Electronic book borrowing management system based on OPAC bidirectional hitching

Abstract

The invention provides an electronic book borrowing management system based on OPAC bidirectional hitching, and relates to the technical field of library informatization. Comprising the steps that a hardware acceleration layer constructs a dynamic resource distribution matrix through an FPGA coprocessor and calculates a matrix norm in real time, and request queue scanning and a redundant copy generation algorithm per 100 ms are achieved in combination with an ASIC anti-conflict controller; a system service layer deploys a dynamic priority scheduling engine, realizes intelligent resource distribution based on a cross-campus coordination coefficient and an edge cloud coordination strategy, records borrowing operation through a block chain evidence storage service and executes automatic copyright division auditing; the user interaction layer provides a bidirectional association retrieval interface and a priority borrowing channel based on credit scores. According to the system, the resource scheduling efficiency in a high-concurrency scene is improved through matrix norm calculation accelerated by hardware, an elastic copy allocation mechanism and an anti-conflict algorithm solidified in ASIC, and cross-campus resource collaboration and accurate copyright revenue allocation are realized in combination with geographic position scoring and block chain technologies.

Description

Technical Field

[0001] The present invention relates to the field of library informatization technology, and particularly to an e - book borrowing management system based on bidirectional OPAC connection. Background Art

[0002] In the field of e - book borrowing in libraries, cross - campus resource collaborative management and accurate copyright sharing are core challenges. Traditional systems rely on manual scheduling and paper ledger records, suffering from high response latency and frequent borrowing conflicts. Especially in the scenario of concurrent requests from multiple campuses, the resource scheduling efficiency decreases, and the error rate of manual statistical copyright sharing is generally high.

[0003] In the prior art, some solutions attempt to optimize resource allocation through software algorithms. However, limited by the pure CPU computing architecture, they are unable to process large - scale matrix operations in real time, resulting in a lag in dynamic block granularity adjustment. Other solutions use a single blockchain for evidence storage. Although they improve data credibility, they do not cooperate with the hardware acceleration module and are difficult to meet the timeliness requirements in high - concurrency scenarios. In addition, the fragmented management of paper and e - book metadata further restricts system performance and user experience. Summary of the Invention

[0004] The present invention provides an e - book borrowing management system based on bidirectional OPAC connection to solve the problems of cross - campus resource collaborative management and accurate copyright sharing in the prior art.

[0005] The present invention provides an e - book borrowing management system based on bidirectional OPAC connection, including: A hardware acceleration layer, including: an FPGA - OPAC co - processor for performing the following operations: In the resource initialization stage, scan the OPAC metadata database to construct an initial resource distribution matrix and calculate it in real time through the matrix norm calculation unit ; In the request processing stage, dynamically adjust the priority weight parameters according to the system load The reinforcement learning coefficient, The system load index, restricted by the reinforcement learning model ; An ASIC anti - collision controller for: in the conflict resolution stage, scan the request queue every 100 ms to calculate the conflict probability and generate the number of redundant copies according to the fixed formula The resource conflict probability, when the real - time conflict probability ​​​​​When it is > 0.2, trigger the redundant copy generation algorithm, Base copy number; In the post-audit phase, verify the validity of redundant copies ; The system service layer includes: a dynamic priority scheduling engine that allocates resource copies according to the cross-campus collaboration coefficient , , geographical location score , cross-campus resource request volume , , elasticity adjustment factor , baseline geographical threshold, and dynamically adjust the shunt ratio through the edge-cloud collaboration strategy ; The blockchain evidence storage service generates a hash value and writes it into the Hyperledger Fabric consortium chain during the user borrowing operation, and performs a copyright sharing deviation audit every morning.

[0006] Preferably, the method for allocating resource copies according to the cross-campus collaboration coefficient includes the following steps: Step 1: Calculate the initial block granularity according to the chapter independence score and the cross-campus collaboration coefficient ; ; Step 2: When the Frobenius norm of the resource distribution matrix changes, calculate the tanh function through the DSP array of the FPGA to dynamically adjust the block granularity , , , matrix Frobenius norm , matrix rank; Step 3: Update the resource distribution matrix using the exponential decay strategy, and the decay rate is controlled by the PLL clock divider of the FPGA.

[0007] Preferably, the copyright sharing method executed by the blockchain evidence storage service includes the following process: Dynamically calculate the sharing income according to the user reading duration and the proportion of the copyright owner ; ; Use the anti-tampering fuse memory of the FPGA to store parameters , and execute ; Compare the actual sharing with the theoretical value every day. If the deviation exceeds the threshold of 0.5% + 0.1 ETH, freeze the account and notify the administrator. .

