开的实施例的用于分割出待识别原始数据中的数字词形式的关键词的方法的流程图。
图5示出了根据本公开的实施例的用于针对分词结果和参考名称进行语义相似性分析的方法的流程图。
图6示出了根据本公开的实施例的用于生成分词结果的方法的流程图。
图7示出了根据本公开的实施例的电子设备的框图。
 具体实施方式
以下结合附图对本公开的示范性实施例做出说明，其中包括本公开实施例的各种细节以助于理解，应当将它们认为仅仅是示范性的。因此，本领域普通技术人员应当认识到，可以对这里描述的实施例做出各种改变和修改，而不会背离本公开的范围和精神。同样，为了清楚和简明，以下的描述中省略了对公知功能和结构的描述。
在本文中使用的术语“包括”及其变形表示开放性包括，即“包括但不限于”。除非特别申明，术语“或”表示“和/或”。术语“基于”表示“至少部分地基于”。术语“一个示例实施例”和“一个实施例”表示“至少一个示例实施例”。术语“另一实施例”表示“至少一个另外的实施例”。术语“第一”、“第二”等等可以指代不同的或相同的对象。下文还可能包括其他明确的和隐含的定义。
如前文所描述，传统的、基于纯粹人工的识别方法，其识别效率不高，并且存在因识别主体的经验差异而使得识别结果存在差异性，因此，难以适应针对大数据量的待识别医药行业目标对象的准确而快速地识别，进而无法满足医药行业的服务平台对医药行业目标对象的识别需求。传统的、基于简单分词技术的识别方法因缺乏医药行业的分词与模式逻辑，因而使得针对目标对象的识别准确率相对较低。因此，传统的用于识别待识别医药行业目标对象的方法存在的不足之处在于：难以快速并且准确地针对医药行业目标对象进行识别。例如，传统的用于识别待识别医药行业目标对象的方法难以快速而准确地识别“三门医药有限责任公司”和“华润三门峡医药有限公司”。
为了至少部分地解决上述问题以及其他潜在问题中的一个或者多个，本公开的示例实施例提出了一种用于识别待识别医药行业目标对象的方案，在本公开方案中，通过针对所获取的、用于指示医药行业目标对象的待识别原始数据进行行政区划信息和渠道类型信息的识别，以便基于所识别的行政区划信息、渠道类型信息、以及噪音词库、语义等价词库和固定词库中的至少一个词库，对待识别原始数据进行噪音去除和分词，以生成分词结果，本公开可以使得分词结果为经由噪音去除、经由语义等价词和/或固定词标准化后的分词结果，而且辅助渠道类型信息加以判断，因而能够克服医药行业目标对象的原始数据结构差异、表达不规范和容易混淆的问题。另外，本公开利用针对分词结果所包括的多个关键词进行哈希计算，以便确认分词结果与参考名称是否匹配；以及如果确认分词结果与参考名称不匹配，针对由分词结果所生成的预处理数据和参考名称进行语义相似性分析，以便基于相似性分析的结果识别待识别医药行业目标对象，本公开可以基于在标准化后的分词结果的基础上，先经由哈希计算准确识别分词结果与参考名称的匹配关系，在无法匹配的基础上利用语义相似性分析结果识别待识别医药行业目标对象，因而本公开能够更为快速和准确地识别待识别医药行业目标对象。
图1示出了用于实现根据本发明的实施例的用于识别待识别医药行业目标对象的方法的系统100的示意图。如图1中所示，系统100包括计算设备110和服务器130和网络140。计算设备110、服务器130可以通过网络140(例如，因特网)进行数据交互。
服务器130，其例如可以将用于指示医药行业目标对象的待识别原始数据发送给计算设备110。
关于计算设备110，其例如用于获取服务器130所提供的用于指示医药行业目标对象的待识别原始数据；以及识别待识别原始数据中的行政区划信息和渠道类型信息。计算设备110还可以基于行政区划信息、渠道类型信息、以及噪音词库、语义等价词库和固定词库中的至少一个词库，对待识别原始数据进行噪音去除和分词，以便生成分词结果；针对分词结果所包括的多个关键词进行哈希计算，以便确认分词结果与参考名称是否匹配；以及如果确认分词结果与参考名称不匹配，针对分词结果和参考名称进行语义相似性分析，以便基于相似性分析的结果识别待识别医药行业目标对象。计算设备110可以具有一个或多个处理单元，包括诸如GPU、FPGA和ASIC等的专用处理单元以及诸如CPU的通用处理单元。另外，在每个计算设备110上也可以运行着一个或多个虚拟机。在一些实施例中，计算设备110与医学影像成像设备110可以集成在一起，也可以是彼此分立设置。在一些实施例中，计算设备110例如包括待识别原始数据获取单元112、行政区划和渠道类型信息识别单元114、分词结果生成单元116、哈希计算单元118、待识别医药行业目标对象识别单元120。
关于待识别原始数据获取单元112，其用于获取用于指示医药行业目标对象的待识别原始数据。
关于行政区划和渠道类型信息识别单元114，其用于识别待识别原始数据中的行政区划信息和渠道类型信息。
关于分词结果生成单元116，其用于基于行政区划信息、渠道类型信息、以及噪音词库、语义等价词库和固定词库中的至少一个词库，针对待识别原始数据进行噪音去除和分词，以便生成分词结果，分词结果包括多个关键词。
关于哈希计算单元118，基于针对分词结果所包括的多个关键词进行哈希计算，以便确认分词结果与参考名称是否匹配。
关于待识别医药行业目标对象识别单元120，其用于如果确认分词结果与参考名称不匹配，针对参考名称和基于分词结果所组合的预处理数据进行语义相似性分析，以便基于相似性分析的结果识别待识别医药行业目标对象。
以下结合图2描述用于识别待识别医药行业目标对象的方法200。图2示出了根据本公开的实施例的用于识别待识别医药行业目标对象的方法200的流程图。方法200可由如图1所示的计算设备110执行，也可以在图7所示的电子设备700处执行。应当理解的是，方法200还可以包括未示出的附加框和/或可以省略所示出的框，本公开的范围在此方面不受限制。
在步骤202，计算设备110获取用于指示医药行业目标对象的待识别原始数据。例如，计算设备110获取来自服务器130的关于医药分销领域中的未知机构的待识别原始数据。
