Case resource analysis matching method and system based on user information

Abstract

The invention relates to the technical field of case resource matching, in particular to a case resource analysis and matching method and system based on user information, and the method comprises the steps: confirming a historical case data set based on an information collection instruction, obtaining an identified case data set through the historical case data set, building an initial retrieval database based on the identified case data set, and storing the initial retrieval database in a database library; optimizing the initial retrieval database to obtain a case retrieval database, obtaining a user identity identification code of a user, obtaining a user information node based on the information analysis instruction and the user identity identification code, performing retrieval in the case retrieval database by using the user information node to obtain an initial retrieval data set, and storing the initial retrieval data set in the case retrieval database; and determining a target retrieval data sequence based on the initial retrieval data set, and sending the target retrieval data sequence to an initiating end of a case matching instruction by utilizing the result feedback unit to realize analysis and matching of case resources. According to the invention, case resource analysis and matching can be carried out in combination with user information.

Description

Technical Field

[0001] The present invention relates to the technical field of case resource matching, and in particular to a case resource analysis and matching method and system based on user information. Background Art

[0002] With the continuous improvement of residents' legal awareness and the increasing number of disputes of all kinds, the demand for case handling is also growing. Correspondingly, how to accurately and intelligently retrieve a large number of similar cases from the existing database is of great significance to improving the efficiency of case handling.

[0003] Currently, manual retrieval is mostly used to retrieve cases similar to user information from known case databases.

[0004] Although the above methods can realize the analysis and matching of user information, they consume a lot of human resources and energy when searching, and the final search results are related to the case handling experience of the search personnel. Therefore, how to realize case resource analysis and matching based on user information has become an urgent problem to be solved. Summary of the invention

[0005] The present invention provides a case resource analysis and matching method based on user information and a computer-readable storage medium, the main purpose of which is to realize case resource analysis and matching in combination with user information.

[0006] To achieve the above purpose, the present invention provides a case resource analysis and matching method based on user information, comprising:

[0007] receiving a case matching instruction, and confirming a case matching system based on the case matching instruction, wherein the case matching system includes: an information collection unit, an information analysis unit, an information matching unit, and a result feedback unit;

[0008] Confirming receipt of an information collection instruction from an information collection unit, and confirming a historical case data set based on the information collection instruction, wherein the historical case data set includes a plurality of historical case data, and the historical case data includes: case file images, case storage texts, and case judgment results;

[0009] Using the historical case data set to obtain a marked case data set, and constructing an initial search database based on the marked case data set;

[0010] Optimizing the initial search database to obtain a case search database;

[0011] Obtaining a user identification code of the user, confirming receipt of an information analysis instruction from the information analysis unit, obtaining a user information node based on the information analysis instruction and the user identification code, wherein the user information node includes m user case data and known user data, and m is an integer greater than or equal to 0, and using the user information node to search in a case search database to obtain an initial search data set;

[0012] Confirming a target retrieval data sequence based on the initial retrieval data set, wherein the target retrieval data sequence includes a plurality of target retrieval data;

[0013] The result feedback unit is used to send the target search data sequence to the initiator of the case matching instruction to achieve analysis and matching of case resources.

[0014] Optionally, the using the historical case dataset to obtain the identification case dataset includes:

[0015] Extract historical case data from the historical case data set in sequence, and perform the following operations on the extracted historical case data:

[0016] Parse the case storage text corresponding to the historical case data, obtain the actor identification code and case implementation time, and obtain the case completion time of the historical case data;

[0017] The extracted historical case data are identified using the actor identification code, case implementation time and case completion time corresponding to the historical case data to obtain identified case data;

[0018] The identified case data are aggregated to obtain an identified case data set.

[0019] Optionally, constructing an initial search database based on the identified case data set includes:

[0020] According to the actor identification code, the identification case data in the identification case data set are respectively aggregated to obtain an analysis case data set, wherein the analysis case data set includes a plurality of analysis case data sets, and the analysis case data set includes one or more identification case data, and the actor identification code corresponding to the one or more identification case data is the same;

[0021] The following operations are performed for each analysis case data group in the analysis case data group set:

[0022] Sort the identification case data in the analysis case data group in the order of case completion time from earliest to latest to obtain an identification case sequence;

[0023] Extract initial case data from the identified case sequence in sequence, and perform the following operations on the extracted initial case data:

[0024] Based on the initial case data, identifying target case data in the identification case sequence, wherein the target case data is identification case data that is adjacent to the initial case data and lags behind the initial case data;

[0025] Obtain the effective prosecution time based on the case completion time, and compare the effective prosecution time with the case implementation time corresponding to the target case data;

[0026] If the case implementation time is less than or equal to the effective prosecution time, determining whether to associate the initial case data with the target case data;

[0027] If the association between the initial case data and the target case data is confirmed, the initial case data and the target case data are associated to obtain chain case data, and the target case data in the chain case data is used as the initial case data, and the step of confirming the target case data in the identification case sequence based on the initial case data is returned to obtain a single case data set, and the chain case data is aggregated to obtain a chain case data set;

[0028] An initial retrieval database is constructed based on the single case data set and the chained case data set.

[0029] Optionally, the determining whether to associate the initial case data with the target case data includes:

[0030] Confirming receipt of an information matching instruction from an information matching unit, and confirming a character recognition model and a text summary generation model based on the information matching instruction;

[0031] Denoising the case file image corresponding to the initial case data to obtain an initial file image, and using a text recognition model to identify initial matching text in the initial file image;

[0032] Using a text summary generation model, a case matching summary and a case storage summary are identified in the initial matching text and the case storage text corresponding to the initial case data, respectively;

[0033] Perform vectorization transformation on the case matching summary and the case storage summary respectively to obtain the case matching vector and the case storage vector;

[0034] Acquire case similarity based on the case matching vector and the case storage vector, and compare the case similarity with a preset similarity threshold;

[0035] If the case similarity is less than the similarity threshold, then obtaining the case update text based on the initial file image and the case storage text, using the case update text to update the case storage text, and obtaining the update storage vector based on the updated case storage text, using the update storage vector as the case storage vector, and returning to the step of obtaining the case similarity based on the case matching vector and the case storage vector until the case similarity is greater than or equal to the similarity threshold;

[0036] If the case similarity is greater than or equal to the similarity threshold, the target case similarity corresponding to the target case data is obtained. After confirming that the target case similarity is greater than or equal to the similarity threshold, the initial case data and the target case data are confirmed to be associated.

