Data verification method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of artificial intelligence, and discloses a data verification method and device, computer equipment and a storage medium, and the method comprises the steps: detecting an agricultural insurance clause document reporting instruction, and obtaining a to-be-reported agricultural insurance clause document; identifying the claim payment standard of the to-be-reported agricultural insurance clause document, converting the identified claim payment standard into structured data, and recording the structured data as a target claim payment standard; storing the target compensation standard to a preset agricultural insurance compensation standard database, calculating semantic similarities between the target compensation standard and a plurality of original agricultural insurance compensation standards in the agricultural insurance compensation standard database, and verifying a repetition condition between the target compensation standard and the plurality of original agricultural insurance compensation standards according to the semantic similarities; and if so, generating alarm information. According to the embodiment of the invention, automatic processing can be realized, clause review time is reduced, and labor cost is saved by reducing manual review workload; conflicts caused by repetition of terms are reduced, and operation cost is saved.

Description

Technical Field

[0001] The present invention relates to the fields of artificial intelligence technology and financial technology, and in particular to a data verification method, device, computer equipment and storage medium. Background Art

[0002] In the field of agricultural insurance (abbreviated as agricultural insurance), the design of insurance clauses is usually customized according to different insurance objects (such as crops, livestock, etc.) and different regions. This customized clause design needs to consider many factors, such as climate, geographical environment, agricultural production methods, etc. Therefore, various types of agricultural insurance clauses are highly heterogeneous. With the continuous expansion and changes in business, agricultural insurance clauses often need to be updated and adjusted to adapt to new market demands and environmental changes. The emergence of new clauses often brings about duplication of compensation scope and compensation ratio, which in turn increases the management cost and complexity of insurance companies, and may also lead to compensation disputes.

[0003] Agricultural insurance clauses have different characteristics from other types of insurance. They have high requirements for regionality, specificity, and customization. It is impossible to cover all the subject matters through unified insurance clauses. Therefore, the current agricultural insurance clauses have a large number of reports and a high stock. In the past two years, the number of agricultural insurance clauses reported has reached 1,000+. Under such a business background, institutions may plan new insurance clauses for new subjects, which will inevitably lead to duplication of insurance liability and overlapping compensation scope, resulting in duplication and homogeneity in the reporting process of institutions. At present, clause verification requires manual verification, with an average processing time of 11 hours / clause, and cross-comparison of different clauses requires further time. Therefore, the current work of repetitive verification of clause responsibilities is still a time-consuming and labor-intensive pain point in the business process, and the cost of adding manpower is too high and cannot bring corresponding value.

[0004] At present, the scope of compensation and the ratio of compensation in agricultural insurance clauses require manual data verification during the updating and comparison process of the clauses. Manual comparison of the contents of these clauses is time-consuming and prone to errors.

[0005] Therefore, the prior art still needs to be improved and developed. Summary of the invention

[0006] In view of the above-mentioned deficiencies in the prior art, the present invention provides a data verification method, device, computer equipment and storage medium, aiming to solve the problem that the scope of compensation and the compensation ratio in the agricultural insurance clauses in the prior art need to be manually verified during the updating and comparison of the clauses, and manual comparison of the contents of these clauses is time-consuming and prone to errors.

[0007] The technical solution of the present invention is as follows:

[0008] A first embodiment of the present invention provides a data verification method, the method comprising:

[0009] Detect the agricultural insurance clause document reporting instruction and obtain the agricultural insurance clause document to be reported;

[0010] Identify the compensation standard of the agricultural insurance clause document to be reported, and convert the identified compensation standard into structured data, which is recorded as the target compensation standard;

[0011] Storing the target compensation standard in a preset agricultural insurance compensation standard database, wherein the preset agricultural insurance compensation standard database stores a plurality of original agricultural insurance compensation standards;

[0012] Calculating the semantic similarity between the target compensation standard and the several original agricultural insurance compensation standards respectively, and verifying the duplication between the target compensation standard and the several original agricultural insurance compensation standards according to the semantic similarity;

[0013] If the target compensation standard overlaps with the several original agricultural insurance compensation standards, an alarm message is generated.

[0014] Another embodiment of the present invention provides a data verification device, the device comprising:

[0015] A data acquisition module, used to detect an agricultural insurance clause document reporting instruction and obtain the agricultural insurance clause document to be reported;

[0016] A data conversion module is used to identify the compensation standard of the agricultural insurance clause document to be reported, and convert the identified compensation standard into structured data, which is recorded as the target compensation standard;

[0017] A data storage module, used to store the target compensation standard into a preset agricultural insurance compensation standard database, wherein the preset agricultural insurance compensation standard database stores a plurality of original agricultural insurance compensation standards;

[0018] A data verification module, used to respectively calculate the semantic similarity between the target compensation standard and the several original agricultural insurance compensation standards, and verify the duplication between the target compensation standard and the several original agricultural insurance compensation standards according to the semantic similarity;

[0019] The warning information generation module is used to generate a warning message if the target compensation standard is repeated with the several original agricultural insurance compensation standards.

