Marking item address confirmation method and device, electronic equipment and storage medium

Abstract

The invention relates to a beacon item address confirmation method and device, electronic equipment and a storage medium, and the method comprises the steps: decomposing a beacon item address text, and obtaining a fuzzy modifier in a decomposition result; the fuzzy modifier in the mapping item address text is identified; a candidate coordinate set of a decomposition result is obtained through multiple pieces of geocoding service software, so that the coverage rate and accuracy of address resolution are improved; the fuzzy modifiers and the candidate coordinate set are quantified based on a two-dimensional Gaussian probability distribution model, a fuzzy range is obtained, the spatial range of'nearby 'fuzzy modifiers and other fuzzy modifiers is quantified, and the problem of insufficient fuzziness processing is solved; and performing data fusion on the candidate coordinate set and the fuzzy range to obtain the optimal longitude and latitude coordinates, and solving the problem of lack of mathematical optimization through multi-source data fusion, so that the method can be adaptive to complex scenes, a more accurate beacon project address can be obtained according to the optimal longitude and latitude coordinates, and a project position visual display effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of geographic information systems, and in particular to a method, device, electronic equipment and storage medium for confirming an address of a bidding project. Background Art

[0002] In the bidding information system, the project address is usually provided in text form. The address description is often unstructured and contains administrative divisions, street names, house numbers and vague modifiers (such as "nearby"), which are difficult to directly convert into precise geographic coordinates (latitude and longitude).

[0003] Existing geocoding technologies mainly rely on address resolution services (such as Baidu Map API and AutoNavi API) to convert text addresses into coordinates by matching standard address libraries. However, existing geocoding technologies have the following problems: 1. Insufficient fuzziness processing: For fuzzy descriptions such as "nearby" and "approximately", traditional methods find it difficult to quantify their spatial range, resulting in reduced positioning accuracy. 2. Dependence on a single data source: Relying on a single API may fail due to incomplete data coverage, especially in remote areas or non-standard address scenarios. 3. Lack of mathematical optimization: Existing methods are mostly rule-based matching, lack mathematical model support, and cannot adapt to complex scenarios. These problems make it difficult to calculate the precise project location coordinates from unstructured standard information addresses, affecting subsequent spatial analysis and visualization.

[0004] Therefore, it is urgent to propose a method, device, electronic device and storage medium for confirming the address of a bidding project to solve the technical problem that the existing methods in the prior art are difficult to calculate the precise project location coordinates from the unstructured bidding address, resulting in unsatisfactory visualization of the project location. Summary of the Invention

[0005] In view of this, it is necessary to provide a method, device, electronic device and storage medium for confirming the address of a bidding project to solve the technical problem that the existing methods in the prior art are difficult to calculate the precise project location coordinates from the unstructured bidding address, resulting in unsatisfactory visualization of the project location.

[0006] In order to solve the above problems, in a first aspect, the present invention provides a method for confirming a bidding project address, comprising: Obtaining a bidding project address text, decomposing the bidding project address text to obtain a decomposition result; the decomposition result includes fuzzy modifiers; the fuzzy modifiers are words that express spatial uncertainty; Identify the decomposition results in multiple geocoding service software respectively to obtain a candidate coordinate set corresponding to each geocoding service software; quantizing the fuzzy modifier and the candidate coordinate set based on a two-dimensional Gaussian probability distribution model to obtain a fuzzy range; The candidate coordinate set and the fuzzy range are subjected to data fusion to obtain the optimal longitude and latitude coordinates, and the optimal longitude and latitude coordinates are used as the address of the bidding project.

[0007] In a possible implementation, decomposing the bid information item address text to obtain a decomposition result includes: Setting the decomposition type; the decomposition type includes administrative division, specific address and the fuzzy modifier; The bid information project address text is decomposed according to regular expressions and word segmentation tools to obtain decomposition results corresponding to each decomposition type.

[0008] In a possible implementation, the quantizing of the fuzzy modifier and the candidate coordinate set based on a two-dimensional Gaussian probability distribution model to obtain a fuzzy range includes: Determine the address scenario based on the address text of the bidding project; According to the address scenario, setting the range radius of the fuzzy modifier; Obtaining a reference coordinate according to the spatial distribution center point of the candidate coordinate set; quantifying the range radius and the reference coordinates according to the two-dimensional Gaussian probability distribution model to obtain a position probability density; An initial range is obtained according to the range radius and the reference coordinates, and a geographical area boundary in the initial range where the position probability density is greater than a preset threshold is confirmed as a fuzzy range.

