System for quantifying lost years of service and the economic impact of fatal workplace accidents

Abstract

A system for the automated calculation of the loss of professional service years and the associated economic and labor market-related impairments, comprising: a processor configured to execute machine-readable instructions stored in non-volatile memory; the non-volatile storage, consisting of a hierarchical data set structure that includes demographic reference data sets, occupational pension data sets, wage development data sets, labor force survey data sets, and historical occupational transition data sets; a data acquisition unit configured to receive workplace accident reports via protected wired communication interfaces, authenticated wireless interfaces, and machine-readable optical acquisition interfaces; the processing circuit is configured to validate each received death record through redundancy checking, demographic reconstruction and attribute completion using correlation routines stored in non-volatile memory; a demographic classification unit configured to classify each death record based on age distribution records, historical occupational entry records, and survival distribution records stored in non-volatile memory; a unit for determining retirement age that is configured to retrieve a retirement age value for each classified death, corresponding to the identified occupation and aligned with the temporal context of the death; a service forecasting unit configured to calculate a probability-adjusted professional service time that would have occurred after the death by applying nonlinear progress profiles derived from the historical career transition datasets, the turnover distribution datasets, and the labor force volatility datasets stored in non-volatile memory; an aggregation unit configured to combine the calculated service periods into an aggregated value for loss of professional years of service by applying an age-dependent significance scaling based on the deviation of the age at death from the expected career mean values; an economic analysis unit configured to calculate a long-term economic loss value by propagating wage development data sets, experience-based compensation adjustments, and inflation-indexed income trajectories across the aggregate occupational service loss value; a unit for ensuring workforce sustainability, configured to calculate an attrition rate by comparing the aggregate loss of years of professional service with updated Labour Force Survey data retrieved from authenticated external registers; and a secure output unit configured to digitally sign, encrypt, and record the aggregated value of occupational year loss, the value of economic loss, and the exhaustion rate in an immutable archive area within the non-volatile storage.

Description

Technical field

[0001] The present invention relates to systems for analyzing human resources and assessing occupational safety. In particular, it relates to a system and a hardware-integrated analysis device for quantifying losses of years of service lost (YSL), economic service lost (ESL), years of productivity lost (YPL), and the workforce sustainability index (WSI) caused by fatal workplace accidents. The invention comprises machine learning pipelines, modules for structured data acquisition, embedded computing hardware, and an integrated decision support system for analyzing the economic and operational impact of premature work-related deaths on the entire workforce. Background of the invention

[0002] Existing instruments for assessing occupational safety primarily focus on evaluating injury rates, fatalities, or days lost from work, but do not capture the total years of service lost within an occupation due to fatal accidents. This document describes the conceptual formulation of the Years of Service Lost (YSL) metric for this purpose. Although this metric provides fundamental mathematical insights, no automated system, intelligent device, or industrial machine currently exists that is specifically designed to calculate YSL and its associated economic indices in real time using large datasets.Current workflows often rely on manual data collection, non-standardized actuarial estimates, and inconsistent analytical methods, hindering unified policy decisions.

[0003] Therefore, there is a need for a dedicated computer with integrated actuarial functions, sensor-based data acquisition modules, machine learning for forecasting personnel development, and cryptographically secured storage, capable of automatically and on a large scale calculating years of service loss (YSL) caused by workplace accidents and comparing this data with economic indicators. The present invention addresses these shortcomings with a novel device and system framework for quantifying years of service loss due to fatal workplace accidents. This framework integrates actuarial models, connectivity to national labor market databases, and predictive analytics into a unified hardware-software package.

[0004] The technical background of the present invention is based on the long-standing need for precise, scalable, and standardized methods for quantifying the impact of fatal workplace accidents on the workforce and the economy. To date, occupational safety analysis has focused on descriptive indicators such as accident rates, the frequency of fatal accidents per industry, the total number of days lost due to injury, and general epidemiological measures such as years of life lost (YLL). While these indicators provide some insight into the health burden on the population, they do not capture the loss of years of service within a specific occupation. This loss, however, represents the actual economic and labor market disruption caused by premature work-related deaths.Current systems for monitoring fatal workplace accidents are fragmented, spread across government labor agencies, insurance records, employer-maintained safety logs, and national health registries. These systems are largely retrospective, manually maintained, not standardized across industries, and incompatible with automated analytics workflows. Consequently, no existing solution provides an integrated, real-time, and occupation-specific analytics framework capable of calculating metrics such as Years of Service Lost (YSL), as conceptually introduced in the aforementioned document. Existing systems were not designed to quantify years of service lost due to premature death within an industry, nor to model the economic consequences that unfold over decades.

