Digital transformation multi-dimensional evaluation method and system for small and medium-sized enterprises

Abstract

The invention relates to the technical field of digital transformation, in particular to a multi-dimensional assessment method and system for digital transformation of small and medium-sized enterprises, which comprises the following steps: analyzing task resume and flow node differences based on small and medium-sized enterprise organizational structures, extracting flow structure offset features, and extracting trend fluctuation features in combination with production efficiency and equipment utilization rate. Evaluating an information basis, an equipment digitization rate and a management process, obtaining a basic adaptive capacity parameter, judging fund, technology and talent resource guarantee capacity, outputting a resource support coefficient, analyzing the number and distribution of risk points, and obtaining a transformation risk index. According to the method, the task resume sequence of each business unit of a small and medium-sized enterprise is dynamically analyzed, so that the real-time capture of flow execution offset, the feedback of abnormal links and structure changes by quantitative indexes, the dynamic trend analysis of flow structure offset characteristics, production efficiency in a period and equipment utilization rate are formed, the abnormal performance can be positioned at the initial stage of the trend, and the real-time analysis of the flow execution offset is realized. And sharp identification of continuous sections and fluctuation nodes.

Description

Technical Field

[0001] This invention relates to the field of digital transformation technology, and in particular to a multi-dimensional evaluation method and system for digital transformation of small and medium-sized enterprises. Background Technology

[0002] Digital transformation focuses on deeply integrating digital technologies and information technology into the production, management, service, and operation processes of various enterprises and organizations, achieving intelligent upgrades to business processes, management systems, and decision-making mechanisms. This field encompasses the integrated application of technologies such as cloud computing, big data, artificial intelligence, the Internet of Things, and mobile internet within and outside enterprises, driving organizational structure optimization, data-driven operations, collaborative resource allocation, and enhanced innovation capabilities. Digital transformation not only focuses on technology applications but also emphasizes corporate culture transformation, business model innovation, and the continuous evolution of organizational management, aiming to build an efficient, agile, and intelligent modern enterprise system to adapt to the ever-changing market and technological environment.

[0003] Among them, the multi-dimensional evaluation method for digital transformation of SMEs refers to the comprehensive evaluation of the transformation status and effectiveness of SMEs in promoting digital transformation by using a multi-dimensional indicator system. This evaluation covers aspects such as digital infrastructure, information management, intelligent business processes, and data application capabilities. It is mainly used to scientifically diagnose the progress of digital transformation of SMEs, identify transformation bottlenecks, and provide quantitative references and decision-making basis for subsequent digital strategy optimization, resource allocation, and capability enhancement.

[0004] Existing technologies primarily rely on static scoring or single-point indicator collection, lacking continuous monitoring of the dynamic evolution and structural shifts in business processes. This leads to limitations such as difficulty in timely detection of process anomalies. Production indicator trends are often based on phased statistics, failing to capture early changes and abnormal fluctuations, resulting in delayed responses to operational bottlenecks. Enterprise infrastructure and equipment digitization capabilities are roughly assessed through list scoring or questionnaires, making it difficult to identify system shortcomings and integration barriers. Resource allocation focuses on total investment, failing to consider resource intensity and availability, making it difficult to determine the true needs at different stages of digital transformation. Risk analysis often remains at a superficial classification level, lacking in-depth analysis of the spatial distribution, weight, and combined effects of risk points. Some key risk items are not effectively identified, hindering the continuity of transformation. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a multi-dimensional evaluation method and system for the digital transformation of small and medium-sized enterprises.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a multi-dimensional evaluation method for the digital transformation of SMEs, comprising the following steps: S1: Based on the organizational structure of small and medium-sized enterprises, analyze the task history of each business unit, compare the distribution of operation action types and the sequence of process nodes, calculate the node interval offset, summarize the repetition and path changes, and obtain the process structure offset characteristics. S2: Based on the process structure offset characteristics, combined with cycle production efficiency and equipment utilization, calculate the direction of sequence trend change, screen trend change segments, determine the length of continuous segments and jump frequency, and obtain trend fluctuation characteristics. S3: Based on the aforementioned trend fluctuation characteristics, analyze the enterprise's information infrastructure, equipment digitization rate, and management processes; calculate the matching degree between elements and transformation needs; determine the adaptability of each basic element; and obtain basic adaptability parameters. S4: Based on the aforementioned basic adaptability parameters, select currently available funds, technology, and human resources, analyze their supporting role in key aspects, compare the coverage of resources and upgrade needs, determine resource guarantee capabilities, and obtain resource support coefficients.

[0007] The present invention improves upon this invention by including the following: the process structure offset characteristics include structure offset distribution, process consistency parameters, and node link characteristics; the trend fluctuation characteristics include periodic fluctuation amplitude, trend turning point, and change duration range; the basic adaptability parameters include digital compatibility, intelligent integration, and process support; and the resource support coefficient includes funding guarantee level, technology matching capability, and personnel support capability.

[0008] The present invention is improved in that the step of obtaining the process structure offset feature is specifically as follows: S111: Based on the organizational structure of small and medium-sized enterprises, analyze the task history sequence of each business unit, compare the difference between the operation action in the actual sequence and the process node order, determine the distribution change of action type, calculate the order change, and obtain the node order offset distribution. S112: Based on the node sequence offset distribution, compare the timestamps of each node, determine the correspondence between the actual history interval and the node baseline interval, analyze the time changes between nodes, and summarize the interval change data to obtain the process time offset amplitude. S113: Based on the process time offset amplitude, the repetition frequency of actions under different path combinations is statistically analyzed, the changing trend of action distribution and structure in the path combination is analyzed, the path disturbance situation is determined, and the process structure offset characteristics are obtained.

[0009] The present invention is improved in that the step of obtaining the trend fluctuation characteristics is specifically as follows: S211: Based on the process structure offset characteristics, analyze the production efficiency and equipment utilization rate sequence of each cycle, and determine whether the trend direction of each cycle has changed by comparing the efficiency and utilization rate change direction of adjacent cycles, identify the change location, and generate a set of direction change locations. S212: Based on the set of direction change locations, calculate the length of the continuously changing segments, analyze the frequency of direction changes in each segment, and by comparing the segment length and the jump frequency, screen key periodic segments to obtain trend fluctuation characteristics.