关于待识别的医药行业目标对象，其例如而不限于是医药分销领域中的未知机构。例如，计算设备110需要识别某个医药分销领域中的未知的公司机构名称代表哪个标准的机构名称。应当理解，同一个医药行业目标对象(例如而不限于是同一医药商店)与不同的医药机构(例如经销商)之间有供货关系，这家医药行业目标对象可能在不同的医药机构(例如经销商)处其名称或者叫法不一致。
在步骤204，计算设备110识别待识别原始数据中的行政区划信息和渠道类型信息。
关于行政区划信息，其例如包括：省份、城市、区县三级行政机构的所属信息。
关于识别待识别原始数据中的行政区划信息的方法，其例如包括：计算设备110基于关于省份、城市、区县的全称、简称、曾用名和排除词来识别待识别的机构名称中的行政区划信息，行政区划信息包括省份信息、城市信息和区县信息；如果确认所识别的区县信息或者城市信息没有指示唯一的区县或者城市，利用所识别的区县信息或者城市信息的下级行政区划信息、或者待识别目标对象的关联目标对象的行政区划信息来识别待识别原始数据中的行政区划信息。具体而言，如果计算设备110确定待识别原始数据中所含有的省份信息包括省份全称、简称、省会城市或省会城市的曾用名，并且不包括关于省份或省会城市的排除名称，则确定识别省份信息；如果确定待识别原始数据中所含有的城市信息包括城市全称、简称或曾用名，并且不包含城市的排除名称，则确定识别城市信息；以及确定待识别原始数据中所含有的区县信息包括区县的全称、简称或曾用名，并且不包括区县的排除名称，则确定识别区县信息；如果计算设备110确定满足以下任一项，则确定识别出行政区划信息：确认识别省份信息、城市信息和区县信息；确认识别行政区划信息和区县信息、确认识别城市信息和区县信息；确认所识别区县信息或者城市信息指示唯一的区县或者城市。
例如，如果计算设备110确定待识别的机构名称中同时含有省市区县三级行政机构、省和区县两级行政机构(例如，省+县/二级市/区)、市和区县两级行政机构(例如，地市级+区县级)，就不需要后续的检测就可以直接识别出待识别的机构名称所属的行政区划信息，即认为已经准确找到待识别的机构名称所属的省市县。
例如，如果计算设备110确定待识别的机构名称中包括区县的全称或者城市的全称，且该区县的全称或者城市的全称是唯一的，则确定识别出待识别的机构名称所属的行政区划信息。应当理解，全国的城市和县是唯一的，如果待识别的机构名称中包含了唯一的城市/县的全称或简称或曾用名，即认为能唯一识别到待识别的机构名称所属的省市县。
如果计算设备110确认所识别的区县信息或者城市信息没有指示唯一的区县或者城市，利用所识别的区县信息或者城市信息的下级行政区划信息、或者待识别目标对象的关联目标对象的行政区划信息来识别待识别原始数据中的行政区划信息。例如，“通州区永顺镇果元村卫生室”和“通州区金沙镇本草药房”，其中，通州区并未指示唯一的区县，例如，北京包括通州、江苏省也包括通州。因此，可以借助于下级行政区划信息(例如，乡镇和区县关系)来识别待识别的机构名称中的行政区划信息。例如，通过“通州”+“永顺”能够找到唯一行政区划关系“北京+通州+永顺”，则此时将定位到北京市通州区；同理，通过“通州”+“金沙”能够找到唯一行政区划关系“江苏省+南通市+通州区”。
再例如，如下表一所示，待识别的目标对象(例如买方机构)的名称例如为“北沟卫生院”，单从“北沟卫生院”是无法找到其所属省市区县的地理信息或者行政区划信息的。计算设备110可以识别关联的目标对象(例如，卖方机构“华润烟台医药有限公司”)是山东烟台的，计算设备110可以在烟台地区找下游乡镇是否存在“北沟”，最终能够找到唯一的“蓬莱区”下有一个北沟镇。
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表一
在一些实施例中，计算设备110基于关于省份、城市、区县的全称、简称、曾用名和排除词来识别待识别的机构名称中的行政区划信息，行政区划信息包括省份信息、城市信息和区县信息省份、城市、区县。以下表二示例性示出了关于市、区县的全称、简称、曾用名和排除词。在表二中，关于省的全称、简称、曾用名和排除词未示出。
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表二
再例如，“三门医药有限责任公司”、“华润三门峡医药有限公司”和“夹江县医药公司三门市”等，存在一些容易混淆的区县简称。计算设备110可以基于关于省份、城市、区县的排除词辅助识别别的机构名称中的行政区划信息。例如，三门县，其简称是三门，排除词包括：三门峡、三门市、第三门、三门店。通过采用上述手段，本发明可以准确识别容易混淆的行政区划信息，进而有利于提高识别目标对象的准确度。
关于渠道类型信息，其例如包括：渠道类型子分类名称、渠道类型子分类名称和渠道类型分类序号。应当理解，医药流通行业机构数据分为三大类：经销商、医疗终端、零售终端，每一个类别下又有细分类，比如零售终端下又分为单体药店和连锁药店分店，机构名称通常含有渠道类型信息这类属性信息的，这些属性信息有助于提高待识别的医药行业目标对象的识别准确性，比如零售终端不能识别到医疗终端。因此，通过识别待识别原始数据中的渠道类型信息，有利于提高待识别的医药行业目标对象的识别精确度。
关于识别待识别原始数据中的行政区划信息和渠道类型信息的方法，其例如包括：计算设备110确定分别与不同优先级顺序关联的多个关键词集合，每个关键词集合包括多个预定关键词；在多个关键词集合中，确定待识别原始数据中所包括的预定关键词所在的目标关键词集合；基于目标关键词集合所关联的优先级顺序，确定与待识别原始数据匹配的渠道类型子分类名称；以及基于所确定的渠道类型子分类名称，确定与待识别原始数据匹配的渠道类型分类名称和渠道类型分类序号。
在步骤206，计算设备110基于行政区划信息、渠道类型信息、以及噪音词库、语义等价词库和固定词库中的至少一个词库，针对待识别原始数据进行噪音去除和分词，以便生成分词结果，分词结果包括多个关键词。
