Method for automatically generating report

Abstract

The invention provides an automatic report generation method, which comprises the following steps of: S1, comprehensively collecting historical reports, production parameter changes and field real-time data, customizing an exclusive template according to production procedures and quality detection requirements, and accurately anchoring a data processing framework by virtue of simulated annealing optimization, so that the system identification precision is improved; and S2, constructing a fitness function by applying genetic and ant colony algorithms, balancing resources through selection, crossing, variation and ant colony search, formatting after shortening progress deviation, and deeply mining a data value assistance decision. S3, a template calling rule is set according to department business characteristics, dynamic optimization is carried out through a particle swarm algorithm, it is ensured that the department obtains highly-adaptive data, and report guidance is enhanced. And S4, automatically generating a report, comparing the report with the actual report, returning the deviation to the S2 for calibration, and constructing a traceability verification system by means of the block chain and homomorphic encryption to ensure the authenticity and completeness of the data and provide a stable decision basis. And S5, intelligently allocating bandwidth according to access and report characteristics by means of the hybrid cloud architecture, and coordinating edge computing cache with CDN optimization transmission to realize instant update and download of the report.

Description

Technical Field

[0001] The present invention relates to the field of automatic report generation, and in particular to a method for automatic report generation. Background Art

[0002] Traditional report generation mainly focuses on reflecting the current production status and operating performance, but cannot provide more comprehensive information on relevant value creation capabilities, risk management capabilities, future development potential, etc. In today's enterprise production management field, report generation is a key task. Its traditional model mainly focuses on reflecting the current production status and operating performance. However, it is gradually becoming weak in the face of complex and changing production environments and diverse information needs. Traditional report generation methods mostly rely on manual collection, organization and analysis of data. The process is cumbersome and error-prone, and it is difficult to integrate deep-level key information such as value creation capabilities, risk management levels, and future development potential. This not only restricts the company's accurate decision-making, but also fails to meet the urgent demands of modern enterprises for efficient and accurate data management.

[0003] With the rapid development of information technology, the demand for automated data processing is increasing day by day. Although the existing report generation technology has improved efficiency and accuracy to a certain extent, there are still many bottlenecks. Most system data templates are rigid and difficult to flexibly adapt to the diverse data structures and demand characteristics of different departments; the data processing link lacks intelligent optimization strategies, and the resource allocation is unbalanced and the processing is slow in the face of massive and complex production data, resulting in the accumulation of progress deviations and waste of resources; the verification mechanism is weak, and it is difficult to trace the source of data errors. Insufficient security protection can easily lead to the risk of data leakage and tampering, endangering the foundation of enterprise operations. In this context, it is urgent to develop an efficient, accurate, intelligent and secure automatic report generation method to fill the technical shortcomings of the industry and help enterprises achieve data-driven refined management and innovative development transformation in the fierce market competition. Summary of the invention

[0004] The main purpose of the present invention is to provide a method for automatically generating reports to solve the problem that traditional report generation mainly focuses on reflecting the current production status and operating performance, but cannot provide more comprehensive information about related value creation capabilities, risk management capabilities, future development potential, etc.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for automatically generating a report, the method comprising: S1. Collect data and create a data folder, and preset the data in the data file into a data template; S2. Perform simple processing, calculation and formatting of data to facilitate rapid identification and comparison by the system; S3. Setting data template call rules is helpful to quickly and efficiently distinguish data with different needs; S4. Generate a report based on the data and compare it with the data template to check whether the data is wrong. If there is an error in the data, adjust the data content of the data folder and update the report data; S5. Reports are automatically updated and downloaded.

[0006] In the preferred solution, in step S1, historical report data, updated data obtained due to parameter modification during production, and data obtained in real time at the production site are collected to create a data folder; According to the data format of each process on the production line, quality inspection index format and other characteristics of different production links, we customize exclusive template formats and parameter requirements for the data to achieve data template preset; The simulated annealing algorithm is introduced as an auxiliary optimization method to further optimize the structure and parameter settings of the data template to assist the system in accurately identifying and processing data.

[0007] In the preferred embodiment, the steps of introducing the simulated annealing algorithm to optimize the template are: The data template structure and parameter vector are , the error measure of the system for data recognition processing is , the objective function is , aims to maximize the objective function value, that is, minimize the recognition processing error; Generate neighborhood solutions using Gaussian perturbation ,in To obey Gaussian distribution A random vector of According to the current iteration number Adaptive adjustment, the formula is: ,in is the total number of iterations, is the initial standard deviation. This formula balances the global search and local search capabilities of the algorithm. In the early stage, a large step size explores a broad solution space, and in the later stage, a small step size focuses on the neighborhood of high-quality solutions. Current solution The objective function value is , neighborhood solution The objective function value is , the temperature parameter is , depending on the cooling progress Decreasing, , is the initial temperature, if or , then accept This Metropolis criterion uses a probabilistic mechanism to jump out of the local optimum, drive the algorithm to find the best solution, ensure that the template structure and parameters reach the near-optimal configuration, and improve the system data processing accuracy and efficiency.

[0008] In the preferred solution, in step S2, a data optimization algorithm based on a genetic algorithm is introduced, and an ant colony optimization algorithm is combined to optimize resource allocation and path selection, and a fitness function is constructed; Through selection, crossover, mutation operations and ant colony path search iterative optimization, production resources are balanced and progress deviation is reduced; Calculate and format data according to production information rules.