[0010] The present invention is improved in that the steps for obtaining the basic adaptability parameters are as follows: S311: Based on the aforementioned trend fluctuation characteristics, analyze the coverage, data integration, and data processing flow of the enterprise information system, compare the degree of integration and coordination between different systems, determine whether the data processing flow meets the standard process requirements, and obtain the basic information matching degree. S312: Based on the information foundation matching degree, calculate the device's network connection ratio, automation level, and sensor application status, analyze the compatibility of factors with the information foundation matching degree, screen out key device digital characteristics, and obtain the device's adaptability to the information system. S313: Based on the adaptability of the equipment and information system, determine the standardization execution ratio, automation implementation ratio, and optimization and improvement status of the management process, calculate the maturity level of the management process, and obtain basic adaptability parameters.

[0011] The present invention is improved in that the step of obtaining the resource support coefficient is specifically as follows: S411: Based on the aforementioned basic adaptability parameters, screen funding, technical resources, and talent reserves, calculate the type and quantity of each resource, determine whether they meet the needs of digital transformation, optimize resource combination and configuration, and obtain resource configuration matching quantity; S412: Based on the resource allocation matching amount, compare it with the actual needs of key links in digital transformation, analyze the support status of each resource for different links, screen the supply of key resources, and adjust the allocation of each link to obtain the resource support intensity. S413: Invoke the resource support strength, determine the coverage of process and upgrade requirements, analyze the priority of process support and upgrade support, calculate each matching relationship, and obtain the resource support coefficient.

[0012] The present invention is improved in that the steps further include: S5: Based on the resource support coefficient, calculate the number and distribution of risk points in the transformation process, analyze the scope and weight of the risk points, determine the role of key risks in the continuity of transformation, identify risk items with high impact, and obtain transformation risk indicators. The transformation risk indicators include risk classification items, impact intensity factors, and continuous interference items.

[0013] The present invention is improved in that the steps for obtaining the transformation risk indicators are as follows: S511: Based on the resource support coefficient, identify the risk points in the process of digital transformation, calculate the resource support strength of the risk points, determine their distribution characteristics in the business process, screen the risks that affect the stability of resource support, and obtain the support strength of the risk points. S512: Based on the support strength of the risk points, compare the functional nodes of each risk point in the enterprise's digital transformation system, analyze the impact range and risk weight of each risk point, screen the risks that have a key impact on the transformation, and obtain the impact magnitude of key risks. S513: Based on the impact magnitude of the key risks, determine the intensity of interference to the continuity of enterprise digital transformation, analyze the matching between the scope of impact and the strength of resource support, calculate the interference level of key risks in the transformation path, and obtain transformation risk indicators.

[0014] A multi-dimensional evaluation system for digital transformation of SMEs, the system comprising: The process structure analysis module is based on the organizational structure of small and medium-sized enterprises. It analyzes the task history sequence of each business unit, compares the actual distribution of each type of operation action with the difference in the order of process nodes, calculates the time interval offset of each node, and evaluates the gap between the actual process and the standard process by summarizing the repetition of operations and path changes, thus obtaining the process structure offset characteristics. The trend feature extraction module compares the process structure offset features with the production efficiency and equipment utilization rate sequences of each cycle, calculates the trend change direction of each sequence in each cycle, filters out the cycle segments where the direction changes, determines the length and jump frequency of continuous segments, and obtains the trend fluctuation features. Based on the aforementioned trend fluctuation characteristics, the capability adaptability analysis module analyzes the enterprise's information infrastructure, equipment digitization rate, and management process maturity, calculates the degree of matching between each element and the conditions required for digital transformation, determines whether the adaptability of each basic element meets the feasibility requirements, and obtains basic adaptability parameters. Based on the aforementioned basic adaptability parameters, the resource support assessment module screens the company's currently available funds, technology resources, and talent reserves, analyzes the resources' ability to support key aspects of digital transformation, compares the resources' coverage of process and upgrade needs, judges the resource investment's guarantee capability, and obtains the resource support coefficient. Based on the resource support coefficient, the risk assessment module calculates the number and distribution of risk points in the enterprise's digital transformation process, analyzes the scope and weight of the impact of risk points, judges the impact of key risks on the continuity of transformation, identifies risk items with critical impact, and obtains transformation risk indicators.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: This invention achieves real-time capture of process execution deviations through dynamic analysis of the task history sequences of various business units in SMEs. Abnormal links and structural changes are fed back with quantitative indicators, forming process structure deviation characteristics. Dynamic trend analysis of production efficiency and equipment utilization within a cycle allows for the early identification of abnormal behavior and the keen recognition of continuous segments and fluctuation nodes. Enterprise basic conditions and equipment digitization levels are characterized parametrically, promoting comprehensive quantification of multi-level condition adaptability. Resources such as capital, technology, and talent are compared horizontally based on intensity and availability, avoiding subjective judgment and achieving transparent expression of resource guarantee capabilities. Risk analysis links factors such as quantity, distribution, and weight, highlighting key risk items and their impact on continuous digitalization. Data levels are progressively interconnected, achieving information flow between indicators, promoting intelligent, dynamic, and systematic feasibility assessment, and significantly strengthening decision support capabilities for digital transformation. Attached Figure Description