[0005] Currently, many countries rely on fatal workplace accident reporting systems that serve primarily for compliance rather than analysis. These systems merely record the death, the associated accident category, and limited demographic data. Such registers do not calculate long-term labor losses or differences in retirement age between occupational groups. Furthermore, these systems lack integrated actuarial modeling components, meaning they cannot determine how many productive years each deceased worker would have contributed had they not died prematurely. Government labor market databases occasionally attempt to calculate rough estimates of lost earnings due to fatal accidents.However, these calculations are often based on crude wage multipliers or generalized averages of labor force participation that fail to account for differences in age of entry, occupation-specific career paths, or trends toward early retirement. The lack of actuarial models capable of automatically processing heterogeneous accident statistics forces policymakers to rely on imprecise or ad-hoc estimates that do not yield precise results and preclude targeted intervention strategies.

[0006] Another significant limitation of existing tools for recording fatal workplace accidents is their inability to process data on a large scale and in real time. Reports of fatalities to occupational safety and health authorities often go through multi-stage bureaucratic processes, with validation, classification, and archiving occurring months after the incident. This structural delay hinders proactive risk mitigation strategies. Furthermore, existing systems use static databases instead of adaptive machine learning models. This makes it impossible to predict future skills shortages under various scenarios of industrial growth, demographic shifts, or changes in occupational hazards.For example, certain dashboards provide annual death figures but do not calculate how these events affect a sector's long-term capacity, nor do they integrate economic models that can predict the financial impact of losing experienced professionals in highly skilled occupations.

[0007] Current analysis tools do not offer automatic integration with actuarial life expectancy tables. Life expectancy assumptions, when used, are often manually embedded in spreadsheet-based workflows that lack audit trails, version control, and standardized calculation logic. This leads to inconsistencies, calculation errors, and methodological variations between institutions studying similar phenomena. Furthermore, conventional systems do not account for the occupational retirement ages defined in the YSL formulation from the uploaded document, as retirement ages vary considerably by industry and therefore must be directly integrated into the calculation models. Without this integration, the estimation of remaining working years becomes unreliable, particularly in high-risk sectors such as emergency services, mining, construction, and aviation.

[0008] Existing systems for handling workplace accidents are typically designed for reporting to regulatory authorities and not for in-depth economic modeling. Insurance-based systems calculate compensation payments using statutory multipliers or fixed compensation tables, ignoring dynamic wage growth, inflation-adjusted productivity curves, and the cost of replacing lost workers. These systems do not quantify Years of Productivity Lost (YPL) or Loss of Economic Services Loss (ESL)—metrics that, while conceptually described in the uploaded document, are not operationalized by existing tools.Consequently, there is no automated mechanism that can weight each death based on the economic value of future work performance, nor an algorithmic method to estimate how the loss of an employee affects the long-term availability of skilled workers in specialized industries.

[0009] Most current solutions are not linked to real-time workforce development registers. This lack of integration prevents the accurate calculation of the Workforce Sustainability Index (WSI), which requires knowledge of the entire active workforce of an occupation as well as the cumulative years of service lost. Without automated linking to national labor market datasets, the calculations are based on static and outdated occupational figures, leading to distorted sustainability estimates. Accurate WSI calculation requires synchronized, continuously updated datasets—a capability lacking in traditional workplace accident recording systems.

[0010] Technological limitations also affect data collection. Modern industries generate multimodal data, including digital fatality reports, biometric employee attendance records, and data from device-connected safety sensors. However, existing occupational safety analysis systems cannot process this diverse data. They lack powerful processing modules capable of validating and harmonizing heterogeneous datasets. This deficiency prevents the automatic reconstruction of accident time periods and age-specific accident profiles, both essential for accurately calculating life expectancy in fatal cases. Furthermore, existing systems lack formalized methods for reconstructing missing values, detecting anomalies, or harmonizing data formats.These tasks are often performed manually, leading to data loss, inconsistencies, and delays.

[0011] Even when data is available, analytical weaknesses persist. Traditional statistical tools used by occupational health and safety agencies are not optimized for the continuous execution of actuarial calculations with millions of data points. Many agencies continue to rely on spreadsheet programs or isolated statistical scripts that lack version control, distributed processing, and secure computing environments. These tools are neither suited for the complex calculation of age-weighted mortality rates across numerous demographic strata, nor can they operate efficiently with extensive temporal analyses. Consequently, the results often remain approximations rather than exact values, diminishing their value for policy planning and economic modeling.

[0012] Another drawback of the current technological infrastructure is the insufficient consideration given to secure data processing and data protection. Workplace accident and fatality datasets often contain sensitive demographic information that requires encrypted storage and controlled access. Older systems typically lack hardware-based encryption modules, trusted execution environments, or tamper-proof protocols. This compromises data integrity and exposes the entire workflow to the risk of unauthorized modifications, thereby jeopardizing the reliability of calculated indices such as YSL.