[0009] In the preferred embodiment, the algorithm steps of the above steps are: S2.1, Genetic Algorithm: Set the task volume in the production plan to The actual progress is , resource consumption vector ,in is the number of resource types; define the fitness function ,in and is the trade-off coefficient, It is a resource The ideal average consumption value; this function comprehensively considers the progress deviation and resource consumption imbalance, and guides the algorithm to optimize resource allocation to reduce the deviation; Ant colony optimization algorithm part: build a production task resource allocation network, with nodes as tasks and resources, and edge weights as Representation Task About Resources The urgency of the demand, according to the priority of the task and remaining resources definition ,in To prevent zero constant; let ant On the path The concentration of pheromone left on , the inspiration information is ;According to the pheromone update rules ,in is the volatility rate, It's an ant In this iteration, The remaining pheromone increment is determined by the ant path selection probability, where For the task Optional resource set, and It is determined by the weight parameter, which reflects the resource allocation tendency and works with the genetic algorithm to optimize the efficiency of resource path selection; S2.2, iterative optimization operation: select genetic algorithm: according to the fitness function value Sort the individuals in the population using the tournament selection method; set the population size , each time randomly selected Individual competition ( ), the winner enters the offspring population, and the replication probability , ensure the inheritance of excellent genes and promote the evolution of the population; Crossover operation: select the parent individuals and , according to the uniform crossover probability Perform uniform crossover; generate random masks ( ), offspring and element , , expand the diversity of solution space and explore potential optimal solution combinations; Mutation Operation: Individual According to the probability of mutation mutation; for randomly selected sites ,set up Value range , new value , , Follows Cauchy distribution ,in is a scale parameter that helps the algorithm escape from the local optimum and maintain the exploratory nature of the population; Ant colony path search iteration: Each ant moves according to probability Select a path to build a resource allocation plan, and update the pheromone according to the path length after completing one iteration. long ,but , It is a pheromone constant that guides the ant colony to search for the best result, interacts with genetic operations to optimize the resource allocation pattern, balances production resources, and reduces progress deviations; S2.3. Data calculation, processing and formatting: After searching for the best solution, obtain the optimal resource allocation plan and schedule adjustment strategy. In the preferred solution, in step S3, a deep insight is gained into the essential differences in departmental data, and template calling rules are accurately set to differentiate data based on the core business needs and decision-making focus characteristics of each department.

[0010] The sales department builds a sales data panoramic report template based on sales volume, order volume, customer geographical distribution, customer purchase frequency, and sales channel conversion rate; The production department designs a holographic data template for the production process based on the output, quality qualification rate, equipment operation rate, equipment failure predictive maintenance indicators and material loss rate of each link in the production process, ensuring that the department accurately obtains highly adapted data, greatly enhancing the precise guidance value of the report to the department's operations, deeply optimizing resource allocation and decision-making benefits, and driving efficient business collaboration; The particle swarm optimization algorithm is introduced to dynamically adjust the template calling rules to ensure that departments obtain adaptive data, enhance the value of reports for departmental operation guidance, and optimize resource allocation and decision-making benefits.

[0011] In the preferred solution, in step S4, the automatically generated report is compared with the actual data report in multiple dimensions, and if there is a deviation, the process returns to step S2 to recalculate and calibrate; This process uses blockchain technology and homomorphic encryption algorithms to collaboratively build an ultra-secure data traceability and verification system; The report data is assigned a hash value based on the national secret SM3 hash algorithm and stored in the blockchain. During verification, the hash value and related data integrity and source authenticity are compared in the ciphertext state through homomorphic encryption technology to accurately identify the root cause of erroneous data, and comprehensively ensure that the report truthfully, accurately and securely reflects the production and operation status, consolidates the basis for management decision-making, and builds a solid line of defense for corporate data security and the foundation of decision-making credibility.

[0012] In the preferred embodiment, the algorithm in step S4 is: S4.1. Data National Secret SM3 Hash Processing and Blockchain Storage: For each data record in the report data set ,in For the data item value, calculate its hash value based on the national secret SM3 hash algorithm SM3 algorithm internal compression function is CF, message grouping , initial value IV, the iterative calculation process is , , is the number of groups, the final hash value ; This hash value is the unique identifier of the data and is used to verify the integrity of the data. Any data tampering after being stored in the blockchain will cause the hash value to change and can be monitored; Record the data With its hash value Composed key-value pairs , written into the specific data structure of the blockchain distributed ledger through the blockchain's smart contract interface, and a Merkle tree structure is constructed for storage. The leaf node is the data hash value, and the non-leaf node is the hash combination calculation value of the child node. The root hash value is stored in the blockchain header as the integrity authentication mark of the entire data set, ensuring that the data cannot be tampered with and can be traced, laying the foundation for subsequent verification; S4.2, Homomorphic encryption auxiliary verification process: Use homomorphic encryption algorithm to encrypt report data; set plaintext data , randomly select a large prime number and ,calculate , , select satisfy , the public key is , the private key is ; The encryption operation is ( is a random number), ciphertext Participating in calculations without leaking plaintext information; During verification, the ciphertext report data Report with ciphertext actual data Compare; Define the difference metric function ,like , If the threshold is set according to the data characteristics, data deviation is determined and needs to be traced back to the source for correction; this function uses the logarithmic operation properties of homomorphic encryption to measure data differences in the ciphertext domain, ensure data privacy during the verification process, accurately lock erroneous data, and ensure report quality and reliable decision-making.

[0013] In the preferred solution, in step S5, the dynamically modified and updated report is robustly downloaded and stored locally or smoothly transmitted to the user, relying on the hybrid cloud architecture to integrate the massive resources of the public cloud and the security advantages of the private cloud, and intelligently allocate storage and transmission bandwidth according to the real-time fluctuation of user access frequency, the scale and complexity of report data; Use edge computing nodes to cache popular reports and update data fragments in real time, and use content delivery network CDN to optimize transmission paths and achieve efficient processing.

