Project cost data query processing method, device and equipment

Abstract

The invention provides a query processing method, device and equipment for project cost data. The method comprises the following steps: acquiring a target query parameter of the project cost data; analyzing the target query parameter to obtain target key feature data; according to the target key feature data, querying target project cost data matched with the target key feature data from a distributed database, and outputting the target project cost data; wherein the target project cost data stored in the distributed database is stored through the following processes: obtaining a standard project cost data file, and performing rule analysis on the standard project cost data file to obtain a plurality of indexes; generating an index code according to the configuration information of the index; extracting and analyzing the index code to obtain output project cost data; and performing reverse index conversion processing on the output project cost data to obtain target project cost data and storing the target project cost data in a distributed database. According to the scheme, million-level index second-level response can be achieved, and efficiency is high.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method, device and equipment for querying and processing engineering cost data. Background Art

[0002] Construction cost data includes classification systems such as project division, quota, equipment, and main materials. The data structure of construction cost data involves different data sources in different formats, such as Excel files for different projects, specific software formats, and more than 30% of the table structures are non-standard. Currently, files of different formats are usually stored directly in the database, resulting in the following problems during query:

[0003] 1. Inefficient queries due to complex conditions: Traditional systems struggle to achieve responses within seconds when processing over 100,000 indicators in a single project. Performance degrades significantly, especially in scenarios involving multiple combined conditions.

[0004] 2. Insufficient data positioning accuracy: Engineering cost data has a multi-layer classification system (project division / quota / equipment / main materials, etc.) and a complex table structure, and traditional methods have coordinate positioning errors.

[0005] 3. Weak adaptability of business rules: Faced with frequently changing cost management business rules (such as classification system adjustments and matching rule updates), traditional systems require manual reconstruction of data models.

[0006] These problems jointly restrict the in-depth exploration of the value of engineering cost data. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a query processing method, device and equipment for engineering cost data. Through the distributed storage architecture of the distributed database combined with the inverted index mechanism, it can achieve a second-level response for millions of indicators and high query processing efficiency.

[0008] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0009] A method for querying and processing engineering cost data, comprising:

[0010] Get the target query parameters of the project cost data;

[0011] Parsing the target query parameters to obtain target key feature data;

[0012] According to the target key feature data, target engineering cost data matching the target key feature data is queried from a distributed database, and the target engineering cost data is output; wherein, the target engineering cost data stored in the distributed database is stored through the following process: obtaining a standard engineering cost data file, performing rule parsing on the standard engineering cost data file to obtain multiple indicators; generating an indicator code according to the configuration information of the indicator; extracting and parsing the indicator code to obtain output engineering cost data; performing inverted index conversion processing on the output engineering cost data to obtain target engineering cost data; and storing the target engineering cost data in the distributed database.

[0013] Optionally, parsing the target query parameters to obtain target key feature data includes:

[0014] Preprocessing the target query parameters to obtain standard target query parameters;

[0015] Performing feature extraction on the standard target query parameters to obtain key feature data;

[0016] Performing relationship analysis on the key feature data to obtain key feature data to be output;

[0017] The key feature data to be output is structured and output to obtain target key feature data.

[0018] Optionally, the standard engineering cost data file is parsed according to rules to obtain multiple indicators, including:

[0019] According to the preset index extraction rules, the standard engineering cost data file is parsed according to the rules to obtain multiple indicators.

[0020] Optionally, generating an indicator code according to the configuration information of the indicator includes:

[0021] According to the preset indicator extraction rules, the configuration information of the indicator is logically converted to generate indicator code.

[0022] Optionally, the indicator code is extracted and parsed to obtain output engineering cost data, including:

[0023] Perform multi-mode table name quick matching on the indicator code to obtain table number information;

[0024] Locating the target row of the indicator code to obtain row number information;

[0025] Performing compound conditional judgment on the indicator code to obtain column number information;

[0026] Positioning mark information is generated according to the table number information, the row number information, and the column number information.

[0027] Optionally, extracting and parsing the indicator code to obtain output engineering cost data also includes:

[0028] According to the preset indicator extraction rules, a general indicator data structure is obtained;

[0029] Output engineering cost data is obtained based on the general indicator data structure, the positioning mark information and metadata.