[0013] The landscape of existing solutions is characterized by fragmentation, manual workflows, delayed processing, low analytical granularity, and the lack of standardized computational platforms. These limitations collectively prevent an accurate assessment of the true impact of fatal workplace accidents on the workforce. The technical background clearly demonstrates a continued significant need for an integrated, automated, AI-powered system and dedicated hardware capable of accurately, scalably, and in real time calculating YSL (Years of Loss of Workplace) and derived metrics. Such an invention would bridge the technological gap between fatal workplace accident reporting systems and advanced economic labor market modeling, opening up fundamentally new possibilities for governments, industry, and safety agencies. Summary of the invention

[0014] The invention relates to an AI-powered actuarial calculation device (AACD) comprising a multi-core processor unit, a structured data acquisition subsystem, an integrated workforce development forecasting engine, an economic impact modeling module, and a secure interface for visualizing and exporting results. The device is configured to receive workplace accident statistics, continuously updated age-based personnel records, occupational retirement ages, and actuarial mortality tables, and to automatically calculate the "Years of Lost Work" (YSL) indicator according to the original wording in the referenced document. The system also converts the YSL values ​​into secondary indicators such as "Loss of Economic Services" (ESL), "Years of Lost Productivity" (YPL), and the "Workforce Sustainability Index" (WSI).

[0015] The invention also utilizes a machine learning system trained to predict future changes in workforce sustainability and economic impacts under different accident patterns. The hardware device features a modular slot that allows integration with biometric sensors for employee identification, personnel tracking systems, and workplace accident reporting systems, thus enabling fully automated, real-time collection of accident and demographic data.

[0016] The present invention aims to create a unified, automated, and standardized framework for quantifying the labor market and economic policy consequences of fatal workplace accidents and to provide a dedicated hardware and software infrastructure capable of performing such calculations in real time. One objective of the invention is the development of a fully integrated system for calculating the Years of Service Lost (YSL) metric using validated actuarial principles. This eliminates the reliance on fragmented manual calculations and spreadsheet-based models. The direct integration of actuarial models into the system architecture is intended to ensure the methodological consistency of YSL calculations across industries, thereby enabling objective comparisons, benchmarking, and long-term analyses of workforce development.Another objective of the invention is the integration of derived indicators such as Economic Service Loss (ESL), Years of Productivity Loss (YPL), and Workforce Sustainability Index (WSI) into a single automated pipeline. This transforms raw workplace accident data into comprehensive economic indicators that can be used by policymakers, industry regulators, and labor market economists for risk mitigation and strategic workforce planning.

[0017] A further objective of the invention is the real-time acquisition and processing of data on fatalities and employee demographics from various heterogeneous sources, including company servers, workplace accident reporting systems, on-site monitoring systems, and biometric employee registers. This enables the system to generate timely and accurate impact assessments immediately after fatal accidents and reduces the latency between data acquisition and analysis. The invention also aims to provide a hardware device that can be deployed at industrial sites or in occupational health services and can perform YSL, ESL, YPL, and WSI calculations on-site without external IT infrastructure. Such a device ensures operational continuity even in locations with limited or intermittent cloud connectivity, thereby improving the accessibility, reliability, and portability of workplace accident analysis.

[0018] Another key objective is the integration of machine learning models into the system to predict future labor market disruptions, training needs, and economic burdens based on historical patterns, demographic shifts, sectoral growth trends, and emerging risk environments. By generating predictive insights instead of purely descriptive statistics, the invention contributes to the advancement of occupational health risk analysis and supports proactive policy development. A further objective of the invention is to ensure the secure, verifiable, and tamper-proof calculation of all key performance indicators. The system attempts to achieve this through hardware-based encryption, secure execution zones, digital signature mechanisms, and immutable audit logs, thereby guaranteeing a high degree of data integrity and analytical authenticity.

[0019] Furthermore, the invention aims to standardize the methodology for recording year-of-service losses and their economic impact. By integrating a unified export framework that generates encrypted reports, machine-readable data outputs, and legally compliant formats, the system ensures seamless interoperability with national databases for occupational safety, insurance systems, and economic modeling platforms. Another objective is to reduce the cognitive and operational workload for analysts and safety officers through automated validation, error detection, and data harmonization. This minimizes human error and improves the accuracy of fatality assessments.The invention also promotes consistency in the assessment of workforce sustainability by ensuring that all key performance indicators are calculated using synchronized and continuously updated workforce registers.

[0020] The invention ultimately aims to improve the quality of decisions in the field of occupational health and safety by providing a platform that can immediately quantify staff reductions, calculate long-term economic consequences, and generate needs-based recommendations for action. By combining actuarial calculations, AI-supported forecasting, secure data management, and robust hardware integration, the invention achieves its goal of developing the analysis of fatal workplace accidents from a fragmented, manual estimation to a rigorous, automated, and scientifically sound discipline.