[0037] Optionally, constructing an initial search database based on the single case data set and the chained case data set includes:

[0038] Extract chain case data from the chain case data set in sequence, and perform the following operations on the extracted chain case data:

[0039] Using the chain case data, a chain case sequence is identified in the identification case sequence, the last identification case data is extracted from the chain case sequence, and the following operations are performed on the extracted last identification case data:

[0040] Parse the case judgment result corresponding to the identified case data to obtain the case accountability type, where the case accountability type includes: recidivism or non-recidivism;

[0041] After confirming that the case accountability type is a recidivism, the judgment result of the case is parsed to obtain multiple judgment text summaries, and the last identification case data is removed from the chain case sequence to obtain a screening case sequence, and multiple screening text summary groups are obtained using the screening case sequence, wherein the screening text summary group includes one or more screening text summaries, and the screening text summary group corresponds to the identification case data in the screening case sequence one by one;

[0042] In a combined form, obtaining matching text nodes based on the judgment text summaries in the plurality of judgment text summaries and the screening text summaries corresponding to each screening text summary group in the plurality of screening text summary groups, obtaining text node similarities based on the matching text nodes, and comparing the text node similarities with a preset recidivism similarity threshold;

[0043] If the text node similarity is greater than or equal to the recidivism similarity threshold, the identification case data corresponding to the text node similarity is retained; otherwise, the identification case data corresponding to the text node similarity is removed, and the removed identification case data is identified as single case data;

[0044] Summarize the retained identification case data to obtain an updated identification data set, use the updated identification data set to update the chain case data to obtain updated chain data, identify the case identification node of each identification case data in the multiple identification case data corresponding to the updated chain data, wherein the case identification node includes a case definition and a case result, and use the case identification node to identify the updated chain data to obtain the identification chain data;

[0045] Based on the single case data, identification single data is obtained, and the identification chain data and identification single data are aggregated to obtain an initial search database.

[0046] Optionally, the matching text node is as follows:

[0047] P = {p i ,s j(k)}

[0048] Among them, P represents the matching text node, p i represents the i-th judgment text summary among multiple judgment text summaries, s j(k) Indicates the kth screening text summary corresponding to the jth screening text summary group among multiple screening text summary groups.

[0049] Optionally, the optimizing the initial search database to obtain a case search database includes:

[0050] Obtaining the type of the implementation object for classification, wherein the type of the implementation object includes an adult or a minor, and confirming the classification type according to the type of the implementation object and the type of case accountability, wherein the classification types include: non-recidivist-adult, non-recidivist-minor, and recidivist;

[0051] According to the classification type, the initial search data in the initial search database are summarized respectively to obtain three classification data sets, wherein the initial search data is the identification single data or the identification chain data, and the three classification data sets are the non-recidivist-adult classification data set, the non-recidivist-juvenile classification data set and the recidivist classification data set;

[0052] The non-recidivist-adult classification data set is stored in a pre-constructed first storage unit, the non-recidivist-juvenile classification data set is stored in a pre-constructed second storage unit, and the recidivist classification data set is stored in a pre-constructed third storage unit to obtain a case retrieval database.

[0053] Optionally, the using the user information node to search in a case search database to obtain an initial search data set includes:

[0054] Based on the user information node, identification analysis data identified with an analysis identification node or analysis chain data identified with an analysis identification node is obtained, and using the identification analysis data or analysis chain data, identification single data identical to the analysis identification node or identification chain data identical to the analysis node is retrieved from the case retrieval database to obtain an initial retrieval data set, wherein the initial retrieval data set includes multiple initial retrieval data.

[0055] Optionally, the step of confirming a target retrieval data sequence based on the initial retrieval data set includes:

[0056] The following operations are performed for each initial search data in the initial search data set:

[0057] The target search vector and the user search vector are obtained respectively by using the initial search data, the user information node and the pre-built information dimension reduction model, wherein the target search vector is as follows:

[0058] M={S,A}

[0059] Wherein, M represents the target retrieval vector, S represents the case implementation time, and A represents the case judgment result after being processed based on the information dimensionality reduction model;

[0060] Calculate the analytical similarity between the target search vector and the user search vector. The calculation formula is as follows:

[0061]

[0062] Wherein, X represents the analysis similarity, Y represents the user search vector, * represents taking the dot product, and || || represents taking the modulus length;

[0063] The analysis similarities are summarized to obtain an analysis similarity set, and the analysis similarities in the analysis similarity set are sorted in descending order to obtain an initial analysis similarity sequence, and a target retrieval data sequence is confirmed based on the initial analysis similarity sequence, wherein the analysis similarity corresponding to the target retrieval data in the target retrieval data sequence is greater than a preset retrieval similarity threshold.

[0064] To achieve the above object, the present invention also provides a case resource analysis and matching system based on user information, comprising:

[0065] An initial database construction preparation module is used to receive a case matching instruction and confirm a case matching system based on the case matching instruction, wherein the case matching system includes: an information collection unit, an information analysis unit, an information matching unit and a result feedback unit;

[0066] an initial database construction module, configured to confirm receipt of an information collection instruction from an information collection unit, and to confirm a historical case data set based on the information collection instruction, wherein the historical case data set includes a plurality of historical case data, and the historical case data includes: case file images, case storage texts, and case judgment results;

[0067] Using the historical case data set to obtain a marked case data set, and constructing an initial search database based on the marked case data set;

[0068] An initial database optimization module, used to optimize the initial search database to obtain a case search database;

[0069] A user information analysis and matching module, used to obtain a user identification code of the user, confirm receipt of an information analysis instruction from the information analysis unit, obtain a user information node based on the information analysis instruction and the user identification code, wherein the user information node includes m user case data and known user data, and m is an integer greater than or equal to 0, and use the user information node to search in a case search database to obtain an initial search data set;

[0070] Confirming a target retrieval data sequence based on the initial retrieval data set, wherein the target retrieval data sequence includes a plurality of target retrieval data;

[0071] The result feedback unit is used to send the target search data sequence to the initiator of the case matching instruction to achieve analysis and matching of case resources.

[0072] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0073] A memory storing at least one instruction; and a processor executing the instruction stored in the memory to implement the above-mentioned case resource analysis and matching method based on user information.

[0074] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned case resource analysis and matching method based on user information.