[0009] In a possible implementation, fusing the candidate coordinate set and the fuzzy range to obtain the optimal latitude and longitude coordinates includes: Obtaining corresponding confidence levels according to the candidate coordinate sets of each geocoding service software; Set constraint weights based on the address scenario; The confidence, the constraint weight and the location probability density of each geocoding service software are optimized based on an optimization formula to obtain the optimal latitude and longitude coordinates.

[0010] In a possible implementation, using the optimal longitude and latitude coordinates as the address of the bidding project includes: Obtaining a confidence score according to all confidences and the number of candidates of the plurality of geocoding service software; When the confidence score meets the conditions, the optimal longitude and latitude coordinates are determined as the bidding project address.

[0011] In a possible implementation, the two-dimensional Gaussian probability distribution model is:

[0012] Where, is the location probability density, is the reference coordinate, is the coordinate in the candidate coordinate set, For the i Service No. j The coordinate values ​​of candidate coordinates, is the scale parameter, , is the range radius.

[0013] In a possible implementation, the optimization formula is:

[0014]

[0015] Where, is the optimal longitude and latitude coordinates, is the reference coordinate, For the i The number of candidates for geocoding service software, is the total number of geocoding service software, For the i Service No. j The confidence of the candidate coordinates, For the i Service No. j The coordinate values ​​of candidate coordinates, is the constraint weight, is the scale parameter, .

[0016] In a second aspect, the present invention further provides a device for confirming a bid information project address, comprising: A text decomposition module is used to obtain a bidding project address text, decompose the bidding project address text, and obtain a decomposition result; the decomposition result includes fuzzy modifiers; the fuzzy modifiers are words that express spatial uncertainty; A coordinate identification module is used to identify the decomposition results in multiple geocoding service software respectively to obtain a candidate coordinate set corresponding to each geocoding service software; a range quantization module, configured to quantify the fuzzy modifier and the candidate coordinate set based on a two-dimensional Gaussian probability distribution model to obtain a fuzzy range; The data fusion module is used to perform data fusion on the candidate coordinate set and the fuzzy range to obtain the optimal longitude and latitude coordinates, and use the optimal longitude and latitude coordinates as the address of the bidding project.

[0017] In a third aspect, an embodiment of the present invention discloses an electronic device comprising: a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the various steps of the above-mentioned method for confirming the address of a bidding project.

[0018] In a fourth aspect, an embodiment of the present invention discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the various steps of the above-mentioned method embodiment for confirming the address of a bidding project are implemented.

[0019] The beneficial effects of the present invention are: decomposing the address text of the bidding project to obtain a decomposition result; the decomposition result includes fuzzy modifiers; thereby, the fuzzy modifiers in the address text of the bidding project can be identified; the decomposition results can also be identified in multiple geocoding service software respectively to obtain a candidate coordinate set corresponding to each geocoding service software, thereby improving the coverage and accuracy of address resolution by using multiple geocoding service software to obtain candidate coordinates, and solving the problem of dependence on a single data source; based on a two-dimensional Gaussian probability distribution model, the fuzzy modifiers and the candidate coordinate set are quantified to obtain a fuzzy range, and the spatial range of fuzzy modifiers such as "nearby" is quantified, solving the problem of insufficient fuzziness processing; data fusion is performed on the candidate coordinate set and the fuzzy range to obtain the optimal longitude and latitude coordinates, and the problem of lack of mathematical optimization is solved by multi-source data fusion, so that the method can adapt to complex scenes, and a more accurate bidding project address can be obtained according to the optimal longitude and latitude coordinates, thereby improving the visualization display effect of the project location. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flow chart of an embodiment of a method for confirming a bid information project address provided by the present invention; Figure 2 For the present invention Figure 1 A schematic flow chart of an embodiment of step S103; Figure 3 For the present invention Figure 1 A schematic flow chart of an embodiment of step S104; Figure 4 A schematic structural diagram of an embodiment of a device for confirming an address of a bidding project provided by the present invention; Figure 5 This is a schematic structural diagram of an embodiment of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0021] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0022] In existing technologies, project addresses in bidding information systems usually exist in the form of unstructured text, and address descriptions often contain administrative divisions, street names, and ambiguous modifiers. Traditional geocoding technology relies on a single address resolution service and cannot effectively handle ambiguous spatial descriptions, resulting in insufficient coordinate conversion accuracy. For example, when the address text contains ambiguous modifiers such as "nearby" and "approximately", existing methods have difficulty quantifying its spatial range, and address matching in remote areas is prone to failure due to incomplete data coverage. This situation often leads to coordinate offset or positioning failure when the project location needs to be accurately located for visual display.

