Cultural relic repairing and tracing system and method based on artificial intelligence

Abstract

The invention discloses a cultural relic repair traceability system and method based on artificial intelligence, and relates to the technical field of traceability, and the system comprises a historical event extraction module, a to-be-analyzed repair object determination module, an optimal repair algorithm analysis module, a traceability feature pair construction module, a matching early warning module, and a real-time repair verification module. The historical event extraction module is used for extracting historical events for repairing cultural relics by utilizing artificial intelligence; the to-be-analyzed repair object determination module is used for determining to-be-analyzed repair objects in the repair event subset; the optimal repair algorithm analysis module is used for outputting an optimal repair algorithm of the to-be-analyzed repair object; the traceability feature pair construction module is used for constructing a traceability feature pair of a restoration object corresponding to each type of cultural relics; and the matching early warning module is used for searching the type of the cultural relic and the type containing the repaired object when the newly-added cultural relic needs to be repaired, and performing matching early warning of the traceability feature pair.

Description

Technical Field

[0001] The present invention relates to the field of traceability technology, and in particular to a cultural relic restoration traceability system and method based on artificial intelligence. Background Art

[0002] At present, in the analysis process of tracing the restoration of cultural relics, machine learning technology is often used to complete the cultural relic protection review work more quickly and accurately. By training algorithms, the laws and patterns of cultural relic protection can be found from the far data obtained by previous experiments, and then the problems in the cultural relics and the places that need to be repaired can be quickly and accurately identified. For example, machine learning methods can identify places with the same color and texture as cultural relics, and then determine where the cultural relics are located, and find the normal / abnormal places of cultural relics. However, the current situation is that different mathematical algorithms are applied to the same places that need to be repaired for the same type of cultural relics or different types of cultural relics. However, the selection of these algorithms generally needs to be compared and screened with other algorithms before it can be finally determined. If this operation is performed for each repair object in a huge data repair event, it will generate a lot of extra workload and greatly reduce the repair efficiency, which loses the meaning of using artificial intelligence to repair cultural relics. Summary of the invention

[0003] The purpose of the present invention is to provide a cultural relics restoration and tracing system and method based on artificial intelligence to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solution: a method for tracing the source of cultural relics restoration based on artificial intelligence, the method comprising the following steps:

[0005] Step S100: extracting historical events of cultural relic restoration using artificial intelligence, classifying and storing the historical events by cultural relic type, traversing all historical events of cultural relic restoration of each type, and storing the historical events that record the same type of restoration objects as a restoration event subset of the corresponding type of cultural relic; determining the restoration objects with algorithm differences in the restoration event subset as the restoration objects to be analyzed for the corresponding type of cultural relic;

[0006] Step S200: Based on the restoration object to be analyzed, the restoration process and restoration evaluation results of the historical event records of the corresponding type of cultural relics are searched, and the optimal restoration algorithm of the restoration object to be analyzed is output;

[0007] Step S300: Mark the restoration process of the cultural relics corresponding to the restoration object to be analyzed and recording the optimal restoration algorithm and other types of cultural relics, and construct a traceability feature pair corresponding to the restoration object of each type of cultural relic;

[0008] Step S400: When a new cultural relic needs to be repaired, the type of cultural relic and the type of repair object are searched, and a matching warning of the traceability feature pair is issued and verified based on the real-time repair evaluation result.

[0009] Further, determining the restoration objects with algorithm differences in the restoration event subset as restoration objects to be analyzed for the corresponding type of cultural relics includes the following steps:

[0010] Restoration objects refer to all restoration objects targeted by the restoration process of the historical event recording algorithm; the restoration order of each type of restoration object corresponding to each restoration event subset is the same in the historical event; when the restoration order of restoration objects of the same type of cultural relics is different, the historical events corresponding to different recording orders are stored as independent restoration event subsets, and belong to the parallel subsets of the corresponding type of cultural relics under the same restoration object record;

[0011] Traverse and search for all historical events in the restoration event subset corresponding to each type of cultural relic, obtain the restoration algorithm applied to each restoration object in any historical event as the target restoration algorithm, and find out whether the restoration algorithms corresponding to the same restoration object of the remaining historical events in the same restoration event subset are the same as the target restoration algorithm. If there is a difference, output that there is an algorithm difference for the corresponding restoration object; if all are the same, output that there is no algorithm difference for the corresponding restoration object.