[0020] Another embodiment of the present invention provides a computer device, the computer device comprising at least one processor; and

[0021] a memory communicatively connected to the at least one processor; wherein,

[0022] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the above-mentioned data verification method.

[0023] Another embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by one or more processors, the one or more processors can perform the steps of the above-mentioned data verification method.

[0024] Beneficial effect: The data verification method of the embodiment of the present invention can obtain the agricultural insurance clause document to be reported by detecting the agricultural insurance clause document reporting instruction; identify the compensation standard of the agricultural insurance clause document to be reported, and convert the identified compensation standard into structured data, which is recorded as the target compensation standard; store the target compensation standard in a preset agricultural insurance compensation standard database, wherein the preset agricultural insurance compensation standard database stores a number of original agricultural insurance compensation standards; calculate the semantic similarity between the target compensation standard and the several original agricultural insurance compensation standards respectively, and verify the duplication of the target compensation standard and the several original agricultural insurance compensation standards according to the semantic similarity; if the target compensation standard is repeated with the several original agricultural insurance compensation standards, an alarm message is generated. In the present invention, the automated processing of extracting and comparing the compensation standards of agricultural insurance clause documents reduces the clause review time by more than 80%, from an average of 11 hours for each clause review to less than 20 minutes; the automated semantic similarity calculation reduces the workload of manual review and saves labor costs; when there is duplication in agricultural insurance compensation standards, an alarm message is generated, and the staff can modify the agricultural insurance clauses based on the alarm message, thereby reducing the duplication and conflict of clauses and saving operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0026] Figure 1 A schematic diagram of an application environment of an embodiment of a data verification method of the present invention;

[0027] Figure 2 A flow chart of a preferred embodiment of a data verification method of the present invention;

[0028] Figure 3 for Figure 2 A flowchart of a specific embodiment of step S200;

[0029] Figure 4 for Figure 2 A flowchart of a specific embodiment of step S300;

[0030] Figure 5 for Figure 2 A flowchart of a specific embodiment of step S400;

[0031] Figure 6 for Figure 5 A flowchart of a specific embodiment of step S402;

[0032] Figure 7 A schematic diagram of functional modules of a preferred embodiment of a data verification device of the present invention;

[0033] Figure 8 A structural schematic diagram of a preferred embodiment of a computer device of the present invention;

[0034] Fig. 9 Another structural schematic diagram of a preferred embodiment of a computer device of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail below. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0036] The embodiments of the present invention are described below in conjunction with the accompanying drawings. Those skilled in the art will appreciate that, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0037] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, which is merely a way of distinguishing the objects of the same attributes when describing them in the embodiments of the present application. Here, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, so that the process, method, system, product or equipment comprising a series of units need not be limited to those units, but may include other units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0038] The method provided in this application can be applied to artificial intelligence (AI) scenarios. AI is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines so that the machines have the functions of perception, reasoning and decision-making. Research in the field of artificial intelligence includes robots, natural speech processing, computer vision, decision-making and reasoning, human-computer interaction, recommendation and search, basic AI theory, etc.

[0039] The intelligent question-answering processing method based on artificial intelligence provided by the embodiment of the present invention can be applied in Figure 1 In an application environment, the client communicates with the server through a network. The server can detect the agricultural insurance terms document reporting instruction through the client, and obtain the agricultural insurance terms document to be reported; identify the compensation standard of the agricultural insurance terms document to be reported, and convert the identified compensation standard into structured data, which is recorded as the target compensation standard; store the target compensation standard in a preset agricultural insurance compensation standard database, wherein the preset agricultural insurance compensation standard database stores several original agricultural insurance compensation standards; calculate the semantic similarity between the target compensation standard and the several original agricultural insurance compensation standards respectively, and compare the duplication between the target compensation standard and the several original agricultural insurance compensation standards according to the semantic similarity. Verification is performed; if the target compensation standard is repeated with the several original agricultural insurance compensation standards, an alarm message is generated. In the present invention, the automatic processing of the extraction and comparison of the compensation standards of the agricultural insurance clause documents reduces the clause review time by more than 80%, from the original average of 11 hours for each clause review to less than 20 minutes; the automatic semantic similarity calculation reduces the workload of manual review and saves manpower costs; when there is duplication of agricultural insurance compensation standards, an alarm message is generated, and the staff can modify the agricultural insurance clauses through the alarm message, thereby reducing the duplication and conflict of clauses and saving operating costs. Among them, the client can be but not limited to various personal computers, laptops, smart phones, tablet computers and portable wearable devices. The server can be implemented with an independent server or a server cluster composed of multiple servers. The present invention is described in detail below through specific embodiments.

[0040] In view of the above problems, an embodiment of the present invention provides a data verification method, please refer to Figure 2 , Figure 2 FIG. 1 is a flow chart of a preferred embodiment of a data verification method of the present invention. Figure 2 As shown, it includes:

[0041] Step S100: Detect an agricultural insurance clause document reporting instruction and obtain the agricultural insurance clause document to be reported.

[0042] The data verification method in the embodiment of the present invention is applied to a server side that verifies the duplication of the compensation standard of the reported agricultural insurance clause with the existing compensation standard in the agricultural insurance scenario, and the server side can verify the duplication.