[0023] To address these issues, we first need to solve the quantification challenge of fuzzy modifiers. Traditional rule-based matching methods cannot adapt to dynamic range changes, so we are considering introducing probabilistic models to model spatial distribution. To address the insufficient coverage of a single data source, we can improve data reliability by integrating the results of multiple geocoding service software. Finally, we need to design a mathematical optimization model to fuse heterogeneous data from multiple sources, rather than simply using a weighted average. This approach breaks away from the single matching model of traditional geocoding technology and combines fuzzy semantic analysis with multi-source data fusion.

[0024] like Figure 1 As shown, a specific embodiment of the present invention discloses a method for confirming a bidding project address, comprising: S101. Obtain a bidding project address text, decompose the bidding project address text, and obtain a decomposition result; the decomposition result includes fuzzy modifiers; and the fuzzy modifiers are words that express spatial uncertainty.

[0025] Address text for bidding projects refers to unstructured address information containing administrative divisions, road names, and fuzzy spatial descriptions. It can be presented in natural language text, such as "near the east side of XX Road, XX City, XX Province." Fuzzy modifiers are words that express spatial uncertainty and can be identified using semantic analysis tools. For example, words like "near," "around," and "approximately" are used to describe the spatial distribution range of coordinate points.

[0026] S102: Identify the decomposition results in multiple geocoding service software respectively to obtain a candidate coordinate set corresponding to each geocoding service software.

[0027] Among them, geocoding service software refers to the application program interface that provides the function of converting addresses to coordinates. Specifically, third-party services such as Baidu Map API and Amap API can be used to improve the reliability of address resolution through the complementarity of multi-source data. A , set up the i The candidate coordinates returned by the service are shown in formula (1): (1) Where, and Represents latitude and longitude respectively, No. i The number of candidates for a service.

[0028] Shared n services, total candidate coordinate set As shown in formula (2): (2) S103 , quantifying the fuzzy modifiers and the candidate coordinate set based on a two-dimensional Gaussian probability distribution model to obtain a fuzzy range.

[0029] Among them, the two-dimensional Gaussian probability distribution model refers to a mathematical model that describes the probability density of two-dimensional space. Specifically, it can be parameterized using a covariance matrix and a mean vector to convert fuzzy modifiers into a computable spatial probability distribution.

[0030] S104: Perform data fusion on the candidate coordinate set and the fuzzy range to obtain the optimal longitude and latitude coordinates, and use the optimal longitude and latitude coordinates as the address of the bidding project.

[0031] Data fusion refers to the process of optimizing the calculation of multi-source coordinate data. Specifically, the maximum likelihood estimation or Bayesian inference method can be used to combine the probability distribution characteristics to generate the optimal longitude and latitude coordinates.

[0032] Specifically, after obtaining the address text containing fuzzy modifiers, semantic decomposition is used to extract structured information such as administrative divisions and road names. These structured information are input into multiple geocoding service software to obtain the candidate coordinate sets returned by each service. For example, inputting "east side of XX Road" may result in ten candidate coordinates returned by three services. The spatial expansion radius is determined based on the fuzzy modifiers. For example, "nearby" corresponds to a range of 500 meters, and a two-dimensional Gaussian distribution model is constructed with the mean of the candidate coordinates as the reference point. By calculating the distribution weight of each candidate coordinate in the probability density space and combining the confidence of multi-source data for weighted optimization, the optimal longitude and latitude coordinates located in the area with the maximum probability density are finally generated, and the optimal longitude and latitude coordinates are used as the address of the bidding project. This process effectively integrates the processing of semantic parsing, multi-source data verification, and mathematical optimization at three levels to ensure that the output coordinates are consistent with both the text description and the spatial distribution characteristics.

[0033] Compared with existing technologies, traditional methods rely on a single geocoding service software to directly output coordinates, are unable to handle fuzzy descriptions, and lack a data verification mechanism. The embodiments of the present invention effectively circumvent the problem of missing or parsed data from a single service through multi-service collaborative identification. Furthermore, a probabilistic model is used to quantify the fuzzy spatial extent, converting natural language descriptions into computable mathematical parameters, addressing the poor adaptability of traditional rule-matching methods. Furthermore, the optimized calculation process comprehensively considers the confidence distribution of multi-source data, ensuring higher accuracy than simple coordinate averaging methods.

