Business duplication judgment method and device, equipment, medium and computer product

Abstract

The invention discloses a business duplication judgment method, device and equipment, a medium and a computer product, and the method comprises the steps: extracting business feature information of a target business, and carrying out the data standardization processing of the business feature information, and obtaining standard feature data; performing hash processing on the standard feature data to obtain unique feature data; carrying out numeralization processing and splicing processing on the unique feature data to obtain a first service feature value; accumulating the first service characteristic value of the target service to obtain a target service characteristic value; and comparing the target service feature value with a feature value set to obtain a service duplicate judgment result. According to the method and the device, the efficiency and the precision of service duplication judgment are improved, and the manual intervention and the misjudgment rate are reduced.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device, equipment, medium and computer product for service duplication detection. Background Art

[0002] In banking business processing, it is crucial to ensure the uniqueness and accuracy of accounts. Currently, many banking systems identify and prevent duplicate business processing by generating unique key values ​​or continuous serial numbers for each business. These key values ​​or serial numbers are used as unique identifiers in the business processing process. The system filters and rejects duplicate business requests by checking the uniqueness of these identifiers, thereby ensuring the accuracy and uniqueness of account processing.

[0003] However, when the banking system itself fails, such as database crash, network interruption, etc., the generation and recording of key values ​​or serial numbers may be affected, resulting in inaccurate duplicate detection. In the scenario of distributed systems or multi-system interaction, due to delays or inconsistencies in data synchronization, key values ​​or serial numbers may be repeated or missing in different systems, thereby affecting the accuracy of duplicate detection. In addition, human operational errors or negligence may also lead to repeated or incorrect use of key values ​​or serial numbers, which in turn causes problems with duplicate entry or exit. Summary of the invention

[0004] The main purpose of the present invention is to provide a method, device, equipment, medium and computer product for service duplication detection, aiming to solve the technical problem of how to perform service duplication detection more efficiently, flexibly and reliably.

[0005] To achieve the above object, the present invention provides a method for determining duplicate services, the method comprising the following steps:

[0006] Extracting service characteristic information of the target service, and performing data standardization processing on the service characteristic information to obtain standard characteristic data;

[0007] Performing hash processing on the standard feature data to obtain unique feature data;

[0008] The unique characteristic data is digitized and concatenated to obtain a first service characteristic value;

[0009] Accumulating the first service characteristic value of the target service to obtain a target service characteristic value;

[0010] The target service characteristic value is compared with the characteristic value set to obtain a service duplication determination result.

[0011] In one embodiment, the step of extracting service feature information of the target service and performing data standardization processing on the service feature information to obtain standard feature data includes:

[0012] Determine the key business fields of the target business to obtain business feature information;

[0013] The service feature information is cleaned and formatted to obtain standard feature data.

[0014] In one embodiment, the step of digitizing and concatenating the unique characteristic data to obtain the first service characteristic value includes:

[0015] The unique characteristic data is numerically converted to obtain a non-negative sequence;

[0016] The non-negative sequences are concatenated bit by bit to obtain a first service characteristic value.

[0017] In one embodiment, the step of converting the unique characteristic data into a numerical value to obtain a non-negative sequence includes:

[0018] Add the unique characteristic data and the preset value bit by bit to obtain a non-negative sequence

[0019] In one embodiment, the step of converting the unique characteristic data into a numerical value to obtain a non-negative sequence further includes:

[0020] The unique characteristic data is converted into an unsigned integer sequence to obtain a non-negative sequence.

[0021] In one embodiment, the step of comparing the target service feature value with the feature value set to obtain a service duplication determination result includes:

[0022] If there is a characteristic value in the characteristic value set that is the same as the characteristic value of the target service, then the target service is determined to be a duplicate service;

[0023] If there is no characteristic value identical to the characteristic value of the target service in the characteristic value set, the target service is determined to be a non-repetitive service.

[0024] In addition, to achieve the above-mentioned purpose, the present invention also provides a service duplication detection device, the service duplication detection device comprising:

[0025] A first acquisition module is used to extract service feature information of a target service, and perform data standardization processing on the service feature information to obtain standard feature data;

[0026] A second acquisition module is used to perform hash processing on the standard feature data to obtain unique feature data;

[0027] A third acquisition module is used to perform numerical processing and splicing processing on the unique characteristic data to obtain a first service characteristic value;

[0028] An accumulation module, used for accumulating the first service characteristic value of the target service to obtain a target service characteristic value;

[0029] The comparison module is used to compare the target service feature value with the feature value set to obtain a service duplication determination result.