[0075] The present invention is to solve the problem described in the background technology. The present invention confirms the reception of an information collection instruction from an information collection unit, and confirms a historical case data set based on the information collection instruction, wherein the historical case data set includes multiple historical case data, and the historical case data includes: case file image, case storage text and case judgment result. It can be seen that the present invention not only considers the electronic version of the case storage text, but also considers the case file image corresponding to the case storage text. The method of identifying the information in the case file image is used to realize the judgment of whether the case storage text is correct, and the update and correction of the case storage text is realized. The present invention uses the historical case data set to obtain the identification case data set, and constructs an initial retrieval database based on the identification case data set. It can be seen that when constructing the initial retrieval database, the embodiment of the present invention only considers the time factor, classifies different types of cases to reduce the energy consumption required when classifying the cases, optimizes the initial retrieval database, and obtains the case retrieval database. It can be seen that the present invention combines different case situations to revise the classified cases again to improve the accuracy of constructing the case retrieval database. The present invention obtains the user's user identification code, confirms the reception of the information analysis instruction from the information analysis unit, obtains the user information node based on the information analysis instruction and the user identification code, wherein the user information node includes m user case data and known user data, and m is an integer greater than or equal to 0, and uses the user information node to search in the case search database to obtain an initial search data set. It can be seen that when the present invention searches in combination with user information, the known case information and unknown case information in the user information are considered, and a user search vector for retrieval is obtained based on the user information, and when the user search vector is obtained, the user information is subjected to dimensionality reduction processing to improve the accuracy and timeliness of the search. Therefore, the present invention can realize case resource analysis and matching in combination with user information. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 A flowchart of a case resource analysis and matching method based on user information provided by an embodiment of the present invention;

[0077] Figure 2 A functional module diagram of a case resource analysis and matching system based on user information provided by an embodiment of the present invention;

[0078] Figure 3 A schematic diagram of the structure of an electronic device for implementing the case resource analysis and matching method based on user information provided by an embodiment of the present invention.

[0079] Description of reference numerals:

[0080] 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0081] 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

[0082] 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.

[0083] The embodiment of the present application provides a case resource analysis and matching method based on user information. The execution subject of the case resource analysis and matching method based on user information includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the case resource analysis and matching method based on user information can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0084] Reference Figure 1 FIG. 1 is a flow chart of a case resource analysis and matching method based on user information provided by an embodiment of the present invention. In this embodiment, the case resource analysis and matching method based on user information includes:

[0085] S1. Receive a case matching instruction, and confirm a case matching system based on the case matching instruction, wherein the case matching system includes: an information collection unit, an information analysis unit, an information matching unit, and a result feedback unit.

[0086] It should be explained that the case matching instruction refers to an instruction for matching case resources. The case matching system refers to an APP or mini-program for matching cases, and the case matching system includes: an information collection unit, an information analysis unit, an information matching unit and a result feedback unit. For the specific application of the units, please refer to the subsequent embodiments. The main purpose of the embodiment of the present invention is to achieve analysis and matching of case resources based on user information. Generally speaking, criminal cases usually require the combination of multiple factors in sentencing, and the circumstances of criminal cases are relatively complicated. Therefore, the embodiment of the present invention mainly focuses on achieving matching analysis of criminal cases.

[0087] For example, in order to obtain historical case data as a reference for judgment, Xiao Zhang issues the case matching instruction, and based on the case matching instruction, identifies a case matching system that can match the historical case data set in combination with the user's case information, and then analyzes the judgment result corresponding to the case information in combination with the matched historical case data.

[0088] S2. Confirm receipt of an information collection instruction from an information collection unit, and confirm a historical case data set based on the information collection instruction, wherein the historical case data set includes a plurality of historical case data, and the historical case data includes: case file images, case storage texts, and case judgment results.

[0089] It should be explained that the historical case data refers to criminal cases that have been judged and recorded. Optionally, the historical case data set is obtained by recording or summarizing the historical case data that has been stored in the records.

[0090] Furthermore, a criminal case refers to a case in which the perpetrator violates the criminal law and commits an act that is seriously harmful to society, criminally illegal, and punishable by criminal penalties. A case file image refers to an image of a file that stores case information. A case storage text refers to a text that stores case information in an electronic device. Ideally, the case information stored in the case file image is consistent with the case information stored in the case storage text. The case judgment result refers to the result of the judgment on the case corresponding to the historical case data, wherein the case judgment result includes but is not limited to the sentence, economic compensation, whether forgiveness is obtained, etc.

[0091] For example, in order to improve the accuracy of the judgment on the perpetrator, cases similar to the case in which the perpetrator was involved are retrieved from the historical case data set, and the similar cases are used as references to improve the accuracy of the judgment on the perpetrator.

[0092] S3. Utilize the historical case data set to obtain a marked case data set, and construct an initial search database based on the marked case data set.

[0093] It should be understood that the use of the historical case data set to obtain the identification case data set includes:

[0094] Extract historical case data from the historical case data set in sequence, and perform the following operations on the extracted historical case data:

[0095] Parse the case storage text corresponding to the historical case data, obtain the actor identification code and case implementation time, and obtain the case completion time of the historical case data;

[0096] The extracted historical case data are identified using the actor identification code, case implementation time and case completion time corresponding to the historical case data to obtain identified case data;

[0097] The identified case data are aggregated to obtain an identified case data set.

[0098] It is understandable that the actor identification code refers to the identification code of the person being executed when the case is judged. Optionally, the actor's ID number is used as the actor identification code. Generally speaking, if a crime is committed again during the effective accountability period of the case judgment, it may have an impact on the sentencing of the case. For example: within 5 years after the execution of the previous crime or pardon, committing a crime again may result in multiple crimes being punished together. The case completion time refers to the time after the actor completes the punishment or is pardoned. The case implementation time refers to the time when the actor commits the crime. The purpose of identifying historical case data is to increase the speed of analyzing different historical case data to adapt to the special circumstances of different historical case data, so as to improve the accuracy of analyzing and matching user information.

[0099] Exemplarily, the actor identification code corresponding to the historical case data is 1, and the case completion time is A / B / C, and the case implementation time is A / B / D. Then, after using the actor identification code, case implementation time and case completion time to identify the extracted historical case data, the identified case data obtained is: 1-A / B / D-A / B / C.

[0100] Furthermore, the constructing of an initial search database based on the identified case data set includes:

[0101] According to the actor identification code, the identification case data in the identification case data set are respectively aggregated to obtain an analysis case data set, wherein the analysis case data set includes a plurality of analysis case data sets, and the analysis case data set includes one or more identification case data, and the actor identification code corresponding to the one or more identification case data is the same;

[0102] The following operations are performed for each analysis case data group in the analysis case data group set:

[0103] Sort the identification case data in the analysis case data group in the order of case completion time from earliest to latest to obtain an identification case sequence;

[0104] Extract initial case data from the identified case sequence in sequence, and perform the following operations on the extracted initial case data:

[0105] Based on the initial case data, identifying target case data in the identification case sequence, wherein the target case data is identification case data that is adjacent to the initial case data and lags behind the initial case data;

[0106] Obtain the effective prosecution time based on the case completion time, and compare the effective prosecution time with the case implementation time corresponding to the target case data;

[0107] If the case implementation time is less than or equal to the effective prosecution time, determining whether to associate the initial case data with the target case data;

[0108] If the association between the initial case data and the target case data is confirmed, the initial case data and the target case data are associated to obtain chain case data, and the target case data in the chain case data is used as the initial case data, and the step of confirming the target case data in the identification case sequence based on the initial case data is returned to obtain a single case data set, and the chain case data is aggregated to obtain a chain case data set;

[0109] An initial retrieval database is constructed based on the single case data set and the chained case data set.