[0034] Through the above technical solution, this application can accurately parse unstructured address text containing fuzzy modifiers, improve data reliability through collaborative verification of multi-source geocoding service software, quantify the range of spatial uncertainty using probabilistic models, and ultimately output optimal coordinates that conform to the actual location distribution characteristics. This effectively solves the accuracy issues of traditional methods in processing fuzzy addresses and locating in remote areas, providing accurate geographic coordinate data support for the visualization of project locations.

[0035] In some embodiments of the present invention, the bid information project address text is decomposed to obtain the decomposition result, including: Set the decomposition type; decomposition types include administrative divisions, specific addresses and fuzzy modifiers; The address text of the bidding project is decomposed according to regular expressions and word segmentation tools to obtain the decomposition results corresponding to each decomposition type.

[0036] In a specific embodiment of the present invention, the decomposition type refers to a predefined classification category of address elements, which can be implemented by using a three-category classification method of administrative divisions, specific addresses, and fuzzy modifiers. This classification can structure the processing of different semantic units in non-standardized address texts. Regular expressions refer to matching fixed-format administrative division information in address texts through predefined pattern strings. Specifically, it can be implemented using Python's regular expression operation module, which can effectively identify regular expressions such as "XX Province XX City". The word segmentation tool refers to a dictionary-based text segmentation component, which can be implemented using the Jieba word segmentation tool. By loading a custom address dictionary, the continuous text is segmented to extract non-structured elements such as specific addresses and fuzzy modifiers.

[0037] Specifically, a structured processing framework for address text is established by setting up three decomposition types: administrative divisions, specific addresses, and fuzzy modifiers. Regular expressions are used to accurately match administrative division information with fixed formats, for example, matching administrative division levels containing keywords such as "province," "city," and "district." A word segmentation tool is simultaneously used to intelligently segment the remaining text. By loading an address dictionary containing specialized terms such as street names and landmarks, specific address elements and fuzzy modifiers are accurately identified. The combined application of these two technical approaches achieves effective synergy between rule matching and semantic segmentation, transforming unstructured address text into a structured data set with clear semantic tags.

[0038] Compared with existing technologies, traditional methods typically fail to clearly define the decomposition type hierarchy and often rely on single regular expression matching or simple word segmentation to process address text, resulting in incomplete recognition of address elements. The present invention significantly improves the completeness and accuracy of address element extraction by establishing a three-level decomposition system and combining the complementary advantages of regular expressions and word segmentation tools. In particular, it can effectively separate fuzzy modifiers that affect location accuracy.

[0039] Through the above technical solution, this application achieves a refined decomposition of the address text in the bid information, ensuring the independent processing of various address elements in the subsequent geocoding process. In particular, by separating fuzzy modifiers, this provides key input parameters for the subsequent establishment of a two-dimensional Gaussian probability distribution model. This overcomes the difficulty of quantitative modeling caused by the mixing of address elements in traditional methods, and improves the accuracy of coordinate positioning from the source of data processing.

[0040] In some embodiments of the present invention, Figure 2 As shown, step S103 includes: S201. Determine the address scenario according to the address text of the bidding project.

[0041] Among them, the address scenario refers to the administrative level or functional type to which the project address belongs. This can be achieved by using natural language processing technology to identify the administrative division level in the address text, such as distinguishing between provincial, municipal or community-level scenarios.

[0042] S202: Set the range radius of the fuzzy modifier according to the address scenario.

[0043] The range radius refers to the spatial coverage distance corresponding to the fuzzy modifier, which can be achieved by pre-setting the radius value according to different address scenarios. For example, the urban scenario is set to 500 meters, and the suburban scenario is set to 500 meters.

[0044] S203. Obtain reference coordinates according to the spatial distribution center point of the candidate coordinate set.

[0045] Among them, the reference coordinates refer to the spatial distribution center point of the candidate coordinate set, which can be specifically achieved by calculating the arithmetic mean or geometric median of the candidate coordinates. The specific zone confirmation process can be set according to actual conditions, and the embodiment of the present invention is not limited here.

[0046] S204: quantify the range radius and the reference coordinates according to a two-dimensional Gaussian probability distribution model to obtain a position probability density.

[0047] Among them, the two-dimensional Gaussian probability distribution model refers to a mathematical model used to describe the probability density of spatial positions. Specifically, it can be achieved by adjusting the probability attenuation rate by setting a scale parameter, and this parameter is positively correlated with the range radius.

[0048] S205: Obtain an initial range according to the range radius and the reference coordinates, and confirm the geographical area boundary in the initial range whose position probability density is greater than a preset threshold as a fuzzy range.

