Data compression method and device for property right data characteristics and electronic equipment
By semantic analysis and encoding processing of property rights data of energy central enterprises, the problem that the existing technology cannot effectively compress property rights data of energy central enterprises has been solved, and more efficient data compression and transmission is achieved.
Patent Information
- Application Number
- CN202510103205.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-22
- Publication Date
- 2025-06-10
AI Technical Summary
The amount of property rights data of energy-related central enterprises is large, and existing compression methods cannot be effectively compressed, resulting in high consumption of data storage and transmission resources.
By obtaining the property rights data set of the target type enterprises, the property rights data is decomposed into several strings based on semantic analysis, and the repetition frequency is counted, the string is selected based on the repetition frequency and the string length for encoding, and finally the property rights data is compressed based on the encoding.
On the premise of ensuring data accuracy, the storage space and transmission resource consumption of property rights data are significantly reduced, and data compression efficiency is improved.
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Figure CN120128189A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of data compression, and specifically to a data compression method for property right data characteristics, a data compression device for property right data characteristics, an electronic device, and a corresponding storage medium. Background Art
[0002] In the context of the digital age, data has become an important asset for national economic development. Property right data plays a crucial role in economic activities and social operations. Property right data clearly defines the ownership of various assets, ensuring the clarity and security of property rights, thereby promoting the establishment of asset transactions and transfers.
[0003] In large group enterprises, especially in central state-owned enterprises in the energy sector, they are characterized by a large enterprise scale, a large number of subsidiaries and branches, and a large number of property right data items, resulting in a very large amount of data. The resources occupied by data storage and data transmission are relatively high, especially the long time taken for data transmission. In the prior art, compression can be performed according to methods such as the Huffman tree algorithm. However, due to the huge amount of property right data of central state-owned enterprises in the energy sector, traditional compression methods cannot effectively compress the data. How to more effectively and accurately compress the property right data of central state-owned enterprises in the energy sector is the key research direction. Summary of the Invention
[0004] The purpose of the embodiments of this application is to provide a data compression method, device, and electronic device for property right data characteristics, analyze the property right data of each central state-owned enterprise in the energy sector, optimize the data compression algorithm, and compress the property right data to a greater extent on the premise of ensuring complete data accuracy, so as to solve at least some of the problems in the background art.
[0005] To achieve the above purpose, this application provides a data compression method for property right data characteristics, which includes: obtaining a set of property right data of a target type of enterprise, where the property right data in the set of property right data includes at least one of enterprise name, enterprise credit code, and property right attribute; decomposing the property right data into several strings based on semantic analysis, and counting the repetition frequency of the several strings in the set of property right data; selecting strings from the several strings for encoding based on the repetition frequency and string length, and the storage space occupied by the encoded code is less than its corresponding string; compressing the property right data based on the encoding to obtain the compressed property right data.
[0006] Optionally, the target type is divided and selected according to the ownership nature of the enterprise, and the property right data in the set of property right data belongs to the same group enterprise.
[0007] Optionally, when compressing the enterprise names in the property rights data, strings are selected from the several strings for encoding based on the repetition frequency and string length, including: encoding the selected strings into encoded codes based on the correspondence relationship, where the correspondence relationship is obtained through the following steps: obtaining a set of encoded codes, where the set of encoded codes includes several encoded codes; sorting the encoded codes in ascending order based on the storage space occupied by the several encoded codes to obtain a first sequence; sorting the several strings in descending order based on the product of the repetition frequency and string length to obtain a second sequence; and obtaining the correspondence relationship between the several strings and the encoded codes based on the correspondence relationship between the first sequence and the second sequence.
[0008] Optionally, when compressing the enterprise credit codes in the property rights data, strings are selected from the several strings for encoding based on the repetition frequency and string length, including: ignoring the first two digits of the codes identifying the registration management department and the institution category code in the enterprise credit code through encoding; for the string identifying the administrative division code of the registration management authority in the enterprise credit code, if the region corresponding to the string is the same as the enterprise name, encoding it as a specific boolean value, and if the region corresponding to the string is different from the enterprise name, encoding it as a preset reduced value according to the repetition frequency of the string; and for the string identifying an overseas enterprise in the enterprise credit code, encoding the string with a high repetition frequency as a null value.