[0016] Figure 1 This is a flowchart of the main steps of the present invention; Figure 2 This is a flowchart illustrating the acquisition of process structure offset features in this invention. Figure 3 This is a flowchart illustrating the process of obtaining trend fluctuation characteristics in this invention. Figure 4 This is a flowchart illustrating the acquisition of basic adaptability parameters in this invention. Figure 5 This is a flowchart illustrating the process of obtaining the resource support coefficient in this invention. Figure 6 This is a flowchart illustrating the process of obtaining transformation risk indicators in this invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0019] Example Please see Figure 1 This invention provides a technical solution: a multi-dimensional evaluation method for the digital transformation of SMEs, comprising the following steps: S1: Based on the organizational structure of small and medium-sized enterprises, analyze the task history sequence of each business unit, compare the actual distribution of each type of operation action with the difference in the order of process nodes, calculate the time interval offset of each node, and evaluate the gap between the actual process and the standard process by summarizing the repetition of operations and path changes, and obtain the process structure offset characteristics. S2: Based on the process structure offset characteristics, compare with the production efficiency and equipment utilization rate sequence of each cycle, calculate the trend change direction of each sequence in each cycle, screen the cycle segments where the direction changes, determine the length and jump frequency of continuous segments, and obtain the trend fluctuation characteristics. S3: Based on trend fluctuation characteristics, analyze the enterprise's information infrastructure, equipment digitization rate and management process maturity, calculate the matching degree of each element with the conditions required for digital transformation, determine whether the adaptability of each basic element meets the feasibility requirements, and obtain basic adaptability parameters. S4: Based on basic adaptability parameters, screen the company's currently available funds, technology resources and talent reserves, analyze the resources' ability to support key aspects of digital transformation, compare the resources' coverage of process and upgrade needs, judge the guarantee capability of resource investment, and obtain the resource support coefficient. S5: Based on the resource support coefficient, calculate the number and distribution of risk points in the enterprise's digital transformation process, analyze the impact range and weight of risk points, determine the impact of key risks on the continuity of transformation, identify risk items with critical impact, and obtain transformation risk indicators.

[0020] Process structure deviation characteristics include structure deviation distribution, process consistency parameters, and node link characteristics; trend fluctuation characteristics include periodic fluctuation amplitude, trend turning point, and change duration range; basic adaptability parameters include digital compatibility, intelligent integration, and process support; resource support coefficients include funding guarantee level, technology matching capability, and personnel support capability; and transformation risk indicators include risk classification items, impact intensity factors, and continuous interference items.

[0021] Business units refer to departments or groups with independent responsibilities and functions in the organizational structure of small and medium-sized enterprises (SMEs), such as production departments, purchasing departments, and sales departments. Operational action types refer to the specific operational behaviors that actually occur in the business process, such as data entry, approval, scheduling, equipment operation, and task allocation. Actual distribution refers to the frequency, sequence, and distribution of all operational action types in the business process, and the proportion of different operation types at each node. Process nodes refer to the key links or steps in the business process; each node typically represents a task, decision, or operation point, such as task start, approval, and material delivery. Path changes refer to the changes in the process route that occur during the execution of the actual business process compared to the standard process, such as skipping a node, adjusting the order of process links, or task duplication. Trend change direction refers to the numerical trend of indicators (such as production efficiency and equipment utilization) within each period, i.e., continuous rise, decline, or no change. Length and jump frequency: Length refers to the number of periods in which the trend continues in the same direction, and jump frequency refers to the number of times the trend changes from rising to falling or the opposite direction. Matching degree refers to the degree of fit between an enterprise's existing information infrastructure, equipment digitization rate, management processes, etc., and the requirements of digital transformation; that is, the level of conformity or adaptation between the two. Adaptability refers to the capacity of an enterprise's existing infrastructure and management system to support, adapt to, and respond to each stage of digital transformation. Support capacity for key stages refers to the enterprise's existing resources, such as capital, technology, and talent, to support and guarantee each core step in digital transformation (such as system construction, data integration, and process reengineering). Process and upgrade requirements refer to the requirements of existing business processes involved in digital transformation, while upgrade requirements refer to the new resource and capability requirements imposed on the enterprise after the introduction of new technologies, systems, or management models. Key risk items refer to the main risk factors that have a decisive or significant impact on the success or failure of the enterprise's digital transformation process, such as system compatibility risks, data security risks, technology substitution risks, and personnel adaptation risks.

[0022] Please see Figure 2 The specific steps for obtaining the process structure offset features are as follows: S111: Based on the organizational structure of small and medium-sized enterprises, analyze the task history sequence of each business unit, compare the difference between the operation action in the actual sequence and the process node order, determine the distribution change of action type, calculate the order change, and obtain the node order offset distribution. Based on the organizational structure of SMEs, historical task records are extracted from each independent business unit, such as the purchasing group, production group, and sales group, to obtain the actual sequence of operations and their corresponding process node numbers. An operation sequence is then constructed based on the action order determined by the time tags in the operation records. Using this sequence as a basis, the node sequence template set in the standard business process is compared sequentially. At each process node, the required action category and action order are preset. The difference between the position of each action in the actual execution sequence and the standard node order is compared to identify any positional misalignment. The actual sequence of operations is then mapped to a numerical vector sequence, and distance comparison is performed with the numerical sequence of the standard node position to determine if the action occurred too early or too late. For example, in the standard, "material confirmation" should be in the third node, but if it actually occurs in the fifth node... If a point is found, it is recorded as a positive offset of 2 nodes. The actual position offset value of each type of action is statistically analyzed, and the distribution of the offset degree in the multiple occurrence records of the action is extracted. For example, if an operation action is offset to node positions 1, 2, 1, and 0 in multiple tasks, the difference is used as the original sample to generate an offset trend map of this type of operation action. The benchmark value for judging offset behavior is set to the difference of node position 1. Any action exceeding this difference is marked as having a sequential offset phenomenon. The threshold is set with reference to the common task connection time between adjacent nodes in the standard process. If the connection time is stable and the task relationship is close, the threshold is set to 1. The offset value and frequency of operation actions in all business units are statistically analyzed. Finally, each type of operation action and its corresponding actual offset are plotted as a distribution map to further judge the sequential offset between the actual execution of the overall process nodes and the standard process setting, and obtain the node sequential offset distribution.

[0023] S112: Based on the node sequence offset distribution, compare the timestamps of each node, determine the correspondence between the actual history interval and the node baseline interval, analyze the time changes between nodes, and summarize the interval change data to obtain the process time offset amplitude. Based on the obtained node sequence offset, for each process node with an offset, the time record information of the corresponding operation action is called to extract the start and end times of each node. The actual execution duration of the action within the node is calculated, and the expected execution interval time of the corresponding node in the standard process file is searched. The two are compared to determine whether the actual time consumption is higher or lower than expected. This difference is used as the time offset value of the current node. The time offset result of each node is recorded in the direction of advance or delay. The time offset data of all nodes in different periods and different tasks are categorized and summarized. Then, all offset values ​​are divided into several segments according to duration, such as less than 15 minutes, 15 to 30 minutes, 30 to 60 minutes, and greater than 60 minutes. The offset levels are divided and marked as normal, slight, and significant segments. The statistics of each level are then compiled. The number of actions and their corresponding proportions are analyzed. For example, the standard time for the "approval confirmation" action in a certain node is 30 minutes. In actual operation, there are records of 20 minutes, 35 minutes, 55 minutes, and 80 minutes, which indicates different situations of early and late. Among them, 80 minutes is a significant delay, which provides support for subsequent process efficiency evaluation. Then, the actual time consumption between consecutive nodes is vertically linked to analyze whether there is a chain offset between nodes caused by the delay of a certain node, resulting in a chain delay in subsequent nodes, and to determine whether it is an abnormal phenomenon in process execution. For example, the standard setting is that the interval between "material warehousing" and "production start" in a certain task is 1 hour. If it exceeds 2 hours in multiple actual records, it is identified as exceeding the normal range. Finally, the time offset data of each node and its adjacent nodes in the actual task are summarized to form a complete process time offset range.