[0030] In addition, to achieve the above-mentioned purpose, the present invention also provides a terminal device, which includes a memory, a processor, and a service duplication detection program stored in the memory and executable on the processor, and the service duplication detection program, when executed by the processor, implements the steps of the service duplication detection method described above.

[0031] In addition, to achieve the above purpose, the present invention also provides a computer-readable storage medium, on which a service duplication detection program is stored, and when the service duplication detection program is executed by a processor, the steps of the service duplication detection method described above are implemented.

[0032] One or more technical solutions proposed in this application have at least the following technical effects:

[0033] The present application first extracts the business feature information of the target business and performs data standardization processing, maintaining a unified and standardized format during the processing process. This standardization helps to reduce errors or anomalies caused by inconsistent data formats in subsequent processing steps.

[0034] Furthermore, the standardized feature data is converted into unique feature data, thereby ensuring the unique expression of business features, greatly reducing the probability of collision between business data, and improving the accuracy of duplicate detection.

[0035] Subsequently, by digitizing and concatenating the unique feature data, the feature information is further refined and compressed, generating the first business feature value that is easy to compare and calculate. This process not only reduces the complexity of data processing, but also improves the system processing speed, meeting the needs of real-time duplication detection of large-scale business data.

[0036] Finally, by accumulating the corresponding first service characteristic values, the target service characteristic values ​​are obtained and compared with the existing data, the goal of service duplication detection is achieved. The above accumulation process does not depend on the original order or batch of the data, so the influence of these factors on the duplication detection results can be eliminated. Compared with the prior art, this method significantly improves the efficiency and accuracy of service duplication detection and reduces manual intervention and misjudgment rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flowchart of a first exemplary embodiment of a method for determining duplicate business transactions in this application;

[0038] Figure 2 This is a flow chart of a second exemplary embodiment of the method for determining duplicate business transactions of this application;

[0039] Figure 3 This is a schematic diagram of the module structure of the service duplicate judgment device according to an embodiment of the present application;

[0040] Figure 4 Schematic diagram of the device structure of the hardware operating environment involved in the service duplication detection method in the embodiment of the present application.

[0041] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0042] 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.

[0043] The main solution of the embodiment of the present invention is: extract the business characteristic information of the target business, perform data standardization processing on the business characteristic information to obtain standard characteristic data, perform hash processing on the standard characteristic data to obtain unique characteristic data, perform numerical processing and splicing processing on the unique characteristic data, obtain the first business characteristic value, accumulate the first business characteristic value of the target business to obtain the target business characteristic value, and finally compare the target business characteristic value with the characteristic value set to obtain the business duplication judgment result. Compared with the traditional method of identifying duplicate businesses by generating a unique key value or a continuous serial number for each business, it is more efficient, flexible and reliable. Based on the solution of the present application, the business of complex scenarios is subjected to a series of simple transformations and finally converted into a comparison of two numbers, so that the comparison process is very intuitive and efficient. The system only needs to simply compare whether the two values ​​are equal to quickly determine whether the two businesses are exactly the same.

[0044] Technical terms involved in the embodiments of this application:

[0045] JSON (JavaScript Object Notation)

[0046] JSON is a data exchange format used in the business duplicate detection method of this application. JSON is used to represent the business feature information of each record, namely, card number, amount, transaction summary and other information. This information is encapsulated into a JSON object, making the data structure clear, easy to understand and process. JSON stores data in the form of key-value pairs. In this scenario, the card number, amount, and transaction summary are used as keys, and their corresponding values ​​constitute the content of the JSON object. This structured storage method allows the data to maintain its original structure and meaning during transmission and storage.

[0047] MD5(Message-Digest Algorithm 5)

[0048] MD5 is a widely used cryptographic hash function that produces a 128-bit (16-byte) hash value. The MD5 algorithm works by accepting an input of arbitrary length (usually a string or file) and producing an output of a fixed length, which is often called a "message digest" or "fingerprint." In theory, it is almost impossible for two different inputs to produce the same output.

[0049] The execution subject of the method of this embodiment can be a business duplication detection device, or a business duplication detection terminal device or server. This embodiment takes a business duplication detection device as an example, and the business duplication detection device can be integrated in a terminal device such as a smart phone, a tablet computer, etc. with a data processing function.