[0012] Further, step S2 includes the following:

[0013] The restoration process refers to the process of first marking the defects and sampling the data of the cultural relics, and then executing the corresponding algorithm restoration in sequence based on the marked different restoration objects. After each restoration object completes the algorithm restoration process, it is necessary to conduct manual visual standard judgment. After the standard is met, the algorithm restoration of the next restoration object is executed. If the standard is not met, the restoration object is returned to be restored again. The algorithm restoration of the next restoration object is executed when the manual visual standard is judged to be restored. After all restoration objects are executed and judged to be restored to the standard, the restoration evaluation results of the corresponding type of cultural relics are output;

[0014] The restoration evaluation results record the number of times each type of cultural relic is restored by performing the algorithm restoration on each restoration object and the restoration algorithm applied;

[0015] Capture all types of repair algorithms of the historical event records of the repair object to be analyzed in the same repair event subset and store them in the first algorithm set. Extract the average return execution times D of the corresponding historical event records for the i-th type of repair algorithm in the first algorithm set. 0i , D 0i =(1 / n i )∑d 0i , where d 0iIndicates the number of times different historical event records corresponding to the execution of the i-th type of repair algorithm in the same repair event subset for the same repair object to be analyzed, n i represents the total number of historical events corresponding to the same repair object to be analyzed and the execution of the i-th type repair algorithm in the same repair event subset;

[0016] Using the formula: U i =D 0i / [(1 / m)∑(D 0i )];

[0017] Calculate the return rate U of each type of repair algorithm executed for each repair object to be analyzed i , i≤m, m represents the total number of repair algorithm types recorded in the first algorithm set for the repair object to be analyzed;

[0018] The smaller the return rate of the execution link is, the more effective and accurate the repair algorithm corresponding to the repair object to be analyzed is;

[0019] Mark the restoration stage of the historical event record corresponding to the i-th type restoration algorithm. The restoration stage refers to the execution period from the start of the restoration algorithm execution on the corresponding restoration object to be analyzed to the start of the restoration algorithm execution on the next restoration object; extract the image area Q of the restoration object to be analyzed corresponding to the disease annotation of the cultural relics in the restoration stage corresponding to the i-th type restoration algorithm 1i and the overall image area Q of the artifact display 0i , and obtain the image area q marked as effectively repaired for the repair object to be analyzed in the corresponding repair stage of the i-type repair algorithm 1i and the image area q marked as multiple restorations 2i ;

[0020] Effective repair means that the image of the repair object to be analyzed and repaired after the first execution of the algorithm in the repair stage remains unchanged in the remaining repair stages. Multiple repair means that the image of the repair object to be analyzed and repaired after the first execution of the algorithm in the repair stage remains unchanged in the remaining repair stages. Using the formula: A i =(Q 1i / Q 0i )×(q 1i / q 2i );Calculate the repair coefficient A of the i-th type repair algorithm i ;

[0021] The larger the repair coefficient is, the higher the repair efficiency of the corresponding repair algorithm for the disease annotation is and the better the overall repair result is.

[0022] Extract the repair coefficient A of the i-th type repair algorithm corresponding to each repair object to be analyzed in the same repair event subset i and the execution phase return rate Ui , calculate the evaluation index R of the i-th type repair algorithm for each repair object to be analyzed in the repair event subset i , R i =e1×A i +e2×(1 / U i );

[0023] Based on the evaluation index, the m repair algorithms of the repair object to be analyzed are sorted from large to small, and the repair algorithm at the first place in the sequence is selected as the optimal repair algorithm corresponding to the repair object to be analyzed in the repair event subset.

[0024] By analyzing the return rate and repair coefficient of the execution link of the restoration algorithm, we can effectively solve the difficulty of algorithm selection in the process of tracing and restoring a large number of cultural relics based on intelligent data, reduce the errors and manpower pressure caused by human judgment of the pros and cons of the algorithm, and improve the accuracy, intelligence, efficiency and convenience of cultural relics restoration using artificial intelligence.

[0025] Furthermore, step S300 includes:

[0026] Extract and record the restoration object to be analyzed corresponding to the optimal restoration algorithm, as well as the restoration event subset and cultural relic type of the restoration object to be analyzed;

[0027] The type of cultural relic is used as the traceability target of the traceability feature pair, the restoration process and the restoration object in the process recorded in the restoration event subset are used as the traceability clue of the traceability feature pair, and the optimal restoration algorithm of the restoration object to be analyzed is used as the traceability result of the traceability feature pair, thus forming the traceability feature pair of each restoration object to be analyzed;

[0028] For other cultural relics that are not marked as restoration objects to be analyzed, traceability feature pairs are constructed with the cultural relic type as the traceability target and the restoration algorithm of the restoration object as the traceability result.