[0021] Below is a newly formulated set of highly technical, detailed system claims, written entirely in paragraph form and strictly avoiding the terms "engine," "module," "platform," "framework," abbreviations, or formulas. The claims use permissible terms such as processor, circuit, unit, and memory. All dependent claims are narrowly defined, implementation-specific, and detailed, and, where necessary, relate exclusively to one system. Brief description of the image

[0022] These and other features, aspects and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols represent the same parts: Fig. Figure 1 shows a block diagram of a system for determining years of service lost and the corresponding economic impact of fatal workplace accidents.

[0023] Furthermore, those skilled in the art will recognize that the elements in the drawing are simplified and not necessarily drawn to scale. For example, the flowcharts illustrate the process by highlighting the main steps to facilitate understanding of the present disclosure. With regard to the construction of the device, one or more components may be represented in the drawing by conventional symbols. The drawing may show only the specific details relevant to understanding the embodiments of the present disclosure, so as not to clutter the drawing with details that are already apparent to those skilled in the art from the description contained herein. Detailed description of the invention

[0024] To facilitate understanding of the principles of the invention, reference is made below to the embodiment shown in the drawing, which is described using specific terms. It is understood, however, that this does not limit the scope of protection of the invention. Rather, modifications and further developments of the depicted system, as well as further applications of the inventive principles shown therein, are conceivable, insofar as they would normally occur to a person skilled in the art in the field of the invention.

[0025] It will be clear to those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not to be understood as a limitation of it.

[0026] References to “an aspect”, “another aspect”, or similar phrases in this description mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, phrases such as “in one embodiment”, “in another embodiment”, and similar expressions in this description may, but do not necessarily, all refer to the same embodiment.

[0027] The terms "includes," "comprehensive," or similar expressions denote non-exclusive inclusion. Thus, a procedure or method containing a list of steps does not only include those steps but may also include further steps not explicitly listed or inherent in the procedure or method. Likewise, the statement "includes..." for one or more devices, subsystems, elements, structures, or components, without further limitations, does not preclude the existence of other devices, subsystems, elements, structures, or components.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meanings generally known to those skilled in the art in the field to which this invention belongs. The systems, methods, and examples described herein serve only for illustration and are not to be understood as limiting.

[0029] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0030] Fig.Figure 1 shows a block diagram of a computer-based system for determining lost years of service and the corresponding economic consequences of fatal workplace accidents. The system 100 comprises: a processor (102) for executing machine-readable instructions from permanent memory; the permanent memory (104) with a hierarchical data structure (104a) including demographic reference datasets, occupational pension datasets, wage development datasets, labor force survey data, and historical data on career transitions; a data acquisition unit (106) for receiving workplace accident datasets via secure wired communication interfaces, authenticated wireless interfaces, and machine-readable optical interfaces;a processing circuit (108) for validating each received accident record by redundancy check, demographic reconstruction, and attribute completion using correlation routines from persistent memory; a demographic classification unit (110) configured to classify each death record based on age distribution records, historical occupational entry records, and survival distribution records stored in non-volatile memory; a retirement age determination unit (112) configured to retrieve, for each classified death record, a pension value corresponding to the identified occupation and aligned with the temporal context of the death;a service-time prediction unit (114) configured to calculate a probability-adjusted professional service length that would have occurred after the death event by applying nonlinear progress profiles derived from the historical career transition datasets, turnover distribution datasets, and labor force volatility datasets stored in non-volatile memory; an aggregation unit (116) configured to combine the calculated service lengths into an aggregated value for lost professional service years by applying an age-dependent significance scaling based on the deviation of the age at death from the expected career means;An economic analysis unit (118) configured to calculate a long-term economic loss value by extrapolating wage development datasets, experience-based pay adjustments, and inflation-indexed income trajectories to the aggregate loss value of years of professional service; a labor force security unit (120) configured to calculate a rate of exhaustion by comparing the aggregate loss value of years of professional service with updated labor force survey datasets retrieved from authenticated external registers; and a secure output unit (122) configured to digitally sign, encrypt, and store the aggregate loss value of years of professional service, the economic loss value, and the rate of exhaustion in an immutable archive area within non-volatile storage.

[0031] In one embodiment, the processing circuit (108) comprises a multi-stage validation circuit configured to detect anomalies in incoming death records by performing field-wise consistency checks against demographic thresholds, occupational attribute ranges, and historical age distribution limits stored in non-volatile memory; wherein the processing circuit further reconstructs incomplete death records by applying probabilistic inference routines that correlate missing occupational identifiers or demographic attributes with neighboring occupational clusters and historical survival histories; and wherein the processor forwards each death record that fails threshold validation to a protected quarantine buffer within the volatile memory area for controlled exclusion from subsequent computation.

[0032] In one embodiment, the demographic classification unit (110) comprises a hierarchical classification circuit configured to generate a demographic state vector containing an estimated deviation in career entry, an expected occupational maturity level, and a long-term occupational longevity probability; wherein the classification circuit further applies temporal alignment by matching the time of death with period-specific demographic profiles stored in the persistent data set area; and wherein the processor transmits the demographic state vector to the retirement age determination unit to refine the selection of the occupational pension value according to historical and country-specific employment conditions.

