Liquid tissue quantification assessment and adaptive intervention system and method

Abstract

This invention discloses a quantitative assessment and adaptive intervention system and method for fluidized tissue, belonging to the fields of enterprise management informatization and artificial intelligence technology. The system includes: a data acquisition module for acquiring role change logs, reporting relationship change logs, and process instance generation logs; a DRSF calculation unit for calculating the dynamic role switching frequency; an RSI calculation unit for calculating the reporting relationship stability index; a PGA calculation unit for calculating the process generation autonomy; a comprehensive index generation module for standardizing and weighting DRSF, RSI, and PGA to generate a comprehensive fluidized tissue index (LOI); and an intervention recommendation module for automatically generating organizational intervention suggestions when the indicators exceed thresholds. This invention proposes for the first time three quantifiable organizational fluidization assessment indicators: DRSF, RSI, and PGA, achieving multi-dimensional comprehensive diagnosis of organizational fluidity and automatically triggering interventions based on thresholds to form a closed-loop optimization. This invention can be embedded into existing enterprise management systems, providing a quantitative tool for digital transformation.

Description

Technical Field

[0001] This invention belongs to the field of enterprise management informatization and artificial intelligence technology, specifically involving an organizational fluidity assessment system based on multi-dimensional quantitative indicators, and a method for automatically triggering organizational intervention measures based on the assessment results. Background Technology

[0002] Traditional enterprise management systems (such as OA, BPM, and HRM) are primarily designed around fixed job responsibilities, pre-defined approval processes, and stable reporting relationships. While these systems were highly efficient in the industrial era, in the digital economy, enterprises face frequent organizational adjustments, cross-departmental collaborations, and dynamic task allocation, making the rigidity of traditional systems increasingly apparent.

[0003] In recent years, concepts such as "liquid organization," "agile organization," and "boundaryless organization" have emerged to describe new organizational forms characterized by high adaptability, dynamic roles, hierarchical flexibility, and adaptive processes. However, current technologies cannot quantitatively assess the degree of "liquidity" in an organization, leaving managers to rely on subjective experience to judge whether the organization is too rigid or too chaotic. More importantly, existing systems lack the ability to automatically trigger optimization interventions based on quantitative indicators, resulting in organizational transformation relying on manual analysis and decision-making, which is inefficient.

[0004] Some patents involve "dynamic process orchestration" (such as CN112132530A), but they only focus on the issue of "how to configure process templates in a visual way" and do not combine it with role fluidity and hierarchical flexibility. Therefore, there is an urgent need for a technical solution that can comprehensively assess the fluidity of organizational roles, hierarchies, and processes and automatically provide intervention suggestions. Summary of the Invention

[0005] 3.1 Technical problems to be solved

[0006] The present invention aims to solve the technical problems of existing technologies that cannot quantify the degree of tissue liquefaction, cannot automatically identify abnormal tissue fluidity, and cannot adaptively trigger tissue intervention measures.

[0007] 3.2 Technical Solution

[0008] This invention provides a system for quantitative assessment and adaptive intervention of liquid tissue, comprising:

[0009] The data acquisition module is configured to retrieve the following three types of log data from the enterprise management system:

[0010] • Role Change Log: Records changes in an employee's job responsibilities or task type switching within a given time period;

[0011] • Reporting Relationship Change Log: Records changes in the formal reporting relationship between an employee and their superior;

[0012] • Process Instance Generation Log: Records how business process instances are generated (generated from a preset process template or dynamically generated by AI).

[0013] The indicator calculation module includes:

[0014] • DRSF calculation unit, used to calculate Dynamic Role Switching Frequency (DRSF) based on the role change log;

[0015] • RSI calculation unit, used to calculate the reporting relationship stability index (RSI) based on the reporting relationship change log;

[0016] • PGA calculation unit, used to calculate process generation autonomy (PGA) based on the process instance generated logs.

[0017] The comprehensive index generation module is used to standardize the DRSF, RSI, and PGA and then weight and fuse them to generate the liquid tissue comprehensive index (LOI).

[0018] The intervention recommendation module is used to monitor whether the DRSF, RSI, PGA or Liquid Tissue Composite Index (LOI) exceed the preset threshold range. When the threshold is exceeded, tissue intervention suggestions are automatically generated.