[0029] Further, step S400 includes the following:

[0030] When the type of cultural relic does not record the traceability feature pair of the restoration object to be analyzed, the restoration object corresponding to the newly added cultural relic is extracted to match the traceability result recorded by the corresponding traceability feature pair for early warning;

[0031] When the type of cultural relic is a traceability feature pair that records the repair object to be analyzed, determine the type of the newly added cultural relic to lock the traceability target, extract the traceability feature pairs recorded with the same traceability target, and match the traceability feature pairs with the same traceability clues as the final matching feature pairs. Output the traceability result in the final matching feature pair as the optimal repair algorithm for the newly added cultural relic containing the repair object, and issue an early warning;

[0032] Extract all historical events corresponding to the optimal repair algorithm of the real-time matching warning, obtain the evaluation index of the repair algorithm implemented for the repair object to be analyzed recorded in the historical events, select the minimum evaluation index as the evaluation index for the optimal repair algorithm implemented for the corresponding repair object; and calculate the real-time evaluation index. If the real-time evaluation index is greater than or equal to the evaluation index, continue monitoring; if the real-time evaluation index is less than the evaluation index, mark the repair event as a special repair event;

[0033] When the number of special repair event records is greater than the threshold, a response signal is output to provide an early warning response to the traceability feature pair.

[0034] An artificial intelligence-based cultural relic restoration and traceability system, the system includes a historical event extraction module, a to-be-analyzed restoration object determination module, an optimal restoration algorithm analysis module, a traceability feature pair construction module, a matching warning module, and a real-time restoration verification module;

[0035] The historical event extraction module is used to extract historical events for cultural relic restoration using artificial intelligence;

[0036] The module for determining the restoration object to be analyzed is used to determine the restoration object with algorithm differences in the restoration event subset as the restoration object to be analyzed of the corresponding type of cultural relic;

[0037] The optimal repair algorithm analysis module is used to output the optimal repair algorithm for the repair object to be analyzed;

[0038] The traceability feature pair construction module is used to construct the traceability feature pair of the restoration object corresponding to each type of cultural relic;

[0039] The matching warning module is used to find the type of cultural relics and the types of restoration objects when new cultural relics need to be repaired, and to conduct matching warnings for traceability feature pairs;

[0040] The real-time repair verification module is used to perform verification based on the real-time repair evaluation results.

[0041] Furthermore, the optimal repair algorithm analysis module includes a repair process determination unit, a repair evaluation result recording unit, an average return execution times calculation unit, an execution link return rate calculation unit, a repair coefficient calculation unit and an evaluation index calculation unit;

[0042] The restoration process determination unit is used to determine the overall process of cultural relic restoration;

[0043] The restoration evaluation result recording unit is used to record the number of times each type of cultural relic is restored by performing the algorithm restoration on each restoration object and the restoration algorithm applied;

[0044] The average return execution times calculation unit is used for extracting the average return execution times of the corresponding historical event records by each type of repair algorithm in the first algorithm set;

[0045] The execution link return rate calculation unit is used to calculate the execution link return rate of each type of repair algorithm recorded in each repair object to be analyzed;

[0046] The repair coefficient calculation unit is used to calculate the repair coefficient of the corresponding repair algorithm;

[0047] The evaluation index calculation unit is used to calculate the evaluation index of each type of repair algorithm corresponding to each repair object to be analyzed in the repair event subset. Based on the evaluation index, the repair algorithms of the repair objects to be analyzed are sorted from large to small, and the repair algorithm with the first sequence is selected as the optimal repair algorithm corresponding to the repair object to be analyzed in the repair event subset.

[0048] Furthermore, the traceability feature pair construction module includes a traceability target determination unit, a traceability clue induction unit, a traceability result output unit and a traceability feature pair output unit;

[0049] The traceability target determination unit is used to use the cultural relic type as the traceability target of the traceability feature pair;

[0050] The traceability clue induction unit is used to repair the repair process and the repair object in the process recorded in the repair event subset as the traceability clue of the traceability feature pair;

[0051] The traceability result output unit is used to use the optimal repair algorithm of the repair object to be analyzed as the traceability result of the traceability feature pair.

[0052] The traceability feature pair output unit is used to construct a traceability feature pair with the cultural relic type as the traceability target and the restoration algorithm of the restoration object as the traceability result.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] 1. The present invention distinguishes and divides the restoration objects of different types of cultural relics, thereby solving the problem that the restoration objects are complicated and diverse and difficult to summarize in cultural relic restoration events; and lays the foundation for subsequent data processing and analysis;

[0055] 2. The present invention analyzes the historical execution of restoration algorithms for the summarized restoration objects, and uses data processing and analysis methods to screen out the corresponding restoration algorithm that can effectively reflect the highest efficiency of the restoration object as the restoration algorithm of the restoration object under the final corresponding type and restoration process, so that the scene matching is clearer, and after the restoration algorithm is determined, it can be effectively applied to subsequent additional cultural relics restoration events. It can effectively solve the algorithm selection difficulties in the process of tracing the source of a large number of cultural relics based on intelligent data, reduce the errors and manpower pressure caused by human judgment of the advantages and disadvantages of the algorithm, and improve the accuracy, intelligence, efficiency and convenience of using artificial intelligence for cultural relics restoration. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a structural schematic diagram of an artificial intelligence-based cultural relic restoration and traceability system of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Example: Figure 1 As shown, the present invention provides a cultural relic restoration and traceability system based on artificial intelligence, the system includes a historical event extraction module, a to-be-analyzed restoration object determination module, an optimal restoration algorithm analysis module, a traceability feature pair construction module, a matching warning module and a real-time restoration verification module;