[0049] In a specific embodiment of the present invention, when fuzzy modifiers such as "nearby" and "surrounding" appear in the address text, the address scene category is first determined through semantic analysis. For example, for the address "near XXX, XX village, XX district, XX city", it is identified as belonging to the urban core area scene and automatically matched with the preset 500-meter range radius. Then, the coordinate point with the highest frequency of occurrence is selected as the reference coordinate from the candidate coordinates returned by multiple geocoding service software, or the geometric center of the coordinate set is calculated by a clustering algorithm. The range radius is then input as the standard deviation parameter into the two-dimensional Gaussian probability distribution model, and the reference coordinate is used as the probability density peak point to generate a probability distribution surface that exponentially decays with increasing distance. The two-dimensional Gaussian probability distribution model is shown in formula (3): (3) Where, is the location probability density, is the reference coordinate, is the coordinate in the candidate coordinate set, For the i Service No. j The coordinate values ​​of candidate coordinates, is the scale parameter, , is the range radius.

[0050] Draw a circle with the reference coordinate as the center and the radius as the range radius to obtain the initial range. Then, the boundary of the geographic area in the initial range whose position probability density is greater than the preset threshold is output as the fuzzy range. The range appears as an irregular ellipse centered on the reference point.

[0051] Compared to existing technologies, traditional geocoding techniques only use fixed-distance buffers for fuzzy modifiers, such as a uniform 500-meter range, which fails to distinguish the spatial characteristics of different scenarios. This solution, however, dynamically adjusts the range radius by classifying address scenarios and builds a probabilistic model based on the distribution characteristics of candidate coordinate sets. This allows the quantification of fuzzy ranges to take into account both semantic characteristics and spatial data distribution patterns, effectively addressing the limited adaptability of a single buffer approach in diverse scenarios, such as mountainous areas and urban areas.

[0052] Through the above technical solution, this application can dynamically generate probabilistic fuzzy ranges that match the scene based on the actual semantic context of the address text and the spatial distribution characteristics of the candidate coordinates, significantly improving the spatial quantification accuracy of fuzzy modifiers. This technical solution combines semantic logic and spatial statistics during the address resolution process, allowing the resulting fuzzy range to accurately reflect the uncertainty in the address description while adapting to the spatial expansion characteristics of different geographical environments.

[0053] In some embodiments of the present invention, Figure 3 As shown, step S104 includes: S301: Obtain corresponding confidence levels according to the candidate coordinate sets of each geocoding service software.

[0054] Confidence refers to the reliability of the candidate coordinate set returned by the geocoding service software. It can be calculated based on the historical matching accuracy or the precision index provided by the service provider, and is used to measure the credibility of the results returned by different services.

[0055] S302: Set constraint weights according to the address scenario.

[0056] Among them, constraint weight refers to dynamically adjusting the importance of different geocoding service software in the optimization process according to the address scenario. It can be implemented using a preset scenario-weight mapping table. For example, high-precision map services are given higher weights in the city center area, while the weights of open map services are increased in rural areas.

[0057] S303: Optimize the confidence, constraint weight, and location probability density of each geocoding service software based on the optimization formula to obtain the optimal longitude and latitude coordinates.

[0058] The optimization formula is a mathematical expression that performs weighted calculations on confidence, weight, and probability density. It can be implemented in the form of multivariate linear combination or probability product to generate the optimal solution based on multi-source data. The goal is to find the optimal longitude and latitude coordinates. , so that it minimizes the weighted sum of squared distances while considering the probability constraints of the fuzzy range. The optimization problem formula is defined as shown in formula (4): (4) Where, is the optimal longitude and latitude coordinates, is the reference coordinate, For the i The number of candidates for geocoding service software, is the total number of geocoding service software, For the i Service No. j The confidence of the candidate coordinates, For the i Service No. j The coordinate values ​​of candidate coordinates, is the constraint weight, is the scale parameter, , is the range radius.

[0059] Let the objective function be as shown in formula (5): (5) right and Calculate the partial derivatives and set them to zero. As shown in formulas (6) and (7): (6) (7) The optimization formulas are shown in formulas (8) and (9): (8) (9) Specifically, during the data fusion process, the confidence score is first assigned to the candidate coordinates returned by each geocoding service software. For example, the coordinates returned by the Baidu Map API can be assigned a confidence score of 0.9 based on its officially announced positioning accuracy level. Then, the preset constraint weight parameters are loaded according to the current address scenario. For example, when an address contains the word "industrial park," the weight allocation scheme corresponding to the industrial zone scenario is automatically applied. Finally, the confidence level, weight, and location probability density calculated by the two-dimensional Gaussian model are substituted into the optimization formula, and the final coordinates are determined through weighted calculation. For example, for an industrial park address, the calculation combines the candidate coordinate confidence level of 0.85 and the preset weight of 0.7 from Amap with the Gaussian probability value of 0.92 within the fuzzy range to generate the optimal longitude and latitude.