[0024] S113: Based on the process time offset amplitude, the repetition frequency of actions under different path combinations is statistically analyzed, the changing trend of action distribution and structure in the path combination is analyzed, the path disturbance is determined, and the process structure offset characteristics are obtained. Based on the time offset results of each process node, we focus on those nodes with significant offsets, statistically analyze the changes in the task paths of these nodes, and record whether any operations within the nodes are called repeatedly. If an operation appears twice or more in a task process, we correlate its repetition with the path to determine whether the process sequence has changed due to a delay or advancement of a node. For example, if the original process should be from node A to B and then to C, but it actually occurs from A to C and then back to B, or skips B and directly enters D, then this path is recorded as a disturbance path. We then compare the distribution and repetition count of all actions in the path with the standard process. If we find that an operation frequently appears repeatedly in multiple disturbance paths, for example, "production instruction issued" appears 6 times in different process paths, while the standard process should only require 6 times... If a repetition occurs once, its repetition rate is calculated to be far higher than the standard requirement. The path distribution is then analyzed to identify which node combinations are most prone to path disturbances. For example, actions that frequently occur early or whose positions drift outside the end node are marked as structural mutations. Furthermore, actions that repeat more than 20% of the original set frequency and whose node positions shift more than two nodes are used as criteria for determining significant shifts in the process structure. For example, if the standard process has 8 nodes, and an action that should have occurred at node 7 appears multiple times at nodes 4 and 5 with a high frequency, it is considered a shift. By statistically analyzing the repetition behavior, sequence position changes, and corresponding frequencies of actions in all path disturbances, various disturbance paths and their combinations are integrated to form process structure shift characteristics, providing a basis for subsequent trend analysis and resource matching.

[0025] Please see Figure 3 The specific steps for obtaining trend fluctuation characteristics are as follows: S211: Based on the process structure offset characteristics, analyze the production efficiency and equipment utilization rate sequence of each cycle. By comparing the efficiency and utilization rate change directions of adjacent cycles, determine whether the trend direction of each cycle has changed, identify the change location, and generate a set of direction change locations. The production efficiency and equipment utilization rates for each operational cycle of the enterprise are extracted separately. Production efficiency data is derived from the effective output quantity recorded per unit time, while equipment utilization rate data is derived from the ratio of total equipment operating time to theoretical available time. These two types of data are read cycle by cycle and arranged as a continuous sequence along the time axis. A direction determination operation is performed on the data of adjacent cycles. First, the difference between the production efficiency and equipment utilization rate of each cycle and the previous cycle is calculated. The sign of the difference determines whether the trend is upward, downward, or unchanged. For example, if the efficiency of cycle 1 and cycle 2 is 82% and 86% respectively, the trend is upward. If the efficiency drops to 80% from cycle 2 to cycle 3, the trend is considered to have changed, and this cycle is recorded as the direction change point. This judgment logic is repeated throughout the entire cycle. In the periodic sequence, a complete trend direction sequence is formed and the periodic index positions where the direction changes from rising to falling or from falling to rising are extracted. Further, a threshold for identifying direction changes is set. When the change in efficiency or utilization rate during a period is less than the set 2%, the change is judged as an invalid jump and is not included in the change record. The threshold is set based on the equipment characteristics and process stability assessment. If the capacity fluctuation of a certain type of production equipment does not exceed ±2% per cycle under stable operation, this value can be used as a judgment boundary. Based on this threshold, the misjudgment of noise fluctuations is eliminated. Finally, the positions of all valid direction change cycles are combined into a direction change position set. For example, if the periodic sequence has 12 periods and the positions where the direction changes effectively are the 3rd, 5th, and 7th periods, then the change position set [3, 5, 7] is formed and used for the analysis of subsequent trend fluctuation segments.

[0026] S212: Based on the set of direction change locations, calculate the length of continuously changing segments, analyze the frequency of direction changes in each segment, and by comparing the segment length and the jump frequency, screen key periodic segments to obtain the characteristics of periodic changing segments. The periodic sequence is segmented to identify segments where the direction remains consistent throughout consecutive cycles. Within each segment between points of directional change, the cycle length is calculated as the length of the continuously changing segment. Simultaneously, the frequency of directional jumps within this segment is recorded. By traversing each new segment formed after a directional change, the segment length and the number of directional changes are recorded. For example, a continuous segment between cycle 3 and cycle 5, where efficiency changes from increasing to decreasing and then back to increasing twice, is recorded as 2 times / 3 cycles. The length and jump frequency data of all segments are then analyzed to identify key segments based on a set filtering rule. This filtering rule is set to... The criteria are set as follows: the period length is not less than 3, and the jump frequency is greater than 1 time / 3 periods. That is, the frequency exceeds 0.33 times / per period and is considered to be frequent jump. This rule is used to remove segments that are too short or have too stable trends. For example, if the period from 4 to 8 constitutes a 5-period length, and there are 2 changes in the internal direction, the jump frequency is 0.4, which meets the screening criteria. This segment is marked as a critical segment. Finally, the set of all period segments that meet the screening conditions of segment length and jump frequency is recorded as the characteristics of periodic variation segments. The start and end period positions, number of periods, jump frequency value and direction change type of each segment are output to form the input basis for subsequent evaluation of the fluctuation trend structure.