[0043] When a new agricultural insurance clause appears in an insurance institution, it is necessary to report the agricultural insurance clause and generate an agricultural insurance clause document. The agricultural insurance clause document includes but is not limited to the clause table data generated by the institution number, underwriting institution, product code, product name, crop growth period and compensation scope.

[0044] The table data in the agricultural insurance clause document to be reported may be a non-docx format, an unclearly described table, a simple table, or a complex table (such as a table with merged cells).

[0045] Step S200: Identify the compensation standard of the agricultural insurance clause document to be reported, and convert the identified compensation standard into structured data, which is recorded as the target compensation standard.

[0046] The compensation standards for agricultural insurance vary depending on factors such as crop type, growing period, degree of loss and regional policies.

[0047] Agricultural insurance includes crop insurance and livestock insurance compensation. For example, an example of the compensation standard for major crop insurance in crop insurance is as follows:

[0048] Corn: The premium is usually 18 yuan / mu, and the insured amount is 400 yuan / mu. The compensation amount is calculated based on the loss rate and the damaged area. When the loss rate reaches a certain proportion (such as more than 80%), it may be regarded as a total loss and compensated in full according to the insured amount.

[0049] Wheat: Premium 18 yuan / mu, insured amount 450 yuan / mu. Compensation method is the same as corn.

[0050] Rice: The premium is 30 yuan / mu, and the insured amount may vary according to different policies. For example, in some places, the insured amount is 650 yuan / mu, while in other places it may be higher (such as 1,100 yuan / mu). The compensation amount is also calculated based on the loss rate and the damaged area, and there is a starting point for compensation (such as 30%).

[0051] Other crops: such as peanuts, apples, etc., the premiums and insured amounts are also different, and the compensation standards need to be determined based on the specific crops and insurance terms.

[0052] The compensation standard for livestock insurance (such as breeding sows, dairy cows, fattening pigs, etc.) is usually determined by the insurance amount and the extent of the loss. For example, the insurance amount for breeding sows may be 1,500 yuan per head. When a loss occurs within the scope of insurance liability, the compensation amount will be determined according to the extent of the loss and the insurance terms.

[0053] Use image recognition technology or artificial intelligence technology to identify the compensation standards of the agricultural insurance clause documents to be reported, and extract the identified content. The extracted content includes but is not limited to the agency number, underwriting agency, product code, product name, crop growth period and compensation scope. Convert the extracted compensation standards into structured data and record them as target compensation standards.

[0054] Among them, Figure 3 As shown, in step S200, the compensation standard of the agricultural insurance clause document to be reported is identified, and the identified compensation standard is converted into structured data, which is recorded as the target compensation standard, including:

[0055] Step S201: identifying the compensation standard of the agricultural insurance clause document to be reported based on the multimodal large model to obtain the identified compensation standard;

[0056] Step S202: convert the identified compensation standard into structured data in a preset format, which is recorded as the target compensation standard.

[0057] Use the multimodal big model to automatically identify and extract table content from agricultural insurance clause documents. Specifically, use the multimodal big model optical character recognition (OCR) technology to extract text information from scanned or image-formatted clause documents. Structure the table content into a comparable data format.

[0058] The modal big model is a machine learning technology based on deep learning. Its core idea is to integrate different media data (such as text, images, audio and video) and achieve more intelligent information processing by learning the relationship between different modalities. The multimodal big model refers to a model that combines multimodal information such as text, images, video, audio, etc. for training.

[0059] The multimodal large model can achieve: Cross-modal understanding: It can not only understand the information of a single modality, but also understand the relationship between them across modalities. Superior performance: It has achieved excellent performance in many fields, such as medical imaging diagnosis, intelligent security, intelligent transportation, etc. Strong scalability: It can be applied to different tasks and provide a variety of intelligent services.

[0060] The multimodal large model first learns through modal representation: data of different modalities are converted into a unified data representation, such as converting images into feature vectors and text into word vectors.

[0061] Next, feature fusion is performed: the representations of different modalities are fused to obtain more comprehensive information. Common fusion methods include concatenation, weighted averaging, and attention mechanisms.

[0062] The attention mechanism is introduced again: enabling the model to focus on the most important information in different modalities and improve the accuracy and efficiency of information processing.

[0063] Finally, multi-task learning; by learning multiple related tasks at the same time, more comprehensive information fusion and more efficient model training can be achieved.

[0064] Among them, OCR (Optical Character Recognition) is a technology that converts text in an image into editable and searchable electronic text by scanning or taking a photo. OCR technology is widely used in document processing, digital libraries, automated office, data entry, barrier-free reading and many other fields.

[0065] OCR technology usually includes the following steps:

[0066] Image preprocessing: Preprocess the input image, including removing noise, adjusting contrast, binarization, etc., to better recognize text.

[0067] Layout analysis: Perform layout analysis on the preprocessed image to identify text areas, image areas, table areas, etc., and determine the arrangement direction of the text (such as horizontal, vertical, inclined, etc.).

[0068] Character segmentation: Segmenting text in a text area into individual characters or words is one of the key steps in OCR recognition.