[0030] Optionally, performing inverted index conversion processing on the output engineering cost data to obtain target engineering cost data; and storing the target engineering cost data in the distributed database includes:

[0031] According to the preset system indicator parameter structure and distributed inverted index mechanism, the output engineering cost data is converted into an inverted index to obtain target engineering cost data;

[0032] The target project cost data is stored in the distributed database.

[0033] The present invention also provides a query processing device for engineering cost data, comprising:

[0034] The acquisition module is used to obtain the target query parameters of the engineering cost data;

[0035] A processing module is used to parse the target query parameters to obtain target key feature data; based on the target key feature data, query the target engineering cost data that matches the target key feature data from the distributed database, and output the target engineering cost data; wherein, the target engineering cost data stored in the distributed database is stored through the following process: obtaining a standard engineering cost data file, performing rule parsing on the standard engineering cost data file to obtain multiple indicators; generating an indicator code based on the configuration information of the indicator; extracting and parsing the indicator code to obtain output engineering cost data; converting the output engineering cost data to obtain target engineering cost data; and storing the target engineering cost data in the distributed database.

[0036] The present invention further provides a computing device, comprising: a processor and a memory storing a computer program, wherein the computer program executes the method described above when executed by the processor.

[0037] The present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above.

[0038] The above solution of the present invention includes at least the following beneficial effects:

[0039] The above-mentioned scheme of the present invention obtains the target query parameters of the engineering cost data; parses the target query parameters to obtain the target key feature data; queries the target engineering cost data matching the target key feature data from the distributed database according to the target key feature data, and outputs the target engineering cost data; wherein, the target engineering cost data stored in the distributed database is stored through the following process: obtains the standard engineering cost data file, performs rule parsing on the standard engineering cost data file to obtain multiple indicators; generates an indicator code according to the configuration information of the indicator; extracts and parses the indicator code to obtain the output engineering cost data; performs inverted index conversion processing on the output engineering cost data to obtain the target engineering cost data; stores the target engineering cost data in the distributed database; through the distributed storage architecture of the distributed database combined with the inverted index mechanism, it is possible to achieve a second-level response for millions of indicators, and the query processing efficiency is high. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flow chart of a method for querying and processing engineering cost data provided by an embodiment of the present invention;

[0041] Figure 2 It is a schematic diagram of the structure of the query processing system for engineering cost data provided by an embodiment of the present invention;

[0042] Figure 3 A module diagram of a query processing device for engineering cost data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0044] like Figure 1 As shown, an embodiment of the present invention provides a query processing method for engineering cost data, comprising:

[0045] Step 11: Obtain target query parameters of engineering cost data;

[0046] Step 12: parsing the target query parameters to obtain target key feature data;

[0047] Step 13: According to the target key feature data, the target engineering cost data that matches the target key feature data is searched from the distributed database, and the target engineering cost data is output; wherein, the target engineering cost data stored in the distributed database is stored through the following process: obtaining a standard engineering cost data file, performing rule parsing on the standard engineering cost data file to obtain multiple indicators; generating an indicator code according to the configuration information of the indicator; extracting and parsing the indicator code to obtain output engineering cost data; performing inverted index conversion processing on the output engineering cost data to obtain target engineering cost data; and storing the target engineering cost data in the distributed database.

[0048] In this embodiment, target query parameters of engineering cost data are obtained; the target query parameters are parsed to obtain target key feature data; according to the target key feature data, target engineering cost data matching the target key feature data is queried from a distributed database, and the target engineering cost data is output; wherein, the target engineering cost data stored in the distributed database is stored through the following process: a standard engineering cost data file is obtained, and rule parsing of the standard engineering cost data file is performed to obtain multiple indicators; according to the configuration information of the indicator, an indicator code is generated; the indicator code is extracted and parsed to obtain output engineering cost data; the output engineering cost data is converted into an inverted index to obtain target engineering cost data; the target engineering cost data is stored in the distributed database; through the distributed storage architecture of the distributed database combined with the inverted index mechanism, a second-level response for millions of indicators can be achieved, and the query processing efficiency is high.