[0008] Preferably, the strategies executed by the ASIC anti-collision controller include: When the system load is, dynamically adjust the elastic coefficient according to a linear formula , and limit the adjustment step size ; Combine the elastic coefficient and the request queue length to generate a virtual queuing time, with the error controlled by a high-precision clock of ±2ppm.

[0009] Preferably, the edge-cloud collaboration method executed by the dynamic priority scheduling engine includes: Based on resource popularity and geographical location , decide whether to pre-cache at the edge node, otherwise eliminate it; Adjust the cloud shunt ratio through load parameters , and ensure that when the edge load exceeds 75%, the shunt ratio rises rapidly.

[0010] Preferably, the hardware acceleration layer includes a dynamic verification mechanism including: Calculate the performance degradation index every 24 hours , and if it exceeds 5%, trigger FPGA recalibration; Verify in the ASIC , the address range of 0x3FF0x401 in the ASIC mask ROM is solidified to ensure that the parameters cannot be tampered with.

[0011] Preferably, the system further includes a user interaction layer: an OPAC retrieval interface that supports two-way association of metadata between paper books and e-books, marking the borrowable campuses and copyright restrictions; a priority borrowing channel based on credit scoring , and the score is weighted and calculated by 60% of the historical on-time rate, 30% of the device credibility, and 10% of the sharing contribution.

[0012] Preferably, the system further includes an anomaly detection and self-healing module, configured as: Real-time monitor the topology mapping delay of the FPGA-OPAC co-processor. When the delay > 1.2ms is detected continuously for 3 times, trigger the redundancy copy number doubling mechanism of the ASIC anti-collision controller; Periodically verify the DCI code generation interval of the blockchain evidence storage service. If the detected interval time > 30 seconds, automatically switch to the standby smart contract instance; When the parameters of the reinforcement learning model of the dynamic priority scheduling engine or exceed the preset range, forcibly reset to the initial value and trigger model retraining.

[0013] The present invention works in cooperation with a hardware-accelerated matrix operation module and a dynamic elastic block strategy, significantly improving the resource scheduling efficiency in high-concurrency scenarios and effectively reducing the probability of resource conflicts; based on a geographically sensitive cross-campus cooperation model, it realizes the intelligent matching and load balancing of multi-region resources, greatly optimizing the storage space utilization rate; combined with blockchain evidence storage technology and an intelligent contract auditing mechanism, it ensures the transparency and immutability of the copyright sharing process. At the same time, through a two-way associated retrieval architecture, it breaks through the metadata barriers between paper books and e-books, providing users with a seamless cross-media retrieval experience. The overall system has achieved a breakthrough improvement in terms of resource management accuracy, response real-time performance, and operation compliance, building a secure and efficient e-book management ecosystem for multi-campus libraries. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a system architecture diagram of an e-book borrowing management system based on two-way connection of OPAC according to the present invention; Figure 2 It is a flow chart of dynamic block reorganization in the present invention; Figure 3 It is a timing diagram of blockchain evidence storage in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] The present invention relates to an e-book borrowing management system based on two-way connection of OPAC, aiming to solve the problems of cross-campus resource collaborative management and accurate copyright sharing. The overall idea adopted is as follows: adopting a hardware acceleration layer, a system service layer, and a user interaction layer, and combining OPAC metadata and blockchain technology to achieve efficient scheduling and copyright management of e-book resources. The overall architecture is as Figure 1 shown. The core processes include: resource topology mapping: the FPGA parses the OPAC metadata to generate a dynamic resource network; conflict prediction and redundancy allocation: the ASIC calculates the redundant copies in real time and distributes them; cross-campus collaborative scheduling: dynamically adjusts the resource distribution matrix; blockchain evidence storage and sharing: records the operation logs and executes the intelligent contract; user interaction and credit management: provides a priority borrowing channel and personalized services.

[0016] The above technical solutions will be described in detail below in combination with the accompanying drawings of the specification and specific embodiments to better understand the above technical solutions. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used to explain the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention. In addition, it should be noted that for the sake of description, only parts related to the present invention are shown in the drawings, not all of them.