[0110] It should be explained that the effective prosecution time refers to the sum of the case completion time and the preset effective accountability time, and under normal circumstances the effective accountability time is 5 years. The purpose of obtaining the effective prosecution time is to determine whether the criminal is a repeat offender from a time perspective. Here, the purpose of using only the effective prosecution time for screening is to increase the speed of screening the identified case data and reduce the energy consumption required during screening.

[0111] Furthermore, when a case is tried, the perpetrator's previous case situation is usually considered, and then the perpetrator's case is sentenced. For example, if the perpetrator has behaved badly, violated the law many times within the effective prosecution period, and has an impact on social security, the perpetrator will be sentenced more severely. The purpose of determining whether to associate the initial case data and the target case data is to determine whether the initial case data will have an impact on the target case data, thereby improving the accuracy of case analysis and matching of user information.

[0112] Exemplarily, there are 6 identification case data in the analysis case data group. By sorting the case completion time corresponding to the 6 identification case data from first to last, an identification case sequence is obtained, wherein the case completion time corresponding to the second identification case data in the identification case sequence is less than or equal to the effective prosecution time calculated by the first identification case data, and the initial case data and the target case data are confirmed to be associated, then it is believed that the case corresponding to the first identification case data will have an impact on the case corresponding to the second identification case data, therefore, the initial case data is associated with the target case data. Generally speaking, the perpetrator may not commit a crime again within the effective prosecution time after committing a crime, therefore, the identification case data in the identification case sequence may not all constitute the chain case data, and the identification case data that does not constitute the chain case data can be used as a reference for sentencing of a single crime, and only the time factor is considered when obtaining the chain case data, therefore, the confirmed chain case data may not necessarily have a relationship, and for the specific steps of further screening the chain case data, please refer to the subsequent embodiments.

[0113] Furthermore, the determining whether to associate the initial case data with the target case data includes:

[0114] Confirming receipt of an information matching instruction from an information matching unit, and confirming a character recognition model and a text summary generation model based on the information matching instruction;

[0115] Denoising the case file image corresponding to the initial case data to obtain an initial file image, and using a text recognition model to identify initial matching text in the initial file image;

[0116] Using a text summary generation model, a case matching summary and a case storage summary are identified in the initial matching text and the case storage text corresponding to the initial case data, respectively;

[0117] Perform vectorization transformation on the case matching summary and the case storage summary respectively to obtain the case matching vector and the case storage vector;

[0118] Acquire case similarity based on the case matching vector and the case storage vector, and compare the case similarity with a preset similarity threshold;

[0119] If the case similarity is less than the similarity threshold, then obtaining the case update text based on the initial file image and the case storage text, using the case update text to update the case storage text, and obtaining the update storage vector based on the updated case storage text, using the update storage vector as the case storage vector, and returning to the step of obtaining the case similarity based on the case matching vector and the case storage vector until the case similarity is greater than or equal to the similarity threshold;

[0120] If the case similarity is greater than or equal to the similarity threshold, the target case similarity corresponding to the target case data is obtained. After confirming that the target case similarity is greater than or equal to the similarity threshold, the initial case data and the target case data are confirmed to be associated.

[0121] It should be explained that the text recognition model refers to a model, unit or algorithm that can recognize text in an image. Optionally, an optical character recognition algorithm is used as the text recognition model, and other technologies can achieve the same effect, which will not be repeated here. The text summary generation model refers to a model, unit or algorithm that generates a summary of the speech according to a given speech. Optionally, a natural language processing model is used as the text summary generation model. Optionally, a bilateral filtering algorithm is used to reduce the noise of the case file image, and the technology of using the bilateral filtering algorithm to reduce the noise of the image is a prior art, which will not be repeated here. Optionally, a natural language processing model is used to vectorize the case matching summary and the case storage summary respectively. Here, the vectorization transformation refers to converting the text into a vector that can be used for calculation, and the technology of vectorizing the text is a prior art, which will not be repeated here.

[0122] Furthermore, the case file will go through multiple steps such as review and proofreading before being stored, and when the case file corresponding to the case file image is stored, it is usually properly stored. Therefore, the information corresponding to the case file image should be accurate. The case storage text may cause errors in the case storage text due to factors such as human input. Therefore, in this application document, the case file image and the case storage text are mutually verified to ensure the accuracy of the constructed case retrieval database, thereby improving the accuracy of case resource matching for user information. Optionally, the Euclidean distance between the case matching vector and the case storage vector is used as the case similarity, and other technologies can achieve the same effect, which will not be repeated here. Using the initial file image and the case storage text to obtain the case update text refers to comparing the text information corresponding to the initial file image and the text information corresponding to the case storage text, and correcting the erroneous information therein. Optionally, the case storage text is corrected manually. The method of obtaining the target case similarity using the target case data is the same as the method of obtaining the case similarity using the initial case data, which will not be repeated here. Generally speaking, if the initial file image is difficult to identify due to accidental factors, the initial case data should be eliminated.

[0123] It should be explained that the initial search database constructed based on the single case data set and the chained case data set includes:

[0124] Extract chain case data from the chain case data set in sequence, and perform the following operations on the extracted chain case data:

[0125] Using the chain case data, a chain case sequence is identified in the identification case sequence, the last identification case data is extracted from the chain case sequence, and the following operations are performed on the extracted last identification case data:

[0126] Parse the case judgment result corresponding to the identified case data to obtain the case accountability type, where the case accountability type includes: recidivism or non-recidivism;

[0127] After confirming that the case accountability type is a recidivism, the judgment result of the case is parsed to obtain multiple judgment text summaries, and the last identification case data is removed from the chain case sequence to obtain a screening case sequence, and multiple screening text summary groups are obtained using the screening case sequence, wherein the screening text summary group includes one or more screening text summaries, and the screening text summary group corresponds to the identification case data in the screening case sequence one by one;

[0128] In a combined form, obtaining matching text nodes based on the judgment text summaries in the plurality of judgment text summaries and the screening text summaries corresponding to each screening text summary group in the plurality of screening text summary groups, obtaining text node similarities based on the matching text nodes, and comparing the text node similarities with a preset recidivism similarity threshold;

[0129] If the text node similarity is greater than or equal to the recidivism similarity threshold, the identification case data corresponding to the text node similarity is retained; otherwise, the identification case data corresponding to the text node similarity is removed, and the removed identification case data is identified as single case data;

[0130] Summarize the retained identification case data to obtain an updated identification data set, use the updated identification data set to update the chain case data to obtain updated chain data, identify the case identification node of each identification case data in the multiple identification case data corresponding to the updated chain data, wherein the case identification node includes a case definition and a case result, and use the case identification node to identify the updated chain data to obtain the identification chain data;

[0131] Based on the single case data, identification single data is obtained, and the identification chain data and identification single data are aggregated to obtain an initial search database.