[0069] Feature extraction: Extract features of each character or word, including shape, size, strokes, etc., for subsequent classification and recognition.

[0070] Character recognition: Using machine learning or pattern recognition algorithms, the extracted features are matched with predefined character templates or training sets to determine the specific content of each character or word.

[0071] Post-processing: Post-process the recognition results, including error correction, format adjustment, and reformatting, to improve the accuracy and readability of the recognition results.

[0072] OCR can be used to identify the compensation standard table of agricultural insurance terms and conditions documents in image format, and the identified data can be organized into structured data in the specified format and recorded as the target compensation standard.

[0073] The accuracy of table extraction and data verification using a multimodal large model reached over 95%, significantly higher than the average accuracy of 70% for traditional comparisons.

[0074] Step S300: storing the target compensation standard in a preset agricultural insurance compensation standard database, wherein the preset agricultural insurance compensation standard database stores a plurality of original agricultural insurance compensation standards.

[0075] Acquire the existing agricultural insurance clauses in advance. Based on the OCR capability of the multimodal large model, we will identify and extract the tables in the document. After extraction, we will sort out the clauses of each institution by institution and store them in the preset agricultural insurance compensation standard database. In this application, when obtaining the target compensation standard, in order to facilitate data comparison, the target compensation standard is stored in the preset agricultural insurance compensation standard database. It is identified through the crop insurance clauses and the extracted fields are stored in the warehouse to become the basis of agricultural insurance data.

[0076] Among them, Figure 4 As shown, in step S300, the target compensation standard is stored in a preset agricultural insurance compensation standard database, wherein the preset agricultural insurance compensation standard database stores a number of original agricultural insurance compensation standards, including:

[0077] Step S301: based on the agricultural insurance clause document to be reported, obtaining the issuing institution corresponding to the target compensation standard;

[0078] Step S302: storing the target compensation standard in a preset agricultural insurance compensation standard database according to the issuing institution, wherein the preset agricultural insurance compensation standard database stores a plurality of original agricultural insurance compensation standards.

[0079] The agricultural insurance clause document to be reported is parsed to obtain the issuing agency of the target compensation standard in the agricultural insurance clause document to be reported. For the convenience of management, the target compensation standard can be stored in a preset agricultural insurance compensation standard database according to the issuing agency.

[0080] Insurance issuing institutions mainly refer to insurance companies, which are the core entities of the insurance market and are responsible for designing, issuing and managing various insurance products to disperse and transfer risks and provide economic protection for customers. Insurance institutions are financial institutions established and operated in accordance with the law. They collect insurance premiums, establish insurance funds, and bear the responsibility of compensating insurance money for property losses caused by the agreed insurance accidents. The insurance institutions in this invention are divided by province.

[0081] The present invention extracts a table corresponding to the compensation standard and stores it in a database, which can be used as the basic input business data of the loss estimation project and become the data basis of agricultural insurance.

[0082] Step S400: Calculate the semantic similarity between the target compensation standard and the plurality of original agricultural insurance compensation standards respectively, and verify the duplication between the target compensation standard and the plurality of original agricultural insurance compensation standards according to the semantic similarity.

[0083] The natural language processing (NLP) technology is used to perform semantic analysis and comparison on the table contents in different agricultural insurance clauses. By calculating the semantic similarity of the agricultural insurance compensation standard, the duplication of the compensation scope between the agricultural insurance compensation clause to be reported and the existing original agricultural insurance compensation standard is obtained. In an embodiment of the present invention, the duplication of the target compensation standard and several original agricultural insurance compensation standards is verified one by one, wherein the duplication includes but is not limited to the overlap of the compensation scope and compensation ratio in the compensation standard. For example, if the target compensation standard corresponding to the agricultural insurance compensation clause to be reported stipulates that the damage area of ​​wheat during the growing period is greater than or equal to 40%, then compensation is required. If the original agricultural insurance compensation standard of the original agricultural insurance compensation clause stipulates that the damage area of ​​wheat during the growing period is greater than or equal to 30%, then compensation is required. At this time, it is determined that there is a duplication of the compensation scope between the agricultural insurance compensation clause to be reported and the existing original agricultural insurance compensation standard. Among them, NLP, the full name of Natural Language Processing, is an important direction in the fields of computer science and artificial intelligence. It studies various theories and methods for effective communication between people and computers using natural language. NLP is a science that integrates linguistics, computer science, and mathematics. Therefore, research in this field will involve natural language, that is, the language people use in daily life, so it is closely related to the study of linguistics. Natural language processing technology usually includes text processing, semantic understanding, paragraph analysis, machine translation, information extraction, question-answering system, dialogue system, text classification, sentiment analysis and other aspects. Through natural language processing technology, the workload of manual review can be reduced and labor costs can be saved.

[0084] Among them, Figure 5 As shown, step S400, namely respectively calculating the semantic similarity between the target compensation standard and the plurality of original agricultural insurance compensation standards, and verifying the duplication between the target compensation standard and the plurality of original agricultural insurance compensation standards according to the semantic similarity, comprises:

[0085] Step S401, inputting the target compensation standard and the several original agricultural insurance compensation standards into a semantic similarity calculation model;

[0086] Step S402: obtaining the semantic similarity output by the semantic similarity calculation model, and verifying the duplication between the target compensation standard and the plurality of original agricultural insurance compensation standards according to the semantic similarity.