[0019] The proposed organizational interventions include, but are not limited to: deploying more digital employees to alleviate role pressure, adjusting reporting relationships, increasing the autonomy of AI process generation, and initiating organizational network analysis for specific departments.

[0020] Preferably, the formula for calculating DRSF is:

[0021]

[0022] Where N is the total number of employees, and T is the number of time periods within the time window. This is an indicator function.

[0023] Preferably, the formula for calculating RSI is:

[0024]

[0025] Preferably, the formula for calculating the PGA is:

[0026]

[0027] Preferably, the standardization process uses Min-Max module normalization and weight coefficient configuration module, with the default weights of DRSF, RSI, and PGA being 0.3, 0.3, and 0.4, respectively.

[0028] Preferably, the intervention recommendation module is further configured as follows:

[0029] • When DRSF exceeds the first threshold, it is determined that the position is overloaded, and it is recommended to split responsibilities or add digital staff;

[0030] • When the RSI is above the second threshold, the reporting relationship is considered too volatile, and it is recommended to fix the core reporting line.

[0031] • When the RSI is below the third threshold, the organization is considered too rigid, and it is recommended to introduce project-based floating reporting.

[0032] • When the PGA is below the fourth threshold, the ability to adapt the judgment process is insufficient, and it is recommended to introduce an AI dynamic process generation engine.

[0033] Preferably, the system further includes a feedback verification module, which is used to automatically re-collect data and calculate LOI changes after the intervention measures are implemented, forming a closed loop of "measurement-diagnosis-intervention-remeasurement".

[0034] 3.3 Beneficial Effects

[0035] 1. Quantitative assessment of tissue fluidization: For the first time, three operational quantitative indicators, DRSF, RSI, and PGA, are proposed, transforming the abstract concept of tissue fluidity into calculable and comparable technical parameters.

[0036] 2. Multi-dimensional comprehensive diagnosis: It integrates three independent dimensions: role, level, and process, which overcomes the one-sidedness of single indicator evaluation and improves the accuracy of diagnosis.

[0037] 3. Automated intervention loop: Automatically triggers optimization suggestions based on indicator thresholds, reducing manual analysis costs and accelerating organizational transformation response speed.

[0038] 4. Highly scalable: It can be combined with technologies such as Organizational Network Analysis (ONA) to achieve deeper organizational health diagnosis.

[0039] 5. Industrial applicability: It can be directly embedded into existing OA, BPM, and HRM systems, providing quantitative tools for enterprise digital transformation. Attached Figure Description

[0040] Figure 1 System structure block diagram of the present invention

[0041] Figure 2 DRSF calculation process diagram

[0042] Figure 3 RSI calculation flowchart

[0043] Figure 4 PGA calculation process diagram

[0044] Figure 5 Flowchart for the generation and intervention decision-making of the comprehensive index of liquid tissue

[0045] Figure 6 Closed-loop feedback verification flowchart Detailed Implementation

[0046] Example 1: Simulated Deployment by an Example Technology Company

[0047] like Figure 1 As shown, the liquid tissue quantitative assessment and adaptive intervention system of the present invention includes: a data acquisition module (100), a DRSF calculation unit (200), an RSI calculation unit (300), a PGA calculation unit (400), a comprehensive index generation module (500), an intervention recommendation module (600), and a feedback verification module (700).

[0048] Reference Figure 1 The structural diagram shown is used as an example to simulate deployment in a technology company, assuming the company has approximately 150 employees. The system follows... Figures 2-4 The indicator calculation process shown simulates continuous operation for 12 months with data collection and maintenance on a weekly basis.

[0049] Initial state: such as Figure 2 As shown, the system calculates DRSF = 0.3 times / week by collecting role change logs; Figure 3 As shown, the RSI (Relative Strength Index) was calculated to be 0.05 (extremely low volatility) by collecting and reporting relationship change logs; Figure 4 As shown, the PGA is calculated to be 0.1 by generating logs from the data collection process instance (approximately 90% of the processes use a preset template). Subsequently, as... Figure 5 As shown in steps 501-502, the system performs Min-Max standardization on the three indicators and then weights and fuses them to generate LOI=0.25, indicating that the organization is in an overly rigid state.