[0014] In the preferred solution, the algorithm steps for automatically updating and downloading the report in step S5 are: User access frequency and report data size and complexity , construct the following fuzzy logic bandwidth allocation mechanism: define Fuzzy subsets and membership functions of : Low Fuzzy Subset : ; Medium fuzzy subset : ; High fuzzy subset : ; definition Fuzzy subsets and membership functions of: small fuzzy subsets , medium fuzzy subset , large fuzzy subset ; Constructing fuzzy rule base: for and for ,but for There are 9 rules in total. The ratio of storage and transmission bandwidth is allocated, and its fuzzy subset is low ,middle ,high ; Fuzzy reasoning and clarification: Using Mamdani reasoning method, according to the input and Membership activation rule calculation output Fuzzy set membership, and then use the centroid method to clarify the exact bandwidth allocation ratio , for Representative value, used to intelligently allocate hybrid cloud storage and transmission bandwidth based on user access and report characteristics, improving resource utilization and transmission efficiency; In the fuzzy logic bandwidth allocation algorithm, the parameters of the membership function can be dynamically adjusted based on the statistical analysis of the system's historical operation data and performance feedback, optimizing the accuracy of the bandwidth allocation strategy to adapt to the dynamic changes in the production environment and user behavior, and ensuring smooth and stable report transmission; Edge node cache optimization algorithm: Assume that the cache capacity of the edge computing node is , cache reports and data fragment sets and its data heat value For new data : Calculating caching benefits , is the trade-off coefficient, for Size, which measures the cache value of new data; Calculating cache costs , is the cost coefficient, Evaluate the resource cost of caching new data for potential replacement datasets; like ,according to from Select Replace Element Cache , optimize edge node cache content, accelerate access to popular data, and collaborate with CDN to enhance report distribution efficiency; In the edge node cache optimization algorithm, data heat value The calculation takes into account the frequency of data access , access time interval and data update frequency ,like , is the weighting coefficient, , , To correspond to the normalized benchmark value, accurately quantify data popularity, improve the scientificity and timeliness of cache replacement decisions, and ensure that cache data meets user needs and dynamic updates of reports.

[0015] The present invention provides a method for automatic report generation, which integrates multiple advanced algorithms to achieve full-process optimization. In the data collection stage, multi-source data is integrated and optimized by the simulated annealing algorithm template preset, accurately adapting to each link of production, laying the foundation for system identification. The processing and calculation link integrates genetic and ant colony algorithms, accurately allocates resources and corrects progress according to the dynamic fitness function, and the processed data format is standardized and the indicators are rich, which improves the depth of decision support. Report generation and verification use blockchain and homomorphic encryption to ensure data quality, and multi-dimensional comparison is combined with traceability correction mechanism to eliminate errors from the source. The report truthfully reflects production and operation, and firmly supports the scientific and efficient formulation of management decisions.

[0016] In-depth analysis of departmental business characteristics to build exclusive templates and dynamic call rules. Sales and production departments customize templates based on core indicators, and dynamically optimize rules through particle swarm algorithm to adapt to dynamic changes in business, ensuring that departments obtain highly consistent data. Reports become the "navigator" for precise management of departments, significantly improving the scientific nature of resource allocation and operational coordination efficiency, driving the integrated upgrade of enterprise operations, and achieving remarkable results in reducing costs and increasing efficiency.

[0017] The powerful combination of blockchain and homomorphic encryption builds a strong security barrier. The data hashing blockchain has an architecture that cannot be tampered with and can be traced; homomorphic encryption enables ciphertext verification operations, accurately locks the source of errors without losing privacy, and the data is safely transferred in the "safe" throughout its life cycle, resolving data risks, consolidating the company's commercial confidentiality defense line and the cornerstone of decision-making trust, injecting a "booster" into the digital transformation of the company and escorting sustainable and steady development.

[0018] The hybrid cloud architecture integrates the advantages of public and private clouds, and uses fuzzy logic to intelligently allocate bandwidth according to access frequency and report characteristics to ensure the immediacy and stability of transmission. Edge computing caches popular data and collaborates with CDN to optimize the path, so that reports can be delivered to the user end in seconds, and the interactive experience is smoothly upgraded. Users can make real-time decisions based on their needs, and the agility of enterprise market response has leapt forward, seizing the initiative in the wave of competition in the digital age and leading the new development trend. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described below in conjunction with the accompanying drawings and embodiments: Figure 1 It is a flow chart of the method for automatically generating reports of the present invention; DETAILED DESCRIPTION Example 1 like Figure 1 As shown, a method for automatically generating a report, the method comprising: S1. Collect data and create a data folder, and preset the data in the data file into a data template; S2. Perform simple processing, calculation and formatting of data to facilitate rapid identification and comparison by the system; S3. Setting data template call rules is helpful to quickly and efficiently distinguish data with different needs; S4. Generate a report based on the data and compare it with the data template to check whether the data is wrong. If there is an error in the data, adjust the data content of the data folder and update the report data; S5. Reports are automatically updated and downloaded.

[0020] Example 2 Further illustrate with reference to Example 1, Figure 1 As shown, in step S1, historical report data, update data obtained due to parameter modification during production, and data obtained in real time at the production site are collected to create a data folder; According to the data format of each process on the production line, quality inspection index format and other characteristics of different production links, we customize exclusive template formats and parameter requirements for the data to achieve data template preset; The simulated annealing algorithm is introduced as an auxiliary optimization method to further optimize the structure and parameter settings of the data template to assist the system in accurately identifying and processing data.

[0021] In the preferred embodiment, the steps of introducing the simulated annealing algorithm to optimize the template are: The data template structure and parameter vector are , the error measure of the system for data recognition processing is , the objective function is , aims to maximize the objective function value, that is, minimize the recognition processing error; Generate neighborhood solutions using Gaussian perturbation ,in To obey Gaussian distribution A random vector of According to the current iteration number Adaptive adjustment, the formula is: ,in is the total number of iterations, is the initial standard deviation. This formula balances the global search and local search capabilities of the algorithm. In the early stage, a large step size explores a broad solution space, and in the later stage, a small step size focuses on the neighborhood of high-quality solutions. Current solution The objective function value is , neighborhood solution The objective function value is , the temperature parameter is , depending on the cooling progress Decreasing, , is the initial temperature, if or , then accept This Metropolis criterion uses a probabilistic mechanism to jump out of the local optimum, drive the algorithm to find the best solution, ensure that the template structure and parameters reach the near-optimal configuration, and improve the system data processing accuracy and efficiency.