关于对待识别原始数据进行噪音去除和分词的方法，其例如包括：确认经由噪音去除和归一化处理的预处理数据是否匹配参考名称的全称、别名和曾用名中的至少一项；如果确认经由噪音去除和归一化处理的预处理数据与参考名称的全称、别名和曾用名均不匹配，针对预处理数据进行分词，以便生成分词结果。如果预处理数据等于参考名称的别名或曾用名、或者预处理数据加上其上游名称等于参考名称或其别名、或者预处理数据加上其上游名称与参考名称或其别名同音字全,则计算设备110确定待识别原始数据与参考名称匹配，而不需要针对预处理数据进行分词。
关于生成分词结果的方法，其例如包括：基于所识别出的行政区划信息，获取待识别原始数据中除去行政区划信息之外的非行政区划数据；针对非行政区划数据，进行噪音词去除和等价词替换；以及基于固定词库，针对经由噪音词去除和等价词替换后的数据进行分割，以便生成与待识别原始数据对应的分词结果，分词结果包括多个关键词和指示分割位的多个预定标识符；识别待识别原始数据中的数字型的词；针对所识别的数字型的词进行归一化处理，以便分割出待识别原始数据中的数字词形式的关键词；以及把分词结果所包括的多个关键词组合成不含地理信息的预处理数据，以用于与参考名称的匹配。下文将结合图6详细说明用于针对分词结果和参考名称进行语义相似性分析的方法，在此，不再赘述。
关于分割出待识别原始数据中的数字词形式的关键词的方法，其例如包括：计算设备110将待识别原始数据中的大写中文数字和/或小写中文数字转换为阿拉伯数字；确定经转换的阿拉伯数字的位数是否大于或者等于预定位数阈值；响应于确定经转换的阿拉伯数字的位数大于或者等于预定位数阈值，去除经转换的阿拉伯数字；响应于确定经转换的阿拉伯数字的位数小于预位数定阈值，确定经转换的阿拉伯数字是否位于待识别原始数据的起始位置或者终止位置；响应于确定经转换的阿拉伯数字位于待识别原始数据的起始位置或者终止位置，确定与位于起始位置或者终止位置的阿拉伯数字位相邻的数据是否指示预定渠道类型；以及响应于确定与位于起始位置或者终止位置的阿拉伯数字位相邻的数据未指示预定渠道类型，去除经转换的阿拉伯数字。下文将结合图4详细说明用于分割出待识别原始数据中的数字词形式的关键词的方法，在此，不再赘述。关于针对非行政区划数据进行噪音词去除和等价词替换的方法，其例如包括：计算设备110确定多组关联词，确定多组关联词，每组关联词包括原始词和等价词，原始词和等价词在指示医药行业目标对象时具有一致的语义；为每组关联词确定关联的顺序号和所属分类，顺序号指示每组关联词的优先级；以及基于所确定的关联的顺序号，使用等价词替换和分割待识别原始数据，使得经由等价词替换和分割的数据中包括等价词和预定标示符，预定标示符指示分割位。
在步骤208，计算设备110针对分词结果所包括的多个关键词进行哈希计算，以便确认分词结果与参考名称是否匹配。
关于确认分词结果与参考名称是否匹配的方法，其例如包括：计算设备110计算分词结果所包括的多个关键词的哈希值之和，以便生成分词结果哈希值之和；计算参考名称所包括的多个关键词的哈希值之和，以便生成参考名称哈希值之和；确认分词结果哈希值之和参考名称哈希值之和是否相等；以及响应于确认分词结果哈希值之和参考名称哈希值之和相等，确定分词结果与参考名称匹配。通过采用被分词后的关键词的哈希值之和的算法逻辑，能够使得匹配结果不因关键词词序不同而影响。
以下公式(1)示意性示出了确认分词结果与参考名称是否匹配的算法。
在上述公式(1)中，ora_hash(key reference i)代表针对参考数据的分词结果所包括的第i个关键词所计算的哈希值。i代表关键词的序号。代表参考名称哈希值之和。n代表关键词的总数量，例如表三或表四中，关键词的总数量n是19。ora_hash(key original i)代表针对待识别原始数据的分词结果所包括的第i个关键词所计算的哈希值。代表分词结果哈希值之和。
例如，以下表三示意性示出了参考名称的分词结果。其中，参考名称例如为“阜阳市延生大药房零售连锁有限公司梦廉分店”，参考名称例如被分为表三中关键词1至关键词19的十九个关键词。表三中仅示意性示出其中的九个关键词。
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表三
例如，以下表四示意性示出了待识别原始数据的分词结果。其中，待识别原始数据例如为“阜阳市延生大药房零售连锁公司(梦廉)”，待识别原始数据例如被分为表四中的中关键词1至关键词19的十九个关键词。表四中仅示意性示出其中的九个关键词。
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表四
上述待识别原始数据“阜阳市延生大药房零售连锁公司(梦廉)”通过去噪和分词，打破了词序，并且大小写被归一化，生成表四中的关键词1至关键词19的十九个关键词。待识别原始数据的分词结果的关键词1至关键词19所有关键词的哈希值之和(即，分词结果哈希值之和)等于参考名称之和(参考名称指标准的目标对象名称)，这样，计算设备110确定分词结果与参考名称匹配。
以下示例性示出用于实现确认分词结果与参考名称是否匹配的算法的示意性程序代码。
select*
from(select a.collatejobdetailid，a.orgname，o.ovalmasterid asstdorgid，o.orgcode as stdorgcode，
o.orgname as stdorgname,2as status,2as gradelevel，length(o.orgname)asorglen，
case when a.channelname＝o.channel and substr(a.keyword05，-1)in(′-，′店′，′药′，′生′，′诊′，′院′)then′99％′
when a.channelname＝o.channel then′98％′else′95％′end as grade，′拆词全等推荐′as splitstatus_std
from collatejobdetaila,ovalmaster o
where a.jobid＝v_jobid......