[0009] Optionally, when compressing the property rights attributes in the property rights data, strings are selected from the several strings for encoding based on the repetition frequency and string length, including: counting the string with the highest repetition frequency in each attribute field in the property rights attributes and encoding the string as a null value; if two attribute fields in the property rights attributes are relevant and the content represented by the strings under the relevant fields is the same, encoding one of the strings as a null value.
[0010] Optionally, the method further includes: if multiple attribute fields in the property rights attributes of an enterprise are all encoded as null values, establishing a separate object to save the multiple null values and setting the strings corresponding to the multiple null values in the response attributes in the object.
[0011] Optionally, after obtaining the compressed property rights data, the method further includes: calculating the compression ratio and using the compression ratio as the verification of the compressed property rights data; the compression ratio includes the field compression ratio and the overall compression ratio.
[0012] In this application, a data compression device for property right data characteristics is also provided. The device includes: a set acquisition module for acquiring a set of property right data of target type enterprises, where the property right data in the set of property right data includes at least one of enterprise name, enterprise credit code, and property right attribute; a rule statistics module for decomposing the property right data into several strings based on semantic analysis and counting the repetition frequency of the several strings in the set of property right data; a coding determination module for selecting strings from the several strings for coding based on the repetition frequency and string length, and the storage space occupied by the code after coding is less than its corresponding string; and a result output module for compressing the property right data based on the coding to obtain the compressed property right data.
[0013] In this application, an electronic device is also provided, including: at least one processor; a memory connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the at least one processor realizes the aforementioned data compression method for property right data characteristics by executing the instructions stored in the memory.
[0014] In this application, a machine-readable storage medium is also provided. Instructions are stored on the machine-readable storage medium, and when the instructions are executed by a processor, the processor is configured to execute and implement the aforementioned data compression method for property right data characteristics.
[0015] In this application, a computer program product is also provided, including a computer program, and the computer program realizes the aforementioned data compression method for property right data characteristics when executed by a processor.
[0016] The above technical solutions have the following beneficial effects:
[0017] By analyzing the property right data, a targeted data compression algorithm is designed and optimized to compress the storage data volume and transmission data volume of the property right data on the premise of ensuring complete data accuracy.
[0018] Other features and advantages of the embodiments of this application will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The drawings are used to provide a further understanding of the embodiments of this application, and constitute a part of the specification. Together with the following specific implementation, they are used to explain the embodiments of this application, but do not constitute a limitation to the embodiments of this application. In the drawings:
[0020] Figure 1 Schematically shows a step schematic diagram of the data compression method for property right data characteristics according to an embodiment of this application;
[0021] Figure 2 Schematically shows a structural diagram of a data compression device for property rights data characteristics according to an embodiment of the present application;
[0022] Figure 3 Schematically shows an internal structure diagram of an electronic device according to an embodiment of the present application. Specific embodiments
[0023] The following will describe in detail the specific embodiments of the embodiments of the present application with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for explaining and illustrating the embodiments of the present application, and are not used to limit the embodiments of the present application.
[0024] Figure 1 Schematically shows a step diagram of a data compression method for property rights data characteristics according to an embodiment of the present application. As Figure 1 shown, a data compression method for property rights data characteristics, the method includes:
[0025] S01. Obtain a set of property rights data of target type enterprises, where the property rights data in the set of property rights data includes at least one of enterprise name, enterprise credit code, and property rights attributes;
[0026] S02. Decompose the property rights data into several strings based on semantic analysis, and count the repetition frequencies of the several strings in the set of property rights data;
[0027] S03. Select strings from the several strings for encoding based on the repetition frequencies and string lengths, and the storage space occupied by the encoded code is less than its corresponding string;
[0028] S04. Compress the property rights data based on the encoding to obtain compressed property rights data.
[0029] Among them, step S02 can be implemented by the following code:
[0030]
[0031]
[0032] Thus, the strings contained more in the enterprise name are "Co., Ltd.", "China National Energy Group", "Guoneng", "Limited Liability Company", "Guodian Power", "Guodian", "Longyuan", "Shenhua", etc. In the enterprise name, the strings are presented in the form of phrases; in the enterprise credit code, the strings are presented in the form of arrays.