[0049] In an optional embodiment of the present invention, step 12 includes:

[0050] Step 121: pre-process the target query parameters to obtain standard target query parameters;

[0051] Step 122: extracting features from the standard target query parameters to obtain key feature data;

[0052] Step 123: performing relationship analysis on the key feature data to obtain key feature data to be output;

[0053] Step 124 : structure the key feature data to be output to obtain target key feature data.

[0054] In this embodiment, step 121 includes:

[0055] Step 1211: remove noise from the target query parameter, uniformly replace punctuation marks, special symbols, etc. in the target query parameter with spaces, and obtain a first preprocessing parameter;

[0056] Step 1212: normalize the format of the first preprocessing parameter to obtain a second preprocessing parameter;

[0057] Step 1213: Perform word segmentation processing on the second preprocessing parameter using a domain word segmentation tool to obtain a third preprocessing parameter;

[0058] Step 1214: Perform field validity check and value range verification on the third pre-processing parameter to obtain a standard target query parameter.

[0059] The target query parameters are pre-processed by a multi-dimensional query engine. Pre-processing the target query parameters facilitates the smooth progress of subsequent processes and improves the efficiency of subsequent processes.

[0060] Step 122 includes:

[0061] Step 1221: Perform multi-dimensional entity recognition on the standard target query parameters through a multi-dimensional query engine, extracting time features, spatial features, business entity types, values and units, and logical conditions as key feature data;

[0062] Extracting features from the standard target query parameters through a multi-dimensional query engine can ensure the accuracy of key feature data and help improve the accuracy of subsequent processing;

[0063] Step 123 includes:

[0064] Step 1231: Perform context dependency analysis using a multi-dimensional query engine. Specifically, perform syntax analysis using a syntax tree to determine feature associations and analyze and process nested relationships.

[0065] Step 1232: perform ambiguity resolution using a multi-dimensional query engine. Specifically, the ambiguity resolution is performed using a domain dictionary or a probabilistic model.

[0066] When performing relationship analysis on the key feature data, fuzzy expression processing can be used. Specifically, fuzzy word mapping rules can be defined (such as "nearby" → radius of 5 kilometers), or the user can be guided to add details through an interactive interface. Multi-language mixed analysis can be used. Specifically, a multi-language entity recognition model is used to first identify the language type and then perform module-based analysis. Cross-modal parameter analysis can be used. Specifically, automatic speech recognition is used to convert text, and then image recognition is used to identify key information in the image, and the text analysis process is uniformly connected.

[0067] Step 124 includes:

[0068] Step 1241: Using a multi-dimensional query engine, perform feature standardization and format conversion according to business requirements to output standardized feature data, thereby obtaining target key feature data.

[0069] The target query parameters are parsed through a multi-dimensional query engine to obtain target key feature data, thereby upgrading the semantic cognitive dimension and realizing multimodal hybrid query, thereby supporting multi-semantic expansion (supporting pinyin error correction → synonym replacement → word vector expansion → context reasoning → business rule mapping); at the same time, semantic intelligence is realized. On the basis of fuzzy matching, it supports expansion into a triple expansion mechanism of integrated pinyin initials (such as "GL" → "water supply and drainage"), fuzzy sounds (no distinction between n / l), and synonyms ("concrete" ≡ "concrete"), thereby improving query coverage and improving parsing accuracy and efficiency.

[0070] In an optional embodiment of the present invention, step 13 includes:

[0071] Step 131: Using a columnar storage query engine, perform a shard query on the structured data in the target key feature data according to the shard key routing to obtain query data;

[0072] Step 132, performing word segmentation search through a distributed search engine to obtain search data;

[0073] Step 133: Aggregate and merge the query data and the search data to obtain merged data;

[0074] Step 133 : Match the merged data with the engineering cost data in the distributed database to obtain target engineering cost data that matches the target key feature data, and output the target engineering cost data.