[0060] Compared to existing technologies, existing geocoding methods only use coordinate matching results from a single data source, failing to consider differences in confidence levels among different service providers or the impact of scenario characteristics on weight allocation. This method, by establishing a mathematical optimization model, dynamically integrates the results of multiple geocoding service software. It also introduces a confidence assessment mechanism and scenario-based weight adjustment during the data processing phase, effectively addressing the positioning errors caused by single-data source bias in traditional methods.

[0061] Through the above technical solution, this application can realize the adaptive fusion of multi-source geographic data. In the presence of fuzzy modifiers and multiple candidate coordinates, it can generate the optimal positioning results through quantitative evaluation and scenario-based parameter configuration, significantly improving the accuracy and reliability of the address coordinates of the bidding project, and providing accurate coordinate input for subsequent geographic information visualization.

[0062] In some embodiments of the present invention, step S104 includes: A confidence score is obtained based on all confidence levels and the number of candidates from multiple geocoding service software.

[0063] Among them, confidence refers to the degree of trust that each geocoding service software has in the candidate coordinates. Specifically, it can be achieved by calculating the matching score or historical accuracy rate returned by the service to measure the credibility of the coordinates. The number of candidates refers to the total number of candidate coordinates returned by each geocoding service software. Specifically, it can be achieved by counting the number of coordinate entries in the service response results, reflecting the coverage and diversity of the data source. The confidence score refers to an evaluation indicator that combines the confidence and the number of candidates. Specifically, it can be achieved by weighted summation or product operation. For example, the confidence and the number of candidates are combined in proportion to quantify the comprehensive credibility of the coordinate results. The calculation of the confidence score is shown in formula (10): (10) When the confidence score meets the conditions, the optimal longitude and latitude coordinates are determined as the bid information project address.

[0064] Among them, the conditions refer to preset thresholds or logical judgment rules. For example, coordinates with a score higher than 0.7 or ranking in the top 10% are set as valid results, which are used to screen geographic coordinates that meet the reliability standards.

[0065] Specifically, after the data is fused to obtain the optimal longitude and latitude coordinates, its validity needs to be further verified. By counting the number of candidates of all geocoding service software and combining the confidence of each candidate coordinate, a comprehensive confidence score is calculated. For example, a geocoding service software returns 5 candidate coordinates with an average confidence of 0.8, and another service returns 3 candidate coordinates with an average confidence of 0.9. The two can be normalized and the total score can be calculated weightedly. When the score reaches the preset threshold, for example, the total score exceeds 0.75, the optimal longitude and latitude coordinates are determined to be valid and used as the final address of the bidding project; if the threshold is not reached, the re-analysis or manual intervention process is triggered to avoid the wrong coordinates from being adopted.

[0066] Compared to existing technologies, traditional methods rely solely on the confidence level of a single geocoding service or a simple majority voting mechanism, failing to consider the impact of differences in the number of results returned by different services on credibility. For example, when a service returns a large number of low-quality candidate coordinates, this superiority in numbers can lead to misjudgments. However, this solution utilizes a dual verification mechanism based on confidence level and candidate number, dynamically adjusting evaluation weights and effectively avoiding positioning bias caused by uneven data source coverage or noise interference.

[0067] Through the above technical solution, this application can establish dynamic verification standards based on the statistical characteristics of multi-source data, solve the misjudgment problem caused by a single evaluation indicator in traditional methods, and ensure that the final coordinates meet the requirements in terms of confidence and data coverage completeness, thereby improving the accuracy of the bidding project address and the robustness of the system.

[0068] In the embodiment of the present invention, the address text of the bidding project can be "XX City XX District XX Village XXX Nearby", and the decomposition result can be "XX City XX District XX Village", "XXX" and "Nearby". Baidu Map: (39.9691, 116.4892) (confidence level 0.95). Amap: (39.9690, 116.4895) (confidence level 0.90). Fuzzy range: Set r =500 m, =166.7 m, reference coordinates (39.9691,116.4892). : 0.95 and 0.90 respectively. λ =0.1. Substituting into the formula, we get =39.9691, =39.9691. Output: The optimal longitude and latitude coordinates are (39.9691, 116.4892), with a confidence score of 0.90. If the condition is greater than 50%, the confidence score is determined to meet the condition, and the optimal longitude and latitude coordinates (39.9691, 116.4892) are determined as the project coordinates.