[0050] The solution of this embodiment mainly realizes the effect of converting the business of complex scenarios into a comparison of two numbers through a series of simple conversions, so that the system can quickly determine whether two businesses are exactly the same by simply comparing whether the two values ​​are equal.

[0051] This embodiment starts from the actual problem of duplicate transaction information in banking business processing, and designs a business duplicate detection method based on the limitations of business identification by key value or serial number in the prior art. This method extracts the core feature information of the business, generates unique feature data using a hash algorithm, and then converts it into a numerical form that can be accumulated, thereby realizing rapid duplicate detection of multiple batches and large amounts of transaction information. This method not only improves the efficiency of duplicate detection, but also ensures accuracy in different transmission orders and multi-batch data transmission, effectively solving the problem of duplicate business identification and processing in banking business processing.

[0052] Specifically, the following are detailed steps of an exemplary embodiment of the service duplicate detection method:

[0053] Reference Figure 1 , Figure 1 This is a flow chart of the first exemplary embodiment of the service duplicate detection method of the present application. The service duplicate detection method comprises:

[0054] Step S10: extracting service characteristic information of the target service, and performing data standardization processing on the service characteristic information to obtain standard characteristic data.

[0055] It should be noted that the financial transaction characteristics that need to be monitored must be clearly identified first, which can be transaction amount, transaction time, transaction type, transaction account number of both parties, transaction IP address, transaction device type, etc. Transaction data containing the above characteristic information can be collected from databases, log files or real-time transaction flows. According to business needs, some non-essential fields can be added or reduced. Adding fields can improve the uniqueness and accuracy of the target business characteristic value, but it will also increase processing complexity and storage space. Reducing fields can simplify the processing process, but may reduce the uniqueness of the target business characteristic value.

[0056] The extracted business feature information can be processed through data cleaning, format unification, classification coding and other data standardization processes to obtain standard feature data. In the process of data cleaning, missing values ​​and abnormal values ​​(such as abnormally large transactions, transactions at non-standard times, etc.) can be removed or corrected; in the process of format unification, all time data can be formatted in a unified format, and all numerical data can be removed from units and unified to the same magnitude (such as converting amounts into yuan or US dollars); in the process of classification coding, classification features (such as transaction types) can be encoded and converted into digital form, such as using One-Hot Encoding or Label Encoding.

[0057] After data cleaning, format unification and classification coding, the processed standard feature data can be organized into JSON format for subsequent data processing, transmission or storage. JSON (JavaScript Object Notation) is very popular in data exchange due to its lightweight, easy to read and write characteristics. It can also be converted into other data formats: such as XML, CSV or custom binary format.

[0058] Specifically, for each transaction record in each business, extract its card number, amount, and transaction summary as business feature information, and encapsulate the card number, amount, and transaction summary into a JSON object, for example: {"cardNumber":"1234567890123456","amount":"100.00","summary":"Shopping consumption"}.

[0059] Step S20, performing hash processing on the standard feature data to obtain unique feature data.

[0060] It should be noted that in this step, you can choose a suitable hash algorithm according to business needs, such as MD5, SHA-256, BLAKE2b, etc. These algorithms can transform inputs of any length into fixed-length outputs through hash algorithms, and the probability of different inputs producing the same output is extremely low. Then, the hash function is applied to each standardized feature or feature combination, and the corresponding hash value is generated based on the hashlib library. The hash algorithm will generate a fixed-length hash value. These hash values ​​will be used as unique feature data.

[0061] Specifically, for example, there is a JSON object containing business feature information, and the string is hashed using the MD5 hash algorithm. The MD5 algorithm generates a 128-bit hash value, which is a 32-character hexadecimal number. Each hexadecimal character pair (that is, every two characters) can be converted to a byte. Therefore, the above hash value can be converted into an array containing 16 bytes, and the value range of each byte is between -128 and 127.

[0062] It should be noted that although the hash value has a high degree of uniqueness, it is not absolutely unique. In theory, two different inputs may produce the same hash value. Therefore, SHA-256 or BLAKE2b can be used in situations where higher security is required.

[0063] Step S30, digitizing and concatenating the unique characteristic data to obtain a first service characteristic value;

[0064] It should be noted that the unique characteristic data is digitized and concatenated in order to convert the hashed data into a numerical form suitable for accumulation and comparison.

[0065] In a feasible implementation, step S30 may include steps S31 to S32:

[0066] Step S31, converting the unique characteristic data into a numerical value to obtain a non-negative sequence;

[0067] In a feasible implementation, step S31 may include step A10:

[0068] Step A10, adding the unique characteristic data to the preset value bit by bit to obtain a non-negative sequence

[0069] For example, add 129 to each bit of the byte array to ensure that all values ​​are positive.