[0087] The target compensation standard and the several original agricultural insurance compensation standards are input into a semantic similarity calculation model, which is an artificial intelligence model. By pre-collecting annotated samples of agricultural insurance compensation standard data, the artificial intelligence model is trained based on the annotated samples to obtain a semantic similarity model. The semantic similarity output by the semantic similarity calculation model is obtained, and the duplication of the target compensation standard and the compensation scope and compensation ratio of the several original agricultural insurances is verified according to the value of the semantic similarity.

[0088] Among them, Figure 6 As shown, step S402, i.e., obtaining the semantic similarity output by the semantic similarity calculation model, and verifying the duplication between the target compensation standard and the plurality of original agricultural insurance compensation standards according to the semantic similarity, includes:

[0089] Step S421: Obtaining the semantic similarity output by the semantic similarity calculation model, wherein the semantic similarity calculation model is a deep learning model;

[0090] Step S422, determining whether the semantic similarity is greater than or equal to a preset similarity threshold, if so, executing step S423, if not, executing step S424;

[0091] Step S423, determining whether the target compensation standard and the plurality of original agricultural insurance compensation standards are repeated;

[0092] Step S424: determine whether the target compensation standard and the plurality of original agricultural insurance compensation standards are overlapping.

[0093] The semantic similarity calculation model uses a deep learning model. The deep learning model is a machine learning model based on artificial neural networks (ANN). Its core idea is to learn the feature representation and pattern of data through a multi-level neural network structure. The deep learning model includes:

[0094] Multi-layer structure: Deep learning models consist of multiple layers, including an input layer, multiple hidden layers, and an output layer. Each layer contains multiple neurons, and each neuron is connected to all neurons in the previous layer. This multi-layer structure enables the model to gradually learn abstract features of the data.

[0095] Feature representation learning: Deep learning models achieve pattern recognition and data modeling by learning feature representations of data in multiple hidden layers. Each layer can be seen as an abstract representation of the data, and high-level representations contain more abstract features of the data.

[0096] End-to-end learning: Deep learning models are usually end-to-end learning models, that is, they learn directly from the original input data to the final output results without manually designing feature extractors or intermediate representations. This makes the model more flexible and powerful.

[0097] Backpropagation algorithm: Deep learning models usually use the backpropagation algorithm to train the model. This algorithm calculates the gradient of the loss function with respect to the model parameters and then uses an optimization algorithm such as gradient descent to update the model parameters to minimize the loss function.

[0098] Through the deep learning model, the semantic similarity of the table content is calculated to determine whether the semantic similarity is greater than or equal to the preset similarity threshold. The preset similarity threshold can be set according to the situation. When the semantic similarity is greater than or equal to the preset similarity threshold, it is determined that the target compensation standard and the several original agricultural insurance compensation standards are repeated, otherwise, there is no repetition. In this way, the duplication of the compensation scope and compensation ratio can be detected.

[0099] Step S500: If the target compensation standard is repeated with the several original agricultural insurance compensation standards, an alarm message is generated.

[0100] If the target compensation standard is repeated with any original agricultural insurance compensation standard in the database, an alarm message is generated and can also be sent to the staff terminal.

[0101] There are various ways to send alarms, aiming to ensure that the alarm information can be conveyed to relevant personnel in a timely and accurate manner. The following are some common ways to send alarms:

[0102] Email Alert: Send the alert information to relevant personnel via email. This method is suitable for alert information that needs to be recorded or reviewed later.

[0103] SMS alert: The alert information is sent to a specified mobile phone number in the form of a text message. This method is suitable for emergency alerts that require immediate action or response.

[0104] WeChat Alarm: Send alarm information to WeChat and notify through WeChat public account, enterprise WeChat or personal WeChat.

[0105] Telephone alarm: directly notify relevant personnel via telephone voice. This method is usually used for the most urgent alarm situations.

[0106] API push: Provide an API interface to allow third-party systems to regularly call the interface to pull alarm events. This method is suitable for scenarios where alarm information needs to be integrated into existing systems.

[0107] Message queue push: put the alarm information into the message queue, and the consumer takes the alarm from the queue and then notifies. This method helps to achieve decoupling between services and asynchronous processing of alarm events.

[0108] WebSocket / SSE push: Maintain a long connection through the WebSocket or SSE (Server-Sent Events) protocol to achieve real-time message push from the server to the client.

[0109] When choosing the method of sending an alarm, you need to consider the urgency of the alarm, the characteristics of the receiver, and the actual needs of the system. For example, for emergency alarms that require immediate action, you can choose SMS, phone calls, or WebSocket / SSE push; for alarms that need to be known but do not require immediate action, you can choose email or WeChat; for scenarios where the alarm information needs to be integrated into the existing system, you can choose API interface push or message queue push.

[0110] This embodiment can realize batch extraction of table-based compensation scopes in agricultural insurance clauses, and intelligently check consistency and issue alarms.