[0069] The embodiment of the present invention solves the problem of parsing unstructured addresses through address text preprocessing, breaking down the address into processable components. Multi-source geocoding improves the coverage and accuracy of address resolution by using multiple services to obtain candidate coordinates, and solves the problem of dependence on a single data source. Fuzzy range modeling quantifies the spatial range of fuzzy modifiers such as "nearby" by constructing a two-dimensional Gaussian probability distribution model, solving the problem of insufficient fuzziness processing. The multi-source data fusion and optimization step introduces a mathematical optimization model, which fuses multi-source data through weighted least squares and Bayesian inference to calculate the optimal latitude and longitude coordinates. This solves the problem of lack of mathematical optimization and enables the method to adapt to complex scenarios.

[0070] In order to better implement the bid information project address confirmation method in the embodiment of the present invention, based on the bid information project address confirmation method, the embodiment of the present invention also provides a bid information project address confirmation device, such as Figure 4 As shown, the bidding project address confirmation device 400 includes: The text decomposition module 401 is used to obtain the bidding project address text, decompose the bidding project address text, and obtain a decomposition result; the decomposition result includes fuzzy modifiers; fuzzy modifiers are words that express spatial uncertainty; A coordinate identification module 402 is used to identify the decomposition results in multiple geocoding service software respectively to obtain a candidate coordinate set corresponding to each geocoding service software; A range quantification module 403 is used to quantify the fuzzy modifier and the candidate coordinate set based on a two-dimensional Gaussian probability distribution model to obtain a fuzzy range; The data fusion module 404 is used to perform data fusion on the candidate coordinate set and the fuzzy range to obtain the optimal longitude and latitude coordinates, and use the optimal longitude and latitude coordinates as the address of the bidding project.

[0071] The bid information project address confirmation device 400 provided in the above embodiment can implement the technical solution described in the above bid information project address confirmation method embodiment. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above bid information project address confirmation method embodiment, which will not be repeated here.

[0072] like Figure 5 As shown, the present invention also provides an electronic device 500. The electronic device 500 includes a processor 501, a memory 502 and a display 503. Figure 5 Only some of the components of the electronic device 500 are shown, but it should be understood that implementation of all of the shown components is not required, and more or fewer components may be implemented instead.

[0073] In some embodiments, the memory 502 may be an internal storage unit of the electronic device 500, such as a hard disk or memory of the electronic device 500. In other embodiments, the memory 502 may also be an external storage device of the electronic device 500, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 500.

[0074] Furthermore, the memory 502 may include both an internal storage unit of the electronic device 500 and an external storage device. The memory 502 is used to store application software installed in the electronic device 500 and various data.

[0075] In some embodiments, the processor 501 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program codes or process data stored in the memory 502, such as the bid signal project address confirmation method of the present invention.

[0076] In some embodiments, display 503 can be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 503 is used to display information about electronic device 500 and to display a visual user interface. Components 501-503 of electronic device 500 communicate with each other via a system bus.

[0077] In some embodiments of the present invention, when the processor 501 executes the bid information item address confirmation program in the memory 502, the following steps may be implemented: Obtain the bidding project address text, decompose the bidding project address text, and obtain a decomposition result; the decomposition result includes fuzzy modifiers; the fuzzy modifiers are words that express spatial uncertainty; Identify the decomposition results in multiple geocoding service software respectively to obtain the candidate coordinate set corresponding to each geocoding service software; Based on the two-dimensional Gaussian probability distribution model, the fuzzy modifiers and candidate coordinate sets are quantified to obtain the fuzzy range; Perform data fusion on the candidate coordinate set and the fuzzy range to obtain the optimal longitude and latitude coordinates, and use the optimal longitude and latitude coordinates as the address of the bidding project.

[0078] It should be understood that, when the processor 501 executes the signal item address confirmation program in the memory 502 , in addition to the above functions, it can also implement other functions. For details, please refer to the description of the corresponding method embodiment above.

[0079] Furthermore, the embodiments of the present invention do not specifically limit the type of electronic device 500 mentioned. The electronic device 500 may be a portable electronic device such as a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, or laptop computer. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The portable electronic devices mentioned above may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 500 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0080] Accordingly, an embodiment of the present application also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by the processor, it can implement the steps or functions of the bid signal project address confirmation method provided by the above-mentioned method embodiments.