[0033] In one embodiment, the pension determination unit (112) retrieves pension values ​​from country-specific tables stored in the persistent data set area; wherein the pension determination unit applies time selection routines that compare the date of death with the pension provisions in force at that time; and wherein the pension determination unit further adjusts the pension value by applying occupational deductions representing early retirement allowances, extensions of the late retirement period, and variations in the long-term disability pension associated with the occupational identifier.

[0034] In one embodiment, the service time prediction unit (114) comprises a time-sequencing circuit configured to calculate probability-adjusted estimates of professional service by applying age-dependent promotion probabilities, region-specific indicators of job stability, labor force volatility parameters, and turnover rate histories; wherein the service time prediction unit performs a multi-stage smoothing routine to remove high-frequency fluctuations caused by demographic irregularities; and wherein the processor further adjusts the calculated service time by applying long-term labor force participation probabilities derived from decades-long datasets stored in non-volatile memory.

[0035] In one embodiment, the aggregation unit (116) retrieves significance scaling parameters corresponding to the age-stratified career loss values ​​stored in the persistent data set area; wherein the aggregation unit applies a weighted accumulation procedure that gives greater significance to death records with larger predicted unrealized years of service; and wherein the aggregation unit performs a normalization pass across aggregated occupational cohorts to generate a cohort-comparable aggregated value for loss of years of professional service.

[0036] In one embodiment, the economic analysis unit (118) comprises a circuit for calculating compensation development, configured to retrieve long-term wage development curves, promotion probabilities to higher experience levels, and inflation-adjusted earnings histories from non-volatile memory; wherein the economic analysis unit calculates a predicted financial loss histories for each death by extrapolating the wage development curves over the probability-adjusted working life; and wherein the processor integrates the individual financial loss histories into a single long-term economic loss value by applying multi-period summation routines and compensation weighting factors representing the expected differences in career advancement.

[0037] In one embodiment, the labor force security unit (120) retrieves occupation-related data from the labor force survey from authenticated external registers by means of cryptographically validated communication sessions; wherein the labor force security unit performs multi-period smoothing to compensate for short-term fluctuations and anomalies in the labor force reports; and wherein the labor force security unit calculates the exhaustion rate by applying scarcity adjustment coefficients corresponding to long-term occupational demand, regional availability of skilled workers, and labor replacement delay values.

[0038] In one embodiment, the secure output unit (122) comprises a cryptographic signature circuit configured to use a hardware-embedded private key stored in a protected execution enclave of the processor to digitally sign the aggregated value of occupational year loss, the value of economic loss, and the wear ratio; wherein the secure output unit encrypts the signed outputs using multi-layered encryption methods stored in the persistent record space; and wherein the secure output unit writes the encrypted outputs to the immutable archive space using sequential, append-only memory operations to prevent modification of previously recorded outputs.

[0039] In one embodiment, the hierarchical storage structure (104a) comprises a volatile computation area for real-time processing of death records, a persistent record area for storing demographic, occupational, and economic records, and an immutable archive area for secure long-term archiving of computation results; wherein the immutable archive area is implemented by means of a write-once memory circuit that prevents the overwriting of stored values; and wherein the processor assigns unique, cryptographically verifiable timestamps to each archive entry to ensure a complete chronological computation history.

[0040] Years of Service Lost (YSL) for a given occupation are calculated by adding up the deaths that occur at each age between 18 (the statutory minimum age for employment) and the retirement age set for that occupation or industry, weighting each death by the number of years of service remaining until retirement.

[0041] It should be noted that the upper limit roughly corresponds to the retirement age for the respective profession, although any age limit could be applied. Deaths before the age of 18 are excluded, as they are generally linked to illegal child labor.

[0042] For example, if there were ten deaths in the age of 30, the YSL contribution for this age group due to workplace accidents would be as follows: Number of deaths at age 30 x number of years lost if each individual had retired at age 65 = 10 x 35 years = 350 years

[0043] This calculation is performed for fatal workplace accidents in this industry for each age group up to retirement age for the respective occupation, and the results are then added together. Mathematically, these calculations can be summarized as follows: YSL=∑(ai di) from i=18 to 64

[0044] Where: i = each age group from 18 to 65 years (retirement age) ai = number of years remaining until age 65 if deaths occur between age i and i+1 di = number of observed deaths in the studied population between age i and i+1

[0045] This allows for an estimate of how many years of professional or service are lost due to premature work accidents resulting in death. Other variants regarding the economic impact: a. Loss of economic services (ESL): ESL=YSL×W Here, W is the average annual wage of the affected workforce. This measures the total economic wage loss due to premature workplace accidents resulting in death. b. Years of Productivity Loss (YPL):

[0046] If we take into account investments in workforce training and productivity levels, we can weight YSL by productivity loss to reflect the skills shortage.