[0059] The historical event extraction module is used to extract historical events for cultural relic restoration using artificial intelligence;

[0060] The module for determining the restoration object to be analyzed is used to determine the restoration object with algorithm differences in the restoration event subset as the restoration object to be analyzed of the corresponding type of cultural relic;

[0061] The optimal repair algorithm analysis module is used to output the optimal repair algorithm for the repair object to be analyzed;

[0062] The traceability feature pair construction module is used to construct the traceability feature pair of the restoration object corresponding to each type of cultural relic;

[0063] The matching warning module is used to find the type of cultural relics and the types of restoration objects when new cultural relics need to be repaired, and to conduct matching warnings for traceability feature pairs;

[0064] The real-time repair verification module is used to perform verification based on the real-time repair evaluation results.

[0065] The optimal repair algorithm analysis module includes a repair process determination unit, a repair evaluation result recording unit, an average return execution times calculation unit, an execution link return rate calculation unit, a repair coefficient calculation unit and an evaluation index calculation unit;

[0066] The restoration process determination unit is used to determine the overall process of cultural relic restoration;

[0067] The restoration evaluation result recording unit is used to record the number of times each type of cultural relic is restored by performing the algorithm restoration on each restoration object and the restoration algorithm applied;

[0068] The average return execution times calculation unit is used for extracting the average return execution times of the corresponding historical event records by each type of repair algorithm in the first algorithm set;

[0069] The execution link return rate calculation unit is used to calculate the execution link return rate of each type of repair algorithm recorded in each repair object to be analyzed;

[0070] The repair coefficient calculation unit is used to calculate the repair coefficient of the corresponding repair algorithm;

[0071] The evaluation index calculation unit is used to calculate the evaluation index of each type of repair algorithm corresponding to each repair object to be analyzed in the repair event subset. Based on the evaluation index, the repair algorithms of the repair objects to be analyzed are sorted from large to small, and the repair algorithm with the first sequence is selected as the optimal repair algorithm corresponding to the repair object to be analyzed in the repair event subset.

[0072] The traceability feature pair construction module includes a traceability target determination unit, a traceability clue induction unit, a traceability result output unit and a traceability feature pair output unit;

[0073] The traceability target determination unit is used to use the cultural relic type as the traceability target of the traceability feature pair;

[0074] The traceability clue induction unit is used to repair the repair process and the repair object in the process recorded in the repair event subset as the traceability clue of the traceability feature pair;

[0075] The traceability result output unit is used to use the optimal repair algorithm of the repair object to be analyzed as the traceability result of the traceability feature pair.

[0076] The traceability feature pair output unit is used to construct a traceability feature pair with the cultural relic type as the traceability target and the restoration algorithm of the restoration object as the traceability result.

[0077] A method for tracing the source of cultural relics restoration based on artificial intelligence, the method comprising the following steps:

[0078] Step S100: extracting historical events of cultural relic restoration using artificial intelligence, classifying and storing the historical events by cultural relic type, traversing all historical events of cultural relic restoration of each type, and storing the historical events that record the same type of restoration objects as a restoration event subset of the corresponding type of cultural relic; determining the restoration objects with algorithm differences in the restoration event subset as the restoration objects to be analyzed for the corresponding type of cultural relic;

[0079] For example, cultural relics can be divided into ancient paintings and ancient porcelain;

[0080] Step S200: Based on the restoration object to be analyzed, the restoration process and restoration evaluation results of the historical event records of the corresponding type of cultural relics are searched, and the optimal restoration algorithm of the restoration object to be analyzed is output;

[0081] Step S300: Mark the restoration process of the cultural relics corresponding to the restoration object to be analyzed and recording the optimal restoration algorithm and other types of cultural relics, and construct a traceability feature pair corresponding to the restoration object of each type of cultural relic;

[0082] Step S400: When a new cultural relic needs to be repaired, the type of cultural relic and the type of repair object are searched, and a matching warning of the traceability feature pair is issued and verified based on the real-time repair evaluation result.