[0022] 1. Data collection and basic template construction Comprehensively gather multi-source data: systematically collect data from all aspects of enterprise production management, including historical report data, which carries detailed business records in the past production cycle and provides a basis for trend analysis and experience reference; updated data generated by parameter modifications such as process optimization, equipment adjustment, and raw material changes during production, which reflect dynamic changes in production in real time; Customized templates: In-depth analysis of the technical specifications, process requirements and quality inspection indicator system of each process on the production line, and accurate identification of data format differences and logical associations in different production links.

[0023] 2. Simulated annealing algorithm optimization template Objective function establishment: constructing a data template structure and parameter vector The error metric function for the systematic data identification process of the independent variable It comprehensively considers multi-dimensional error factors such as data entry error rate, format mismatch, logical contradiction and deviation from actual production, and quantifies the error value through complex weighted calculation and statistical analysis. Then define the objective function , clearly define the optimization direction as maximizing , which is essentially full compression To the minimum extent possible, accurate data identification and processing can be achieved, risks of data misjudgment, omission and misuse can be avoided, and high-fidelity data resources can be delivered to enterprises for production decision-making.

[0024] Neighborhood solution generation and search strategy: Generate neighborhood solutions using Gaussian perturbation , where the random vector Follows Gaussian distribution , standard deviation Adaptive adjustment according to the iterative process, formula The algorithm explores breadth and depth in a clever way. In the early stages of optimization, t is close to 0 and larger. The value gives the algorithm a strong global search capability, helping it to overcome the local extreme value trap and widely explore potential high-quality solution areas; as the iteration progresses, t tends to , The step size is gradually reduced according to the exponential decay law, guiding the algorithm to focus on the local optimal solution neighborhood for refined search, exploring near-optimal template structures and parameter combinations, improving optimization efficiency and accuracy, and avoiding falling into meaningless search cycles or local suboptimal dilemmas.

[0025] Acceptance criterion driven optimization: In each iteration, compare the current solution With neighborhood solution Objective function value and , combined with the decreasing temperature parameter (Initial temperature Based on the scale and complexity of the problem, the Metropolis criterion is used to determine whether to accept the .like , that is, the neighboring solution is better, then it is directly accepted; if , then with probability To decide whether to accept, this probability mechanism gives a higher possibility of acceptance to worse solutions in the high temperature stage (early iteration), maintains the global exploration vitality of the algorithm and prevents premature convergence; at low temperatures (later iterations), it tends to retain the best solution and steadily converge to a near-optimal template configuration. After multiple rounds of iterative refinement, the system data processing accuracy and efficiency have leapt to a new level, unlocking the potential of data-driven production management and injecting strong momentum for enterprises to make accurate decisions and operate efficiently in the fierce market competition.

[0026] Example 3 Further illustrate with reference to Example 1, Figure 1 As shown, in step S2, a data optimization algorithm based on a genetic algorithm is introduced, and an ant colony optimization algorithm is combined to optimize resource allocation and path selection, and a fitness function is constructed; Through selection, crossover, mutation operations and ant colony path search iterative optimization, production resources are balanced and progress deviation is reduced; Calculate and format data according to production information rules.

[0027] In the preferred embodiment, the algorithm steps of the above steps are: S2.1, Genetic Algorithm: Set the task volume in the production plan to The actual progress is , resource consumption vector ,in is the number of resource types; define the fitness function ,in and is the trade-off coefficient, It is a resource The ideal average consumption value; this function comprehensively considers the progress deviation and resource consumption imbalance, and guides the algorithm to optimize resource allocation to reduce the deviation; Ant colony optimization algorithm part: build a production task resource allocation network, with nodes as tasks and resources, and edge weights as Representation Task About Resources The urgency of the demand, according to the priority of the task and remaining resources definition ,in To prevent zero constant; let ant On the path The concentration of pheromone left on , the inspiration information is ;According to the pheromone update rules ,in is the volatility rate, It's an ant In this iteration, The remaining pheromone increment is determined by the ant path selection probability, where For the task Optional resource set, and It is determined by the weight parameter, which reflects the resource allocation tendency and works with the genetic algorithm to optimize the efficiency of resource path selection; S2.2, iterative optimization operation: select genetic algorithm: according to the fitness function value Sort the individuals in the population using the tournament selection method; set the population size , each time randomly selected Individual competition ( ), the winner enters the offspring population, and the replication probability , ensure the inheritance of excellent genes and promote the evolution of the population; Crossover operation: select the parent individuals and , according to the uniform crossover probability Perform uniform crossover; generate random masks ( ), offspring and element , , expand the diversity of solution space and explore potential optimal solution combinations; Mutation Operation: Individual According to the probability of mutation mutation; for randomly selected sites ,set up Value range , new value , , Follows Cauchy distribution ,in is a scale parameter that helps the algorithm escape from the local optimum and maintain the exploratory nature of the population; Ant colony path search iteration: Each ant moves according to probability Select a path to build a resource allocation plan, and update the pheromone according to the path length after completing one iteration. long ,but , It is a pheromone constant that guides the ant colony to search for the best result, interacts with genetic operations to optimize the resource allocation pattern, balances production resources, and reduces progress deviations; S2.3. Data calculation, processing and formatting: After searching for the best solution, obtain the optimal resource allocation plan and schedule adjustment strategy. 1. Data optimization algorithm integration Genetic algorithm core optimization mechanism: Precise definition of fitness function: In production management scenarios, accurately quantify the production planning tasks The actual progress Deviation, borrow Highlight the impact of progress differences, Flexibly balance the urgency of production cycle and the cost of delivery delay; deeply consider resource consumption vector With ideal mean Deviate, Accurately measure resource imbalances, The fitness function is constructed by dynamically determining resource scarcity and cost fluctuations Comprehensively weigh progress and resource factors, guide the algorithm to accurately optimize resource allocation, efficiently reduce progress deviations, and lay a solid foundation for the algorithm to ensure balanced and stable production.

[0028] Iterative Evolutionary Operational Coordination: Tournament Selection Selection of the best and elimination of the worst, population size Next Random Meta-contest ( ) Stimulate competition and selection, and the probability of winners replicating Accurately inherit superior genes to consolidate population evolutionary advantages; uniform crossover innovation uses random masking Recombinant Parents and Genes, offspring and Gene Rules such as these expand the diversity boundaries of the solution space and explore potential excellent solution combinations; mutation operations are based on For individuals specific Cauchy distribution Slight change, Break through the confinement of local optimality, maintain the global exploration activity of the population, and use multi-operation collaborative driving algorithms to approach the global optimal solution for production resource allocation and progress management.