and a.keyword01＝o.keyword01
and a.keyword02＝o.keyword02
and a.keyword03＝o.keyword03
and a.hashvalue＝o.hashvalue
/*hashvalue
ora_hash(a.keyword04)+ora-hash(a.keyword05)+
ora_hash(a.keyword06)+ora_hash(a.keyword07)+
ora_hash(a.keyword08)+ora_hash(a.keyword09)+
ora_hash(a.keyword10)+ora_hash(a.keyword11)+
ora_hash(a.keyword12)+ora_hash(a.keyword19)＝
ora_hash(o.keyword04)+ora_hash(o.keyword05)+
ora_hash(o.keyword06)+ora_hash(o.keyword07)+
ora_hash(o.keyword08)+ora_hash(o.keyword09)+
ora_hash(o.keyword10)+ora_hash(o.keyword11)+
ora_hash(o.keyword12)+ora_hash(o.keyword19)*/
)
order by orgname,orglen
在步骤210，如果计算设备110确认分词结果与参考名称不匹配，针对参考名称和基于分词结果所组合的预处理数据进行语义相似性分析，以便基于相似性分析的结果识别待识别医药行业目标对象。例如，如果计算设备110确认分词结果与参考名称相匹配，将待识别医药行业目标对象识别为与参考名称相关联的目标对象。
关于针对分词结果和参考名称进行语义相似性分析的方法，其例如包括：确定预处理数据和参考名称的重合部分；在预处理数据中删除重合部分，以便获得剩余部分；响应于确定满足第一预定可信度条件，确定待识别原始数据和参考名称之间的匹配可信度等级为第一等级，匹配可信度等级为第一等级指示待识别原始数据和参考名称之间相匹配，第一预定条件包括以下任一项：确定剩余部分所包括的字数小于或者等于第一字数阈值；确定剩余部分所包括的字数大于第二字数阈值并且剩余部分和重合部分关联有相同的渠道类型信息，第二字数阈值大于第一字数阈值；剩余部分所包括的字数大于第一字数阈值并且小于第二字数阈值并且剩余部分包含一对括号；剩余部分包含“原”或括号和“原”；剩余部分包含一对括号并且括号的字数小于第三字数阈值，第三字数阈值大于第一字数阈值并且小于第二字数阈值；响应于满足第二预定可信度条件，确定待识别原始数据和参考名称之间的匹配可信度等级为第二等级，第二预定可信度条件包括以下任一项：确定预处理数据和参考名称存在重合部分，并且预处理数据和参考名称具有相同的渠道类型子分类；确定预处理数据和参考名称的分词结果经由结构重组后具有重合部分，并且预处理数据和参考名称的渠道类型分类信息相同；响应于满足第三预定可信度条件，确定待识别原始数据和参考名称之间的不匹配，第三预定可信度条件包括：预处理数据和参考名称的分词结果经由结构重组后具有重合部分，并且预处理数据和参考名称渠道类型分类信息不同。下文将结合图5详细说明用于针对分词结果和参考名称进行语义相似性分析的方法，在此，不再赘述。
在一些实施例中，如果针对分词结果和参考名称的语义相似性分析或针对分词结果进行哈希计算均不能精确识别待识别的医药行业目标对象，那么计算设备110可以基于渠道类型信息调整分词结果和参考名称的语义相似性分析的权重，以便基于经调整后的权重针对分词结果和参考名称进行语义相似性分析。
在上述方案中，通过针对所获取的、用于指示医药行业目标对象的待识别原始数据进行行政区划信息和渠道类型信息的识别，以便基于所识别的行政区划信息、渠道类型信息、以及噪音词库、语义等价词库和固定词库中的至少一个词库，对待识别原始数据进行噪音去除和分词，以生成分词结果，本公开可以使得分词结果为经由噪音去除、经由语义等价词和/或固定词标准化后的分词结果，而且辅助渠道类型信息加以判断，因而能够克服医药行业目标对象的原始数据结构差异、表达不规范和容易混淆的问题。另外，本公开利用针对分词结果所包括的多个关键词进行哈希计算，以便确认分词结果与参考名称是否匹配；以及如果确认分词结果与参考名称不匹配，针对由分词结果所生成的预处理数据和参考名称进行语义相似性分析，以便基于相似性分析的结果识别待识别医药行业目标对象，本公开可以基于在标准化后的分词结果的基础上，先经由哈希计算准确识别分词结果与参考名称的匹配关系，在无法匹配的基础上利用语义相似性分析结果识别待识别医药行业目标对象，因而本公开能够更为快速和准确地识别待识别医药行业目标对象。
以下结合图3说明用于识别待识别原始数据中的行政区划信息和渠道类型信息的方法。图3示出了根据本公开的实施例的用于识别待识别原始数据中的行政区划信息和渠道类型信息的方法300的流程图。方法300可由如图1所示的计算设备110执行，也可以在图7所示的电子设备700处执行。应当理解的是，方法300还可以包括未示出的附加框和/或可以省略所示出的框，本公开的范围在此方面不受限制。
在步骤302，计算设备110确定分别与不同优先级顺序关联的多个关键词集合，每个关键词集合包括多个预定关键词。
关于用于识别渠道类型分类的关键词集合，其例如包括用于识别连锁药店的关键词集合、用于识别单体药店的关键词集合、用于识别连锁公司的关键词集合、用于识别医院的关键词集合和用于识别卫生监督所的关键词集合。
以下表三示例性示意出用于识别连锁药店的关键词集合、用于识别单体药店的关键词集合。
[援引加入（细则20.6）01.02.2024]
表五
在步骤304，计算设备110在多个关键词集合中，确定待识别原始数据中所包括的预定关键词所在的目标关键词集合。例如，计算设备110确定待识别原始数据中包括的预定关键词“％零售中心％”，并且不包括“％连锁％店％”，则所包括的预定关键词所在的目标关键词集合为表五中第二行的关键词集合。
在步骤306，计算设备110基于目标关键词集合所关联的优先级顺序，确定与待识别原始数据匹配的渠道类型子分类名称。例如，表五中第二行的关键词集合所关联的优先级顺序为18，计算设备110基于该优先级顺序18确定与待识别原始数据匹配的渠道类型子分类名称为“单体药店”。应当理解，在识别渠道类型时，基于目标关键词集合所关联的优先级顺序，优先符合条件的，即认为“定位到与待识别原始数据匹配的渠道类型子分类名称”。
在步骤308，计算设备110基于所确定的渠道类型子分类名称，确定与待识别原始数据匹配的渠道类型分类名称和渠道类型分类序号。例如，计算设备110基于所确定的渠道类型子分类名称“单体药店”，确定与待识别原始数据匹配的渠道类型分类名称为“终端药店”和渠道类型分类序号为“114”。
在上述方案中，本公开能够准确确定待识别原始数据所属渠道类型，有利于基于准确的所属渠道类型提高识别医药行业目标对象的准确度。