[0083] Determining the restoration objects with algorithm differences in the restoration event subset as restoration objects to be analyzed for the corresponding type of cultural relics includes the following steps:

[0084] Restoration objects refer to all restoration objects targeted by the restoration process of the historical event recording algorithm; the restoration order of each type of restoration object corresponding to each restoration event subset is the same in the historical event; when the restoration order of restoration objects of the same type of cultural relics is different, the historical events corresponding to different recording orders are stored as independent restoration event subsets, and belong to the parallel subsets of the corresponding type of cultural relics under the same restoration object record;

[0085] One type of cultural relic can record multiple restoration event subsets, and all the restoration objects recorded in each restoration event subset are not exactly the same;

[0086] Traverse and search for all historical events in the restoration event subset corresponding to each type of cultural relic, obtain the restoration algorithm applied to each restoration object in any historical event as the target restoration algorithm, and find out whether the restoration algorithms corresponding to the same restoration object of the remaining historical events in the same restoration event subset are the same as the target restoration algorithm. If there is a difference, output that there is an algorithm difference for the corresponding restoration object; if all are the same, output that there is no algorithm difference for the corresponding restoration object.

[0087] Step S2 includes the following:

[0088] The restoration process refers to the process of first marking the defects and sampling the data of the cultural relics, and then executing the corresponding algorithm restoration in sequence based on the marked different restoration objects. After each restoration object completes the algorithm restoration process, it is necessary to conduct manual visual standard judgment. After the standard is met, the algorithm restoration of the next restoration object is executed. If the standard is not met, the restoration object is returned to be restored again. The algorithm restoration of the next restoration object is executed when the manual visual standard is judged to be restored. After all restoration objects are executed and judged to be restored to the standard, the restoration evaluation results of the corresponding type of cultural relics are output;

[0089] The restoration objects refer to the restoration strategies corresponding to different types of cultural relics based on the disease annotation and data sampling, and each type of cultural relic corresponds to a restoration strategy based on the initial disease annotation and data sampling, which is recorded in the restoration traceability system;

[0090] The restoration evaluation results record the number of times each type of cultural relic is restored by performing the algorithm restoration on each restoration object and the restoration algorithm applied;

[0091] Capture all types of repair algorithms of the historical event records of the repair object to be analyzed in the same repair event subset and store them in the first algorithm set. Extract the average return execution times D of the corresponding historical event records for the i-th type of repair algorithm in the first algorithm set. 0i , D 0i =(1 / n i )∑d 0i , where d 0i Indicates the number of times different historical event records corresponding to the execution of the i-th type of repair algorithm in the same repair event subset for the same repair object to be analyzed, n i represents the total number of historical events corresponding to the same repair object to be analyzed and the execution of the i-th type repair algorithm in the same repair event subset;

[0092] Using the formula: U i =D 0i / [(1 / m)∑(D 0i )];

[0093] Calculate the return rate U of each type of repair algorithm executed for each repair object to be analyzed i , i≤m, m represents the total number of repair algorithm types recorded in the first algorithm set for the repair object to be analyzed;

[0094] The repair event subset will record historical events with the same repair execution process but different repair algorithms applied;

[0095] The smaller the return rate of the execution link is, the more effective and accurate the repair algorithm corresponding to the repair object to be analyzed is;

[0096] Mark the restoration stage of the historical event record corresponding to the i-th type restoration algorithm. The restoration stage refers to the execution period from the start of the restoration algorithm execution on the corresponding restoration object to be analyzed to the start of the restoration algorithm execution on the next restoration object; extract the image area Q of the restoration object to be analyzed corresponding to the disease annotation of the cultural relics in the restoration stage corresponding to the i-th type restoration algorithm 1i and the overall image area Q of the artifact display 0i , and obtain the image area q marked as effectively repaired for the repair object to be analyzed in the corresponding repair stage of the i-type repair algorithm 1i and the image area q marked as multiple restorations 2i ;

[0097] Effective repair means that the image of the repair object to be analyzed and repaired after the first execution of the algorithm in the repair stage remains unchanged in the remaining repair stages. Multiple repair means that the image of the repair object to be analyzed and repaired after the first execution of the algorithm in the repair stage remains unchanged in the remaining repair stages. Using the formula: A i =(Q 1i / Q 0i )×(q 1i / q 2i );Calculate the repair coefficient A of the i-th type repair algorithm i ;

[0098] Image unchanged means that the similarity remains 100%, and image changed means that the similarity changes; image change means that the repair object is not repaired properly when the repair algorithm is applied for the first time in the repair stage, so it needs to be repaired again;

[0099] The larger the repair coefficient is, the higher the repair efficiency of the corresponding repair algorithm for the disease annotation is and the better the overall repair result is.

[0100] Extract the repair coefficient A of the i-th type repair algorithm corresponding to each repair object to be analyzed in the same repair event subset i and the execution phase return rate U i , calculate the evaluation index R of the i-th type repair algorithm for each repair object to be analyzed in the repair event subset i , R i =e1×A i +e2×(1 / U i );

[0101] Based on the evaluation index, the m repair algorithms of the repair object to be analyzed are sorted from large to small, and the repair algorithm at the first place in the sequence is selected as the optimal repair algorithm corresponding to the repair object to be analyzed in the repair event subset.