[0050] Intervention trigger: Refer to Figure 5 In the decision-making process, the system detected that the RSI was below the third threshold (0.1). In step 504, it matched the intervention rule and automatically recommended: 'The stability of the reporting relationship is too high. It is recommended to introduce project-based floating reporting to increase cross-departmental collaboration.'

[0051] Intervention Implementation: The simulated company adopts the recommendations, implements a pilot project, and allows project team members to report to the project manager temporarily.

[0052] Remeasurement: After 3 months of simulation, the system was repeated. Figures 2-4 and Figure 5The recalculated process resulted in an RSI of approximately 0.25, a PGA of approximately 0.35, and an LOI of approximately 0.62. Simulation data shows that the cross-departmental collaboration cycle can be shortened by approximately 57%.

[0053] Closed-loop verification: Refer to Figure 6 The closed-loop feedback verification process is executed automatically by the system. Figure 6 Steps 601-602: Collect measurement data after the intervention (601), calculate the change in LOI (602), generate an effect report (603), store the results in the experience base (604), and update the intervention rule thresholds (605). The intervention effect report shows that LOI increased by approximately 148%, and collaboration efficiency was significantly improved.

[0054] Example 2: Simulation Application of High DRSF Early Warning

[0055] Reference Figure 1 and Figure 2 A simulated monitoring was conducted using a sales department as an example. The system followed... Figure 2 The process shown calculates DRSF on a daily basis. For two consecutive weeks, the department's average DRSF was detected at approximately 5.2 times / day (preset threshold). ).

[0056] Reference Figure 5 In the decision-making process, when the DRSF exceeds the first threshold (step 504), the system matches the "role overload" intervention rule (step 504) and automatically generates an early warning: "The role switching frequency is too high, suspected of responsibility overload." It also recommends the intervention suggestion: "Add one digital sales assistant to take on the work of lead screening and initial communication." (step 505).

[0057] The simulated company adopted the recommendations. Based on system simulation predictions (using historical task allocation data and a role switching cost model), implementation was projected to reduce DRSF (Delivery-Switching-Sales Rate) to approximately 2.8 times per day, and increase sales conversion rate by approximately 20-25%. Actual tracking results largely matched the simulation predictions.

[0058] Example 3: Simulation Application of Multi-Department Joint Diagnosis in Large Manufacturing Enterprises

[0059] Reference Figure 1 , Figure 3 and Figure 5 Taking a large manufacturing enterprise (simulated with approximately 5,000 employees, including five business units: R&D, production, procurement, sales, and after-sales service) as an example, the system of this invention was deployed and simulated for 6 months.

[0060] Initial multi-department assessment: The system runs independently for each business unit. Figures 2-4The calculation process yielded simulated LOI values ​​for each business unit: R&D Department approximately 0.71 (healthy range), Production Department approximately 0.18 (overly rigid), Purchasing Department approximately 0.52 (slightly volatile), Sales Department approximately 0.68 (healthy range), and After-sales Department approximately 0.43 (growth stage).

[0061] Root cause analysis: Review the production department's sub-indicators ( Figure 3 RSI approximately 0.98 (extremely low volatility), PGA approximately 0.05 (almost no AI process), DRSF approximately 0.2. System diagnostics (refer to...) Figure 5 If the RSI is below the third threshold and the PGA is below the fourth threshold, the production department is considered to be in an "overly rigid" state, specifically manifested as "reporting relationships rarely change and production planning processes rely entirely on preset templates".

[0062] Automatic intervention recommendation: The system generates multi-dimensional intervention suggestions based on the rule base (step 505):

[0063] • Regarding the excessively low RSI: It is recommended to introduce project-based floating reporting in the production planning department, allowing temporarily assigned personnel to report to the project manager;

[0064] • Regarding the low PGA: It is recommended to pilot AI-powered dynamic generation of testing processes in the quality inspection stage, transforming standard operating manuals into executable dynamic nodes;

[0065] • For those with moderate DRSF but low performance: It is recommended to introduce a digital work order assistant on the production line to automatically assign tasks and reduce the burden of switching roles for shift leaders.