[0081] Those skilled in the art will appreciate that all or part of the process flow of the above-described method embodiment can be implemented by instructing related hardware (such as a processor, controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0082] The above is a detailed introduction to the bidding project address confirmation method, device, electronic device and storage medium provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for technical personnel in this field, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for confirming the address of a bidding project, characterized in that: include: Obtaining a bid information project address text, decomposing the bid information project address text, and obtaining a decomposition result; The decomposition result includes fuzzy modifiers; The fuzzy modifiers are words that express spatial uncertainty; Identify the decomposition results in multiple geocoding service software respectively to obtain a candidate coordinate set corresponding to each geocoding service software; quantizing the fuzzy modifier and the candidate coordinate set based on a two-dimensional Gaussian probability distribution model to obtain a fuzzy range; The candidate coordinate set and the fuzzy range are subjected to data fusion to obtain the optimal longitude and latitude coordinates, and the optimal longitude and latitude coordinates are used as the address of the bidding project.

2. The method for confirming the address of a bidding project according to claim 1, characterized in that: Decomposing the bid information project address text to obtain a decomposition result includes: Setting the decomposition type; the decomposition type includes administrative division, specific address and the fuzzy modifier; The bid information project address text is decomposed according to regular expressions and word segmentation tools to obtain decomposition results corresponding to each decomposition type.

3. The method for confirming the address of a bidding project according to claim 1, wherein: The quantizing of the fuzzy modifier and the candidate coordinate set based on a two-dimensional Gaussian probability distribution model to obtain a fuzzy range includes: Determine the address scenario based on the address text of the bidding project; According to the address scenario, setting the range radius of the fuzzy modifier; Obtaining a reference coordinate according to the spatial distribution center point of the candidate coordinate set; quantifying the range radius and the reference coordinates according to the two-dimensional Gaussian probability distribution model to obtain a position probability density; An initial range is obtained according to the range radius and the reference coordinates, and a geographical area boundary in the initial range where the position probability density is greater than a preset threshold is confirmed as a fuzzy range.

4. The method for confirming the address of a bidding project according to claim 3, wherein: The step of fusing the candidate coordinate set and the fuzzy range to obtain the optimal longitude and latitude coordinates includes: Obtaining corresponding confidence levels according to the candidate coordinate sets of each geocoding service software; Set constraint weights based on the address scenario; The confidence, the constraint weight and the location probability density of each geocoding service software are optimized based on an optimization formula to obtain the optimal latitude and longitude coordinates.

5. The method for confirming the address of a bidding project according to claim 4, wherein: The step of using the optimal longitude and latitude coordinates as the bidding project address includes: Obtaining a confidence score according to all confidences and the number of candidates of the plurality of geocoding service software; When the confidence score meets the conditions, the optimal longitude and latitude coordinates are determined as the bidding project address.

6. The method for confirming the address of a bidding project according to claim 1, wherein: The two-dimensional Gaussian probability distribution model is: Where, is the location probability density, is the reference coordinate, is the coordinate in the candidate coordinate set, For the i Service No. j The coordinate values ​​of candidate coordinates, is the scale parameter, , is the range radius.

7. The method for confirming the address of a bidding project according to claim 4, wherein: The optimization formula is: Where, is the optimal longitude and latitude coordinates, is the reference coordinate, For the i The number of candidates for geocoding service software, is the total number of geocoding service software, For the i Service No. j The confidence of the candidate coordinates, For the i Service No. j The coordinate values ​​of candidate coordinates, is the constraint weight, is the scale parameter, , is the range radius.

8. A device for confirming the address of a bidding project, characterized in that: include: A text decomposition module is used to obtain a bid information project address text, decompose the bid information project address text, and obtain a decomposition result; The decomposition result includes fuzzy modifiers; The fuzzy modifiers are words that express spatial uncertainty; A coordinate identification module is used to identify the decomposition results in multiple geocoding service software respectively to obtain a candidate coordinate set corresponding to each geocoding service software; a range quantization module, configured to quantify the fuzzy modifier and the candidate coordinate set based on a two-dimensional Gaussian probability distribution model to obtain a fuzzy range; The data fusion module is used to perform data fusion on the candidate coordinate set and the fuzzy range to obtain the optimal longitude and latitude coordinates, and use the optimal longitude and latitude coordinates as the address of the bidding project.

9. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the steps of the method for confirming the address of a bidding project as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for confirming the address of a bidding project according to any one of claims 1 to 7 are implemented.

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