[0132] It is understandable that the matching text nodes are as follows:

[0133] P = {p i ,s j(k)}

[0134] Among them, P represents the matching text node, p i represents the i-th judgment text summary among multiple judgment text summaries, sj(k) Indicates the kth screening text summary corresponding to the jth screening text summary group among multiple screening text summary groups.

[0135] Furthermore, a recidivist refers to a criminal who has been sentenced to a certain penalty and who, after the penalty has been executed or pardoned, commits another crime within the statutory period, and recidivists are usually given heavier sentences during sentencing. Non-recidivists are the complement of recidivists. Generally speaking, if the case accountability type is a recidivist, the previous criminal basis that can be judged as a recidivist will usually be marked during sentencing. The judgment text summary refers to the text of each criminal basis when constituting a recidivist, and the case judgment result usually gives words such as the criminal is a recidivist or is determined to be a recidivist. Therefore, the case accountability type can be parsed from the case judgment result.

[0136] It should be explained that not every identified case data in the screening case sequence can be used as the basis for constituting a recidivist. For example: when there is a negligent crime, the case data cannot be used as the basis for constituting a recidivist. Here, negligent crime refers to an unintentional crime. Generally speaking, the identified case data may correspond to more than one text used to represent the crime. Therefore, there may be more than one screening text summary in the screening text summary group obtained using the identified case data in the screening case sequence. The method of obtaining the screening text summary and the method of obtaining the judgment text summary are the same as the method of obtaining the case matching summary, which will not be repeated here.

[0137] Furthermore, the text node similarity refers to the similarity between the judgment text summary and the screening text summary included in the matching text node, and the method for obtaining the text node similarity is the same as the method for obtaining the case similarity, which will not be repeated here. The difference between the recidivism similarity threshold and the similarity threshold is that the recidivism similarity threshold is used to determine whether the criminal's previous crimes can constitute a basis for being a recidivist, and then to screen out the identification case data that cannot be used as a basis for recidivism, so as to improve the accuracy of case resource matching for user information, while the similarity threshold is used to determine whether the case storage text is correct. Therefore, under normal circumstances, the recidivism similarity threshold should be greater than the similarity threshold. Generally speaking, if there is at least one text node similarity among one or more text node similarities corresponding to the same identification case data, the identification case data can be retained.

[0138] It is understandable that the case definition refers to the definition given in combination with laws and regulations when characterizing a case. For example: burglary, attempted burglary. Case outcome refers to the outcome produced in the case. For example: causing economic losses, disability, injury. The method of using the single case data to obtain identification single data is the same as the method of using the updated chain data to obtain identification chain data, which will not be repeated here. Updated chain data refers to chain case data after eliminating identification case data that does not meet the conditions on the basis of chain case data.

[0139] S4. Optimize the initial search database to obtain a case search database.

[0140] It should be explained that the optimization of the initial search database to obtain the case search database includes:

[0141] Obtaining the type of the implementation object for classification, wherein the type of the implementation object includes an adult or a minor, and confirming the classification type according to the type of the implementation object and the type of case accountability, wherein the classification types include: non-recidivist-adult, non-recidivist-minor, and recidivist;

[0142] According to the classification type, the initial search data in the initial search database are summarized respectively to obtain three classification data sets, wherein the initial search data is the identification single data or the identification chain data, and the three classification data sets are the non-recidivist-adult classification data set, the non-recidivist-juvenile classification data set and the recidivist classification data set;

[0143] The non-recidivist-adult classification data set is stored in a pre-constructed first storage unit, the non-recidivist-juvenile classification data set is stored in a pre-constructed second storage unit, and the recidivist classification data set is stored in a pre-constructed third storage unit to obtain a case retrieval database.

[0144] It is understandable that the adult refers to the perpetrator who was an adult when the crime was committed, and the minor refers to the perpetrator who was a minor when the crime was committed. Non-recidivist-adult means that the offender is an adult and the case accountability type of the crime committed is non-recidivist. Non-recidivist-minor means that the offender is a minor and the case accountability type of the crime committed is non-recidivist. Generally speaking, a recidivist is only possible when the offender is an adult. The purpose of summarizing the initial search data in the initial search database according to the classification type is to achieve classified management of different types of cases, thereby improving the efficiency of users in case retrieval and reducing the energy consumption required by users in case retrieval.

[0145] S5. Obtain the user's user identity identification code, confirm receipt of the information analysis instruction from the information analysis unit, and obtain a user information node based on the information analysis instruction and the user identity identification code, wherein the user information node includes m user case data and known user data, and m is an integer greater than or equal to 0. Use the user information node to search in the case retrieval database to obtain an initial retrieval data set.

[0146] It is understandable that the user identity identification code refers to an identification code that can confirm the identity of the user, and the user identity identification code has the same definition as the actor identification code, which will not be repeated here. Optionally, the user identity identification code is used to search in a known database to obtain the user information node, and the definition of the user case data is the same as the definition of the historical case data, which will not be repeated here. Known user data refers to the information of users known in the case. Optionally, the known user data can be obtained after collecting evidence from the information, and other technologies can achieve the same effect, which will not be repeated here. Generally speaking, the difference between known user data and user case data is that user case data is an established fact, and there are undetermined facts in the known user data. For example: after committing a crime, if the user actively cooperates with the police in collecting evidence and actively obtains a letter of forgiveness, the user's sentence may be reduced.

[0147] Furthermore, the user information node is used to search in a case search database to obtain an initial search data set, including:

[0148] Based on the user information node, identification analysis data identified with an analysis identification node or analysis chain data identified with an analysis identification node is obtained, and using the identification analysis data or analysis chain data, identification single data identical to the analysis identification node or identification chain data identical to the analysis node is retrieved from the case retrieval database to obtain an initial retrieval data set, wherein the initial retrieval data set includes multiple initial retrieval data.