[0111] After step S500, the following steps are also included:

[0112] Generate an agricultural insurance clause comparison report based on the duplication between the target compensation standard and the several original agricultural insurance compensation standards;

[0113] Based on the agricultural insurance terms comparison report, the duplication of compensation standards is marked.

[0114] During specific implementation, the duplication between the target compensation standard and several original agricultural insurance compensation standards is obtained, and a comparison report is generated based on the duplication. In the comparison report, the repeated or similar clauses in the agricultural insurance clause document are marked.

[0115] In this embodiment, the automated processing of table extraction and comparison reduces the clause review time by more than 80%, from an average of 11 hours per clause review to less than 20 minutes.

[0116] After marking the duplication of compensation standards based on the agricultural insurance clause comparison report, it also includes:

[0117] Generate a proposed clause revision plan based on the duplication between the target compensation standard and the several original agricultural insurance compensation standards;

[0118] The overlap between the target compensation standard and several original agricultural insurance compensation standards is displayed.

[0119] According to the data verification results, the proposed clause revision plan is automatically generated to reduce the duplication of compensation scope and compensation ratio. Visual tools can also be provided to help business personnel intuitively view and analyze the comparison results. Furthermore, the data verification process can be integrated into the agricultural insurance product clause declaration business process to improve the efficiency of clause management and reduce labor costs. In some other embodiments, it can also be used with the agricultural insurance core system and product factory for intelligent identification and early interception. Use its semantic understanding ability to perform semantic verification on the compensation scope of different products of different institutions, identify the repeated content of the compensation scope and issue an alarm, prompt modification if there is repeated content, and locate and prompt similar clauses. Reduce conflicts due to repeated clauses.

[0120] Compared with the prior art, the data verification method of the embodiment of the present invention has the following technical advantages: the automated processing of the extraction and comparison of the compensation standards of agricultural insurance clause documents reduces the clause review time by more than 80%, from the original average of 11 hours for each clause review to less than 20 minutes through automated semantic similarity calculation, reducing the workload of manual review and saving labor costs; when there is duplication of agricultural insurance compensation standards, an alarm message is generated, and the staff can modify the agricultural insurance clauses based on the alarm message, thereby reducing the duplication and conflict of clauses and saving operating costs.

[0121] It should be noted that there is not necessarily a certain order between the above-mentioned steps. A person skilled in the art can understand, based on the description of the embodiments of the present invention, that in different embodiments, the above-mentioned steps may have different execution orders, that is, they may be executed in parallel, may be executed interchangeably, and so on.

[0122] Another embodiment of the present invention provides a data verification device, which corresponds one-to-one to the data verification method of the above embodiment. Figure 7 As shown, the device 1 includes a data acquisition module 100, a data conversion module 200, a data storage module 300, a data verification module 400 and an alarm information generation module 500. The functional modules are described in detail as follows:

[0123] The data acquisition module 100 is used to detect the agricultural insurance clause document reporting instruction and obtain the agricultural insurance clause document to be reported;

[0124] The data conversion module 200 is used to identify the compensation standard of the agricultural insurance clause document to be reported, and convert the identified compensation standard into structured data, which is recorded as the target compensation standard;

[0125] The data storage module 300 is used to store the target compensation standard into a preset agricultural insurance compensation standard database, wherein the preset agricultural insurance compensation standard database stores a plurality of original agricultural insurance compensation standards;

[0126] The data verification module 400 is used to respectively calculate the semantic similarity between the target compensation standard and the plurality of original agricultural insurance compensation standards, and verify the duplication between the target compensation standard and the plurality of original agricultural insurance compensation standards according to the semantic similarity;

[0127] The warning information generating module 500 is used to generate a warning message if the target compensation standard is repeated with the several original agricultural insurance compensation standards.

[0128] The specific implementation method is shown in the method embodiment, which will not be described in detail here.

[0129] In one embodiment, the data conversion module 200 is specifically used for:

[0130] Identifying the compensation standard of the agricultural insurance clause document to be reported based on the multimodal large model to obtain the identified compensation standard;

[0131] The identified compensation standard is converted into structured data in a preset format and recorded as the target compensation standard.

[0132] The specific implementation method is shown in the method embodiment, which will not be described in detail here.

[0133] In one embodiment, the data storage module 300 is specifically used for:

[0134] Based on the agricultural insurance clause document to be reported, obtaining the issuing institution corresponding to the target compensation standard;

[0135] The target compensation standard is stored in a preset agricultural insurance compensation standard database according to the issuing institution, wherein the preset agricultural insurance compensation standard database stores a plurality of original agricultural insurance compensation standards.

[0136] The specific implementation method is shown in the method embodiment, which will not be described in detail here.

[0137] In one embodiment, the data verification module 400 is specifically used for:

[0138] Inputting the target compensation standard and the several original agricultural insurance compensation standards into a semantic similarity calculation model;

[0139] The semantic similarity output by the semantic similarity calculation model is obtained, and the duplication between the target compensation standard and the plurality of original agricultural insurance compensation standards is verified according to the semantic similarity.

[0140] The specific implementation method is shown in the method embodiment, which will not be described in detail here.