[0075] In this embodiment, a shard query is performed through the column storage query engine of the data coordinate parser to obtain query data, which can reduce the number of calls; a word segmentation search is performed through the distributed search engine of the data coordinate parser to obtain retrieval data, and the word segmentation retrieval is performed in a batch document acquisition manner, which helps to reduce network latency; the data coordinate parser uses distributed computing to aggregate and merge the query data and the retrieval data to obtain merged data; the data coordinate parser matches the merged data with the engineering cost data in the distributed database, and maps the merged data back to the specific location of the source data file, thereby obtaining target engineering cost data that matches the target key feature data; the surrounding related information of the target engineering cost data is obtained through context extraction, and finally a two-dimensional table display view with highlighted marks is generated through a visual locator to output the target engineering cost data;

[0076] The above process realizes elastic index mapping. The distributed storage architecture of the distributed database is combined with the inverted index mechanism to improve query throughput, break through the bottleneck of real-time retrieval of massive data, and achieve a response of millions of indicators in seconds, with high query processing efficiency.

[0077] In an optional embodiment of the present invention, the storage process of the target project cost data stored in the distributed database includes:

[0078] Step 21, obtaining a standard engineering cost data file;

[0079] Step 22, performing rule parsing on the standard engineering cost data file to obtain multiple indicators;

[0080] Step 23, generating an indicator code according to the configuration information of the indicator;

[0081] Step 24, extracting and parsing the indicator code to obtain output engineering cost data;

[0082] Step 25: Perform inverted index conversion processing on the output engineering cost data to obtain target engineering cost data; and store the target engineering cost data in the distributed database.

[0083] Furthermore, step 22 includes:

[0084] Step 221 , according to preset index extraction rules, the standard engineering cost data file is parsed to obtain a plurality of indicators.

[0085] In this embodiment, the indicator rule loader extracts data from the row and column of the substation total balance sheet according to a preset indicator extraction rule, for example, to obtain multiple indicators.

[0086] The preset indicator extraction rules include but are not limited to numerical indicator extraction rules, text indicator extraction rules and association indicator extraction rules.

[0087] Furthermore, step 23 includes:

[0088] Step 231: Perform a logical conversion on the configuration information of the indicator according to the preset indicator extraction rules to generate an indicator code.

[0089] In this embodiment, the configuration information of the indicator is converted into an executable syntax tree structure as the indicator code by an indicator rule converter according to a preset indicator extraction rule.

[0090] Furthermore, step 24 includes:

[0091] Step 241, perform multi-mode table name quick matching on the indicator code to obtain table number information;

[0092] Step 242, locate the target row of the indicator code to obtain row number information;

[0093] Step 243, performing a composite conditional judgment on the indicator code to obtain column number information;

[0094] Step 244: Generate positioning mark information according to the table number information, the row number information, and the column number information.

[0095] Furthermore, step 24 further includes:

[0096] Step 245, obtaining a general indicator data structure according to a preset indicator extraction rule;

[0097] Step 246: Obtain output engineering cost data based on the general indicator data structure, the positioning mark information and the metadata.

[0098] In this embodiment, a table matching executor of a comparative feature locator performs a multi-mode table name fast match on the indicator code using a string matching algorithm to obtain table number information; wherein the string matching algorithm may be an improved BM (Boyer-Moore) algorithm;

[0099] The target row of the indicator code is located by combining the row feature capture of the comparative feature locator with regular expressions and sliding window technology to obtain row number information;

[0100] By comparing the column condition filter of the feature locator, the parser is deployed to perform compound condition judgment on the indicator code to obtain column number information;

[0101] Generate positioning mark information with table number, row number and column number according to the table number information, the row number information and the column number information by comparing the coordinate traceability recorder of the feature locator;

[0102] The dynamic structure generation framework of the result set encapsulator is used to obtain the general indicator data structure according to the preset indicator extraction rules;

[0103] The metadata injection module of the result set encapsulator obtains output engineering cost data according to the general indicator data structure, the positioning mark information and the metadata; wherein the metadata includes but is not limited to auxiliary fields such as basic engineering information and data version;

[0104] By comparing the feature locator and the result set encapsulator, the indicator code is extracted and parsed to obtain the output engineering cost data; by comparing the feature locator and the result set encapsulator, the preset indicator extraction rules and retrieval logic can be decoupled, and dynamic adaptation of more than 30% non-standard tables can be supported; at the same time, by comparing the feature locator and the result set encapsulator, the structure of a multi-layer classification system (quota / equipment / main material) and a composite matching rule library (table / row / column three-dimensional rules) can be realized to solve the problem of the complexity of the cost data structure.