[0070] It should be noted that byte type data is stored as 8-bit (1-byte) integers in most programming languages, and its value range is usually from -128 to 127. When these byte values ​​need to be further processed, such as concatenating them into a larger value or performing accumulation calculations, ensuring that all values ​​are positive can avoid many potential problems, such as sign extension and value overflow.

[0071] The reason for choosing 129 as the addend is that it is an integer larger than the maximum value of the byte type, and it is not too large to introduce unnecessary complexity or performance overhead in subsequent processing. By adding each byte value to 129, we can ensure that the result is a positive number.

[0072] Step S32: splice the non-negative sequence bit by bit to obtain a first service characteristic value.

[0073] Specifically, the non-negative sequences are concatenated into a long string and then converted into a BigDecimal value as the first business characteristic value of the target business. This step actually converts the byte array into a form that is easier to process and accumulate.

[0074] A feasible implementation method may also adopt a block transfer method to obtain the first service characteristic value, and the byte array of the hash value may be divided into multiple smaller blocks, and then each block is converted separately, and finally the conversion results are combined in some way. The specific implementation steps are as follows:

[0075] First, define a suitable block size according to the target data type or processing requirements. For example, if the goal is to convert to a 64-bit integer, the hash value can be divided into two 64-bit blocks. Then, each block is treated as an independent binary number and converted to the corresponding integer or floating point number. For MD5, if divided into two 64-bit blocks, each block can be directly converted to an integer of type long. The converted blocks can be combined in many ways, such as through bit operations, addition, or other mathematical operations.

[0076] Step S40, accumulating the first service characteristic value of the target service to obtain a target service characteristic value;

[0077] It should be noted that the target business may be composed of multiple transaction records. In this application, the first business characteristic value corresponds to the characteristic value of each transaction record, and the target business characteristic value corresponds to the characteristic value of the target business.

[0078] For example, for 100 transaction records of a business, the business data characteristic value (BigDecimal value) of each transaction record is calculated according to the method of steps S10 to S40, and these 100 BigDecimal values ​​are accumulated to obtain the characteristic value result of this business. By summarizing the characteristics of all transaction records, the overall characteristics or unique identification of the entire business activity can be represented. The accumulation operation can be implemented through the addition method of BigDecimal to ensure that the accuracy is not lost.

[0079] It should be noted that no matter in what order the data arrives, or whether it is sent in multiple batches, it will eventually be converted into a series of hash values ​​and accumulated into a total feature value. This accumulation process does not depend on the original order or batch of the data, so it can eliminate the impact of these factors on the duplicate detection results. As long as all the records of the two business sets are exactly the same, no matter how they are sent in batches, the final feature values ​​will be the same. This stateless duplicate detection method makes the system more flexible and robust.

[0080] In addition, in order to obtain a more concise, easy-to-process and unique feature value, a hash function can be used to hash the business data feature value of each record, and then all hash values ​​can be aggregated in some form (such as concatenating and hashing again). This method can reduce the amount of data while maintaining a certain uniqueness, but it may increase the risk of collision.

[0081] Step S50: Compare the target service feature value with the feature value set to obtain a service duplication determination result.

[0082] Specifically, the target service characteristic value is compared with a characteristic value set, the characteristic value set;

[0083] If there is a characteristic value in the characteristic value set that is the same as the characteristic value of the target service, then the target service is determined to be a duplicate service;

[0084] If there is no characteristic value identical to the characteristic value of the target service in the characteristic value set, the target service is determined to be a non-repetitive service.

[0085] It should be noted that after obtaining the target business characteristic value, the target business characteristic value is compared with other business characteristic values ​​stored in the database. If there is a business characteristic value in the database that is exactly the same as the target characteristic value, it means that the two businesses are exactly the same in key features. The system can determine that the two business information is exactly the same, that is, there is a repeated business. If not, it means that the two businesses have differences in key features, and the system can determine that the two are different businesses. If it is determined to be a repeated business, the system can automatically reject the business request, or prompt the user to confirm whether to continue the operation. If it is determined to be a different business, the system will process the business request normally.