[0027] S213: Based on the characteristics of the periodic variation segment, the following formula is used: ; The trend fluctuation characteristics are obtained, among which, Representing the The trend fluctuation characteristics of each representative segment Indicates the first The first in the section The magnitude of change in production efficiency or equipment utilization corresponding to each directional change point. For the first The length of the continuous cycle of each segment, Indicates the first The frequency of direction changes within a unit period of each segment Indicates the first Within each section The magnitude of the abnormal structure shift in the next cycle For the first The number of direction change points in each section For the first The number of times abnormal structural shifts occur within each segment; For a digital transformation assessment scenario of a small-to-medium-sized manufacturing enterprise, the equipment operation and production process logs of its key production unit are selected. Based on the obtained key trend fluctuation segments, calculations are first performed within these segments to obtain the magnitude of directional changes. Specifically, the enterprise's production line recorded outputs of 95, 90, 98, and 93 units in four consecutive cycles, corresponding to equipment utilization rates of 0.92, 0.88, 0.95, and 0.89, respectively. Therefore: Output changes from cycle 1 to 2: ( ); Output changes from cycle 2 to 3: ( ); Output changes from cycle 3 to 4: ( ); Therefore, the total magnitude of directional changes in this section is: ; This segment has a length of 4 cycles: ; The direction change frequency is 3 jumps / 4 cycles: ; Two abnormal equipment events were recorded during the period: an abnormal rise in equipment temperature to the set upper limit and a brief sensor failure. The offset magnitude was processed by quantitative scoring as follows: Anomaly 1: Temperature rise deviates from 2.0℃, standard tolerance is 1.0℃, quantification range is 2.0℃; Anomaly 2: Signal missing for 3 minutes, standard time limit 2 minutes, quantization amplitude 1.5; Therefore, the total offset magnitude of the abnormal structure is: ; Substitute all the above parameters into the trend fluctuation characteristic formula: ; The negative trend fluctuation characteristic value indicates that although there are high-frequency jumps in the overall trend direction, the positive and negative changes cancel each other out, ultimately presenting a state of "high disturbance and low net volatility". At the same time, due to the large proportion of abnormal deviations, the overall indicators are compressed, which belongs to the risk-induced segment. In the subsequent step S3, this segment can be used as a weak correlation unit to analyze "information infrastructure conditions" and "management process maturity" to further determine whether it has the basic adaptability required for transformation.

[0028] Please see Figure 4 The specific steps for obtaining basic adaptability parameters are as follows: S311: Based on trend fluctuation characteristics, analyze the coverage, data integration and data processing flow of enterprise information systems, compare the degree of integration and coordination between different systems, determine whether the data processing flow meets the standard process requirements, and obtain the information basic matching degree. A structured sampling inspection of the enterprise's internal information systems was conducted to extract detailed deployment data across various business units. By listing the business process scope covered by each system's service modules and calculating their proportion across all standard process nodes, information coverage parameters were obtained. Furthermore, the number of data interfaces, data transmission formats, and actual exchange frequencies between each type of system were analyzed to determine whether stable data connections had been established between different systems. If two systems had bidirectional interfaces, exchanged data at least once a day, and had consistent data field formats, they were considered coordinated. If any of these conditions were not met, they were marked as uncoordinated. The number of coordinated and uncoordinated relationships between all systems was then statistically analyzed to calculate the coordination ratio, serving as an indicator of the system integration coordination level. Finally, based on the system structure upon which each business process depended, various types of data were traced from collection to aggregation. The overall process path involves checking the continuity of data transmission timestamps, the presence of missing fields, and the occurrence of processing interruptions. If a record is found to be collected by a sensor, processed by the MES system, but not received by the ERP system, it is considered a process interruption. If a data transmission delay exceeds a set threshold, such as a processing delay exceeding 60 minutes, the record is considered a time mismatch. After traversing all business process data processing paths, the percentage of time mismatches and processing interruptions is statistically analyzed. The matching degree level is divided into the following ranges: 0-40% is low, 41%-70% is medium, and above 71% is high. If an enterprise has an information system coverage rate of 65%, a system coordination rate of 78%, and a data flow continuity rate of 88%, then the enterprise's basic information matching degree is determined to be medium based on the weighted judgment rules, thus obtaining the final basic information matching degree.

[0029] S312: Based on the information infrastructure matching degree, calculate the network connection ratio, automation level and sensor application of the equipment, analyze the compatibility of the factors with the information infrastructure matching degree, screen the key digital characteristics of the equipment, and obtain the equipment and information system adaptability. The process involves accessing the network records and operation logs of each type of key equipment on the production floor, calculating the proportion of all networked devices in the total number of devices, and using this as the basis for evaluating the network ratio. Simultaneously, the number of control programs embedded in each device is summarized, and the number of devices supporting features such as automatic control and remote command is extracted. This number is then compared with the total number of devices to obtain a reference ratio for the level of automation. Next, the number and types of sensor modules installed in each device are checked to determine whether they have the function of collecting basic physical quantities such as temperature, pressure, and speed. The sampling frequency of each sensor is recorded to ensure it reaches at least once per minute. If it does, it is included in the sensor application index statistics. After completing the collection of three types of digital characteristic data at the device level, the results are compared with the aforementioned information basis matching assessment. The system analyzes whether various equipment characteristic indicators match the system. The adaptation judgment rules are set as follows: when the basic information matching degree is medium, the networking ratio should not be less than 60%, the automation level should not be less than 50%, and the sensor application coverage should not be less than 40%. If the three values ​​in a factory are 72%, 55%, and 45% respectively, it is judged as basic adaptation. If any one of them is lower than the corresponding matching degree threshold, it is considered as insufficient adaptation. The system selects the equipment characteristic parameters that play a decisive role in the judgment result in the adaptation analysis. If the adaptation degree changes from unsuitable to suitable after the sensor application coverage increases by 10%, "sensor application" is marked as a key feature. Finally, the system outputs the digital characteristics of all key equipment that meet the adaptation judgment to obtain the equipment and information system adaptability.