[0105] Furthermore, step 25 includes:

[0106] Step 251: Perform inverted index conversion on the output project cost data according to a preset system indicator parameter structure and a distributed inverted index mechanism to obtain target project cost data;

[0107] Step 252: storing the target project cost data in the distributed database.

[0108] In this embodiment, a dynamic mapping generator performs an inverted index conversion on the output engineering cost data according to a preset system indicator parameter structure and a distributed inverted index mechanism to obtain target engineering cost data; and the target engineering cost data is stored in the distributed database; combined with the distributed storage architecture and inverted index mechanism of the distributed database, it is convenient to improve the query processing efficiency of subsequent engineering cost data;

[0109] The preset system indicator parameter structure includes but is not limited to:

[0110] The indicator name is used to uniquely identify the indicator and should have a clear business meaning; for example, in the engineering cost system, "total cost", "unit cost", "concrete usage", etc.

[0111] Indicator description: A detailed explanation of the indicator, including its calculation method, data source, and purpose. For example, the description of "unit cost" could be "the cost per square meter obtained by dividing the total project cost by the building area. The data comes from project cost accounting and is used to assess the project's cost level."

[0112] Data type, specifies the data type of the indicator parameter. Common types include numeric type (such as integer, floating point number), text type, date type, etc. For example, "Total Cost" is a floating point type, "Project Name" is a text type, and "Start Date" is a date type.

[0113] Value range: defines the valid value range of indicator parameters to ensure the rationality of the data; for example, "building area" should be greater than 0, and "construction period" should be a positive integer;

[0114] Indicator unit: For numerical indicators, specify the unit; for example, the unit of "concrete consumption" may be "cubic meters", and the unit of "material price" may be "yuan / ton";

[0115] Correlation relationship, which describes the relationship between indicators. For example, the relationship between "total construction cost", "unit construction cost" and "construction area" is "total construction cost = unit construction cost × construction area";

[0116] Data sources, record the channels for obtaining indicator data, such as "bill of quantities", "financial statements", "market research", etc.;

[0117] Update frequency, which indicates the update cycle of indicator data, such as "daily", "monthly", "quarterly", etc.; for example, "market material prices" may be updated daily, and "project progress indicators" may be updated weekly.

[0118] In an optional embodiment of the present invention, the storage process of the target project cost data stored in the distributed database further includes:

[0119] Step 26, obtaining change information of the preset indicator extraction rules, updating the preset indicator extraction rules according to the change information, and re-obtaining the target project cost data according to the updated preset indicator extraction rules, and storing it in the distributed database.

[0120] In this embodiment, the data synchronization tasker updates the preset indicator extraction rules according to the change information, deletes all the original old project cost data, and re-obtains the target project cost data according to the updated preset indicator extraction rules and stores them in the distributed database;

[0121] The data synchronization tasker uses a multi-threaded batch processing framework to support asynchronous data writing of 200+ concurrent threads; the data synchronization tasker uses a transaction compensation mechanism to achieve full reconstruction and incremental compensation; through the data synchronization tasker, a dynamic adaptation system is implemented, and changes to preset indicator extraction rules take effect within minutes, achieving full reconstruction in less than 20 minutes and incremental compensation in less than 30 seconds.

[0122] The query processing method of the above engineering cost data is based on the following Figure 2 The query processing system for engineering cost data shown in the figure is implemented, and the query processing system for engineering cost data includes:

[0123] Distributed storage engine: used to store target project cost data and related custom technical parameter indicators;

[0124] Business rule parsing module: Indicator rule loader, used to configure a multi-layer data classification system: a tree-like classification architecture for quotas / equipment / main materials / consumable materials; used to build a table row and column coordinate matching rule library: in the table dimension, it supports full matching / compatible matching (fuzzy similarity ≥ 95%) / regular expression / continuous n-gram matching; in the row dimension, it implements exact matching / semantic fuzzy matching / continuous pattern matching; in the column dimension, it deploys compound condition matching, including prefix exclusion rules, non-null constraints, and enumeration value verification; indicator rule converter, used to convert business rules into an executable syntax tree structure;