以下结合图4说明用于分割出待识别原始数据中的数字词形式的关键词的方法。图4示出了根据本公开的实施例的用于分割出待识别原始数据中的数字词形式的关键词的方法400的流程图。方法400可由如图1所示的计算设备110执行，也可以在图7所示的电子设备700处执行。应当理解的是，方法400还可以包括未示出的附加框和/或可以省略所示出的框，本公开的范围在此方面不受限制。
在步骤402，计算设备110将待识别原始数据中的大写中文数字和/或小写中文数字转换为阿拉伯数字。应当理解，在待识别原始数据中，可能会含有大小写数字、电话、邮编。这些数字可能出现在待识别目标对象的名称的前面、后面或者中间。例如，计算设备110可以将出现在待识别原始数据中的所有大小写中文数字统一变成阿拉伯数字，例如，将“一百五十一”、“一五一”、或者“壹佰五十一”最终都转换为阿拉伯数字“151”。通过利用上述手段，利于使得待识别原始数据中的数字型词进行归一化。
在步骤404，计算设备110确定经转换的阿拉伯数字的位数是否大于或者等于预定位数阈值。
关于预定位数阈值，其例如而不限于是6以及以上的一个数。
如果计算设备110确定经转换的阿拉伯数字的位数大于或者等于预定位数阈值，在步骤406，去除经转换的阿拉伯数字。例如，如果确定经转换的阿拉伯数字的位数大于或者等于6(或者6位及以上)，无论经转换的阿拉伯数字在何处出现，都直接去掉经转换的阿拉伯数字。因为，该经转换的阿拉伯数字可能是电话、邮编等信息。
在步骤408，如果计算设备110确定经转换的阿拉伯数字的位数小于预位数定阈值，确定经转换的阿拉伯数字是否位于待识别原始数据的起始位置或者终止位置。
在步骤410，如果计算设备110确定经转换的阿拉伯数字位于待识别原始数据的起始位置或者终止位置，确定与位于起始位置或者终止位置的阿拉伯数字位相邻的数据是否指示预定渠道类型。例如，如果计算设备110确定经转换的阿拉伯数字小于预位数定阈值，并且出现在待识别目标对象名称的起始位置，则也可以去除经转换的阿拉伯数字，因为该经转换的阿拉伯数字很可能是在提供目标对象名称时不小心加入的序号。
如果计算设备110确定与位于起始位置或者终止位置的阿拉伯数字位相邻的数据未指示预定渠道类型，跳转至步骤406，去除经转换的阿拉伯数字。
在步骤412，如果计算设备110确定与位于起始位置或者终止位置的阿拉伯数字位相邻的数据指示预定渠道类型，不去除经转换的阿拉伯数字。
例如，如果计算设备110确定经转换的阿拉伯数字位于待识别原始数据的起始位置或者终止位置，并且紧随经转换的阿拉伯数字的不是药店类型或者医疗机构类型(渠道类型不是药店)，则可以去除阿拉伯数字；如果计算设备110确定与位于终止位置的阿拉伯数字位是在终止位置出现，并且紧随经转换的阿拉伯数字的是药店类型或者医疗机构类型且经转换的阿拉伯数字小于或者等于预定数字阈值，则不能去除阿拉伯数字。例如，以下表六中示例的“56店王志恒”，其中，阿拉伯数字“56”位于待识别原始数据的起始位置，并且紧随经转换的阿拉伯数字“56”的是药店类型，同时，阿拉伯数字“56”小于市级医药有限公司所关联的预定数字阈值，此时，计算设备110确定不能去除阿拉伯数字“56”。
例如，以下表六中示例的“56店王志恒”，其中，阿拉伯数字“56”位于待识别原始数据的起始位置，并且紧随经转换的阿拉伯数字“56”的是药店类型，同时，阿拉伯数字“56”小于市级医药有限公司所关联的预定数字阈值，此时，计算设备110确定不能去除阿拉伯数字“56”。
再例如，以下表六中示例的“骑鹤乡王楼卫生室50”或“骑鹤乡王楼卫生室1”，其中，阿拉伯数字“50”或“1”位于待识别原始数据的终止位置，并且紧随经转换的阿拉伯数字“50”或“1”是医疗机构类型，假设阿拉伯数字“50”大于乡级卫生室所关联的预定数字阈值，此时，计算设备110确定去除阿拉伯数字“50”；而阿拉伯数字“1”小于乡级卫生室所关联的预定数字阈值，此时，计算设备110确定不能去除阿拉伯数字“1”。
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表六
在上述方案中，本公开能够准确识别并剔除待识别原始数据中的数字型噪音，而利于准确分割出利于识别目标对象的数字型关键词。
以下结合图5说明用于针对分词结果和参考名称进行语义相似性分析的方法。图5示出了根据本公开的实施例的用于针对分词结果和参考名称进行语义相似性分析的方法500的流程图。方法500可由如图1所示的计算设备110执行，也可以在图7所示的电子设备700处执行。应当理解的是，方法500还可以包括未示出的附加框和/或可以省略所示出的框，本公开的范围在此方面不受限制。
在步骤502，计算设备110确定预处理数据和参考名称的重合部分。
在步骤504，计算设备110在预处理数据中删除重合部分，以便获得剩余部分。
例如，预处理数据为“宣化区田志东诊所1”和参考名称“宣化区田志东诊所”，预处理数据和参考名称的重合部分为“宣化区田志东诊所”。预处理数据中删除重合部分之后的剩余部分为“1”
在步骤506，如果计算设备110确定满足第一预定可信度条件，确定待识别原始数据和参考名称之间的匹配可信度等级为第一等级，匹配可信度等级为第一等级指示待识别原始数据和参考名称之间相匹配，第一预定条件包括以下任一项：确定剩余部分所包括的字数小于或者等于第一字数阈值；确定剩余部分所包括的字数大于第二字数阈值并且剩余部分和重合部分关联有相同的渠道类型信息，第二字数阈值大于第一字数阈值；剩余部分所包括的字数大于第一字数阈值并且小于第二字数阈值并且剩余部分包含一对括号；剩余部分包含“原”或括号和“原”；或者剩余部分包含一对括号并且括号的字数小于第三字数阈值，第三字数阈值大于第一字数阈值并且小于第二字数阈值；确定预处理数据和参考名称存在重合部分，并且预处理数据和参考名称具有相同的渠道类型子分类。
关于第一字数阈值，其例如而不限于是2。例如，上述剩余部分“1”所包括的字数小于第一字数阈值，确定待识别原始数据和参考名称之间的匹配可信度等级为第一等级，例如，匹配相似度为100％，即，待识别原始数据和参考名称之间相匹配。关于第二字数阈值，其例如而不限于是10。例如，预处理数据为“七台河市源福大药房(七台河市源鸿福医疗器械商店)”和参考名称“七台河市源福大药房”之间的重合部分为“宣化区田志东诊所”。预处理数据中删除重合部分之后的剩余部分为“(七台河市源鸿福医疗器械商店)”。剩余部分所包括的字数大于10，并且剩余部分和相同部分的渠道类型信息是一样的，则确定待识别原始数据和参考名称之间的匹配可信度等级为第一等级，例如，匹配相似度为98％，即，待识别原始数据和参考名称之间高度相似，因而相匹配。
例如，预处理数据为“获嘉县亢村镇亢北村卫生室(原亢北联合卫生室)”和参考名称“亢村镇亢北村卫生室”之间的重合部分为“亢村镇亢北村卫生室”。预处理数据中删除重合部分之后的剩余部分为“获嘉县(原亢北联合卫生室)”。剩余部分包含“原”或“(原”，则确定待识别原始数据和参考名称之间的匹配可信度等级为第一等级，例如，匹配相似度为99％，即，待识别原始数据和参考名称之间高度相似，因而相匹配。