[0102] As shown in the embodiment: taking the mural as the cultural relic to be repaired, the mural is firstly marked with defects and data is sampled. If the repair object is classified into image shedding and crack after the defect marking, if the image shedding is repaired first, two types of algorithms are recorded in the historical events, namely the mask repair algorithm based on Fourier convolution and the ACP-LaMa algorithm;

[0103] That is, the historical event that the restoration process of the mural is first image detachment repair and then crack repair can be recorded in the restoration event subset, and this subset records two algorithms for the restoration object of image detachment;

[0104] Based on several historical events corresponding to the two types of algorithms, the final comparative results can be obtained by analyzing the repair coefficient and the return rate of the execution link.

[0105] By analyzing the return rate and repair coefficient of the execution link of the restoration algorithm, we can effectively solve the difficulty of algorithm selection in the process of tracing and restoring a large number of cultural relics based on intelligent data, reduce the errors and manpower pressure caused by human judgment of the pros and cons of the algorithm, and improve the accuracy, intelligence, efficiency and convenience of cultural relics restoration using artificial intelligence.

[0106] Step S300 includes:

[0107] Extract and record the restoration object to be analyzed corresponding to the optimal restoration algorithm, as well as the restoration event subset and cultural relic type of the restoration object to be analyzed;

[0108] The type of cultural relic is used as the traceability target of the traceability feature pair, the restoration process and the restoration object in the process recorded in the restoration event subset are used as the traceability clue of the traceability feature pair, and the optimal restoration algorithm of the restoration object to be analyzed is used as the traceability result of the traceability feature pair, thus forming the traceability feature pair of each restoration object to be analyzed;

[0109] For other cultural relics that are not marked as restoration objects to be analyzed, traceability feature pairs are constructed with the cultural relic type as the traceability target and the restoration algorithm of the restoration object as the traceability result.

[0110] Step S400 includes the following:

[0111] When the type of cultural relic does not record the traceability feature pair of the restoration object to be analyzed, the restoration object corresponding to the newly added cultural relic is extracted to match the traceability result recorded by the corresponding traceability feature pair for early warning;

[0112] When the type of cultural relic is a traceability feature pair that records the repair object to be analyzed, determine the type of the newly added cultural relic to lock the traceability target, extract the traceability feature pairs recorded with the same traceability target, and match the traceability feature pairs with the same traceability clues as the final matching feature pairs. Output the traceability result in the final matching feature pair as the optimal repair algorithm for the newly added cultural relic containing the repair object, and issue an early warning;

[0113] Extract all historical events corresponding to the optimal repair algorithm of the real-time matching warning, obtain the evaluation index of the repair algorithm implemented for the repair object to be analyzed recorded in the historical events, select the minimum evaluation index as the evaluation index for the optimal repair algorithm implemented for the corresponding repair object; and calculate the real-time evaluation index. If the real-time evaluation index is greater than or equal to the evaluation index, continue monitoring; if the real-time evaluation index is less than the evaluation index, mark the repair event as a special repair event;

[0114] When the number of special repair event records is greater than the threshold, the output response signal is used to respond to the traceability feature pair. The purpose of the early warning response is to remind the system or management personnel that the optimal repair algorithm for the corresponding repair object may change with the increase of data and needs further update.

[0115] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for tracing the source of cultural relics restoration based on artificial intelligence, characterized in that: The method comprises the following steps: Step S100: extracting historical events of cultural relic restoration using artificial intelligence, classifying and storing the historical events by cultural relic type, traversing all historical events of cultural relic restoration of each type, and storing the historical events that record the same type of restoration objects as a restoration event subset of the corresponding type of cultural relic; determining the restoration objects with algorithm differences in the restoration event subset as the restoration objects to be analyzed for the corresponding type of cultural relic; Step S200: Based on the restoration object to be analyzed, the restoration process and restoration evaluation results of the historical event records of the corresponding type of cultural relics are searched, and the optimal restoration algorithm of the restoration object to be analyzed is output; Step S300: Mark the restoration process traceability of the cultural relics corresponding to the restoration object to be analyzed and recording the optimal restoration algorithm and other types of cultural relics, and construct a traceability feature pair corresponding to the restoration object of each type of cultural relic; Step S400: When a new cultural relic needs to be repaired, the type of cultural relic and the type of repair object are searched, and a matching warning of the traceability feature pair is issued and verified based on the real-time repair evaluation result.