[0125] Intelligent Indicator Extraction Module: Comparative feature locator, including: table matching executor, applying the improved BM algorithm to achieve fast matching of multi-mode table names; row feature capturer, combining regular expressions and sliding window technology to locate target rows; column condition filter, deploying a parser to perform composite condition judgment; coordinate traceability recorder, generating location markers with table numbers, row numbers, and column numbers; result set encapsulator, including: dynamic structure generation framework, automatically constructing a common indicator data structure according to extraction rules; metadata injection module, embedding auxiliary fields such as basic project information and data version;

[0126] Distributed Storage Optimization Module: A dynamic mapping generator automatically converts Elasticsearch (a distributed search and analytics engine) mapping index data based on the system's indicator parameter structure; a data synchronization tasker implements a multi-threaded batch processing framework that supports asynchronous data writing with 200+ concurrent threads; a transaction compensation mechanism that implements full rebuilds and incremental compensation; and an extended parameter adaptation layer, including a universal structure converter, that standardizes custom technical parameter indicators into Elasticsearch document-formatted index data.

[0127] The standard engineering cost data file is parsed by a business rule parsing module to obtain a plurality of indicators; an indicator code is generated based on the configuration information of the indicator; the indicator code is extracted and parsed by an indicator intelligent extraction module to obtain output engineering cost data; the output engineering cost data is converted into an inverted index to obtain target engineering cost data; and the target engineering cost data is stored in the distributed database by a dynamic mapping generator in a distributed storage optimization module;

[0128] Intelligent query analysis module: A multi-dimensional query engine, with a joint query optimizer, supports combined retrieval of multiple conditions such as category / table name / time range; data traceability services, including: a data coordinate parser that maps query results back to the specific location of the source data file; a context extraction interface that supports obtaining surrounding related information of the target data; a visual locator that generates a two-dimensional table display view with highlighted tags;

[0129] The target query parameters are parsed through the multi-dimensional query engine and data traceability service in the intelligent query analysis module to obtain target key feature data; based on the target key feature data, the target engineering cost data that matches the target key feature data is queried from the distributed database, and the target engineering cost data is output.

[0130] The implementation process of a specific embodiment of the present invention is as follows: The storage and query processing process of engineering cost data includes the following steps:

[0131] storage

[0132] Step 1: Obtain standard engineering cost data file;

[0133] Step 2: performing rule parsing on the standard engineering cost data file to obtain a plurality of indicators; specifically, performing rule parsing on the standard engineering cost data file according to a preset indicator extraction rule to obtain a plurality of indicators;

[0134] Step 3: Generate an indicator code based on the configuration information of the indicator; specifically, perform a logical conversion on the configuration information of the indicator according to a preset indicator extraction rule to generate the indicator code;

[0135] Step 4: extract and parse the indicator code to obtain output engineering cost data; specifically, perform multi-mode table name quick matching on the indicator code to obtain table number information; locate the target row on the indicator code to obtain row number information; perform compound condition judgment on the indicator code to obtain column number information; generate positioning mark information based on the table number information, the row number information, and the column number information; obtain a general indicator data structure according to a preset indicator extraction rule; and obtain output engineering cost data based on the general indicator data structure, the positioning mark information, and metadata;

[0136] Step 5: Perform inverted index conversion on the output project cost data to obtain target project cost data; store the target project cost data in the distributed database; specifically, perform inverted index conversion on the output project cost data according to a preset system indicator parameter structure and a distributed inverted index mechanism to obtain target project cost data; store the target project cost data in the distributed database;

[0137] Query

[0138] Step 6: Obtain target query parameters for engineering cost data;

[0139] Step 7: parsing the target query parameters to obtain target key feature data; specifically, preprocessing the target query parameters to obtain standard target query parameters; extracting features from the standard target query parameters to obtain key feature data; performing relationship analysis on the key feature data to obtain key feature data to be output; and performing structured output on the key feature data to obtain target key feature data.