[0017] Example 1, as Figures 1 - 3 shown: The hardware acceleration layer includes: an FPGA OPAC coprocessor for performing the following operations: In the resource initialization phase, scan the OPAC metadata database to construct an initial resource distribution matrix , and calculate in real time through the matrix norm calculation unit ; In the request processing phase, dynamically adjust the priority weight parameters according to the system load , , , , The reinforcement learning coefficient, The system load index is limited by the reinforcement learning model ; The ASIC anti-collision controller is used for: In the conflict resolution phase, scan the request queue every 100 ms to calculate the conflict probability , and generate the number of redundant copies according to the fixed formula , The resource conflict probability, when the real-time conflict probability > 0.2, trigger the redundant copy generation algorithm, The basic number of copies; In the post-audit phase, verify the validity of the redundant copies ; Otherwise, trigger an alarm; The strategies executed by the ASIC anti-collision controller include: When the system load , dynamically adjust the elasticity coefficient according to the linear formula , and limit the adjustment step size ; Combine the elasticity coefficient and the request queue length to generate the virtual queuing time, and the error is controlled by a high-precision clock of ±2 ppm.

[0018] The edge-cloud collaboration method executed by the dynamic priority scheduling engine includes: The edge-cloud collaboration method executed by the dynamic priority scheduling engine includes: According to the resource popularity and geographical location , decide the edge node pre-caching, otherwise eliminate; Adjust the cloud shunting ratio through the load parameter , ensure that when the edge load exceeds 75%, the shunting ratio rises rapidly.

[0019] The hardware acceleration layer includes a dynamic verification mechanism including: Calculate the performance degradation index every 24 hours , if it exceeds 5%, FPGA recalibration is triggered; Verify ASIC , solidified in the 0x3FF0x401 address range of the ASIC mask ROM to ensure that the parameters cannot be tampered with.

[0020] Specifically, the hardware acceleration layer FPGAOPAC coprocessor: Obtain MARC21 metadata from the library integrated management system (ILS), including: basic information of the book (ISBN, title, author); chapter structure (XML format, with chapter independence score marked); ; Copyright permission status (including the campus where books can be borrowed and the maximum number of concurrent users).

[0021] Convert OPAC metadata into a resource distribution matrix , the matrix dimension is (m=number of campuses, n=number of resource blocks); Matrix Elements Indicates the number of copies of the jth resource block in the i-th campus, initial value =1.

[0022] The FPGA has a built-in matrix norm calculation unit that performs: in It is the matrix singular value, and the calculation cycle is 5ns / time; the output norm value is sent to the dynamic priority scheduling engine.

[0023] Using pipeline optimization technology, 256-bit data stream is processed per clock cycle; the measured data of mapping delay is: 0.8ms (mean) ±0.05ms (standard deviation).

[0024] Furthermore, the redundant copy generation process of the ASIC anti-collision controller is as follows: the edge node request queue is scanned every 100ms to count the request density ;when It is marked as a high conflict area.

[0025] Exponential smoothing forecast based on historical data: ; like >0.3, triggering the generation of redundant copies.

[0026] Formulas solidified in ASIC mask ROM: ; The generated copies are distributed to edge nodes. The distribution strategy is: if the campus type == "teaching area": ​​then the allocation ratio = 70%, which is used to prioritize teaching needs; otherwise: the allocation ratio = 30%; Calculate the redundancy copy utilization rate every 5 minutes ; If < 0.7, trigger an alarm and roll back to the benchmark copy number .

[0027] Furthermore, the system service layer includes: a dynamic priority scheduling engine, which allocates resource copies according to the cross-campus collaboration coefficient , geographical location score , cross-campus resource request volume , , elastic adjustment factor , benchmark geographical threshold, and dynamically adjust the shunt ratio through the edge-cloud collaboration strategy ; The blockchain evidence storage service generates a hash value and writes it into the Hyperledger Fabric consortium chain during the user's borrowing operation, and performs a copyright sharing deviation audit every day at midnight.

[0028] Cross-campus collaboration coefficient The method for allocating resource copies includes the following steps: Step 1: Calculate the initial block granularity according to the chapter independence score and the cross-campus collaboration coefficient ; ; Step 2: When the Frobenius norm of the resource distribution matrix changes, calculate the tanh function through the DSP array of the FPGA to dynamically adjust the block granularity , , matrix Frobenius norm matrix rank; Step 3: Update the resource distribution matrix using the exponential decay strategy, and the decay rate is controlled by the PLL clock divider of the FPGA.

[0029] The copyright sharing method executed by the blockchain evidence storage service includes the following process: Dynamically calculate the sharing income according to the user's reading duration and the proportion of the copyright owner ; ; Use the anti-tamper fuse memory of the FPGA to store parameters , and execute ; Compare the actual sharing with the theoretical value If the deviation exceeds the threshold of 0.5% + 0.1 ETH, freeze the account and notify the administrator. .

[0030] Specifically, the cross-campus collaboration strategy of the dynamic resource scheduling engine in the system service layer: Count the number of requests per hour for each campus ; Calculate the distance attenuation factor: Example: When the distance difference between Campus A and the main resource campus is 5 km, = 0.606.