[0030] S313: Based on the compatibility between equipment and information systems, determine the standardization rate, automation rate, and optimization and improvement status of management processes, and calculate the maturity level of management processes using the formula: ; Obtain basic adaptability parameters ,in, This represents the degree of standardization in management processes, referring to the level of standardization in the execution of business processes by an enterprise. The percentage of automated processes refers to the proportion of automated processing steps in various enterprise processes. The depth of process optimization refers to the scope of a company's process optimization and upgrade efforts, as well as the degree of continuous improvement. This represents the overall adaptability of equipment and information systems, indicating the overall compatibility between the digital capabilities of various enterprise equipment and the collaboration of information systems. The organizational synergy index represents the level of collaboration and resource integration among various departments and business processes within an enterprise. First, determine the standardization execution rate of the enterprise's management processes. Calculate the number of standard processes currently published by each business unit within the enterprise and the number of processes strictly adhered to during actual execution. Taking enterprise E05 as an example, the enterprise has set a total of 20 business process standards. In actual operation, 15 processes are strictly implemented and have complete log records maintained. Therefore, this indicator is calculated as follows: Next, determine the ratio of the number of automated steps in each business process to the total number of steps. For example, in E05 company's 20 processes, there are a total of 40 key execution steps. Of these, 24 steps have already achieved automated functions such as system triggering, information closed-loop processing, or automatic generation of output reports. Therefore, the automation ratio is [percentage missing]. The degree of optimization and perfection of the enterprise processes can then be assessed by analyzing the iteration frequency and optimization coverage of each process over the past three years. If the enterprise has performed process refactoring, node compression, or resource path optimization on 12 of its processes within the past three years, with a coverage rate of 60%, then the depth of process optimization is [insert value here]. Perform the calculation operation, substituting the aforementioned three parameters into the formula to calculate the maturity level of the management process. Set the equipment and information system adaptability. This value originates from the comprehensive evaluation results formed by analyzing the device network connectivity rate, sensor application coverage, and information system matching error in the previous sub-step; further, the organizational collaboration capability index is extracted. This parameter is based on the actual survey results and the on-time rate of cross-departmental collaborative tasks, forming an evaluation system. It is assumed that E05 company achieved an 80% on-plan completion rate, a 90% departmental data synchronization rate, and a 70% monthly active user coverage rate on various shared platforms in five cross-departmental collaborative tasks. After standardization, the collaborative capability index is set as follows: Substitute into the formula: ; Step 1: Calculate the molecular part: ; ; The second step is to calculate the denominator and the division part: ; The third step is to add the collaboration capability index: ; Therefore, the basic adaptability parameters of the enterprise are calculated as follows: ; The results indicate that the enterprise demonstrates a good overall performance in terms of management process standardization, automation coverage, and optimized execution. Specific numerical values... For sections above the general assessment baseline, the basic adaptability grading standard based on numerical ranges is as follows: "Poor adaptability" The ability to adapt is described as "moderate". If the enterprise is described as "highly adaptable", then it corresponds to the "highly adaptable" category. The formula takes process standardization and automation capabilities as the basic structure of management maturity, expands it by weighting the depth of optimization activities, harmonizes it by the compatibility between equipment systems, and finally introduces organizational-level collaborative capabilities to supplement soft capability factors, thus forming a complete set of basic adaptability parameters.

[0031] Please see Figure 5 The specific steps for obtaining the resource support coefficient are as follows: S411: Based on basic adaptability parameters, screen funding, technology resources and talent reserves, calculate the type and quantity of each resource, determine whether they meet the needs of digital transformation, optimize resource combination and configuration, and obtain resource configuration matching quantity; The process involves: 1) Reviewing the company's currently available funds, extracting the balance of special funds accounts and unspent budget plans from the annual financial statements. For example, if a company's annual budget is 1.2 million yuan and the available balance is 720,000 yuan, this fund item is marked as an available resource. 2) Obtaining a ledger of available technical resources from the IT department, counting the quantity and system compatibility of key components such as servers, database tools, and interface modules. For example, if the company currently has two database management platforms supporting mainstream industrial protocols, which can interface with MES and ERP systems, this is considered a usable technology item. 3) Accessing the technical job information related to digital transformation from the human resources system, screening the number and qualification levels of engineers in system development, network maintenance, and data governance. If there are currently 5 people with senior or higher technical titles, the number meets the basic requirements for transformation. 4) Organizing the three types of resources into a resource summary table by category and quantity, comparing them with the resource list required for transformation, marking deficiencies, and finally performing a matching analysis on resource combinations. Prioritizing combinations with high coverage and low configuration conflicts while meeting the needs of the most transformation stages, for example, combination A covers 8 stages, accounting for 80%, and has a budget utilization rate of 70%. This combination is then selected as the recommended solution, and the corresponding resource configuration matching quantity is output.

[0032] S412: Based on the resource allocation matching quantity, compare it with the actual needs of key links in digital transformation, analyze the support status of each resource for different links, screen the supply of key resources, and adjust the allocation of each link to obtain the resource support intensity. Based on the obtained resource allocation matching, the current combination of funding, technology, and personnel resources is compared item by item with the key links involved in digital transformation. For example, "data platform construction" requires 400,000 yuan in funding, two types of middleware support, and one data engineer. If the current combination allocates 420,000 yuan in funding, provides two types of middleware, and two engineers, then the requirements are met, and the resource support for this link is recorded as complete. After comparing the resource support for all key links in sequence, the number of times each type of resource supports each link is summarized and counted. For example, technology tools support 6 links, funding supports 4 links, and personnel supports 5 links, thus forming the support ratio. Screening rules are set, and if a certain type of resource supports a link... If a resource accounts for more than 80% of all key processes and more than three of these processes have no alternative resource dependencies, then the resource is marked as a key resource. For example, if the personnel resource support rate reaches 85% and covers key tasks such as "process refactoring" and "data interface management", then it is identified as a key resource. Then, check whether there is resource overlap and exclusive conflict in the allocation of key resources. For example, if a key engineer is responsible for two tasks at the same time but the total working hours exceed the 8-hour limit, then the priority needs to be reassigned, and the engineer should be assigned to the higher priority task. The lower priority task should be replaced by a reserve candidate. After all adjustments are completed, the proportion of each resource that is effectively satisfied in each process is calculated, and the support strength of each type of resource for the target task is output.