[0140] Step 8: Based on the target key feature data, target engineering cost data matching the target key feature data is searched from a distributed database, and the target engineering cost data is output. Specifically, a column-based storage query engine is used to perform a shard query on the structured data in the target key feature data according to the shard key routing to obtain query data; a distributed search engine is used to perform a word segmentation search to obtain retrieval data; the query data and the retrieval data are aggregated and merged to obtain merged data; the merged data is matched with the engineering cost data in the distributed database to obtain target engineering cost data matching the target key feature data, and the target engineering cost data is output.

[0141] The above process enables the storage and query processing of engineering cost data. By building a dynamic location extraction index model, precise location of engineering cost data is achieved. Index data construction for data types supports precise location queries. A dual-mode data synchronization mechanism ensures eventual consistency in massive data storage, supporting the regeneration and compensation of massive data when indicator extraction rules frequently change. An intelligent semantic expansion system overcomes the technical limitations of traditional keyword retrieval, enabling expanded capabilities such as fuzzy queries, pinyin error correction, synonym expansion, and Chinese word segmentation. An inverted index enables second-level queries for over 100,000 indicators per project. This effectively addresses technical challenges in the engineering cost field, such as inefficient complex query efficiency, inaccurate data location, and weak scalability. Actual testing demonstrates second-level responses for over 100,000 indicators, with 99% query accuracy and less than 0.1% data location accuracy. The dynamic word segmentation strategy increases system query throughput by more than three times.

[0142] like Figure 3 As shown, an embodiment of the present invention further provides a query processing device 30 for engineering cost data, comprising:

[0143] An acquisition module 31 is used to acquire target query parameters of engineering cost data;

[0144] The processing module 32 is used to parse the target query parameters to obtain target key feature data; based on the target key feature data, query the target engineering cost data that matches the target key feature data from the distributed database, and output the target engineering cost data; wherein, the target engineering cost data stored in the distributed database is stored through the following process: obtaining a standard engineering cost data file, performing rule parsing on the standard engineering cost data file to obtain multiple indicators; generating an indicator code based on the configuration information of the indicator; extracting and parsing the indicator code to obtain output engineering cost data; converting the output engineering cost data to obtain target engineering cost data; storing the target engineering cost data in the distributed database.

[0145] Optionally, parsing the target query parameters to obtain target key feature data includes:

[0146] Preprocessing the target query parameters to obtain standard target query parameters;

[0147] Performing feature extraction on the standard target query parameters to obtain key feature data;

[0148] Performing relationship analysis on the key feature data to obtain key feature data to be output;

[0149] The key feature data to be output is structured and output to obtain target key feature data.

[0150] Optionally, the standard engineering cost data file is parsed according to rules to obtain multiple indicators, including:

[0151] According to the preset index extraction rules, the standard engineering cost data file is parsed according to the rules to obtain multiple indicators.

[0152] Optionally, generating an indicator code according to the configuration information of the indicator includes:

[0153] According to the preset indicator extraction rules, the configuration information of the indicator is logically converted to generate indicator code.

[0154] Optionally, the indicator code is extracted and parsed to obtain output engineering cost data, including:

[0155] Perform multi-mode table name quick matching on the indicator code to obtain table number information;

[0156] Locating the target row of the indicator code to obtain row number information;

[0157] Performing compound conditional judgment on the indicator code to obtain column number information;

[0158] Positioning mark information is generated according to the table number information, the row number information, and the column number information.

[0159] Optionally, extracting and parsing the indicator code to obtain output engineering cost data also includes:

[0160] According to the preset indicator extraction rules, a general indicator data structure is obtained;

[0161] Output engineering cost data is obtained based on the general indicator data structure, the positioning mark information and metadata.

[0162] Optionally, performing inverted index conversion processing on the output engineering cost data to obtain target engineering cost data; and storing the target engineering cost data in the distributed database includes:

[0163] According to the preset system indicator parameter structure and distributed inverted index mechanism, the output engineering cost data is converted into an inverted index to obtain target engineering cost data;

[0164] The target project cost data is stored in the distributed database.

[0165] It should be noted that the device is a device corresponding to the above method, and all implementation methods in the above method embodiments are applicable to the embodiments of the device and can achieve the same technical effects.