[0031] Collaboration coefficient generation: If > 1.2, mark it as a high-demand campus.

[0032] Resource redistribution: Transfer at least 20% of the replicas from low-demand campuses to high-demand campuses; Update the resource distribution matrix , and recalculate the norm through FPGA.

[0033] Edge-cloud collaboration: Users initiate borrowing requests: When users need to read or obtain resources, they will initiate borrowing requests to the edge nodes.

[0034] Edge nodes query local resource replicas: After receiving the request, edge nodes will first check whether the relevant resource replicas are stored locally.

[0035] Judge whether the replicas are sufficient: If the replicas are sufficient: Edge nodes will notify the FPGA to schedule resource allocation and return a reading link to the user.

[0036] If the replicas are insufficient: Edge nodes will forward the request to the cloud scheduling center, and the cloud scheduling center will turn to ASIC: Calculate and generate redundant replicas, then return the new replicas to the cloud scheduling center, and the cloud scheduling center will then distribute them to the edge nodes: Distribute the new replicas, and finally the edge nodes will return a reading link to the user.

[0037] Key parameters: Edge node response threshold , shunt ratio Calculation error ≤ 3%.

[0038] The process of blockchain evidence storage service is as follows: Hash generation: When a user initiates borrowing, the system combines the following fields to generate the original data: json { "user_id":"U2023001", "book_id":"B9787111636665", "timestamp":"20231005T14:23:18Z", "location":"Library on the 3rd Floor of Campus X" } Perform SHA3256 hashing operation: (Original data).

[0039] Deposit evidence onto the blockchain: Invoke the Hyperledger Fabric smart contract `recordBorrow` with the input parameter being the hash value H; the blockchain returns the deposit evidence number (such as `DCI20231005142318`) and records it in the local database.

[0040] Copyright sharing trigger: When the cumulative reading time of the user reaches 30 minutes, the system automatically executes the sharing formula: Write the sharing result to the blockchain and allocate it to the author's wallet address.

[0041] Deviation audit: Perform an automatic audit at 2 am every day and calculate: If , freeze the relevant accounts and notify the administrator.

[0042] Furthermore, the user interaction layer: An OPAC retrieval interface that supports two-way association of metadata between physical books and e-books, marking the borrowable campuses and copyright restrictions; a priority borrowing channel based on credit score The credit score is calculated by weighting 60% of the historical on-time rate, 30% of the device credibility, and 10% of the sharing contribution.

[0043] Specifically, the OPAC retrieval in the user interaction layer enhances the association between physical and e-resources: Retrieval result display: On the traditional OPAC result page, add an "e-version" label to the entries containing e-books; click on the label to jump to the e-book reading interface, supporting EPUB / PDF format rendering.

[0044] Metadata synchronization: Physical books and e-books achieve metadata mapping through ISBN, including: author, publisher, publication year; chapter and section directory, reference index.

[0045] Copyright prompt: If there are borrowing restrictions for the e-book (such as only available in specific campuses), display a prompt message: "The current borrowable campuses for this resource are: Campus A, Campus B".

[0046] The credit borrowing channel includes: Credit score calculation: Historical on-time rate, weight 60%: ; Example: The user returns on time 15 times / total borrowings 20 times → on-time rate 75% → score 45 points (75% × 60).

[0047] Device credibility, weight 30%: Registered device + IP whitelist: score 30 points; Non-registered device: score 9 points (30% × 30).

[0048] Historical contribution, weight 10%: ; Example: Share 5 times → contribution 1.0 → score 1 point.

[0049] Total score calculation: , , enter the virtual queue.

[0050] In an application case, handling high-concurrency requests during the exam week: Campus X had explosive requests during the "Advanced Mathematics" exam week, with a peak of 1200 requests per minute, exhausting the local copies.

[0051] Handling process: 1. Load detection: The FPGA detected a fluctuation exceeding 15%, marked as an abnormal area; Real-time request density , triggering a high-conflict warning.

[0052] 2. Redundant copy generation: The ASIC calculates , generating: Transfer 150 copies from Campuses Y and Z to Campus X.

[0053] 3. Resource distribution: The edge nodes direct part of the requests to the cloud according to the shunt ratio ; Dynamically adjust the resource block granularity to 2MB, originally 5MB, to improve the parallel processing ability.

[0054] Result: The average response time is improved; The conflict rate is reduced: 7%.