[0066] Simulation Implementation and Effect Prediction: The simulation assumes the company adopts some suggestions, implements a quarterly project system in the production planning department, and generates AI-powered processes for quality inspection pilot projects. The system simulates and predicts three months later, according to... Figure 5 The process reassessment is expected to increase the production RSI to approximately 0.75, PGA to approximately 0.32, DRSF to approximately 0.7, and LOI to approximately 0.58. Production efficiency is projected to increase by approximately 22%, and the response time to planning changes can be reduced from an average of 2 days to approximately 4 hours.

[0067] Example 4: Simulation Application of Liquid Optimization in the R&D Department of an Internet Company

[0068] Reference Figure 1 , Figure 2 and Figure 5 Taking an internet company (simulated to have approximately 800 employees, including an agile development team) as an example, this invention system is deployed to focus on monitoring the R&D department.

[0069] Anomaly detected: The system follows Figure 2The process calculated the DRSF (Diagnosis Related Free Response) of R&D department members, finding an average DRSF of approximately 6.8 times / day (threshold 3.5). Simultaneously, through... Figure 3 The calculated RSI is approximately 0.62 (slightly volatile). Figure 4 The process calculates a PGA of approximately 0.45 (moderate). System diagnostics ( Figure 5 Steps 503-504): "The frequency of role switching is too high, and there is slight instability in the reporting relationship, which is suspected to be due to the combination of overly detailed division of labor and changes in individual key positions."

[0070] Root cause analysis: The system located the following through the correlation logs (sub-module within step 504): Two key technical personnel changed jobs one after the other within the simulated two months, causing their subordinates to frequently report to different superiors; at the same time, the team's microservice architecture required each developer to maintain multiple code repositories, switching contexts approximately 8-10 times per day.

[0071] Automatic intervention: The system matches the intervention rule base and generates multi-stage recommendations (step 505):

[0072] • Short-term: Add temporary digital management assistants to the changed positions to handle approximately 80% of routine approvals in order to stabilize reporting relationships;

[0073] • Mid-term: Adjust task allocation strategy, aggregate related microservices by business domain, and reduce the frequency of code repository switching for individual developers;

[0074] • Long-term: Establish a succession plan for core positions and set up a deputy position or rotation mechanism to avoid large-scale shocks to reporting relationships caused by changes in a single point.

[0075] Simulation effect: The system follows Figure 5 The remeasurement process predicts that, two months after the intervention, the R&D department's DRSF can be reduced to about 3.2 times / day, the RSI can be restored to about 0.85, the R&D iteration cycle can be shortened by about 30%, and employee satisfaction is expected to increase by about 35%.

[0076] Example 5: Simulated Application of Special Improvement of Process Autonomy in the Compliance Department of Financial Enterprises

[0077] Reference Figure 1 , Figure 4 , Figure 5 and Figure 6 Taking the compliance audit department of a large bank (simulated with approximately 30,000 employees) as an example, this invention's system was deployed. This department has long relied on preset process templates, and simulations show that maintaining the reporting templates alone would consume approximately 20 person-months annually.

[0078] Initial detection: according to Figure 4 The system calculates a PGA of approximately 0.02 based on the process (almost all processes use preset templates). Meanwhile, Figure 3 The calculated RSI is approximately 0.95 (overly stable). Figure 2 The process calculates DRSF to be approximately 0.9 (within the normal range). The system determines ( Figure 5 "The lack of process adaptability leads to rigid audit processes and makes it difficult to respond quickly to changes in regulatory policies."

[0079] Intervention measures: System recommendations ( Figure 5 Step 505) "Dynamic Analysis Pilot of Compliance Clauses": Using the latest anti-money laundering regulatory policies as input, AI dynamically generates compliance inspection process examples, replacing the original manual template modification. The system also recommends retaining the manual review node (confidence threshold set to 0.85) until the PGA reaches 0.3.