[0149] It should be explained that the method for obtaining the analysis identification node is the same as the method for obtaining the case identification node, which will not be repeated here. The definition of the analysis chain data is the same as that of the update chain data, which will not be repeated here. The definition of the identification analysis data is the same as that of the identification case data, which will not be repeated here. Optionally, the analysis chain data or the identification analysis data can be given by the law after analyzing the user information node. The initial retrieval data set includes multiple initial retrieval data, and the initial retrieval data is the identification single data or identification chain data retrieved from the case retrieval database.

[0150] S6. Confirm a target retrieval data sequence based on the initial retrieval data set, wherein the target retrieval data sequence includes a plurality of target retrieval data.

[0151] It is understandable that the step of confirming the target search data sequence based on the initial search data set includes:

[0152] The following operations are performed for each initial search data in the initial search data set:

[0153] The target search vector and the user search vector are obtained respectively by using the initial search data, the user information node and the pre-built information dimension reduction model, wherein the target search vector is as follows:

[0154] M={S,A}

[0155] Wherein, M represents the target retrieval vector, S represents the case implementation time, and A represents the case judgment result after being processed based on the information dimensionality reduction model;

[0156] Calculate the analytical similarity between the target search vector and the user search vector. The calculation formula is as follows:

[0157]

[0158] Wherein, X represents the analysis similarity, Y represents the user search vector, * represents taking the dot product, and || || represents taking the modulus length;

[0159] The analysis similarities are summarized to obtain an analysis similarity set, and the analysis similarities in the analysis similarity set are sorted in descending order to obtain an initial analysis similarity sequence, and a target retrieval data sequence is confirmed based on the initial analysis similarity sequence, wherein the analysis similarity corresponding to the target retrieval data in the target retrieval data sequence is greater than a preset retrieval similarity threshold.

[0160] It should be explained that the purpose of the information dimension reduction model is to remove unnecessary information from the case judgment results and retain only necessary information. For example: the case judgment result is: Wang broke into the house and stole items A and B on a certain day of a certain month of a certain year, causing economic losses of 30,000 yuan. The following judgment was executed on Wang: Wang was sentenced to compensate the victim for economic losses totaling 40,000 yuan and sentenced to 6 months in prison. Here, a certain day of a certain month of a certain year is the implementation time of the case. After processing the case judgment result through the information dimension reduction model, it is obtained that: economic losses are 30,000 yuan, compensation for economic losses is 40,000 yuan, and a fixed-term imprisonment is 6 months. Optionally, a natural language processing model is used as the information dimension reduction model, and other technologies can achieve the same effect, which will not be repeated here. Generally speaking, the purchasing power of currency is different at different times. Therefore, by setting the case implementation time and the case judgment result after processing as the basis, the accuracy of case resource matching for user information can be improved.

[0161] Furthermore, the method for acquiring the user search vector is the same as the method for acquiring the target search vector, and can achieve the same effect, which will not be described in detail herein.

[0162] S7. Utilize the result feedback unit to send the target search data sequence to the initiator of the case matching instruction to achieve analysis and matching of case resources.

[0163] The present invention is to solve the problem described in the background technology. The present invention confirms the reception of an information collection instruction from an information collection unit, and confirms a historical case data set based on the information collection instruction, wherein the historical case data set includes multiple historical case data, and the historical case data includes: case file image, case storage text and case judgment result. It can be seen that the present invention not only considers the electronic version of the case storage text, but also considers the case file image corresponding to the case storage text. The method of identifying the information in the case file image is used to realize the judgment of whether the case storage text is correct, and the update and correction of the case storage text is realized. The present invention uses the historical case data set to obtain the identification case data set, and constructs an initial retrieval database based on the identification case data set. It can be seen that when constructing the initial retrieval database, the embodiment of the present invention only considers the time factor, classifies different types of cases to reduce the energy consumption required when classifying the cases, optimizes the initial retrieval database, and obtains the case retrieval database. It can be seen that the present invention combines different case situations to revise the classified cases again to improve the accuracy of constructing the case retrieval database. The present invention obtains the user's user identification code, confirms the reception of the information analysis instruction from the information analysis unit, obtains the user information node based on the information analysis instruction and the user identification code, wherein the user information node includes m user case data and known user data, and m is an integer greater than or equal to 0, and uses the user information node to search in the case search database to obtain an initial search data set. It can be seen that when the present invention searches in combination with user information, the known case information and unknown case information in the user information are considered, and a user search vector for retrieval is obtained based on the user information, and when the user search vector is obtained, the user information is subjected to dimensionality reduction processing to improve the accuracy and timeliness of the search. Therefore, the present invention can realize case resource analysis and matching in combination with user information.

[0164] like Figure 2 , which is a functional module diagram of a case resource analysis and matching system based on user information provided by an embodiment of the present invention.

[0165] The case resource analysis and matching system 100 based on user information of the present invention can be installed in an electronic device. According to the functions to be implemented, the case resource analysis and matching system 100 based on user information can include an initial database construction preparation module 101, an initial database construction module 102, an initial database optimization module 103 and a user information analysis and matching module 104. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0166] The initial database construction preparation module 101 is used to receive a case matching instruction and confirm a case matching system based on the case matching instruction, wherein the case matching system includes: an information collection unit, an information analysis unit, an information matching unit and a result feedback unit;

[0167] The initial database construction module 102 is used to confirm the receipt of the information collection instruction from the information collection unit, and confirm the historical case data set based on the information collection instruction, wherein the historical case data set includes a plurality of historical case data, and the historical case data includes: case file images, case storage texts and case judgment results;

[0168] Using the historical case data set to obtain a marked case data set, and constructing an initial search database based on the marked case data set;

[0169] The initial database optimization module 103 is used to optimize the initial search database to obtain a case search database;

[0170] The user information analysis and matching module 104 is used to obtain the user identity code of the user, confirm the receipt of the information analysis instruction from the information analysis unit, obtain the user information node based on the information analysis instruction and the user identity code, wherein the user information node includes m user case data and known user data, and m is an integer greater than or equal to 0, and use the user information node to search in the case search database to obtain an initial search data set;

[0171] Confirming a target retrieval data sequence based on the initial retrieval data set, wherein the target retrieval data sequence includes a plurality of target retrieval data;

[0172] The result feedback unit is used to send the target search data sequence to the initiator of the case matching instruction to achieve analysis and matching of case resources.

[0173] In detail, each module in the case resource analysis and matching system 100 based on user information in the embodiment of the present invention is used in the same manner as described above. Figure 1The case resource analysis and matching method based on user information described in the text is the same technical means and can produce the same technical effects, so I will not go into details here.

[0174] like Figure 3 , is a schematic diagram of the structure of an electronic device for implementing a case resource analysis and matching method based on user information provided by an embodiment of the present invention.