[0055] It should be understood that the disclosed embodiments of the present invention and the above descriptions enable those skilled in the art to implement the present invention using the present invention. At the same time, the present invention is not limited to the part of the embodiments mentioned above. It should be understood that ordinary skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. An electronic book borrowing management system based on OPAC bidirectional connection, characterized in that: include: Hardware acceleration layer, including: FPGAOPAC coprocessor, used to perform the following operations: In the resource initialization phase, scan the OPAC metadata database and build the initial resource distribution matrix , and is calculated in real time by the matrix norm calculation unit ; During the request processing phase, based on the system load Dynamically adjust priority weight parameters , , , Reinforcement learning coefficient, System load indicators, limited by reinforcement learning models ; ASIC anti-collision controller, used to: scan the request queue every 100ms and calculate the collision probability during the conflict resolution phase , and generate the number of redundant copies according to the solidification formula , Resource conflict probability, when real-time conflict probability When >0.2, the redundant copy generation algorithm is triggered. Number of base copies; Verify the validity of redundant copies during the post-audit phase ; System service layer, including: dynamic priority scheduling engine, based on cross-campus coordination coefficient Allocate resource copies, , location score , cross-campus resource requests , Elasticity adjustment factor, Benchmark geographic thresholds and dynamically adjust the diversion ratio through edge-cloud collaborative strategies ; The blockchain evidence storage service generates a hash value and writes it to the Hyperledger Fabric consortium chain when the user borrows the book, and performs copyright sharing deviation audits every morning.

2. The electronic book borrowing management system based on OPAC bidirectional connection according to claim 1 is characterized in that: The cross-campus synergy coefficient The method for allocating resource copies comprises the following steps: Step 1: Score based on chapter independence and cross-campus synergy coefficient , calculate the initial block granularity ; Step 2: When the resource distribution matrix When the Frobenius norm changes, the tanh function is calculated through the FPGA's DSP array to dynamically adjust the block granularity , , Matrix Frobenius norm, Matrix rank; Step 3: Update the resource distribution matrix using an exponential decay strategy. Controlled by the FPGA's PLL clock divider.

3. The electronic book borrowing management system based on OPAC bidirectional connection according to claim 1 is characterized in that: The copyright sharing method implemented by the blockchain evidence storage service includes the following process: Based on user reading time and the proportion of copyright holders , dynamically calculate the revenue sharing ; Using FPGA's tamper-proof fuse memory to store parameters ,implement ; Daily comparison of actual share With theoretical value If the deviation exceeds the threshold of 0.5% + 0.1ETH, the account will be frozen and the administrator will be notified. .

4. The electronic book borrowing management system based on OPAC bidirectional connection according to claim 1 is characterized in that: The strategies implemented by the ASIC anti-collision controller include: When the system load When the elastic coefficient is adjusted dynamically according to the linear formula , limit the adjustment step size ; The elasticity coefficient and request queue length are combined to generate the virtual queuing time, and the error is controlled by a high-precision clock of ±2ppm.

5. The electronic book borrowing management system based on OPAC bidirectional connection according to claim 1 is characterized in that: The edge-cloud collaboration method performed by the dynamic priority scheduling engine includes: Based on resource popularity and geographic location ,determine the edge node pre-cache, otherwise eliminate; Adjust the cloud offload ratio by load parameters , ensuring edge load When it exceeds 75%, the diversion ratio increases rapidly.

6. The electronic book borrowing management system based on OPAC bidirectional connection according to claim 1 is characterized in that: The hardware acceleration layer includes a dynamic verification mechanism including: Calculate performance degradation indicators every 24 hours , if it exceeds 5%, FPGA recalibration is triggered; Verify ASIC , solidified in the 0x3FF0x401 address range of the ASIC mask ROM to ensure that the parameters cannot be tampered with.

7. The electronic book borrowing management system based on OPAC bidirectional connection according to claim 1 is characterized in that: The system further includes a user interaction layer: an OPAC search interface that supports bidirectional association of paper book and e-book metadata, annotating the campuses where books can be borrowed and copyright restrictions; The priority borrowing channel is scored based on a weighted calculation of 60% historical punctuality, 30% device credibility, and 10% shared contribution.

8. The electronic book borrowing management system based on OPAC bidirectional connection according to claim 1 is characterized in that: The system further includes an anomaly detection and self-healing module configured to: Real-time monitoring of the topology mapping delay of the FPGA-OPAC coprocessor, when the delay is monitored to be greater than 1.2 ms for three consecutive times, triggering a redundant copy number doubling mechanism of the ASIC anti-collision controller; Periodically check the DCI code generation interval of the blockchain evidence storage service. If the interval is greater than 30 seconds, automatically switch to the backup smart contract instance. When the reinforcement learning model parameters of the dynamic priority scheduling engine or When the value exceeds the preset range, it is forced to reset to the initial value and trigger model retraining.

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