[0033] Through the above embodiments, a corresponding compression algorithm is designed for the characteristics of property rights data, which significantly reduces the storage space occupied by property rights data and the transmission resources occupied during transmission while ensuring data accuracy.
[0034] In some embodiments of the present application, the target type is divided and selected according to the ownership nature of the enterprise, and the property rights data in the property rights data set belong to the same group enterprise. For example, the target type is a central enterprise. Another example is that the property rights data in the property rights data set come from a central energy enterprise, such as the National Energy Group.
[0035] In some embodiments of the present application, when compressing the enterprise names in the property rights data, strings are selected from the several strings for encoding based on the repetition frequency and string length, including: encoding the selected strings into encoded codes based on the corresponding relationship, and the corresponding relationship is obtained through the following steps: obtaining an encoded code set, the encoded code set includes several encoded codes; for example, the encoded code set is (00, 010, 011, 0110, 01110), arranging the encoded codes in ascending order based on the storage space occupied by the several encoded codes to obtain a first sequence; arranging in ascending order according to the number of occupied bits, that is, two bits first, followed by three bits, four bits, etc., to obtain the first sequence as (00, 010, 011, 0110, 01110); arranging the several strings in descending order based on the product of the repetition frequency and string length to obtain a second sequence; for example, (Limited Company 2103, New Energy 942, Guoneng 826, Longyuan 388, Guohua 171), where the number after the string represents the repetition frequency. Based on the corresponding relationship between the first sequence and the second sequence, the corresponding relationship between several strings and the encoded codes is obtained as shown in the following table:
[0036] string repetition frequency encoded code Co., Ltd. 2130 00 new energy 942 010 Guoneng 826 011 Longyuan 388 0110 Guohua 171 01110 ... ... ...
[0037] According to the above table, taking "Guohua" as an example, the binary code is "101011011111101101001101001110" under normal circumstances, and after being simplified by this embodiment, it is "01110", greatly reducing the character length. Through the above embodiments, the enterprise names are compressed well.
[0038] In some embodiments of the present application, when compressing the enterprise credit code in the property rights data, strings are selected from the several strings for encoding based on the repetition frequency and string length, including: ignoring the first two digits of the code identifying the registration management department code and the institution category code in the enterprise credit code through encoding; for the string identifying the administrative division code of the registration authority in the enterprise credit code, if the region corresponding to the string is the same as the enterprise name, it is encoded as a specific Boolean value, and if the region corresponding to the string is different from the enterprise name, it is encoded as a preset reduction value according to the repetition frequency of the string; the specific Boolean value and the preset reduction value here are both the aforementioned encoding codes. For the string identifying an overseas enterprise in the enterprise credit code, the string with a high repetition frequency is encoded as a null value. The enterprise credit code is 18 characters long, and the meaning of each character is determined according to the general coding rules. Therefore, the data length of the credit code field of the entire group can be greatly reduced according to this embodiment when being reduced. Since the composition of the unified social credit code has certain rules, for example, many enterprise names contain the names of the provinces and cities where the enterprises are located, and central enterprises and their subsidiaries do not belong to individual businesses, so they all start with 91. Therefore, the first two digits of the credit code can be ignored when compressing the data. The 3rd to 8th digits are the registration regions of the enterprises. Therefore, the provinces and cities included in the enterprise name can be compared with those in the credit code. Since there is a high probability that the region code in the credit code is the same as the province and city region of the registered address, if they are the same, it is set to 1 to shorten the length of the transmitted string. The 2nd to 8th digits in the unified social credit code are region codes. Since many enterprises under each group have the same registered place, there are many repeated data in the 2-8th digits of their credit codes. A reduction value can be given to these region codes according to the number of repetitions. Since the unified social credit code of overseas enterprises starts with #, the "domestic / overseas" attribute can be set according to this feature. For values that do not start with #, they can be set to null to compress the data. Through the above embodiments, the enterprise credit code is compressed well.