[0175] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a case resource analysis and matching method program based on user information.

[0176] The memory 11 includes at least one type of readable storage medium, including flash memory, mobile hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card, etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the case resource analysis matching method program based on user information, but also can be used to temporarily store data that has been output or is to be output.

[0177] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (such as a case resource analysis and matching method program based on user information, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0178] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0179] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0180] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management system, so that the power management system can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0181] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0182] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0183] The case resource analysis and matching method program based on user information stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0184] receiving a case matching instruction, and confirming a case matching system based on the case matching instruction, wherein the case matching system includes: an information collection unit, an information analysis unit, an information matching unit, and a result feedback unit;

[0185] Confirming receipt of an information collection instruction from an information collection unit, and confirming a historical case data set based on the information collection instruction, wherein the historical case data set includes a plurality of historical case data, and the historical case data includes: case file images, case storage texts, and case judgment results;

[0186] Using the historical case data set to obtain a marked case data set, and constructing an initial search database based on the marked case data set;

[0187] Optimizing the initial search database to obtain a case search database;

[0188] Obtaining a user identification code of the user, confirming receipt of an information analysis instruction from the information analysis unit, obtaining a user information node based on the information analysis instruction and the user identification code, wherein the user information node includes m user case data and known user data, and m is an integer greater than or equal to 0, and using the user information node to search in a case search database to obtain an initial search data set;

[0189] Confirming a target retrieval data sequence based on the initial retrieval data set, wherein the target retrieval data sequence includes a plurality of target retrieval data;

[0190] The result feedback unit is used to send the target search data sequence to the initiator of the case matching instruction to achieve analysis and matching of case resources.

[0191] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0192] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or system that can carry the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0193] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0194] receiving a case matching instruction, and confirming a case matching system based on the case matching instruction, wherein the case matching system includes: an information collection unit, an information analysis unit, an information matching unit, and a result feedback unit;

[0195] Confirming receipt of an information collection instruction from an information collection unit, and confirming a historical case data set based on the information collection instruction, wherein the historical case data set includes a plurality of historical case data, and the historical case data includes: case file images, case storage texts, and case judgment results;

[0196] Using the historical case data set to obtain a marked case data set, and constructing an initial search database based on the marked case data set;

[0197] Optimizing the initial search database to obtain a case search database;

[0198] Obtaining a user identification code of the user, confirming receipt of an information analysis instruction from the information analysis unit, obtaining a user information node based on the information analysis instruction and the user identification code, wherein the user information node includes m user case data and known user data, and m is an integer greater than or equal to 0, and using the user information node to search in a case search database to obtain an initial search data set;

[0199] Confirming a target retrieval data sequence based on the initial retrieval data set, wherein the target retrieval data sequence includes a plurality of target retrieval data;

[0200] The result feedback unit is used to send the target search data sequence to the initiator of the case matching instruction to achieve analysis and matching of case resources.

[0201] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0202] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0203] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0204] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0205] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A case resource analysis and matching method based on user information, characterized in that: The method comprises: receiving a case matching instruction, and confirming a case matching system based on the case matching instruction, wherein the case matching system includes: an information collection unit, an information analysis unit, an information matching unit, and a result feedback unit; Confirming receipt of an information collection instruction from an information collection unit, and confirming a historical case data set based on the information collection instruction, wherein the historical case data set includes a plurality of historical case data, and the historical case data includes: case file images, case storage texts, and case judgment results; Using the historical case data set to obtain a marked case data set, and constructing an initial search database based on the marked case data set; Optimizing the initial search database to obtain a case search database; Obtaining a user identification code of the user, confirming receipt of an information analysis instruction from the information analysis unit, obtaining a user information node based on the information analysis instruction and the user identification code, wherein the user information node includes m user case data and known user data, and m is an integer greater than or equal to 0, and using the user information node to search in a case search database to obtain an initial search data set; Confirming a target retrieval data sequence based on the initial retrieval data set, wherein the target retrieval data sequence includes a plurality of target retrieval data; The result feedback unit is used to send the target search data sequence to the initiator of the case matching instruction to achieve analysis and matching of case resources.

2. The case resource analysis and matching method based on user information according to claim 1, characterized in that: The step of using the historical case data set to obtain the identification case data set includes: The historical case data are sequentially extracted from the historical case data set, and the following operations are performed on the extracted historical case data: Parse the case storage text corresponding to the historical case data, obtain the actor identification code and case implementation time, and obtain the case completion time of the historical case data; The extracted historical case data are identified using the actor identification code, case implementation time and case completion time corresponding to the historical case data to obtain identified case data; The identified case data are aggregated to obtain an identified case data set.

3. The case resource analysis and matching method based on user information as claimed in claim 2, characterized in that: The constructing of an initial search database based on the identified case data set includes: According to the actor identification code, the identification case data in the identification case data set are respectively aggregated to obtain an analysis case data set, wherein the analysis case data set includes a plurality of analysis case data sets, and the analysis case data set includes one or more identification case data, and the actor identification code corresponding to the one or more identification case data is the same; The following operations are performed for each analysis case data group in the analysis case data group set: Sort the identification case data in the analysis case data group in the order of case completion time from earliest to latest to obtain an identification case sequence; Extract initial case data from the identified case sequence in sequence, and perform the following operations on the extracted initial case data: Based on the initial case data, identifying target case data in the identification case sequence, wherein the target case data is identification case data that is adjacent to the initial case data and lags behind the initial case data; Obtain the effective prosecution time based on the case completion time, and compare the effective prosecution time with the case implementation time corresponding to the target case data; If the case implementation time is less than or equal to the effective prosecution time, determining whether to associate the initial case data with the target case data; If the association between the initial case data and the target case data is confirmed, the initial case data and the target case data are associated to obtain chain case data, and the target case data in the chain case data is used as the initial case data, and the step of confirming the target case data in the identification case sequence based on the initial case data is returned to obtain a single case data set, and the chain case data is aggregated to obtain a chain case data set; An initial retrieval database is constructed based on the single case data set and the chained case data set.