[0047] This new indicator would be valuable for economic policy, workforce planning, and investments in occupational safety. c. Workforce Sustainability Index (WSI) WSI=H−YSL / H

[0048] Here, H is the total number of employees in the respective workforce. This represents a measure of workforce sustainability and shows the proportion of remaining years of service after subtracting years of service lost.

[0049] The key performance indicator "Years of Fatality (YSL (EMS))" provides a concrete measure of the impact of fatal workplace accidents on emergency medical service personnel and illustrates the significant loss of years of service and the associated economic costs. This underscores the importance of targeted safety measures and support systems to protect skilled workers in all sectors and to mitigate these losses.

[0050] The system comprises tangible, hardware-based components implemented by physical electronic circuits, wherein the processor includes a hardware-implemented instruction execution pipeline configured to retrieve, decode, and execute the machine-readable instructions stored in non-volatile memory, and the non-volatile memory comprises semiconductor-based persistent memory arranged to physically encode the hierarchical data record structure; the data acquisition unit comprises hardware communication circuits configured for electrical interface via protected wired ports, radio frequency transceivers for authenticated wireless links, and optical sensor arrays for machine-readable optical data acquisition, each interface being implemented by means of discrete analog and digital circuits; the processing circuits,The demographic classification unit, the retirement age determination unit, the service time forecasting unit, the aggregation unit, the economic analysis unit, and the workforce development assurance unit each comprise dedicated hardware logic instantiated as microelectronic processing sub-circuits. These include arithmetic units, data bus controllers, and field-programmable or application-specific logic gates configured to perform the respective validation, classification, retrieval, forecasting, aggregation, calculation, and comparison operations through physical signal transformations. The secure output unit comprises cryptographic hardware implementing encryption and digital signature generation using embedded cryptographic accelerator circuits and non-volatile memory write controllers configured tothat they physically record data in an immutable archiving area. Each operational unit is electrically interconnected via hardware buses and signal paths, enabling direct electronic data exchange without dependence on a remote computing environment, so that every function of the system is performed by physical computing hardware that works with stored electronic representations of data.

[0051] The present invention relates to a computer-based system for generating quantified assessments of lost years of service, corresponding economic impacts, and measures to ensure workforce availability based on workplace accident statistics. The system consists of specialized hardware units, processing circuits, and algorithmic methods that together enable a highly accurate forecast of unrealized years of service and financial losses. The system operates with a computing infrastructure comprising a central processing unit and non-volatile memory. The memory stores instructions which, when executed by the processor, trigger the computational operations described herein. The architecture is designed for processing large occupational datasets, heterogeneous data inputs, and dynamically changing pension and wage structures.As a result, the invention delivers precise and highly granular results.

[0052] During operation, the system receives death records via the data receiving unit. Each death record consists of structured fields, including at least the age at death, an occupational classification identifier that refers to a profession or sector, and a timestamp indicating the date of death. The data receiving unit is configured to process multi-channel input, and the central processor initiates a redundancy check if multiple channels report overlapping records. The system also applies context reconstruction algorithms stored in non-volatile memory to complete partially missing fields. These algorithms work by identifying similarity clusters based on record attributes and selecting the most probable occupational or demographic value using a distance-weighted match derived from historical records stored in memory.

[0053] Once the system has created a verified and complete death record, the central processor transfers it to the demographic interpretation unit. This unit applies a hierarchical classification procedure, assigning the deceased person's demographic profile to standardized, memory-stored categories. These categories include age distribution tables, career progression profiles, expected age of entry into the labor market, and survival patterns related to specific occupational groups. The demographic interpretation unit generates a vector that maps various dimensions of career life expectancy. This vector forms the basis for subsequent calculations, particularly for the precise length of service estimate by the length of service estimation unit.

[0054] The system forwards the demographic interpretation vector and the death record to the interpretation unit responsible for retirement planning. This unit retrieves an occupation-specific retirement threshold from permanent storage, corresponding to the occupational classification in the death record. The search process includes a time-alignment logic that selects the retirement threshold based on the legal provisions in effect at the time of death. If the underlying record contains multiple historical versions for the occupation in question, the system interpolates the threshold or selects it based on the value closest to the date of death. This ensures that the calculated length of service reflects the actual employment conditions at the time of death and not current or future standards.

[0055] Upon reaching the retirement age, the system transmits all relevant parameters to the service length estimation unit. There, the central processor executes numerical procedures that estimate the expected duration of employment the individual would have performed had they not died. The algorithm first calculates the raw range between the age at death and the retirement age. This raw range is then subjected to nonlinear adjustment routines that consider the probability of career interruptions. These include job changes, industry volatility, regional unemployment patterns, expected training intervals, and historical turnover rates.For example, if a profession is characterized by high turnover or irregular work cycles, the algorithm applies a career fragmentation factor derived from stored historical transition probabilities. This transforms the raw range into a more realistic prediction of actual length of service. The system also applies time smoothing using time series models trained on historical labor market data. The result is a refined forecast of length of service that reflects realistic career participation.