[0039] In some embodiments of the present application, when compressing the property attributes in the property rights data, strings are selected from the several strings for encoding based on the repetition frequency and string length, including: counting the string with the most repetition frequency in each attribute field of the property attributes, and encoding this string as a null value; for example, in the field of regulatory agencies, central enterprises and their subsidiaries under the management of the State-owned Assets Supervision and Administration Commission (SASAC) are basically affiliated to the "State-owned Assets Supervision and Administration Commission of the State Council" for supervision except for equity participation units. Therefore, this field can be reset before pushing, setting the attribute value of "State-owned Assets Supervision and Administration Commission of the State Council" to null, and only transmitting other types such as "Local State-owned Assets Supervision and Administration Commission" to reduce the overall data volume. Another example is the enterprise category (SASAC) field. You can first query in the system which types are the most, and set the attribute with the most values to null. For example, in the organizational form field, since most central enterprises and their subsidiaries are "limited liability companies", according to the "more means none" theory, the attribute values of most enterprises can also be set to null to reduce the data transmission space. For example, many attribute values are "yes" and "no", such as "whether it is the main business of a state-owned enterprise", "whether it is consolidated", "whether it is a listed company", "whether there are situations such as dormancy, suspension of business, or closure of business". These fields are mandatory, and their attribute values are 0 or 1. Therefore, it can be judged what the respective proportions of 0 and 1 in the field value are. To reduce the amount of data transmitted, the more value can be set to null, and only the less proportion value is retained to reduce the data volume. For the currency attribute, since most central enterprises are domestic-funded, most enterprises' currency is RMB. Therefore, only non-RMB currencies can be transmitted, and the RMB currency can be set to null. If two attribute fields in the property attributes are relevant and the content represented by the strings under the relevant fields is the same, one of the strings is encoded as a null value. Although the field of the relationship with state-owned enterprises is selected, it can be judged based on the information of the investor. The type of the investor, whether it belongs to the group, and the share ratio and other information can be judged through code to determine whether the information obtained from this field is consistent with the information judged from the investor information. If they are consistent, it can be set to null; if not, the field information is saved. The field of state-owned enterprise information is the headquarters of the central enterprise to which the main investor belongs. Since the total number of central enterprises under the management of the SASAC is 97, the value of this field is within a certain range. The simplified code values can be set for all central enterprises according to the code table. Through the above embodiments, the property attributes are better compressed.
[0040] In some embodiments of the present application, the method further includes: if multiple attribute fields in the property rights attribute of an enterprise are all encoded as null values, a separate object is established to save the multiple null values, and the strings corresponding to the multiple null values are set in the response attribute in the object. This embodiment mainly aims to solve the following problem: when setting the attribute with the most values to null, since there are many attributes, the attributes of its unit may not cover all attributes, so there will be errors in parsing after the data is transmitted to the other party.
[0041] In some embodiments of the present application, after obtaining the compressed property rights data, the method further includes: calculating the compression ratio and using the compression ratio as the verification of the compressed property rights data; the compression ratio includes the field compression ratio and the overall compression ratio. The calculation method of the field compression ratio is shown as follows: The compression ratio of field 1 ITEMS1 = TYPE1 * (ROWC1 / ROWR1) * (DR1 / DN1); The compression ratio of field 2 ITEMS2 = TYPE2 * (ROWC2 / ROWR2) * (DR2 / DN2);.......; The compression ratio of field N ITEMSN = TYPEN * (ROWCN / ROWRN) * (DRN / DNN). The overall compression ratio CRATE = (ITEMS1 + ITEMS2 +.... + ITEMSN) / N. Where CRATE is the overall compression ratio, and ITEMS is the data item to be compressed. TYPE is the field type weight. For example, the weight of the input field is 3, and the weight of the selection field is 2. ROWR is the source data row of the field, ROWC is the compressed field data row, ITEMS is the data item to be compressed, DR is the original data size of the field, and DN is the reduced data size of the field. Through this embodiment, it is possible to verify the compressed property rights data or the decompressed data based on the compressed property rights data, so as to improve data security and decompression accuracy.
[0042] Through the above embodiments, a corresponding compression method is designed based on the characteristics of the property rights data, which has the advantages of strong pertinence and good compression effect.
[0043] Based on the same inventive concept, the present application also provides a data compression device for the characteristics of property rights data, Figure 2 Schematically shows the structural schematic diagram of the data compression device for the characteristics of property rights data according to the embodiments of the present application. As Figure 2As shown in the figure, the device includes: a set acquisition module, configured to acquire a property right data set of target type enterprises, where the property right data in the property right data set includes at least one of enterprise name, enterprise credit code, and property right attribute; a rule statistics module, configured to decompose the property right data into several strings based on semantic analysis, and count the repetition frequency of the several strings in the property right data set; a coding determination module, configured to select strings from the several strings for coding based on the repetition frequency and string length, and the storage space occupied by the code after coding is less than its corresponding string; and a result output module, configured to compress the property right data based on the coding to obtain the compressed property right data.