2. The method for tracing the source of cultural relics restoration based on artificial intelligence according to claim 1, characterized in that: The step of determining the restoration objects with algorithm differences in the restoration event subset as restoration objects to be analyzed for the corresponding type of cultural relics comprises the following steps: The restoration objects refer to all restoration objects targeted by the restoration process of the historical event recording algorithm; the restoration order of each type of restoration object corresponding to each restoration event subset is the same in the historical events; when the restoration order of restoration objects of the same type of cultural relics is different, the historical events corresponding to the different recording orders are stored as independent restoration event subsets, and belong to the parallel subsets of the corresponding type of cultural relics under the same restoration object record; Traverse and search for all historical events in the restoration event subset corresponding to each type of cultural relic, obtain the restoration algorithm applied to each restoration object in any historical event as the target restoration algorithm, and find out whether the restoration algorithms corresponding to the same restoration object of the remaining historical events in the same restoration event subset are the same as the target restoration algorithm. If there is a difference, output that there is an algorithm difference for the corresponding restoration object; if all are the same, output that there is no algorithm difference for the corresponding restoration object.

3. The artificial intelligence-based cultural relic restoration and tracing system and method according to claim 1, characterized in that: The step S2 comprises the following: The restoration process refers to the process of first marking the defects and sampling the data of the cultural relics, and then performing the corresponding algorithm restoration execution process in sequence based on the marked different restoration objects, and after each restoration object completes the algorithm restoration process, it is necessary to perform manual visual standard judgment, and after the standard is met, the algorithm restoration of the next restoration object is performed, and if the standard is not met, the restoration object is returned to perform algorithm restoration again, until the manual visual standard is judged to be restored to the standard, and the algorithm restoration of the next restoration object is performed; after all restoration objects are executed and judged to be restored to the standard, the restoration evaluation results of the corresponding type of cultural relics are output; The restoration evaluation result records the number of times each type of cultural relic is restored by performing the restoration algorithm on each restoration object and the restoration algorithm applied; Capture all types of repair algorithms of the historical event records of the repair object to be analyzed in the same repair event subset and store them in the first algorithm set. Extract the average return execution times D of the corresponding historical event records for the i-th type of repair algorithm in the first algorithm set. 0i , D 0i =(1 / n i )∑d 0i , where d 0i Indicates the number of times different historical event records corresponding to the execution of the i-th type of repair algorithm in the same repair event subset for the same repair object to be analyzed, n i represents the total number of historical events corresponding to the same repair object to be analyzed and the execution of the i-th type repair algorithm in the same repair event subset; Using the formula: U i =D 0i / [(1 / m)∑(D 0i )]; Calculate the return rate U of each type of repair algorithm executed for each repair object to be analyzed i , i≤m, m represents the total number of repair algorithm types recorded in the first algorithm set for the repair object to be analyzed; Mark the restoration stage of the historical event record corresponding to the i-th type restoration algorithm, where the restoration stage refers to the execution period from the start of the restoration algorithm execution on the corresponding restoration object to be analyzed to the start of the restoration algorithm execution on the next restoration object; extract the image area Q of the restoration object to be analyzed corresponding to the disease annotation of the cultural relics in the restoration stage corresponding to the i-th type restoration algorithm 1i and the overall image area Q of the artifact display 0i , and obtain the image area q marked as valid repair for the repair object to be analyzed in the corresponding repair stage of the i-type repair algorithm 1i and the image area q marked as multiple restorations 2i ; The effective repair means that the image of the repair object to be analyzed and repaired after the first execution of the algorithm in the repair stage remains unchanged in the remaining repair stages, and the multiple repairs mean that the image of the repair object to be analyzed and repaired after the first execution of the algorithm in the repair stage changes in the remaining repair stages; using the formula: A i =(Q 1i / Q 0i )×(q 1i / q 2i ); Calculate the repair coefficient A of the i-th type repair algorithm i ; Extract the repair coefficient A of the i-th type repair algorithm corresponding to each repair object to be analyzed in the same repair event subset i and the execution phase return rate U i , calculate the evaluation index R of the i-th type repair algorithm for each repair object to be analyzed in the repair event subset i , R i =e1×A i +e2×(1 / U i ); Based on the evaluation index, the m repair algorithms of the repair object to be analyzed are sorted from large to small, and the repair algorithm at the first place in the sequence is selected as the optimal repair algorithm corresponding to the repair object to be analyzed in the repair event subset.

4. The artificial intelligence-based cultural relic restoration and tracing system and method according to claim 1, characterized in that: The step S300 includes: Extract and record the restoration object to be analyzed corresponding to the optimal restoration algorithm, as well as the restoration event subset and cultural relic type of the restoration object to be analyzed; The type of cultural relic is used as the traceability target of the traceability feature pair, the restoration process and the restoration object in the process recorded in the restoration event subset are used as the traceability clue of the traceability feature pair, and the optimal restoration algorithm of the restoration object to be analyzed is used as the traceability result of the traceability feature pair, thus forming the traceability feature pair of each restoration object to be analyzed; For other cultural relics that are not marked as restoration objects to be analyzed, traceability feature pairs are constructed with the cultural relic type as the traceability target and the restoration algorithm of the restoration object as the traceability result.