[0086] This embodiment extracts the characteristic information of the target business, and obtains standard characteristic data through a series of data sorting processes, such as conversion into a JSON object, which helps to ensure the consistency and comparability of the data, and is also convenient for subsequent data processing. Among them, the characteristic information can include information such as card number, amount, transaction summary, etc., which are the key points to distinguish different businesses. Then, the standard characteristic data is converted into a hash value, a byte array of length 16, using a hash algorithm. This hash value is the unique characteristic data, for example, an MD5 array is obtained. Then, the unique characteristic data is converted into a non-negative sequence and spliced ​​into a long string to obtain the first business characteristic value, and obtain a data form that is easier to process and accumulate. Finally, all the first business characteristic values ​​of the target business are accumulated to obtain the target business characteristic value of the target business. In this way, complex and changeable business data can be converted into a concise and clear target business characteristic value. This characteristic value not only retains the uniqueness of the business data, but also greatly simplifies the complexity of data comparison and improves processing efficiency.

[0087] Reference Figure 2 , Figure 2 This is a flow chart of the second embodiment of the service duplication detection method of this application. Figure 2 In the embodiment shown, in this embodiment, step S31 may include step B10:

[0088] Step B10, converting the unique characteristic data into an unsigned integer sequence to obtain a non-negative sequence.

[0089] Specifically, after the extracted business feature information is processed through a series of processes to obtain unique feature data, the unique feature data after hash processing is directly converted into an unsigned integer sequence. This process is relatively simple and direct, and does not introduce additional calculations, which helps to reduce computational complexity and improve processing efficiency.

[0090] For example, suppose the unique characteristic data of a target business is hash_bytes = bytearray.fromhex('abcdef1234567890'), which is directly converted to an unsigned integer sequence. In most programming languages, this can be achieved directly through type conversion without additional calculation steps. After the conversion, a non-negative integer sequence can be directly obtained. This unsigned integer retains all the information of the original hash value without introducing more errors or complexity.

[0091] It should be noted that in Java, byte is signed, but when you convert it to int type, it is treated as an unsigned number, which can be done by performing a bitwise AND operation on the byte value and 0xFF (that is, 255, or binary 11111111) and then shifting the result left (if a larger range is required), or directly treating it as a positive number of int type, because int is large enough to accommodate all values ​​of byte as an unsigned number.

[0092] This embodiment uses the above method to specifically convert the unique feature data into an unsigned integer sequence to obtain a non-negative sequence. The non-negative sequence is generated directly by converting the data type, avoiding the bit-by-bit addition operation with the preset value, thereby simplifying the calculation steps and improving the processing efficiency. In addition, the unsigned integer can completely represent each bit of the unique feature data, and the structure or information of the original data will not be changed due to the addition with the preset value, thereby maintaining the integrity and accuracy of the data.

[0093] In addition, the present application also proposes a service duplication detection device, the service duplication detection device comprising:

[0094] The first acquisition module 10 is used to extract the service characteristic information of the target service, and perform data standardization processing on the service characteristic information to obtain standard characteristic data;

[0095] A second acquisition module 20 is used to perform hash processing on the standard feature data to obtain unique feature data;

[0096] A third acquisition module 30 is used to perform numerical processing and splicing processing on the unique characteristic data to obtain a first service characteristic value;

[0097] An accumulation module 40, configured to accumulate the first service characteristic value of the target service to obtain a target service characteristic value;

[0098] The comparison module 50 is used to compare the target service feature value with the feature value set to obtain a service duplication determination result.

[0099] The service duplication detection device provided by the present application adopts the service duplication detection method in the above embodiment, which can solve the technical problem of how to perform service duplication detection more efficiently, flexibly and reliably. Compared with the prior art, the beneficial effects of the service duplication detection device provided by the present application are the same as the beneficial effects of the service duplication detection method provided by the above embodiment, and other technical features in the service duplication detection device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0100] The present application provides a business duplicate detection device, which includes: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein 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 execute the business duplicate detection method in the above-mentioned embodiment 1.

[0101] Reference below Figure 4 , which shows a schematic diagram of the structure of a service duplication detection device for implementing an embodiment of the present application. The service duplication detection device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The service duplication detection device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0102] like Figure 4As shown, the service duplication detection device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the service duplication detection device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the service weight determination device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a service weight determination device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0103] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0104] The service duplication detection device provided by the present application adopts the service duplication detection method in the above embodiment, which can solve the technical problem of how to perform service duplication detection more efficiently, flexibly and reliably. Compared with the prior art, the beneficial effects of the service duplication detection device provided by the present application are the same as the beneficial effects of the service duplication detection method provided by the above embodiment, and other technical features in the service duplication detection device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0105] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0106] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0107] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the service duplicate determination method in the above-mentioned embodiment.