[0141] In one embodiment, the data verification module 400 is further used to:

[0142] Obtaining the semantic similarity output by the semantic similarity calculation model, wherein the semantic similarity calculation model is a deep learning model;

[0143] Determine whether the semantic similarity is greater than or equal to a preset similarity threshold;

[0144] If the semantic similarity is greater than or equal to a preset similarity threshold, it is determined that the target compensation standard and the plurality of original agricultural insurance compensation standards are repeated;

[0145] If the semantic similarity is less than a preset similarity threshold, it is determined that there is no duplication between the target compensation standard and the plurality of original agricultural insurance compensation standards.

[0146] The specific implementation method is shown in the method embodiment, which will not be described in detail here.

[0147] In one embodiment, the device further comprises a marking module, the marking module being configured to:

[0148] Generate an agricultural insurance clause comparison report based on the duplication between the target compensation standard and the several original agricultural insurance compensation standards;

[0149] Based on the agricultural insurance terms comparison report, the duplication of compensation standards is marked.

[0150] The specific implementation method is shown in the method embodiment, which will not be described in detail here.

[0151] In one embodiment, the device further comprises a display module, the display module being configured to:

[0152] Generate a proposed clause revision plan based on the duplication between the target compensation standard and the several original agricultural insurance compensation standards;

[0153] The overlap between the target compensation standard and several original agricultural insurance compensation standards is displayed.

[0154] The specific implementation method is shown in the method embodiment, which will not be described in detail here.

[0155] The present invention provides a data verification device, which automatically extracts and compares the compensation standard tables in agricultural insurance clauses, and automatically compares and verifies, performs intelligent alarms on homogeneous compensation ranges, and automatically calculates semantic similarity, thereby reducing the workload of manual review and saving labor costs; when there are duplications in agricultural insurance compensation standards, an alarm message is generated, and the staff can modify the agricultural insurance clauses through the alarm message, thereby reducing the duplication and conflict of clauses and saving operating costs.

[0156] Another embodiment of the present invention provides a computer device, which may be a server, and its internal structure diagram may be as shown in Figure 8As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client via a network connection. When the computer program is executed by the processor, it implements a function or step on the server side of a data verification method.

[0157] In one embodiment, a computer device is provided. The computer device may be a client, and its internal structure diagram may be as follows: Fig. 9 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When the computer program is executed by the processor, the functions or steps on the client side of a data verification method are implemented.

[0158] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0159] Detect the agricultural insurance clause document reporting instruction and obtain the agricultural insurance clause document to be reported;

[0160] Identify the compensation standard of the agricultural insurance clause document to be reported, and convert the identified compensation standard into structured data, which is recorded as the target compensation standard;

[0161] Storing the target compensation standard in a preset agricultural insurance compensation standard database, wherein the preset agricultural insurance compensation standard database stores a plurality of original agricultural insurance compensation standards;

[0162] Calculating the semantic similarity between the target compensation standard and the several original agricultural insurance compensation standards respectively, and verifying the duplication between the target compensation standard and the several original agricultural insurance compensation standards according to the semantic similarity;

[0163] If the target compensation standard overlaps with the several original agricultural insurance compensation standards, an alarm message is generated.

[0164] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0165] Detect the agricultural insurance clause document reporting instruction and obtain the agricultural insurance clause document to be reported;

[0166] Identify the compensation standard of the agricultural insurance clause document to be reported, and convert the identified compensation standard into structured data, which is recorded as the target compensation standard;

[0167] Storing the target compensation standard in a preset agricultural insurance compensation standard database, wherein the preset agricultural insurance compensation standard database stores a plurality of original agricultural insurance compensation standards;

[0168] Calculating the semantic similarity between the target compensation standard and the several original agricultural insurance compensation standards respectively, and verifying the duplication between the target compensation standard and the several original agricultural insurance compensation standards according to the semantic similarity;

[0169] If the target compensation standard overlaps with the several original agricultural insurance compensation standards, an alarm message is generated.

[0170] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant descriptions on the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0171] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchl ink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0172] The embodiments described above are merely illustrative, in which the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0173] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a general hardware platform, and of course can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product can exist in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0174] It should be noted that if software tools or components other than those of our company appear in the embodiments of the present application, they are only used for illustrative purposes and do not represent actual use.

[0175] Conditional language such as "can," "can," "might," or "may," among other things, unless specifically stated otherwise or otherwise understood within the context as used, is generally intended to convey that particular embodiments can include (while other embodiments do not) particular features, elements, and / or operations. Thus, such conditional language is also generally intended to imply that features, elements, and / or operations are anyway required for one or more embodiments or that one or more embodiments must include logic for determining, with or without input or prompting, whether such features, elements, and / or operations are included or will be performed in any particular embodiment.