[0029] Ant Colony Optimization Algorithm Unique Path Planning: Resource Allocation Network Modeling: Building Tasks - Resource Allocation Network, Edge Weights Precise quantification tasks About Resources Demand urgency, based on task priority and remaining resources Dynamic adjustment, Anti-zero ensures stable computing. Pheromone concentration With inspirational information By probability Explore path building solutions, and Intelligently balance pheromone historical experience and heuristic instant guidance, efficiently explore optimal resource allocation paths, and complement and synergize with genetic algorithms to improve resource allocation efficiency.

[0030] Pheromone dynamic update drive: iteratively based on the volatility Path selection with ants precipitates pheromones, paths long Time Ant Leave pheromone increment , In order to constantly regulate the intensity of pheromones, guide the ant colony to iterate towards excellence, interact with genetic iteration to optimize the resource allocation pattern, dynamically balance the supply and demand of production resources, strongly reduce progress deviations, and inject intelligent power into the efficient and smooth production process.

[0031] 2. Deep optimization of template calling rules Customization of department-specific templates: Accurately analyze the business characteristics of the sales department, focus on the core indicators of sales to explore growth drivers, analyze market demand fluctuations from the order volume, gain insight into regional market potential from the geographical distribution of customers, tap into customer loyalty from the purchase frequency, and evaluate marketing effectiveness from the channel conversion rate. Build a sales panoramic template to quantify sales dynamics in all aspects to accurately formulate sales strategies and accurately navigate market expansion; deeply deconstruct the production department process, grasp the production rhythm from the output of each link, control the quality lifeline from the quality pass rate, monitor equipment health from the equipment operation rate, and use predictive maintenance indicators to pre-position equipment operation and maintenance, and optimize cost control from the material loss rate. Design and produce holographic templates to monitor production factors throughout the entire process, build a solid data support for lean management of production scheduling, precise policy implementation for quality improvement, and lean operation of cost control, and empower departmental data-driven operation management changes with highly adaptable templates.

[0032] Particle swarm dynamic optimization empowerment: Introduce particle swarm optimization algorithm to accurately and dynamically adjust template calling rules, adjust according to the ever-changing market environment and corporate strategy, intelligently track sales market trend fluctuations, production technology innovation rhythm, customer demand evolution, and optimize department templates in real time to match the business evolution trajectory. For example, when the e-commerce shopping festival surges in sales orders, intelligently expand the order volume data weight, accurately adjust the quality pass rate parameter sensitivity when the new production process is launched, and agilely optimize the customer geographical distribution data granularity when emerging markets emerge, to ensure that department report data accurately fits the dynamic needs of the business, deeply stimulate the potential of report decision-making guidance, comprehensively optimize the dynamic allocation of resources and the benefits of strategic decision-making, drive the collaborative innovation and development of corporate business, and continuously enhance market competitiveness.

[0033] Example 4 Further illustrate with reference to Example 1, Figure 1 As shown, in step S3, we deeply understand the essential differences between department data, and accurately set template calling rules to distinguish data based on the core business needs and decision-making focus characteristics of each department.

[0034] The sales department builds a sales data panoramic report template based on sales volume, order volume, customer geographical distribution, customer purchase frequency, and sales channel conversion rate; The production department designs a holographic data template for the production process based on the output, quality qualification rate, equipment operation rate, equipment failure predictive maintenance indicators and material loss rate of each link in the production process, ensuring that the department accurately obtains highly adapted data, greatly enhancing the precise guidance value of the report to the department's operations, deeply optimizing resource allocation and decision-making benefits, and driving efficient business collaboration; The particle swarm optimization algorithm is introduced to dynamically adjust the template calling rules to ensure that departments obtain adaptive data, enhance the value of reports for departmental operation guidance, and optimize resource allocation and decision-making benefits.

[0035] Example 5 Further illustrate with reference to Example 1, Figure 1 As shown, in step S4, the automatically generated report is compared with the actual data report in multiple dimensions. If a deviation occurs, the process returns to step S2 to recalculate and calibrate. This process uses blockchain technology and homomorphic encryption algorithms to collaboratively build an ultra-secure data traceability and verification system; The report data is assigned a hash value based on the national secret SM3 hash algorithm and stored in the blockchain. During verification, the hash value and related data integrity and source authenticity are compared in the ciphertext state through homomorphic encryption technology to accurately identify the root cause of erroneous data, and comprehensively ensure that the report truthfully, accurately and securely reflects the production and operation status, consolidates the basis for management decision-making, and builds a solid line of defense for corporate data security and the foundation of decision-making credibility.

[0036] In the preferred embodiment, the algorithm in step S4 is: S4.1. Data National Secret SM3 Hash Processing and Blockchain Storage: For each data record in the report data set ,in For the data item value, calculate its hash value based on the national secret SM3 hash algorithm SM3 algorithm internal compression function is CF, message grouping , initial value IV, the iterative calculation process is , , is the number of groups, the final hash value ; This hash value is the unique identifier of the data and is used to verify the integrity of the data. Any data tampering after being stored in the blockchain will cause the hash value to change and can be monitored; Record the data With its hash value Composed key-value pairs , written into the specific data structure of the blockchain distributed ledger through the blockchain's smart contract interface, and a Merkle tree structure is constructed for storage. The leaf node is the data hash value, and the non-leaf node is the hash combination calculation value of the child node. The root hash value is stored in the blockchain header as the integrity authentication mark of the entire data set, ensuring that the data cannot be tampered with and can be traced, laying the foundation for subsequent verification; S4.2, Homomorphic encryption auxiliary verification process: Use homomorphic encryption algorithm to encrypt report data; set plaintext data , randomly select a large prime number and ,calculate , , select satisfy , the public key is , the private key is ; The encryption operation is ( is a random number), ciphertext Participating in calculations without leaking plaintext information; During verification, the ciphertext report data Report with ciphertext actual data Compare; Define the difference metric function ,like , If the threshold is set according to the data characteristics, data deviation is determined and needs to be traced back to the source for correction; this function uses the logarithmic operation properties of homomorphic encryption to measure data differences in the ciphertext domain, ensure data privacy during the verification process, accurately lock erroneous data, and ensure report quality and reliable decision-making.