[0108] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0109] The computer-readable storage medium may be included in the service duplication detection device; or may exist independently without being installed in the service duplication detection device.

[0110] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0111] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0112] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0113] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned service duplication detection method, and can solve the technical problem of how to perform service duplication detection more efficiently, flexibly, and reliably. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the service duplication detection method provided in the above-mentioned embodiment, and will not be repeated here.

[0114] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned service duplicate detection method when executed by a processor.

[0115] The computer program product provided by this application can solve the technical problem of how to perform business duplication detection more efficiently, flexibly and reliably. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the business duplication detection method provided by the above embodiment, which will not be repeated here.

[0116] Compared with the prior art, the business duplication detection method, device, equipment, medium and computer product proposed in the embodiment of the present application extracts the business characteristic information of the target business, performs data standardization processing on the business characteristic information to obtain standard characteristic data, performs hash processing on the standard characteristic data to obtain unique characteristic data, performs numerical processing and splicing processing on the unique characteristic data, obtains the first business characteristic value, accumulates the first business characteristic value of the target business to obtain the target business characteristic value, and finally compares the target business characteristic value with the characteristic value set to obtain the business duplication detection result. Compared with the traditional method of identifying duplicate businesses by generating a unique key value or a continuous serial number for each business, it is more efficient, flexible and reliable. Based on the solution of the present application, by subjecting the business of complex scenarios to a series of simple transformations and finally converting it into a comparison of two numbers, the comparison process is very intuitive and efficient. The system only needs to simply compare whether the two values ​​are equal to quickly determine whether the two businesses are exactly the same.

[0117] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0118] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0119] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, a controlled terminal, or a network device, etc.) to execute the method of each embodiment of the present invention.

[0120] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for determining duplicate services, characterized in that: The service duplication detection method comprises: Extracting service characteristic information of the target service, and performing data standardization processing on the service characteristic information to obtain standard characteristic data; Performing hash processing on the standard feature data to obtain unique feature data; The unique characteristic data is digitized and concatenated to obtain a first service characteristic value; Accumulating the first service characteristic value of the target service to obtain a target service characteristic value; The target service characteristic value is compared with the characteristic value set to obtain a service duplication determination result.

2. The service duplicate determination method according to claim 1, characterized in that: The step of extracting the service characteristic information of the target service and performing data standardization processing on the service characteristic information to obtain standard characteristic data comprises: Determine the key business fields of the target business to obtain business feature information; The service feature information is cleaned and formatted to obtain standard feature data.

3. The service duplicate determination method according to claim 1, characterized in that: The step of digitizing and concatenating the unique characteristic data to obtain the first service characteristic value comprises: The unique characteristic data is numerically converted to obtain a non-negative sequence; The non-negative sequences are concatenated bit by bit to obtain a first service characteristic value.

4. The service duplicate determination method according to claim 3, characterized in that: The step of converting the unique characteristic data into a numerical value to obtain a non-negative sequence comprises: The unique characteristic data is added to the preset value bit by bit to obtain a non-negative sequence.

5. The service duplicate determination method according to claim 3, characterized in that: The step of converting the unique characteristic data into a numerical value to obtain a non-negative sequence further includes: The unique characteristic data is converted into an unsigned integer sequence to obtain a non-negative sequence.

6. The service duplicate determination method according to claim 1, characterized in that: The step of comparing the target service characteristic value with the characteristic value set to obtain the service duplication determination result comprises: If there is a characteristic value in the characteristic value set that is the same as the characteristic value of the target service, then the target service is determined to be a duplicate service; If there is no characteristic value identical to the characteristic value of the target service in the characteristic value set, the target service is determined to be a non-repetitive service.

7. A service duplicate detection device, characterized in that: The device for determining the service weight includes: A first acquisition module is used to extract service feature information of a target service, and perform data standardization processing on the service feature information to obtain standard feature data; A second acquisition module is used to perform hash processing on the standard feature data to obtain unique feature data; A third acquisition module is used to perform numerical processing and splicing processing on the unique characteristic data to obtain a first service characteristic value; An accumulation module, used for accumulating the first service characteristic value of the target service to obtain a target service characteristic value; The comparison module is used to compare the target service feature value with the feature value set to obtain a service duplication determination result.

8. A service duplication detection device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the service duplication detection method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the service duplicate detection method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the service duplication detection method according to any one of claims 1 to 6 are implemented.

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