4. The case resource analysis and matching method based on user information as claimed in claim 3, characterized in that: The determining whether to associate the initial case data with the target case data includes: Confirming receipt of an information matching instruction from an information matching unit, and confirming a character recognition model and a text summary generation model based on the information matching instruction; Denoising the case file image corresponding to the initial case data to obtain an initial file image, and using a text recognition model to identify initial matching text in the initial file image; Using a text summary generation model, a case matching summary and a case storage summary are identified in the initial matching text and the case storage text corresponding to the initial case data, respectively; Perform vectorization transformation on the case matching summary and the case storage summary respectively to obtain the case matching vector and the case storage vector; Acquire case similarity based on the case matching vector and the case storage vector, and compare the case similarity with a preset similarity threshold; If the case similarity is less than the similarity threshold, then obtaining the case update text based on the initial file image and the case storage text, using the case update text to update the case storage text, and obtaining the update storage vector based on the updated case storage text, using the update storage vector as the case storage vector, and returning to the step of obtaining the case similarity based on the case matching vector and the case storage vector until the case similarity is greater than or equal to the similarity threshold; If the case similarity is greater than or equal to the similarity threshold, the target case similarity corresponding to the target case data is obtained. After confirming that the target case similarity is greater than or equal to the similarity threshold, the initial case data and the target case data are confirmed to be associated.

5. The case resource analysis and matching method based on user information as claimed in claim 4, characterized in that: The constructing of the initial search database based on the single case data set and the chained case data set includes: Extract chain case data from the chain case data set in sequence, and perform the following operations on the extracted chain case data: Using the chain case data, a chain case sequence is identified in the identification case sequence, the last identification case data is extracted from the chain case sequence, and the following operations are performed on the extracted last identification case data: Parse the case judgment result corresponding to the identified case data to obtain the case accountability type, where the case accountability type includes: recidivism or non-recidivism; After confirming that the case accountability type is a recidivism, the judgment result of the case is parsed to obtain multiple judgment text summaries, and the last identification case data is removed from the chain case sequence to obtain a screening case sequence, and multiple screening text summary groups are obtained using the screening case sequence, wherein the screening text summary group includes one or more screening text summaries, and the screening text summary group corresponds to the identification case data in the screening case sequence one by one; In a combined form, obtaining matching text nodes based on the judgment text summaries in the plurality of judgment text summaries and the screening text summaries corresponding to each screening text summary group in the plurality of screening text summary groups, obtaining text node similarities based on the matching text nodes, and comparing the text node similarities with a preset recidivism similarity threshold; If the text node similarity is greater than or equal to the recidivism similarity threshold, the identification case data corresponding to the text node similarity is retained; otherwise, the identification case data corresponding to the text node similarity is removed, and the removed identification case data is identified as single case data; Summarize the retained identification case data to obtain an updated identification data set, use the updated identification data set to update the chain case data to obtain updated chain data, identify the case identification node of each identification case data in the multiple identification case data corresponding to the updated chain data, wherein the case identification node includes a case definition and a case result, and use the case identification node to identify the updated chain data to obtain the identification chain data; Based on the single case data, identification single data is obtained, and the identification chain data and identification single data are aggregated to obtain an initial search database.

6. The case resource analysis and matching method based on user information as claimed in claim 5, characterized in that: The matching text nodes are as follows: P={p i ,s j(k) } Among them, P represents the matching text node, p i represents the i-th judgment text summary among multiple judgment text summaries, s j(k) Indicates the kth screening text summary corresponding to the jth screening text summary group among multiple screening text summary groups.

7. The case resource analysis and matching method based on user information according to claim 6, characterized in that: The step of optimizing the initial search database to obtain a case search database includes: Obtaining the type of the implementation object for classification, wherein the type of the implementation object includes an adult or a minor, and confirming the classification type according to the type of the implementation object and the type of case accountability, wherein the classification types include: non-recidivist-adult, non-recidivist-minor, and recidivist; According to the classification type, the initial search data in the initial search database are summarized respectively to obtain three classification data sets, wherein the initial search data is the identification single data or the identification chain data, and the three classification data sets are the non-recidivist-adult classification data set, the non-recidivist-juvenile classification data set and the recidivist classification data set; The non-recidivist-adult classification data set is stored in a pre-constructed first storage unit, the non-recidivist-juvenile classification data set is stored in a pre-constructed second storage unit, and the recidivist classification data set is stored in a pre-constructed third storage unit to obtain a case retrieval database.

8. The case resource analysis and matching method based on user information according to claim 7, characterized in that: The method of using the user information node to search in the case search database to obtain an initial search data set includes: Based on the user information node, identification analysis data identified with an analysis identification node or analysis chain data identified with an analysis identification node is obtained, and using the identification analysis data or analysis chain data, identification single data identical to the analysis identification node or identification chain data identical to the analysis node is retrieved from the case retrieval database to obtain an initial retrieval data set, wherein the initial retrieval data set includes multiple initial retrieval data.

9. The case resource analysis and matching method based on user information as claimed in claim 8, characterized in that: The step of confirming a target search data sequence based on the initial search data set includes: The following operations are performed for each initial search data in the initial search data set: The target search vector and the user search vector are obtained respectively by using the initial search data, the user information node and the pre-built information dimension reduction model, wherein the target search vector is as follows: M={S,A} Wherein, M represents the target retrieval vector, S represents the case implementation time, and A represents the case judgment result after being processed based on the information dimensionality reduction model; Calculate the analytical similarity between the target search vector and the user search vector. The calculation formula is as follows: Wherein, X represents the analysis similarity, Y represents the user search vector, * represents taking the dot product, and |||| represents taking the modulus length; The analysis similarities are summarized to obtain an analysis similarity set, and the analysis similarities in the analysis similarity set are sorted in descending order to obtain an initial analysis similarity sequence, and a target retrieval data sequence is confirmed based on the initial analysis similarity sequence, wherein the analysis similarity corresponding to the target retrieval data in the target retrieval data sequence is greater than a preset retrieval similarity threshold.

10. A case resource analysis and matching system based on user information, characterized in that: The system comprises: An initial database construction preparation module is used to receive a case matching instruction and confirm a case matching system based on the case matching instruction, wherein the case matching system includes: an information collection unit, an information analysis unit, an information matching unit and a result feedback unit; an initial database construction module, configured to confirm receipt of an information collection instruction from an information collection unit, and to confirm a historical case data set based on the information collection instruction, wherein the historical case data set includes a plurality of historical case data, and the historical case data includes: case file images, case storage texts, and case judgment results; Using the historical case data set to obtain a marked case data set, and constructing an initial search database based on the marked case data set; An initial database optimization module, used to optimize the initial search database to obtain a case search database; A user information analysis and matching module, used to obtain a user identification code of the user, confirm receipt of an information analysis instruction from the information analysis unit, obtain a user information node based on the information analysis instruction and the user identification code, wherein the user information node includes m user case data and known user data, and m is an integer greater than or equal to 0, and use the user information node to search in a case search database to obtain an initial search data set; Confirming a target retrieval data sequence based on the initial retrieval data set, wherein the target retrieval data sequence includes a plurality of target retrieval data; The result feedback unit is used to send the target search data sequence to the initiator of the case matching instruction to achieve analysis and matching of case resources.

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