[0037] In the financial management scenario of large enterprise groups, massive amounts of financial data reports need to be processed every day, including revenue reports of each subsidiary, cost detail reports, cash flow reports, etc.

[0038] 1. Data hash processing and blockchain storage: During the daily financial settlement cycle, the system automatically collects the report data uploaded by each subsidiary and records each report data. (like The revenue of a specific business of a subsidiary, For corresponding cost expenditure items, etc.), the hash value is accurately calculated according to the national secret SM3 hash algorithm During this process, the compression function CF inside the SM3 algorithm groups messages according to strict logic. Iteration operation, starting from the initial value IV through ( , = (number of groups) to derive a unique hash value . Then, the data record is paired with the hash value key The distributed ledger Merkle tree structure is embedded through the blockchain smart contract interface. The leaf node hash value anchors the data, and the non-leaf node integrates the sub-node hash to intelligently build a hierarchical integrity authentication system. The root hash value protects the reliability of the entire data set. Any malicious tampering of the data will cause a dramatic change in the hash value, laying a solid foundation for reliable data traceability.

[0039] 2. Homomorphic encryption auxiliary verification process: In the financial data encryption process, a large prime number is randomly selected for each report data. and calculate and , accurate screening satisfy Establishing the public key With private key ,according to ( is a random number) to encrypt data, ciphertext Conceal data privacy and participate in subsequent verification. At the time of verification, the system ciphertext compares the financial statement data Report with actual data , using the difference metric function Accurately quantify differences and set thresholds based on financial data sensitivity and business tolerance ,when Trigger the traceability correction mechanism. This innovative function cleverly uses the logarithmic operation characteristics of homomorphic encryption to accurately identify data deviations in the ciphertext domain, strictly adhere to the bottom line of data privacy throughout the verification process, and efficiently lock the source of erroneous data, laying a solid foundation for accurate data for corporate financial decision-making, building a strong data security dam, and lighting a beacon of trusted decision-making.

[0040] Example 6 Further illustrate with reference to Example 1, Figure 1 As shown, in step S5, the dynamically modified and updated report is robustly downloaded and stored locally or smoothly transmitted to the user, relying on the hybrid cloud architecture to integrate the massive resources of the public cloud and the security advantages of the private cloud, and intelligently allocate storage and transmission bandwidth according to the real-time fluctuation of user access frequency, the scale and complexity of report data; Use edge computing nodes to cache popular reports and update data fragments in real time, and use content delivery network CDN to optimize transmission paths and achieve efficient processing.

[0041] In the preferred solution, the algorithm steps for automatically updating and downloading the report in step S5 are: User access frequency and report data size and complexity , construct the following fuzzy logic bandwidth allocation mechanism: define Fuzzy subsets and membership functions of : Low Fuzzy Subset : ; Medium fuzzy subset : ; High fuzzy subset : ; definition Fuzzy subsets and membership functions of: small fuzzy subsets , medium fuzzy subset , large fuzzy subset ; Constructing fuzzy rule base: for and for ,but for There are 9 rules in total. The ratio of storage and transmission bandwidth is allocated, and its fuzzy subset is low ,middle ,high ; Fuzzy reasoning and clarification: Using Mamdani reasoning method, according to the input and Membership activation rule calculation output Fuzzy set membership, and then use the centroid method to clarify the exact bandwidth allocation ratio , for Representative value, used to intelligently allocate hybrid cloud storage and transmission bandwidth based on user access and report characteristics, improving resource utilization and transmission efficiency; In the fuzzy logic bandwidth allocation algorithm, the parameters of the membership function can be dynamically adjusted based on the statistical analysis of the system's historical operation data and performance feedback, optimizing the accuracy of the bandwidth allocation strategy to adapt to the dynamic changes in the production environment and user behavior, and ensuring smooth and stable report transmission; Edge node cache optimization algorithm: Assume that the cache capacity of the edge computing node is , cache reports and data fragment sets and its data heat value For new data : Calculating caching benefits , is the trade-off coefficient, for Size, which measures the cache value of new data; Calculating cache costs , is the cost coefficient, Evaluate the resource cost of caching new data for potential replacement datasets; like ,according to from Select Replace Element Cache , optimize edge node cache content, accelerate access to popular data, and collaborate with CDN to enhance report distribution efficiency; In the edge node cache optimization algorithm, data heat value The calculation takes into account the frequency of data access , access time interval and data update frequency ,like , is the weighting coefficient, , , To correspond to the normalized benchmark value, accurately quantify data popularity, improve the scientificity and timeliness of cache replacement decisions, and ensure that cache data meets user needs and dynamic updates of reports.

[0042] Global supply chain management report transmission example: In the global supply chain system of multinational manufacturing companies, suppliers, production bases, logistics hubs and sales outlets around the world generate massive supply chain reports every day, such as raw material inventory reports, work-in-progress reports, finished product distribution reports, sales order reports, etc., which need to be accurately transmitted to the company's global data center and regional decision-making nodes in real time or regularly.

[0043] 1. Intelligent bandwidth allocation under hybrid cloud architecture: based on the frequency of report access by users in each region (including supply chain planners, production schedulers, logistics managers, sales analysts, etc.) Real-time fluctuations and report data size complexity Differences, build a fuzzy logic bandwidth allocation system. Taking Asia as an example, during the preparation period for new product launches, sales order report access The sudden increase in order data is related to multiple product models, customer stratification, regional market policies, etc. The system has increased dramatically. The membership function accurately locates the fuzzy subset (such as )and The corresponding subset (such as ), activate the adaptation rules in the fuzzy rule base to accurately allocate high bandwidth ratio (like The interval value is calculated by Mamdani reasoning and the center of gravity method, and the bandwidth is accurately allocated from the hybrid cloud (the public cloud's massive storage resources carry the historical report big data pool, and the private cloud security fortress protects the core business sensitive data) to ensure that massive report data can instantly reach each decision-making terminal, improve resource allocation and transmission efficiency, and consolidate the foundation of supply chain decision-making timeliness.