关于第三字数阈值，其例如而不限于是4。例如，预处理数据为“NE中山市三乡镇湘鸿堂药品零售店(06)”和参考名称“中山市三乡镇湘鸿堂药品零售店”之间的重合部分为“中山市三乡镇湘鸿堂药品零售店”。预处理数据中删除重合部分之后的剩余部分包含一对括号，且括号的内容长度小于4个字符，则确定待识别原始数据和参考名称之间的匹配可信度等级为第一等级，例如，匹配相似度为96％，即，待识别原始数据和参考名称之间高度相似，因而相匹配。
例如，预处理数据“厦门湖里叮铃医生第一门诊部有限公司”和参考名称“厦门湖里叮铃医生第一门诊部”之间的重合部分为“厦门湖里叮铃医生第一门诊部”。二者有完整相互重合的部分，并且二者有相同的渠道子分类名称，则确定待识别原始数据和参考名称之间的匹配可信度等级为第一等级，例如，匹配相似度为70％，即，待识别原始数据和参考名称之间具有较高相似，因而相匹配。
关于匹配可信度等级为第一等级，其例如指示匹配相似度处于70％至100％之间。
在步骤508，如果计算设备110确定满足第二预定可信度条件，确定待识别原始数据和参考名称之间的匹配可信度等级为第二等级，第二预定可信度条件包括：确定预处理数据和参考名称的分词结果经由结构重组后具有重合部分，并且预处理数据和参考名称的渠道类型分类信息相同。
例如，预处理数据为“淮南市潘集区芦集镇卫生院(城北村卫生室)”，参考名称为“芦集镇城北村卫生室”之间存在重合部分。预处理数据“淮南市潘集区芦集镇卫生院(城北村卫生室)”的分词结果经由结构重组后为“芦集卫生(城北卫生)”，分词结果经由结构重组后的“(城北卫生)”被参考名称“芦集城北卫生”所包含，并且预处理数据和参考名称的渠道类型分类信息相同，确定待识别原始数据和参考名称之间的匹配可信度等级为第二等级，例如，匹配相似度为65％，即，待识别原始数据和参考名称之间具有一定相似性。
在一些实施例中，匹配可信度等级为第二等级可以被认为是预处理数据和参考名称相匹配。
在步骤510，如果计算设备110确定满足第三预定可信度条件，确定待识别原始数据和参考名称之间的不匹配，第三预定可信度条件包括：预处理数据和参考名称的分词结果经由结构重组后具有重合部分，并且预处理数据和参考名称渠道类型分类信息不同。
例如，预处理数据“华润青岛医药有限公司崂山路分店”和参考名称“华润青岛医药有限公司”之间存在重合部分。预处理数据“华润青岛医药有限公司崂山路分店”和参考名称“华润青岛医药有限公司”所关联的渠道类型信息不同，参考名称“华润青岛医药有限公司”是商业公司，预处理数据“华润青岛医药有限公司崂山路分店”是药店，二者不具有相似性。则确定待识别原始数据和参考名称之间的不匹配。
在通过采用上述手段，本公开能够在预处理数据与参考名称存在差别的情况下，依然能够快速并准确地识别待识别原始数据和参考名称之间是否匹配。
以下结合图6说明用于生成分词结果的方。图6示出了根据本公开的实施例的用于生成分词结果的方法600的流程图。方法600可由如图1所示的计算设备110执行，也可以在图7所示的电子设备700处执行。应当理解的是，方法600还可以包括未示出的附加框和/或可以省略所示出的框，本公开的范围在此方面不受限制。
在步骤602，计算设备110基于所识别出的行政区划信息，获取待识别原始数据中除去行政区划信息之外的非行政区划数据。
在步骤604，计算设备110针对非行政区划数据，进行噪音词去除和等价词替换。
关于等价词，其例如是在医学行业中可以视为等价识别的词，或者语义等价的词。例如，“歧河乡李庄卫生室”和“歧河乡李庄卫生所”，这上述两个名称中，在分词结构上只有一个“卫生室”和“卫生所”存在差别，现实中上述两个名称通常属于一个类型，在医学行业中可以视为等价识别，属于“等价词”。
等价词库包括数据庞大的等价词，这些词例如是经由人工标注或者基于机器学习而分类出来的。以下表六示意性示出了等价词库中的部分等价词。
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表六
关于针对非行政区划数据进行等价词替换的方法，其例如包括计算设备110确定多组关联词，每组关联词包括原始词和等价词，原始词和等价词具有在指示医药行业目标对象时具有一致的语义；为每组关联词确定关联的顺序号和所属分类，顺序号指示每组关联词的优先级；以及基于所确定的关联的顺序号，使用等价词替换和分割待识别原始数据，使得经由等价词替换和分割的数据中包括等价词和预定标示符，预定标示符指示分割位。
关于预定标示符，其例如而不限于是“％”，预定标示符表示相应位置处是一个分割位。在使用等价词替换和分割时是有优先顺序的，例如，计算设备110按照“顺序号”(例如表六中所示，一般而言，长度大的原始词优先级高)，使用等价词替换和分割待识别原始数据，比如，“苏州汇仁医药商店有限公司”中的原始词“商店有限公司”被等价词％公司％所替换和分割，经由等价词替换和分割的数据例如为“苏州汇仁医药％公司”。
在步骤606，计算设备110基于固定词库，针对经由噪音词去除和等价词替换后的数据进行分割，以便生成与待识别原始数据对应的分词结果，分词结果包括多个关键词和指示分割位的多个预定标识符。关于固定词，其例如至少包括：省份、城市、区县的全称和简称，以及其他常规固定词组。例如，机构名称“北京师范大学附属中学卫生站”中，师范大学、附属、中学、卫生站这些词属于固定词，不需要再拆分。在一些实施例中，计算设备110进行噪音词去除和等价词替换之后，基于ASCII码表，确认经由噪音词去除和等价词替换后的数据中的每一个字符的ASCII值是否处于第一预定数值范围(例如是48～57)之外的，以便将处于第一预定数值范围(例如是48～57)之外的字符全部去掉。采用上述手段的原因主要在于：通过噪音词去除和等价词替换可以去掉一批噪音词，但机构名称中也经常出现字母、横杠等内容，中国的医疗零售机构和医疗终端不含有大小写字母或其它英文符号，因此，除中文和数字外，其它符号需要去除，因而通过上述手段，本公开能够进一步过滤噪音词。
关于针对经由噪音词去除和等价词替换后的数据进行分割的方法，其例如包括：计算设备110在完成噪音词、等价词替换完成后，用固定词去“分割”待识别原始数据。以“苏州汇仁医药商店有限公司”为例，经由等价词替换和分割后变成“苏州汇仁医药％公司”；通过固定词库被分割后变成“苏州％汇仁％医药％公司”，其中“苏州为地理信息，其会被单独截取并另外存储，剩余部分被逐个分离出来，以便生成与待识别原始数据对应的分词结果。例如，以下表七示例了待识别原始数据“苏州汇仁医药商店有限公司”的分析结果，表八示例了待识别原始数据“医药商店有限公司(苏州汇仁)”的分词结果。表九示例了待识别原始数据“苏州汇仁医药贸易有限公司”的分词结果。