[0176] The content described in this specification and the accompanying drawings herein includes examples that can provide data verification methods and devices. Of course, it is not possible to describe every conceivable combination of elements and / or methods for the purpose of describing the various features of the present disclosure, but it can be recognized that many other combinations and permutations of the disclosed features are possible. Therefore, it is obvious that various modifications can be made to the present disclosure without departing from the scope or spirit of the present disclosure. In addition, or in alternatives, other embodiments of the present disclosure may be obvious from consideration of the present specification and the accompanying drawings and the practice of the present disclosure as presented herein. It is intended that the examples proposed in the present specification and the accompanying drawings are considered to be illustrative and non-restrictive in all respects. Although specific terms are adopted in this article, they are used in a general and descriptive sense and are not used for the purpose of limitation.

Claims

1. A data verification method, characterized in that , the method comprises: Detect the agricultural insurance clause document reporting instruction and obtain the agricultural insurance clause document to be reported; Identify the compensation standard of the agricultural insurance clause document to be reported, and convert the identified compensation standard into structured data, which is recorded as the target compensation standard; Storing the target compensation standard in a preset agricultural insurance compensation standard database, wherein the preset agricultural insurance compensation standard database stores a plurality of original agricultural insurance compensation standards; Calculating the semantic similarity between the target compensation standard and the several original agricultural insurance compensation standards respectively, and verifying the duplication between the target compensation standard and the several original agricultural insurance compensation standards according to the semantic similarity; If the target compensation standard overlaps with the several original agricultural insurance compensation standards, an alarm message is generated.

2. The data verification method according to claim 1, characterized in that: The compensation standard of the agricultural insurance clause document to be reported is identified, and the identified compensation standard is converted into structured data, which is recorded as the target compensation standard, including: Identifying the compensation standard of the agricultural insurance clause document to be reported based on the multimodal large model to obtain the identified compensation standard; The identified compensation standard is converted into structured data in a preset format and recorded as the target compensation standard.

3. The data verification method according to claim 1, characterized in that: The target compensation standard is stored in a preset agricultural insurance compensation standard database, wherein the preset agricultural insurance compensation standard database stores a plurality of original agricultural insurance compensation standards: Based on the agricultural insurance clause document to be reported, obtaining the issuing institution corresponding to the target compensation standard; The target compensation standard is stored in a preset agricultural insurance compensation standard database according to the issuing institution, wherein the preset agricultural insurance compensation standard database stores a plurality of original agricultural insurance compensation standards.

4. The data verification method according to claim 1, characterized in that: The respectively calculating the semantic similarity between the target compensation standard and the plurality of original agricultural insurance compensation standards, and verifying the duplication between the target compensation standard and the plurality of original agricultural insurance compensation standards according to the semantic similarity, comprises: Inputting the target compensation standard and the several original agricultural insurance compensation standards into a semantic similarity calculation model; The semantic similarity output by the semantic similarity calculation model is obtained, and the duplication between the target compensation standard and the plurality of original agricultural insurance compensation standards is verified according to the semantic similarity.

5. The data verification method according to claim 4, characterized in that: The obtaining of the semantic similarity output by the semantic similarity calculation model and verifying the duplication between the target compensation standard and the plurality of original agricultural insurance compensation standards according to the semantic similarity include: Obtaining the semantic similarity output by the semantic similarity calculation model, wherein the semantic similarity calculation model is a deep learning model; Determine whether the semantic similarity is greater than or equal to a preset similarity threshold; If the semantic similarity is greater than or equal to a preset similarity threshold, it is determined that the target compensation standard and the plurality of original agricultural insurance compensation standards are repeated; If the semantic similarity is less than a preset similarity threshold, it is determined that there is no duplication between the target compensation standard and the plurality of original agricultural insurance compensation standards.

6. The data verification method according to any one of claims 1 to 5, characterized in that: If the target compensation standard and the several original agricultural insurance compensation standards are repeated, after generating the alarm information, it also includes: Generate an agricultural insurance clause comparison report based on the duplication between the target compensation standard and the several original agricultural insurance compensation standards; Based on the agricultural insurance terms comparison report, the duplication of compensation standards is marked.

7. The data verification method according to claim 6, characterized in that: After marking the duplication of the compensation standards based on the agricultural insurance clause comparison report, it also includes: Generate a proposed clause revision plan based on the duplication between the target compensation standard and the several original agricultural insurance compensation standards; The overlap between the target compensation standard and several original agricultural insurance compensation standards is displayed.

8. A data verification device, characterized in that: The device comprises: A data acquisition module, used to detect an agricultural insurance clause document reporting instruction and obtain the agricultural insurance clause document to be reported; A data conversion module is used to identify the compensation standard of the agricultural insurance clause document to be reported, and convert the identified compensation standard into structured data, which is recorded as the target compensation standard; A data storage module, used to store the target compensation standard into a preset agricultural insurance compensation standard database, wherein the preset agricultural insurance compensation standard database stores a plurality of original agricultural insurance compensation standards; A data verification module, used to respectively calculate the semantic similarity between the target compensation standard and the several original agricultural insurance compensation standards, and verify the duplication between the target compensation standard and the several original agricultural insurance compensation standards according to the semantic similarity; The warning information generation module is used to generate a warning message if the target compensation standard is repeated with the several original agricultural insurance compensation standards.

9. A computer device, characterized in that: The computer device comprises at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the data verification method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by one or more processors, the one or more processors can execute the steps of the data verification method described in any one of claims 1-7.

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