[0044] 2. Edge node cache efficiency synergy mechanism: Deploy edge computing nodes at key logistics nodes (such as port distribution centers, intercontinental transshipment warehouses) and regional sales headquarters, and set cache capacity Managing cached report sets and heat value . New and popular raw material price fluctuation report When Weighing the benefits of caching ( According to market price sensitivity and storage cost dynamic adjustment), compared Evaluate the switching costs ( Determined by network transmission cost and cache device loss), if ,according to Intelligently swap out low-temperature report cache Heat value according to Precise quantification ( Designed based on business priorities and data timeliness, it uses edge caching of popular reports to collaborate with CDN to optimize transmission paths, accelerate the global flow of key supply chain information, improve supply chain response agility, collaborative efficiency and market competitiveness, and drive the efficient and stable operation of the global supply chain system.

[0045] The above embodiments are only preferred technical solutions of the present invention and should not be regarded as limiting the present invention. The protection scope of the present invention shall be the technical solutions recorded in the claims, including equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A method for automatically generating a report, characterized in that: The method includes: S1. Collect data and create a data folder, and preset the data in the data file into a data template; S2. Perform simple processing, calculation and formatting of data to facilitate rapid identification and comparison by the system; S3. Setting data template call rules is helpful to quickly and efficiently distinguish data with different needs; S4. Generate a report based on the data and compare it with the data template to check whether the data is wrong. If there is an error in the data, adjust the data content of the data folder and update the report data; S5. Reports are automatically updated and downloaded.

2. According to the method of automatic report generation of claim 1, the characteristics are: In S1, historical report data, updated data due to parameter modification during production, and data acquired in real time at the production site are collected to create a data folder; According to the data format of each process on the production line, quality inspection index format and other characteristics of different production links, we customize exclusive template formats and parameter requirements for the data to achieve data template preset; The simulated annealing algorithm is introduced as an auxiliary optimization method to further optimize the structure and parameter settings of the data template to assist the system in accurately identifying and processing data.

3. According to the method of automatic report generation according to claim 2, it is characterized by: introducing The steps of optimizing template by simulated annealing algorithm are: The data template structure and parameter vector are , the error measure of the system for data recognition processing is , the objective function is , aims to maximize the objective function value, that is, minimize the recognition processing error; Generate neighborhood solutions using Gaussian perturbation ,in To obey Gaussian distribution A random vector of According to the current iteration number Adaptive adjustment, the formula is: ,in is the total number of iterations, is the initial standard deviation. This formula balances the algorithm's global search and local search capabilities. In the early stage, a large step size explores a broad solution space, and in the later stage, a small step size focuses on the neighborhood of high-quality solutions. Current solution The objective function value is , neighborhood solution The objective function value is , the temperature parameter is , depending on the cooling progress Decreasing, , is the initial temperature, if or , then accept This Metropolis criterion uses a probabilistic mechanism to jump out of the local optimum, drive the algorithm to find the best solution, ensure that the template structure and parameters reach the near-optimal configuration, and improve the system data processing accuracy and efficiency.

4. The method for automatically generating a report according to claim 1, characterized in that: In step S2, a data optimization algorithm based on a genetic algorithm is introduced, and an ant colony optimization algorithm is combined to optimize resource allocation and path selection, and a fitness function is constructed; Balance production resources and reduce progress deviation through selection, crossover, mutation operations and ant colony path search iterative optimization; Calculate and format data according to production information rules.

5. The method for automatically generating a report according to claim 4, characterized in that: The algorithm steps of the above steps are: S2.1, Genetic Algorithm: Set the task volume in the production plan to The actual progress is , resource consumption vector ,in is the number of resource types; define the fitness function ,in and is the trade-off coefficient, It is a resource The ideal average consumption value; This function comprehensively considers the progress deviation and resource consumption imbalance, and guides the algorithm to optimize resource allocation to reduce the deviation; Ant colony optimization algorithm part: build a production task resource allocation network, with nodes as tasks and resources, and edge weights as Indicates the task About Resources The urgency of the demand, according to the priority of the task and remaining resources definition ,in To prevent zero constant; let ant On the path The concentration of pheromone left on , the inspiration information is ; According to the pheromone update rules ,in is the volatility rate, It's an ant In this iteration, The remaining pheromone increment is determined by the ant path selection probability, where For the task Optional resource set, and It is determined by the weight parameter, which reflects the resource allocation tendency and works with the genetic algorithm to optimize the efficiency of resource path selection; S2.2, iterative optimization operation: select genetic algorithm: according to the fitness function value Sort the individuals in the population using the tournament selection method; set the population size , each time randomly selected Individual competition ( ), the winner enters the offspring population, and the replication probability , ensure the inheritance of excellent genes and promote the evolution of the population; Crossover operation: select parent individuals and , according to the uniform crossover probability Perform uniform crossover; generate random masks ( ), offspring and element , , expand the diversity of solution space and explore potential optimal solution combinations; Mutation Operation: Individual According to the probability of mutation mutation; randomly selected sites ,set up Value range , new value , , Follows Cauchy distribution ,in is a scale parameter that helps the algorithm escape from the local optimum and maintain the exploratory nature of the population; Ant colony path search iteration: Each ant moves according to probability Select a path to build a resource allocation plan, and update the pheromone according to the path length after completing one iteration. long ,but , It is a pheromone constant that guides the ant colony to search for the best result, interacts with genetic operations to optimize the resource allocation pattern, balances production resources, and reduces progress deviations; S2.

3. Data calculation, processing and formatting: After searching for the best solution, obtain the optimal resource allocation plan and schedule adjustment strategy.