[0044] In some alternative embodiments of the present application, the target type is divided and selected according to the ownership nature of the enterprise, and the property right data in the property right data set belongs to the same group enterprise.
[0045] In some alternative embodiments of the present application, when compressing the enterprise name in the property right data, selecting strings from the several strings for coding based on the repetition frequency and string length includes: encoding the selected strings into coded codes based on the corresponding relationship, and the corresponding relationship is obtained through the following steps: obtaining a coded code set, where the coded code set includes several coded codes; sorting the coded codes in ascending order based on the storage space occupied by the several coded codes to obtain a first sequence; sorting the several strings in descending order based on the product of the repetition frequency and string length to obtain a second sequence; and obtaining the corresponding relationship between the several strings and the coded codes based on the corresponding relationship between the first sequence and the second sequence.
[0046] In some alternative embodiments of the present application, when compressing the enterprise credit code in the property right data, selecting strings from the several strings for coding based on the repetition frequency and string length includes: ignoring the first two digits of the code identifying the registration management department code and the institution category code in the enterprise credit code through coding; for the string identifying the administrative division code of the registration management authority in the enterprise credit code, if the region corresponding to the string is the same as the enterprise name, encoding it as a specific Boolean value, and if the region corresponding to the string is different from the enterprise name, encoding it as a preset reduction value according to the repetition frequency of the string; and for the string identifying an overseas enterprise in the enterprise credit code, encoding the string with a high repetition frequency as a null value.
[0047] In some alternative embodiments of the present application, when compressing the property attributes in the property data, strings are selected from the several strings for encoding based on the repetition frequency and string length, including: counting the string with the highest repetition frequency in each attribute field of the property attributes, and encoding this string as a null value; if two attribute fields in the property attributes are correlated and the content represented by the strings under the correlated fields is the same, encoding one of the strings as a null value.
[0048] In some alternative embodiments of the present application, the device further includes an object creation module, which is used to, if multiple attribute fields in the property attributes of an enterprise are all encoded as null values, create a separate object to save the multiple null values, and set the strings corresponding to the multiple null values in the response attributes of the object.
[0049] In some alternative embodiments of the present application, after obtaining the compressed property data, the device further includes a compression ratio calculation module, which is used to calculate the compression ratio and use the compression ratio as the verification of the compressed property data; the compression ratio includes a field compression ratio and an overall compression ratio.
[0050] The specific definitions of the respective functional modules in the above data compression device for property data characteristics can be referred to the definitions of the data compression method for property data characteristics in the foregoing text, and will not be elaborated herein. Each module in the above system can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the electronic device in hardware form or be independent of it, or can be stored in the memory of the electronic device in software form, so that the processor can call and execute the operations corresponding to the above respective modules. It also realizes the advantages of compressing the storage data volume and transmission data volume of the property data while ensuring complete data accuracy.
[0051] In some embodiments of the present application, there is also provided an electronic device, including: at least one processor; a memory connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the at least one processor executes the foregoing data compression method for property data characteristics. Its internal structure diagram can be as Figure 3 shown. Figure 3Schematically shown is the internal structure diagram of an electronic device according to an embodiment of the present application. The electronic device includes a processor A01, a network interface A02, a memory (not shown in the figure), and a database (not shown in the figure) connected through a system bus. Among them, the processor A01 of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes an internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02, and a database (not shown in the figure). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A04. The network interface A02 of the electronic device is used to communicate with an external terminal through a network connection. When the computer program B02 is executed by the processor A01, it implements a data compression method for property data characteristics.
[0052] Those skilled in the art can understand that Figure 3 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.
[0053] In an embodiment provided by the present application, a machine-readable storage medium is provided. Instructions are stored on the machine-readable storage medium, and when the instructions are executed by a processor, the processor is configured to execute the aforementioned data compression method for property data characteristics.
[0054] In an embodiment provided by the present application, a computer program product is provided, including a computer program that implements the aforementioned data compression method for property data characteristics when executed by a processor.