[0166] An embodiment of the present invention further provides a computing device comprising: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described in the above embodiment. All implementations of the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0167] The present invention also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to execute the method described in the above embodiment. All implementations of the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0168] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0169] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0170] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0171] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0172] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0173] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, ROM, RAM, a magnetic disk, or an optical disk.

[0174] In addition, it should be noted that, in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it will be understood that all or any steps or components of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in hardware, firmware, software or a combination thereof, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0175] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general-purpose device. Therefore, the purpose of the present invention can also be achieved simply by providing a program product containing program code that implements the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but do not necessarily need to be performed in chronological order. Certain steps can be performed in parallel or independently of each other.

[0176] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for querying and processing engineering cost data, characterized in that: include: Get the target query parameters of the project cost data; Parsing the target query parameters to obtain target key feature data; According to the target key feature data, target engineering cost data matching the target key feature data is queried from a distributed database, and the target engineering cost data is output; wherein, the target engineering cost data stored in the distributed database is stored through the following process: obtaining a standard engineering cost data file, performing rule parsing on the standard engineering cost data file to obtain multiple indicators; generating an indicator code according to the configuration information of the indicator; extracting and parsing the indicator code to obtain output engineering cost data; performing inverted index conversion processing on the output engineering cost data to obtain target engineering cost data; and storing the target engineering cost data in the distributed database.

2. The query processing method for engineering cost data according to claim 1, characterized in that: Parsing the target query parameters to obtain target key feature data includes: Preprocessing the target query parameters to obtain standard target query parameters; Performing feature extraction on the standard target query parameters to obtain key feature data; Performing relationship analysis on the key feature data to obtain key feature data to be output; The key feature data to be output is structured and output to obtain target key feature data.

3. The query processing method for engineering cost data according to claim 1, characterized in that: The standard engineering cost data file is parsed according to rules to obtain multiple indicators, including: According to the preset index extraction rules, the standard engineering cost data file is parsed according to the rules to obtain multiple indicators.

4. The query processing method for engineering cost data according to claim 1, characterized in that: Generate indicator code based on the configuration information of the indicator, including: According to the preset indicator extraction rules, the configuration information of the indicator is logically converted to generate indicator code.

5. The query processing method for engineering cost data according to claim 1, characterized in that: The indicator code is extracted and parsed to obtain output engineering cost data, including: Perform multi-mode table name quick matching on the indicator code to obtain table number information; Locating the target row of the indicator code to obtain row number information; Performing compound conditional judgment on the indicator code to obtain column number information; Positioning mark information is generated according to the table number information, the row number information, and the column number information.

6. The query processing method for engineering cost data according to claim 5, characterized in that: Extracting and parsing the indicator code to obtain output engineering cost data also includes: According to the preset indicator extraction rules, a general indicator data structure is obtained; Output engineering cost data is obtained based on the general indicator data structure, the positioning mark information and metadata.

7. The query processing method for engineering cost data according to claim 1, characterized in that: Performing inverted index conversion processing on the output engineering cost data to obtain target engineering cost data; and storing the target engineering cost data in the distributed database, including: According to the preset system indicator parameter structure and distributed inverted index mechanism, the output engineering cost data is converted into an inverted index to obtain target engineering cost data; The target project cost data is stored in the distributed database.

8. A query processing device for engineering cost data, characterized in that: include: The acquisition module is used to obtain the target query parameters of the engineering cost data; A processing module, configured to parse the target query parameters to obtain target key feature data; According to the target key feature data, target engineering cost data matching the target key feature data is queried from a distributed database, and the target engineering cost data is output; wherein, the target engineering cost data stored in the distributed database is stored through the following process: obtaining a standard engineering cost data file, performing rule parsing on the standard engineering cost data file to obtain multiple indicators; generating an indicator code according to the configuration information of the indicator; extracting and parsing the indicator code to obtain output engineering cost data; converting and processing the output engineering cost data to obtain target engineering cost data; and storing the target engineering cost data in the distributed database.

9. A computing device, characterized in that include: A processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is performed.

10. A computer-readable storage medium, characterized in that: The device stores instructions, which, when executed on a computer, enable the computer to execute the method according to any one of claims 1 to 7.

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