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表九
在步骤608，计算设备110识别待识别原始数据中的数字型的词。
在步骤610，计算设备110针对所识别的数字型的词进行归一化处理，以便分割出待识别原始数据中的数字词形式的关键词。上文已经结合图4说明了用于分割出待识别原始数据中的数字词形式的关键词的方法，在此，不再赘述。
在步骤612，计算设备110把分词结果所包括的多个关键词组合成不含地理信息的预处理数据，以用于与参考名称的匹配。
在一些实施例中，计算设备110可以把分割后的多个关键词组合成不含地理信息的预处理数据，以用于与参考名称的匹配。例如，以“苏州汇仁医药商店有限公司”为例，所组合成不含地理信息的预处理数据例如为“汇仁医药公司”，以用于与参考名称的匹配。
图7示意性示出了适于用来实现本发明实施例的电子设备700的框图。电子设备700可以是用于实现执行图2至图6所示的方法200至600。如图7所示，电子设备700包括中央处理单元(即，CPU 701)，其可以根据存储在只读存储器(即，ROM 702)中的计算机程序指令或者从存储单元708加载到随机访问存储器(即，RAM 703)中的计算机程序指令，来执行各种适当的动作和处理。在RAM 703中，还可存储电子设备700操作所需的各种程序和数据。CPU 701、ROM 702以及RAM 703通过总线704彼此相连。输入/输出接口(即，I/O接口705)也连接至总线704。
电子设备700中的多个部件连接至I/O接口705，包括：输入单元706、输出单元707、存储单元708，CPU 701执行上文所描述的各个方法和处理，例如执行方法200至600。例如，在一些实施例中，方法200至600可被实现为计算机软件程序，其被存储于机器可读介质，例如存储单元708。在一些实施例中，计算机程序的部分或者全部可以经由ROM 702和/或通信单元709而被载入和/或安装到电子设备700上。当计算机程序加载到RAM 703并由CPU 701执行时，可以执行上文描述的方法200至600的一个或多个操作。备选地，在其他实施例中，CPU 701可以通过其他任何适当的方式(例如，借助于固件)而被配置为执行方法200至600的一个或多个动作。
需要进一步说明的是，本发明可以是方法、装置、系统和/或计算机程序产品。计算机程序产品可以包括计算机可读存储介质，其上载有用于执行本发明的各个方面的计算机可读程序指令。
计算机可读存储介质可以是可以保持和存储由指令执行设备使用的指令的有形设备。计算机可读存储介质例如可以是但不限于电存储设备、磁存储设备、光存储设备、电磁存储设备、半导体存储设备或者上述的任意合适的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、静态随机存取存储器(SRAM)、便携式压缩盘只读存储器(CD-ROM)、数字多功能盘(DVD)、记忆棒、软盘、机械编码设备、例如其上存储有指令的打孔卡或凹槽内凸起结构、以及上述的任意合适的组合。这里所使用的计算机可读存储介质不被解释为瞬时信号本身，诸如无线电波或者其他自由传播的电磁波、通过波导或其他传输媒介传播的电磁波(例如，通过光纤电缆的光脉冲)、或者通过电线传输的电信号。
这里所描述的计算机可读程序指令可以从计算机可读存储介质下载到各个计算/处理设备，或者通过网络、例如因特网、局域网、广域网和/或无线网下载到外部计算机或外部存储设备。网络可以包括铜传输电缆、光纤传输、无线传输、路由器、防火墙、交换机、网关计算机和/或边缘服务器。每个计算/处理设备中的网络适配卡或者网络接口从网络接收计算机可读程序指令，并转发该计算机可读程序指令，以供存储在各个计算/处理设备中的计算机可读存储介质中。
用于执行本发明操作的计算机程序指令可以是汇编指令、指令集架构(ISA)指令、机器指令、机器相关指令、微代码、固件指令、状态设置数据、或者以一种或多种编程语言的任意组合编写的源代码或目标代码，该编程语言包括面向对象的编程语言—诸如Smalltalk、C++等，以及常规的过程式编程语言—诸如“C”语言或类似的编程语言。计算机可读程序指令可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络—包括局域网(LAN)或广域网(WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。在一些实施例中，通过利用计算机可读程序指令的状态信息来个性化定制电子电路，例如可编程逻辑电路、现场可编程门阵列(FPGA)或可编程逻辑阵列(PLA)，该电子电路可以执行计算机可读程序指令，从而实现本发明的各个方面。
这里参照根据本发明实施例的方法、设备(系统)、和计算机程序产品的流程图和/或框图描述了本发明的各个方面。应当理解，流程图和/或框图的每个方框以及流程图和/或框图中各方框的组合，都可以由计算机可读程序指令实现。
这些计算机可读程序指令可以提供给语音交互装置中的处理器、通用计算机、专用计算机或其它可编程数据处理装置的处理单元，从而生产出一种机器，使得这些指令在通过计算机或其它可编程数据处理装置的处理单元执行时，产生了实现流程图和/或框图中的一个或多个方框中规定的功能/动作的装置。也可以把这些计算机可读程序指令存储在计算机可读存储介质中，这些指令使得计算机、可编程数据处理装置和/或其他设备以特定方式工作，从而，存储有指令的计算机可读介质则包括一个制造品，其包括实现流程图和/或框图中的一个或多个方框中规定的功能/动作的各个方面的指令。
也可以把计算机可读程序指令加载到计算机、其它可编程数据处理装置、或其它设备上，使得在计算机、其它可编程数据处理装置或其它设备上执行一系列操作步骤，以产生计算机实现的过程，从而使得在计算机、其它可编程数据处理装置、或其它设备上执行的指令实现流程图和/或框图中的一个或多个方框中规定的功能/动作。
附图中的流程图和框图显示了根据本发明的多个实施例的设备、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段或指令的一部分，该模块、程序段或指令的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个连续的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或动作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
以上已经描述了本发明的各实施例，上述说明是示例性的，并非穷尽性的，并且也不限于所披露的各实施例。在不偏离所说明的各实施例的范围和精神的情况下，对于本技术领域的普通技术人员来说许多修改和变更都是显而易见的。本文中所用术语的选择，旨在最好地解释各实施例的原理、实际应用或对市场中的技术改进，或者使本技术领域的其它普通技术人员能理解本文披露的各实施例。