[0033] S413: Invoke resource support strength, determine the coverage of process and upgrade requirements, analyze the priority of process support and upgrade support, calculate each matching relationship, using the formula: ; Obtain the resource support coefficient This is used to reflect the overall support capability of an enterprise's existing resources for digital transformation processes and upgrade needs, among which, The degree of matching between representative process requirements refers to the extent to which funding, technology, and talent align with the needs of existing business processes. The degree of matching between upgrade requirements and needs refers to the extent to which funding, technology, and talent align with the needs of each stage of the process upgrade. It represents the strength of financial support and measures the ability of available funds to support key aspects. It represents the strength of technical support and measures the ability of available technical resources to support key aspects. The representative process supports prioritization, reflecting the importance ranking of each step in the company's existing business processes. This indicates the priority of upgrade support, reflecting the importance of each step in the process upgrade or optimization. This represents the resource support matching degree, used to measure the extent to which comprehensive resources support the entire process and upgrade tasks; Based on enterprise resource allocation, assess the priority and resource matching relationship of each link (such as process support and upgrade support), and set process support priorities. This indicates that, at the current stage, process support has a high priority in the enterprise's digital transformation process; similarly, setting upgrade support priority is also important. This indicates that while optimizing and upgrading the process is of high importance, resource allocation has a slightly lower priority compared to process support; then, based on the allocated resource support intensity, assuming the financial support intensity is... (This indicates that financial resources have a strong ability to support key process stages), and the technical support intensity is... (This indicates relatively weak technical resources, but still capable of supporting some upgrade needs.) Next, calculate the matching degree of each step, first determining the matching degree of process requirements. This indicates a high degree of matching between funding, technology, and talent requirements and the needs of existing business processes; and a high degree of matching between upgrade requirements and requirements. This indicates that the matching degree of funding, technology, and talent with the needs of the upgrading process is slightly low; finally, through the matching degree of resource guarantees... Quantification is performed to measure the overall resource support for all processes and upgrade tasks, and a formula is used to calculate the resource support coefficient. : Calculate the numerator: ; ; Therefore, the molecule is calculated as follows: ; Calculate the denominator: ; ; ; Therefore, the denominator is calculated as follows: ; Final calculation: ; Calculated resource support coefficient This indicates that the current resource allocation of enterprises is relatively weak in supporting the processes and upgrade tasks of digital transformation. Based on this coefficient value, enterprises need to adjust their resource allocation, especially in terms of increasing financial support, increasing investment in technical resources, and optimizing the overall resource guarantee capabilities. When the value is below 0.3, it can be regarded as a sign of insufficient resource support capabilities. It is recommended that enterprises invest key resources (funds and technology) in key processes and upgrade tasks to increase the possibility of successful transformation.

[0034] Please see Figure 6 The specific steps for obtaining transformation risk indicators are as follows: S511: Based on the resource support coefficient, identify the risk points in the process of digital transformation, calculate the resource support strength of the risk points, determine their distribution characteristics in business processes, screen risks that affect the stability of resource support, and obtain the support strength of the risk points. The entire process of digital transformation projects is scanned to identify resource bottlenecks at each stage and task node. Risk points closely related to personnel, technology, or funding are listed, such as "delayed sensor deployment," "insufficient system debugging personnel," and "funding gaps in interface development." Each risk point corresponds to a resource item. The actual supply capacity of this resource in the resource allocation table is compared with the minimum required configuration. If the supply capacity is insufficient, it is recorded as a low-intensity risk point. For example, if an interface development requires a budget of 400,000 yuan but the current budget is only 300,000 yuan, the resource support intensity of the corresponding risk point is 75%. If it is below 80%, it is marked as a weak point. All risk points are then linked to actual business processes to determine whether the risks are concentrated in a certain module or evenly distributed in the process flow. For example, risks related to equipment data acquisition are mostly concentrated in the "on-site automation link," and are recorded as a concentrated distribution. A distribution density threshold is then set. For example, if there are more than three risk points in the same module and the average support intensity is below 85%, it is determined to be a concentrated risk area. Through the above analysis, a set of risk points that substantially interfere with the stability of resource support can be identified, and their corresponding risk point support intensity is output.

[0035] S512: Based on the support strength of risk points, compare the functional nodes of each risk point in the enterprise's digital transformation system, analyze the impact range and risk weight of each risk point, screen out risks that have a key impact on the transformation, and obtain the impact magnitude of key risks. Based on the identified risk points' support strength, the enterprise's digital transformation process structure is invoked. According to the specific task links corresponding to each risk point, it is located to the corresponding functional node. For example, "insufficient MES system debugging personnel" is located to the "Manufacturing Execution System Setup" node. After completing the functional node mapping for all risk points, the number of risk points covered by each node is counted, and the number of upstream and downstream nodes affected by the corresponding risk point is read to confirm its impact range. For example, if a risk point affects 3 upstream data nodes and 2 subsequent task nodes, it is recorded as an impact range of 5. A risk weight evaluation range is then set. If the support strength is below 70%, the impact range exceeds 4, and the node is a core control node, it is judged as a high-weight risk. All high-weight risks are further summarized, and risk points that only affect non-critical auxiliary nodes are filtered out. Those risk items that cause major process disruptions or delays are retained. For example, if "platform data interruption" affects the three core task modules of data governance, model training, and analysis decision-making, and the resource gap reaches 40%, it is judged as a critical risk. The impact magnitude of the critical risk is output with the risk point name, node, support strength, impact range, and weight level.

[0036] S513: Based on the magnitude of the impact of key risks, determine the intensity of interference to the continuity of enterprise digital transformation, analyze the matching between the scope of impact and the strength of resource support, calculate the interference level of key risks in the transformation path, and obtain transformation risk indicators. Based on the obtained impact magnitude of key risks, the task stage and path information of each key risk item are read, and a path branch mapping relationship is established in the business execution flowchart. The number of dependency chains of the risk node on upstream and downstream tasks is marked, and then the interference level and support strength are paired to form an interference-support correspondence structure. It is determined whether there is a mismatch between resource capacity and the severity of risk impact. For example, if a risk point affects 4 consecutive nodes in the process and the resource support is only 65%, then the interference level of this point in the path is high. Then, the interference level classification standard is set. If the number of interference process nodes is ≥3 and the resource support is ≤70%, it is judged as moderate interference. If it is ≥5 nodes and the support is ≤60%, it is judged as severe interference. After performing interference level identification on all key risks, the severity is ranked according to the impact magnitude. For example, "device network connection abnormality" has a severe interference level and a high impact magnitude, so it is given priority to be included in the risk indicator output list. The interference level of key risks in the overall transformation path is recorded by comparing the risk number, path location, interference level and support, and the results are used to complete the collection of transformation risk indicators.