[0055] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.
[0056] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and combinations of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.
[0057] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.
[0058] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.
[0059] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.
[0060] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.
[0061] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.
[0062] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.
[0063] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.
Claims
1. A data compression method targeting the characteristics of property rights data, characterized in that: The method includes: Acquire a property rights data set of a target type enterprise, wherein the property rights data in the property rights data set includes at least one of an enterprise name, an enterprise credit code, and a property rights attribute; Decomposing the property rights data into a plurality of character strings based on semantic analysis, and counting the repetition frequencies of the plurality of character strings in the property rights data set; Selecting a character string from the plurality of character strings for encoding based on the repetition frequency and the character string length, wherein the storage space occupied by the encoded code is smaller than the corresponding character string; The property rights data is compressed based on the encoding to obtain compressed property rights data.
2. The method according to claim 1, characterized in that The target type is divided and selected according to the ownership nature of the enterprise, and the property data in the property data set belong to the same group enterprise.
3. The method according to claim 1, characterized in that When compressing the enterprise name in the property data, a character string is selected from the plurality of character strings for encoding based on the repetition frequency and the character string length, including: encoding the selected character string into an encoded code based on a corresponding relationship, wherein the corresponding relationship is obtained by the following steps: Obtaining a post-encoding code set, wherein the post-encoding code set includes a plurality of post-encoding codes; Arrange the encoded codes in ascending order based on the storage space occupied by the encoded codes to obtain a first sequence; Arrange the plurality of character strings in descending order based on the product of the repetition frequency and the character string length to obtain a second sequence; Based on the correspondence between the first sequence and the second sequence, a correspondence between a plurality of character strings and the encoded code is obtained.
4. The method according to claim 1, characterized in that: When compressing the enterprise credit code in the property data, a character string is selected from the plurality of character strings for encoding based on the repetition frequency and the character string length, including: The first two digits of the registration management department code and the organization category code in the enterprise credit code are ignored through coding; For the character string identifying the administrative division code of the registration management authority in the enterprise credit code, if the region corresponding to the character string is consistent with the enterprise name, it is encoded as a specific Boolean value; if the region corresponding to the character string is inconsistent with the enterprise name, it is encoded as a preset reduced value according to the repetition frequency of the character string; For the character string identifying the overseas enterprise in the enterprise credit code, the character string with high repetition frequency is encoded as a null value.
5. The method according to claim 1, characterized in that When compressing the property attribute in the property data, a character string is selected from the plurality of character strings for encoding based on the repetition frequency and the character string length, including: Count the most repeated character string in each attribute field of the property right attribute, and encode the character string as a null value; If two attribute fields in the property rights attribute are correlated, and the contents represented by the character strings under the correlated fields are consistent, one of the character strings is encoded as a null value.
6. The method according to claim 5, characterized in that The method also includes: if multiple attribute fields in the property rights attributes of an enterprise are encoded as null values, a separate object is established to store the multiple null values, and the character strings corresponding to the multiple null values are set in the response attributes in the object.
7. The method according to claim 1, characterized in that After obtaining the compressed property data, the method further includes: calculating a compression ratio, and using the compression ratio as a check of the compressed property data; The compression ratio includes a field compression ratio and an overall compression ratio.
8. A data compression device targeting the characteristics of property rights data, characterized in that: The device includes: A set acquisition module, used to acquire a property data set of a target type enterprise, wherein the property data in the property data set includes at least one of an enterprise name, an enterprise credit code, and a property attribute; A rule statistics module, used for decomposing the property rights data into a plurality of character strings based on semantic analysis, and counting the repetition frequencies of the plurality of character strings in the property rights data set; A coding determination module, configured to select a character string from the plurality of character strings for coding based on a repetition frequency and a character string length, wherein the storage space occupied by the coded code is smaller than the corresponding character string; and The result output module is used to compress the property rights data based on the encoding to obtain compressed property rights data.
9. An electronic device, characterized in that: include: at least one processor; a memory connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor implements the steps of the data compression method for property rights data characteristics as described in any one of claims 1 to 7 by executing the instructions stored in the memory.
10. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the data compression method for property rights data characteristics as claimed in any one of claims 1 to 7 are implemented.