[0080] Simulated Implementation and Expansion: Simulating a bank's pilot program in the anti-money laundering sector, the system follows... Figure 4 Dynamic monitoring of process PGA. It is predicted that after three months, the PGA can rise to approximately 0.28, the time to generate compliance inspection reports can be shortened from 5 days to approximately 1 day, and the error rate is expected to decrease by approximately 60%. Subsequently, this model is extended to other compliance areas, and it is simulated that after one year, the overall departmental PGA can rise to approximately 0.45, which is expected to free up the template maintenance workload of approximately 15 compliance personnel, allowing them to focus on high-value risk analysis.

[0081] Closed-loop feedback: Refer to Figure 6 The system automatically performs closed-loop verification: records the PGA change curves before and after intervention and simulated data on the shortening of regulatory response time (steps 601-603), stores the pilot experience in the process generation knowledge base (step 604), and updates the industry benchmark reference threshold (step 605), providing optimization basis for similar financial enterprises.

[0082] Note: All the above embodiments are reasonable deductions and simulations based on the technical solutions of this invention, aiming to fully demonstrate the technical principles, implementation methods, and expected technical effects of this invention. The data, parameters, and effect predictions in each embodiment are calculated based on the system's built-in algorithm model and industry-standard experience values. Those skilled in the art can reasonably foresee the technical effects based on the disclosure of this invention.

Claims

1. A quantitative assessment and adaptive intervention system for liquid tissue, characterized in that, include: o Data acquisition module is used to obtain role change logs, reporting relationship change logs and process instance generation logs from the enterprise management system; o Dynamic Role Switching Frequency (DRSF) Calculation Unit, used to calculate the role switching frequency per unit time based on the role change log; o Reporting Relationship Stability Index (RSI) calculation unit, used to calculate the change ratio of reporting relationships based on the reporting relationship change log; o Process Generation Autonomy PGA Calculation Unit, used to calculate the proportion of AI-dynamically generated process instances based on the process instance generated logs; The comprehensive index generation module is used to standardize and weight the DRSF, RSI, and PGA to generate the liquid tissue comprehensive index LOI. The intervention recommendation module is used to automatically generate organizational intervention suggestions when the DRSF, RSI, PGA, or LOI exceed a preset threshold.

2. The system according to claim 1, characterized in that, The DRSF calculation unit is configured to calculate according to the following formula: Where N is the total number of employees, and T is the number of time periods within the time window. This is an indicator function.

3. The system according to claim 1, characterized in that, The RSI calculation unit is configured to calculate according to the following formula:

4. The system according to claim 1, characterized in that, The PGA calculation unit is configured to calculate according to the following formula:

5. The system according to claim 1, characterized in that, The standardized weighted fusion adopts Min-Max normalization, with a default weight of DRSF:RSI:PGA = 0.3:0.3:0.

4.

6. The system according to claim 1, characterized in that, The intervention recommendation module is also configured to: o When DRSF is above the first threshold, generate suggestions to split responsibilities or add digital staff; o When the RSI is above the second threshold, a suggestion to generate a fixed core reporting line is generated; o When the RSI falls below the third threshold, a recommendation to introduce project-based floating reporting is generated; When the PGA falls below the fourth threshold, a suggestion is generated to introduce an AI dynamic process generation engine.

7. The system according to claim 1, characterized in that, It also includes a feedback verification module, which is used to recollect data and calculate LOI changes after the intervention measures are implemented, generate an effect comparison report, and form a closed-loop optimization.

8. A method for quantitative assessment and adaptive intervention of liquid tissue, characterized in that, Includes the following steps: o Data collection steps: Obtain role change logs, reporting relationship change logs, and process instance generation logs from the enterprise management system; oDRSF calculation steps: Calculate the dynamic character switching frequency per unit time based on the character change log; oRSI calculation steps: Calculate the reporting relationship stability index based on the reporting relationship change log; oPGA calculation steps: Generate logs based on the process instance to calculate the autonomy of the process; o Comprehensive index generation steps: Standardize and weight the DRSF, RSI, and PGA to generate the liquid tissue comprehensive index LOI; o Intervention recommendation steps: When the DRSF, RSI, PGA, or LOI exceed the preset threshold, an organizational intervention recommendation is automatically generated.

9. The method according to claim 8, characterized in that, It also includes a feedback verification step: after the intervention measures are implemented, data is collected again and the changes in LOI are calculated to generate an effect comparison report.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method of claim 8 or 9.

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