[0056] After determining the expected length of service for a single death, the processor passes this value to the cumulative service loss quantification unit. This unit aggregates the expected lengths of service for all records belonging to a user-selected occupation or time period. The aggregation is not a simple arithmetic sum; rather, the system applies age-weighted significance coefficients that vary depending on the age at death relative to the entry and retirement thresholds. These coefficients, stored in permanent memory, give greater weight to earlier deaths, as these result in a greater loss of potential career path. The aggregation algorithm involves iterative summation with coefficient application at each step, followed by a normalization phase that ensures the consistency of the aggregated results across datasets of varying sizes.The resulting aggregate loss of service time represents the total number of years of service lost due to the recorded deaths.

[0057] To determine the financial impact of lost years of service, the system uses the Economic Consequences Unit. This unit utilizes time-indexed earnings histories stored in permanent memory. These histories include annual base pay values, experience-based salary increases, probabilities of transitioning between qualification levels, and inflation-adjusted, occupation-specific growth factors. The processor calculates the financial effect by multiplying the projected years of lost service by the corresponding earnings histories, applying a growth adjustment logic based on the occupation's historical salary development. For occupations with tiered salary structures, the system distributes the projected years of lost service across the likely career levels using a probabilistic level allocation procedure.This ensures that the projected financial impact fully reflects the effects of missed promotions and skills development. The result is an integrated estimate of the economic consequences, representing the total financial loss associated with the aggregated years of service lost.

[0058] The system continues calculating the workforce balance, which determines the sustainability value of the workforce. The central processor retrieves an updated dataset of the labor force from permanent memory, containing the number of active employees in each occupation. Where available, the system regularly synchronizes this dataset with external labor market registers via authenticated communication channels. The processor compares the aggregated number of attrition figures with the number of active employees and calculates a sustainability value representing the proportion of potential employment capacity lost due to deaths. The algorithm considers both absolute employment figures and proportional impacts on the workforce derived from multi-year data trends.This means that the sustainability measure reflects not only the immediate loss of employees, but also the long-term consequences for workforce stability and recruitment needs.

[0059] Once all calculations are complete, the processor calls the secure output generation unit. This unit hardware-signs the generated outputs with cryptographic data using a dedicated private key stored in a secure execution area of ​​the processor. The output generation algorithm formats the aggregated values ​​for service outages, financial impact, and sustainability metrics into encrypted, tamper-proof data structures suitable for regulatory submissions, policy planning, and archiving. The system stores each final output in the immutable archive area of ​​non-volatile memory using append operations to ensure that previous calculations cannot be overwritten or altered.The archive structure also stores metadata, including calculation timestamps, data source identifiers, and verification status, for traceability and auditability.

[0060] The invention further comprises additional algorithmic layers executed by predictive computing circuits within the processor. These layers utilize statistical learning methods to generate forecasts of future workplace accidents and economic burdens. The predictive algorithms analyze historical accident statistics, demographic changes, wage development patterns, labor supply trends, and industry growth forecasts, all of which are stored in permanent memory. They generate multi-year forecasts that support decision-makers and human resources managers in long-term planning and the development of measures. The forecasts are also stored in an immutable archive layer, thus ensuring chronological documentation of the development of labor market risks.

[0061] Throughout the entire process, the system applies validation algorithms via the validation circuit. These algorithms compare demographic interpretation vectors with historical validity ranges stored in memory. If inconsistencies or anomalies are detected—such as implausible age information, discrepancies in job titles, or data conflicts—the system isolates the affected records and excludes them from further processing. Notifications about data inconsistencies are provided in the final secure output to ensure transparency regarding data integrity.

[0062] The system's comprehensive hardware-software integration, combined with the advanced algorithmic methods described herein, enables the automated, highly accurate, and secure calculation of lost years of service, economic impact, and key performance indicators for workforce sustainability based on data from fatal workplace accidents. The detailed algorithmic steps, multi-stage validation procedures, and integrated cryptographically authenticated output ensure that the invention provides reliable, verifiable, and politically relevant workforce impact assessments.

[0063] The drawing and the preceding description illustrate embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another. For example, the process flows described here can be modified and are not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the sequence shown; nor do all actions necessarily need to be carried out. Actions that do not depend on other actions can be performed in parallel with the other actions. The scope of protection of the embodiments is in no way limited by these specific examples. Numerous variations, whether explicitly stated in the description or not, such as...Differences in structure, dimensions, and materials are possible. The scope of protection of the embodiments is at least as comprehensive as described by the following claims.