5. The method for tracing the source of cultural relics restoration based on artificial intelligence according to claim 1 is characterized in that: The step S400 includes the following: When the type of cultural relic does not record the traceability feature pair of the restoration object to be analyzed, the restoration object corresponding to the newly added cultural relic is extracted to match the traceability result recorded by the corresponding traceability feature pair for early warning; When the type of cultural relic is a traceability feature pair that records the repair object to be analyzed, determine the type of the newly added cultural relic to lock the traceability target, extract the traceability feature pairs recorded with the same traceability target, and match the traceability feature pairs with the same traceability clues as the final matching feature pairs. Output the traceability result in the final matching feature pair as the optimal repair algorithm for the newly added cultural relic containing the repair object, and issue an early warning; Extract all historical events corresponding to the optimal repair algorithm of the real-time matching warning, obtain the evaluation index of the repair algorithm implemented for the repair object to be analyzed recorded in the historical events, and select the minimum value of the evaluation index as the evaluation indicator for the optimal repair algorithm implemented for the corresponding repair object; And calculate the real-time evaluation index. If the real-time evaluation index is greater than or equal to the evaluation index, continue monitoring; If the real-time evaluation index is less than the evaluation index, the repair event is marked as a special repair event; When the number of special repair event records is greater than a threshold, a response signal is output to perform an early warning response to the traceability feature pair.

6. An artificial intelligence-based cultural relic restoration and tracing system, such as using an artificial intelligence-based cultural relic restoration and tracing method according to any one of claims 1 to 5, characterized in that: The system includes a historical event extraction module, a to-be-analyzed repair object determination module, an optimal repair algorithm analysis module, a traceability feature pair construction module, a matching warning module, and a real-time repair verification module; The historical event extraction module is used to extract historical events of cultural relics restoration using artificial intelligence; The module for determining the restoration object to be analyzed is used to determine the restoration object with algorithm differences in the restoration event subset as the restoration object to be analyzed of the corresponding type of cultural relic; The optimal repair algorithm analysis module is used to output the optimal repair algorithm of the repair object to be analyzed; The traceability feature pair construction module is used to construct a traceability feature pair corresponding to the restoration object of each type of cultural relic; The matching warning module is used to find the type of cultural relics and the types of restoration objects when new cultural relics need to be restored, and to carry out matching warning of traceability feature pairs; The real-time repair verification module is used to perform verification based on the real-time repair evaluation result.

7. The artificial intelligence-based cultural relic restoration and tracing system according to claim 6 is characterized by: The optimal repair algorithm analysis module includes a repair process determination unit, a repair evaluation result recording unit, an average return execution times calculation unit, an execution link return rate calculation unit, a repair coefficient calculation unit and an evaluation index calculation unit; The restoration process determination unit is used to determine the overall process of cultural relic restoration; The restoration evaluation result recording unit is used to record the number of times each type of cultural relic is restored by performing the algorithm restoration on each restoration object and the restoration algorithm applied; The average return execution times calculation unit is used for extracting the average return execution times of corresponding historical event records by each type of repair algorithm in the first algorithm set; The execution link return rate calculation unit is used to calculate the execution link return rate of each type of repair algorithm for each repair object record to be analyzed; The repair coefficient calculation unit is used to calculate the repair coefficient of the corresponding repair algorithm; The evaluation index calculation unit is used to calculate the evaluation index of each type of repair algorithm corresponding to each repair object to be analyzed in the repair event subset. Based on the evaluation index, the repair algorithms of the repair objects to be analyzed are sorted from large to small, and the repair algorithm first in the sequence is selected as the optimal repair algorithm corresponding to the repair object to be analyzed in the repair event subset.

8. The artificial intelligence-based cultural relic restoration and tracing system according to claim 6 is characterized by: The traceability feature pair construction module includes a traceability target determination unit, a traceability clue induction unit, a traceability result output unit and a traceability feature pair output unit; The traceability target determination unit is used to use the cultural relic type as the traceability target of the traceability feature pair; The traceability clue induction unit is used to repair the repair process and the repair object in the process recorded in the repair event subset as the traceability clue of the traceability feature pair; The traceability result output unit is used to use the optimal repair algorithm of the repair object to be analyzed as the traceability result of the traceability feature pair, The traceability feature pair output unit is used to construct a traceability feature pair with the cultural relic type as the traceability target and the restoration algorithm of the restoration object as the traceability result.

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