以上仅为本发明的可选实施例，并不用于限制本发明，对于本领域的技术人员来说，本发明可以有各种更改和变化。凡在本发明的精神和原则之内，所作的任何修改、等效替换、改进等，均应包含在本发明的保护范围之内。

Claims (14)

  1. 一种用于识别待识别医药行业目标对象的方法，包括：获取用于指示医药行业目标对象的待识别原始数据；识别待识别原始数据中的行政区划信息和渠道类型信息；基于行政区划信息、渠道类型信息、以及噪音词库、语义等价词库和固定词库中的至少一个词库，针对待识别原始数据进行噪音去除和分词，以便生成分词结果，所述分词结果包括多个关键词；针对分词结果所包括的多个关键词进行哈希计算，以便确认分词结果与参考名称是否匹配；以及响应于确认分词结果与参考名称不匹配，针对参考名称核基于分词结果所组和的预处理数据进行语义相似性分析，以便基于相似性分析的结果识别待识别医药行业目标对象。
  2. 根据权利要求1所述的方法，其中针对分词结果所包括的多个关键词进行哈希计算，以便确认分词结果与参考名称是否匹配包括：计算分词结果所包括的多个关键词的哈希值之和，以便生成分词结果哈希值之和；计算参考名称所包括的多个关键词的哈希值之和，以便生成参考名称哈希值之和；确认分词结果哈希值之和参考名称哈希值之和是否相等；以及响应于确认分词结果哈希值之和参考名称哈希值之和相等，确定分词结果与参考名称匹配。
  3. 根据权利要求1或2所述的方法，还包括：响应于确认分词结果与参考名称相匹配，将待识别医药行业目标对象识别为与参考名称相关联的目标对象。
  4. 根据权利要求1所述的方法，其中生成分词结果包括：基于所识别出的行政区划信息，获取待识别原始数据中除去行政区划信息之外的非行政区划数据；针对非行政区划数据，进行噪音词去除和等价词替换；以及基于固定词库，针对经由噪音词去除和等价词替换后的数据进行分割，以便生成与待识别原始数据对应的分词结果，分词结果包括多个关键词和指示分割位的多个预定标识符。
  5. 根据权利要求4所述的方法，还包括：识别待识别原始数据中的数字型的词；针对所识别的数字型的词进行归一化处理，以便分割出待识别原始数据中的数字词形式的关键词；以及把分词结果所包括的多个关键词组合成不含地理信息的预处理数据，以用于与参考名称的匹配。
  6. 根据权利要求2所述的方法，其中针对所识别的数字型的词进行归一化处理，以便分割出待识别原始数据中的数字词形式的关键词包括：将待识别原始数据中的大写中文数字和/或小写中文数字转换为阿拉伯数字；确定经转换的阿拉伯数字的位数是否大于或者等于预定位数阈值；响应于确定经转换的阿拉伯数字的位数大于或者等于预定位数阈值，去除经转换的阿拉伯数字；响应于确定经转换的阿拉伯数字的位数小于预位数定阈值，确定经转换的阿拉伯数字是否位于待识别原始数据的起始位置或者终止位置；响应于确定经转换的阿拉伯数字位于待识别原始数据的起始位置或者终止位置，确定与位于起始位置或者终止位置的阿拉伯数字位相邻的数据是否指示预定渠道类型；以及响应于确定与位于起始位置或者终止位置的阿拉伯数字位相邻的数据未指示预定渠道类型，去除经转换的阿拉伯数字。
  7. 根据权利要求2所述的方法，其中渠道类型信息包括：渠道类型子分类名称、渠道类型分类名称和渠道类型分类序号。
  8. 根据权利要求1所述的方法，其中识别待识别原始数据中的行政区划信息和渠道类型信息包括：确定分别与不同优先级顺序关联的多个关键词集合，每个关键词集合包括多个预定关键词；在多个关键词集合中，确定待识别原始数据中所包括的预定关键词所在的目标关键词集合；基于目标关键词集合所关联的优先级顺序，确定与待识别原始数据匹配的渠道类型子分类名称；以及 基于所确定的渠道类型子分类名称，确定与待识别原始数据匹配的渠道类型分类名称和渠道类型分类序号。
  9. 根据权利要求1所述的方法，其中针对待识别原始数据进行噪音去除和分词包括：确定多组关联词，每组关联词包括原始词和等价词，原始词和等价词在指示医药行业目标对象时具有一致的语义；为每组关联词确定关联的顺序号和所属分类，顺序号指示每组关联词的优先级；以及基于所确定的关联的顺序号，使用等价词替换和分割待识别原始数据，使得经由等价词替换和分割的数据中包括等价词和预定标示符，所述预定标示符指示分割位。
  10. 根据权利要求1所述的方法，其中针对待识别原始数据进行噪音去除和分词包括：确定预处理数据和参考名称的重合部分；在预处理数据中删除重合部分，以便获得剩余部分；响应于确定满足第一预定可信度条件，确定待识别原始数据和参考名称之间的匹配可信度等级为第一等级，匹配可信度等级为第一等级指示待识别原始数据和参考名称之间相匹配，第一预定条件包括以下任一项：确定剩余部分所包括的字数小于或者等于第一字数阈值；确定剩余部分所包括的字数大于第二字数阈值并且剩余部分和重合部分关联有相同的渠道类型信息，第二字数阈值大于第一字数阈值；剩余部分所包括的字数大于第一字数阈值并且小于第二字数阈值并且剩余部分包含一对括号；剩余部分包含“原”或括号和“原”；剩余部分包含一对括号并且括号的字数小于第三字数阈值，第三字数阈值大于第一字数阈值并且小于第二字数阈值；确定预处理数据和参考名称存在重合部分，并且预处理数据和参考名称具有相同的渠道类型子分类。
  11. 根据权利要求10所述的方法，其中针对分词结果和参考名称进行语义相似性分析还包括：响应于确定满足第二预定可信度条件，确定待识别原始数据和参考名称之间的匹配可信度等级为第二等级，第二预定可信度条件包括：确定预处理数据和参考名称的分词结果经由结构重组后具有重合部分，并且预处理数据和参考名称的渠道类型分类信息相同；响应于确定满足第三预定可信度条件，确定待识别原始数据和参考名称之间的不匹配，第三预定可信度条件包括：预处理数据和参考名称的分词结果经由结构重组后具有重合部分，并且预处理数据和参考名称渠道类型分类信息不同。
  12. 根据权利要求1所述的方法，其中识别待识别原始数据中的行政区划信息和渠道类型信息包括：基于关于省份、城市、区县的全称、简称、曾用名和排除词来识别待识别的机构名称中的行政区划信息，行政区划信息包括省份信息、城市信息和区县信息；响应于确认所识别的区县信息或者城市信息没有指示唯一的区县或者城市，利用所识别的区县信息或者城市信息的下级行政区划信息、或者待识别目标对象的关联目标对象的行政区划信息来识别待识别原始数据中的行政区划信息。
  13. 一种计算设备，包括：至少一个处理器；以及与所述至少一个处理器通信连接的存储器；所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行权利要求1-12中任一项所述的方法。
  14. 一种存储有计算机指令的非瞬时计算机可读存储介质，其中所述计算机指令用于使所述计算机执行权利要求1-12中任一项所述的方法。
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