[0064] The advantages, other benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and any components that can effect or enhance an advantage, benefit, or solution are not to be construed as critical, necessary, or essential features or components of the claims. REFERENCES 100 A computer-aided system for determining the years of service lost due to fatal workplace accidents and the associated economic impact. 102 processor 104 Non-transient memory 104a Hierarchical data set structure 106 Data acquisition unit 108 Processing circuit 110 Demographic classification unit 112 Units for Determining the Age Limit 114 Service projection unit 116 Aggregation unit 118 Economic Analysis Unit 120 Units for Workforce Sustainability 122 Secure Dispensing Unit

Claims

[1] A system for the automated calculation of the loss of professional service years and the associated economic and labour market impairments, comprising: a processor configured to execute machine-readable instructions stored in non-volatile memory; the non-volatile storage, consisting of a hierarchical data set structure that includes demographic reference data sets, occupational pension data sets, wage development data sets, labor force survey data sets, and historical occupational transition data sets; a data acquisition unit configured to receive workplace accident reports via protected wired communication interfaces, authenticated wireless interfaces, and machine-readable optical acquisition interfaces; the processing circuit is configured to validate each received death record through redundancy checking, demographic reconstruction and attribute completion using correlation routines stored in non-volatile memory; a demographic classification unit configured to classify each death record based on age distribution records, historical occupational entry records, and survival distribution records stored in non-volatile memory; a unit for determining retirement age that is configured to retrieve a retirement age value for each classified death, corresponding to the identified occupation and aligned with the temporal context of the death; a service forecasting unit configured to calculate a probability-adjusted professional service time that would have occurred after the death by applying nonlinear progress profiles derived from the historical career transition datasets, the turnover distribution datasets, and the labor force volatility datasets stored in non-volatile memory; an aggregation unit configured to combine the calculated service periods into an aggregated value for loss of professional years of service by applying an age-dependent significance scaling based on the deviation of the age at death from the expected career mean values; an economic analysis unit configured to calculate a long-term economic loss value by propagating wage development data sets, experience-based compensation adjustments, and inflation-indexed income trajectories across the aggregate occupational service loss value; a unit for ensuring workforce sustainability, configured to calculate an attrition rate by comparing the aggregate loss of years of professional service with updated Labour Force Survey data retrieved from authenticated external registers; and a secure output unit configured to digitally sign, encrypt, and record the aggregated value of occupational year loss, economic loss, and exhaustion rate in an immutable archive area within non-volatile storage. [2] System according to claim 1, wherein the processing circuit comprises a multi-stage validation circuit configured to detect anomalies in incoming death records by performing field-wise consistency checks against demographic thresholds, occupational attribute ranges, and historical age distribution boundaries stored in non-volatile memory; wherein the processing circuit further performs a reconstruction of incomplete death records by applying probabilistic inference routines that correlate missing occupational identifiers or demographic attributes with neighboring occupational clusters and historical survival histories; and wherein the processor forwards each death record that fails threshold validation to a protected quarantine buffer within the volatile memory area for controlled exclusion from subsequent computation. [3] System according to claim 1, wherein the demographic classification unit comprises a hierarchical classification circuit configured to generate a demographic state vector containing an estimated deviation in career entry, an expected occupational maturity level, and a long-term occupational longevity probability; wherein the classification circuit further applies temporal alignment by matching the time of death with period-specific demographic profiles stored in the persistent data set area; and wherein the processor transmits the demographic state vector to the retirement age determination unit to refine the selection of the occupational pension value in accordance with historical and country-specific employment conditions. [4] System according to claim 1, wherein the service time prediction unit comprises a circuit for calculating probability-adjusted estimates of professional service by applying age-dependent promotion probabilities, region-specific indicators of job stability, labor force volatility parameters, and turnover rate histories; wherein the service time prediction unit performs a multi-stage smoothing routine to remove high-frequency fluctuations caused by demographic irregularities; and wherein the processor further adjusts the calculated service time by applying long-term labor force participation probabilities derived from decades-long datasets stored in non-volatile memory. [5] System according to claim 1, wherein the aggregation unit retrieves significance scaling parameters corresponding to age-stratified career loss values ​​stored in the persistent data set space; wherein the aggregation unit applies a weighted accumulation method that gives greater weight to death records with longer predicted unrealized service years; and wherein the aggregation unit performs a normalization pass across aggregated occupational cohorts to generate a cohort-comparable aggregated value for loss of occupational years of service. [6] System according to claim 1, wherein the hierarchical storage structure comprises a volatile computation area for real-time processing of death records, a persistent record area for storing demographic, occupational, and economic records, and an immutable archiving area for secure long-term archiving of computational results; wherein the immutable archiving area is implemented by means of a write-once memory circuit that prevents the overwriting of stored values; and wherein the processor assigns unique, cryptographically verifiable timestamps to each archive entry to ensure a complete chronological computational history.

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