6. The method for automatically generating a report according to claim 1, characterized in that: In step S3, we deeply understand the essential differences between department data and accurately set template calling rules to differentiate data based on the core business needs and decision-making focus characteristics of each department.

7. The sales department builds a sales data panoramic report template based on sales volume, order volume, customer geographical distribution, customer purchase frequency and sales channel conversion rate; The production department designs a holographic data template for the production process based on the output, quality qualification rate, equipment operation rate, equipment failure predictive maintenance indicators and material loss rate of each link in the production process, ensuring that the department accurately obtains highly adapted data, greatly enhancing the precise guidance value of the report to the department's operations, deeply optimizing resource allocation and decision-making benefits, and driving efficient business collaboration; The particle swarm optimization algorithm is introduced to dynamically adjust the template calling rules to ensure that departments obtain adaptive data, enhance the value of reports for departmental operation guidance, and optimize resource allocation and decision-making benefits.

8. The method for automatically generating a report according to claim 1, characterized in that: In step S4, the automatically generated report is compared with the actual data report in multiple dimensions. If there is a deviation, the process returns to step S2 to recalculate and calibrate. This process uses blockchain technology and homomorphic encryption algorithms to collaboratively build an ultra-secure data traceability and verification system; The report data is assigned a hash value based on the national secret SM3 hash algorithm and stored in the blockchain. During verification, the hash value and related data integrity and source authenticity are compared in the ciphertext state through homomorphic encryption technology to accurately identify the root cause of erroneous data, and comprehensively ensure that the report truthfully, accurately and securely reflects the production and operation status, consolidates the basis for management decision-making, and builds a solid line of defense for corporate data security and the foundation of decision-making credibility.

9. The method for automatically generating a report according to claim 7, characterized in that: The algorithm in step S4 is: S4.

1. Data National Secret SM3 Hash Processing and Blockchain Storage: For each data record in the report data set ,in For the data item value, calculate its hash value based on the national secret SM3 hash algorithm SM3 algorithm internal compression function is CF, message grouping , initial value IV, the iterative calculation process is , , is the number of groups, the final hash value ; This hash value is a unique identifier for data and is used to verify data integrity. Any data tampering after being stored in the blockchain will cause changes in the hash value that can be monitored; Record the data With its hash value Composed key-value pairs , written into the specific data structure of the blockchain distributed ledger through the blockchain's smart contract interface, and a Merkle tree structure is constructed for storage. The leaf node is the data hash value, and the non-leaf node is the hash combination calculation value of the child node. The root hash value is stored in the blockchain header as the integrity authentication mark of the entire data set, ensuring that the data cannot be tampered with and can be traced, laying the foundation for subsequent verification; S4.2, Homomorphic encryption auxiliary verification process: Use homomorphic encryption algorithm to encrypt report data; set plaintext data , randomly select a large prime number and ,calculate , , select satisfy , the public key is , the private key is ; The encryption operation is ( is a random number), ciphertext Participating in calculations without leaking plaintext information; During verification, the ciphertext report data Report with ciphertext actual data Compare; Define the difference metric function ,like , If the threshold is set according to the data characteristics, data deviation is determined and needs to be traced back to the source for correction; this function uses the logarithmic operation properties of homomorphic encryption to measure data differences in the ciphertext domain, ensure data privacy during the verification process, accurately lock erroneous data, and ensure report quality and reliable decision-making.

10. The method for automatically generating a report according to claim 1, characterized in that: In step S5, the dynamically modified and updated report is robustly downloaded and stored locally or smoothly transmitted to the user. Relying on the hybrid cloud architecture to integrate the massive resources of the public cloud and the security advantages of the private cloud, the storage and transmission bandwidth are intelligently allocated according to the real-time fluctuations of the user's access frequency, the scale and complexity of the report data; Use edge computing nodes to cache popular reports and update data fragments in real time, and use content delivery network CDN to optimize transmission paths and achieve efficient processing.

11. The method for automatically generating a report according to claim 9, characterized in that: The algorithm steps for automatic report update and download in step S5 are as follows: User access frequency and report data size and complexity , construct the following fuzzy logic bandwidth allocation mechanism: define Fuzzy subsets and membership functions of : Low Fuzzy Subset : ; Medium fuzzy subset : ; High fuzzy subset : ; definition Fuzzy subsets and membership functions of: small fuzzy subsets , medium fuzzy subset , large fuzzy subset ; Constructing fuzzy rule base: "If for and for ,but for There are 9 rules in total. The ratio of storage and transmission bandwidth is allocated, and its fuzzy subset is low ,middle ,high ; Fuzzy reasoning and clarification: Using Mamdani reasoning method, according to the input and Membership activation rule calculation output Fuzzy set membership, and then use the centroid method to clarify the exact bandwidth allocation ratio , for Representative value, used to intelligently allocate hybrid cloud storage and transmission bandwidth based on user access and report characteristics, improving resource utilization and transmission efficiency; In the fuzzy logic bandwidth allocation algorithm, the parameters of the membership function can be dynamically adjusted based on the statistical analysis of the system's historical operation data and performance feedback, optimizing the accuracy of the bandwidth allocation strategy to adapt to the dynamic changes in the production environment and user behavior, and ensuring smooth and stable report transmission; Edge node cache optimization algorithm: Assume that the cache capacity of the edge computing node is , cache reports and data fragment sets and its data heat value For new data : Calculating caching benefits , is the trade-off coefficient, for Size, which measures the cache value of new data; Calculating cache costs , is the cost coefficient, Evaluate the resource cost of caching new data for potential replacement datasets; like ,according to from Select Replace Element Cache , optimize edge node cache content, accelerate access to popular data, and collaborate with CDN to enhance report distribution efficiency; In the edge node cache optimization algorithm, data heat value The calculation takes into account the frequency of data access , access time interval and data update frequency ,like , is the weighting coefficient, , , To correspond to the normalized benchmark value, accurately quantify data popularity, improve the scientificity and timeliness of cache replacement decisions, and ensure that cache data meets user needs and dynamic updates of reports.

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