[0037] A multi-dimensional evaluation system for digital transformation of SMEs, the system includes: The process structure analysis module is based on the organizational structure of small and medium-sized enterprises. It analyzes the task history sequence of each business unit, compares the actual distribution of each type of operation action with the difference in the order of process nodes, calculates the time interval offset of each node, and evaluates the gap between the actual process and the standard process by summarizing the repetition of operations and path changes, thus obtaining the process structure offset characteristics. The trend feature extraction module compares the process structure offset features with the production efficiency and equipment utilization rate sequences of each cycle, calculates the trend change direction of each sequence in each cycle, filters out the cycle segments where the direction changes, and determines the length and jump frequency of continuous segments to obtain trend fluctuation features. The capability adaptability analysis module analyzes an enterprise's information infrastructure, equipment digitization rate, and management process maturity based on trend fluctuation characteristics. It calculates the degree of matching between each element and the conditions required for digital transformation, judges whether the adaptability of each basic element meets the feasibility requirements, and obtains basic adaptability parameters. The resource support assessment module, based on basic adaptability parameters, screens the company's currently available funds, technology resources, and talent reserves, analyzes the resources' ability to support key aspects of digital transformation, compares the resources' coverage of process and upgrade needs, judges the resource investment's support capability, and obtains the resource support coefficient. The risk assessment module calculates the number and distribution of risk points in the enterprise's digital transformation process based on the resource support coefficient, analyzes the scope and weight of the impact of risk points, judges the impact of key risks on the continuity of transformation, identifies risk items with critical impact, and obtains transformation risk indicators.

[0038] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A multi-dimensional evaluation method for the digital transformation of SMEs, characterized in that, Includes the following steps: S1: Based on the organizational structure of small and medium-sized enterprises, analyze the task history of each business unit, compare the distribution of operation action types and the sequence of process nodes, calculate the node interval offset, summarize the repetition and path changes, and obtain the process structure offset characteristics. S2: Based on the process structure offset characteristics, combined with cycle production efficiency and equipment utilization, calculate the direction of sequence trend change, screen trend change segments, determine the length of continuous segments and jump frequency, and obtain trend fluctuation characteristics. S3: Based on the aforementioned trend fluctuation characteristics, analyze the enterprise's information infrastructure, equipment digitization rate, and management processes; calculate the matching degree between elements and transformation needs; determine the adaptability of each basic element; and obtain basic adaptability parameters. S4: Based on the aforementioned basic adaptability parameters, select currently available funds, technology, and human resources, analyze their supporting role in key aspects, compare the coverage of resources and upgrade needs, determine resource guarantee capabilities, and obtain resource support coefficients.

2. The multi-dimensional evaluation method for digital transformation of SMEs according to claim 1, characterized in that, The process structure offset characteristics include structure offset distribution, process consistency parameters, and node link characteristics; the trend fluctuation characteristics include periodic fluctuation amplitude, trend turning point, and change duration range; the basic adaptability parameters include digital compatibility, intelligent integration, and process support; and the resource support coefficient includes funding guarantee level, technology matching capability, and personnel support capability.

3. The multi-dimensional evaluation method for digital transformation of SMEs according to claim 1, characterized in that, The specific steps for obtaining the process structure offset features are as follows: S111: Based on the organizational structure of small and medium-sized enterprises, analyze the task history sequence of each business unit, compare the difference between the operation action in the actual sequence and the process node order, determine the distribution change of action type, calculate the order change, and obtain the node order offset distribution. S112: Based on the node sequence offset distribution, compare the timestamps of each node, determine the correspondence between the actual history interval and the node baseline interval, analyze the time changes between nodes, and summarize the interval change data to obtain the process time offset amplitude. S113: Based on the process time offset amplitude, the repetition frequency of actions under different path combinations is statistically analyzed, the changing trend of action distribution and structure in the path combination is analyzed, the path disturbance situation is determined, and the process structure offset characteristics are obtained.

4. The multi-dimensional evaluation method for digital transformation of SMEs according to claim 1, characterized in that, The specific steps for obtaining the trend fluctuation characteristics are as follows: S211: Based on the process structure offset characteristics, analyze the production efficiency and equipment utilization rate sequence of each cycle, and determine whether the trend direction of each cycle has changed by comparing the efficiency and utilization rate change direction of adjacent cycles, identify the change location, and generate a set of direction change locations. S212: Based on the set of direction change locations, calculate the length of the continuously changing segments, analyze the frequency of direction changes in each segment, and by comparing the segment length and the jump frequency, screen key periodic segments to obtain trend fluctuation characteristics.

5. The multi-dimensional evaluation method for digital transformation of SMEs according to claim 1, characterized in that, The specific steps for obtaining the basic adaptability parameters are as follows: S311: Based on the aforementioned trend fluctuation characteristics, analyze the coverage, data integration, and data processing flow of the enterprise information system, compare the degree of integration and coordination between different systems, determine whether the data processing flow meets the standard process requirements, and obtain the basic information matching degree. S312: Based on the information foundation matching degree, calculate the device's network connection ratio, automation level, and sensor application status, analyze the compatibility of factors with the information foundation matching degree, screen out key device digital characteristics, and obtain the device's adaptability to the information system. S313: Based on the adaptability of the equipment and information system, determine the standardization execution ratio, automation implementation ratio, and optimization and improvement status of the management process, calculate the maturity level of the management process, and obtain basic adaptability parameters.

6. The multi-dimensional evaluation method for digital transformation of SMEs according to claim 1, characterized in that, The specific steps for obtaining the resource support coefficient are as follows: S411: Based on the aforementioned basic adaptability parameters, screen funding, technical resources, and talent reserves, calculate the type and quantity of each resource, determine whether they meet the needs of digital transformation, optimize resource combination and configuration, and obtain resource configuration matching quantity; S412: Based on the resource allocation matching amount, compare it with the actual needs of key links in digital transformation, analyze the support status of each resource for different links, screen the supply of key resources, and adjust the allocation of each link to obtain the resource support intensity. S413: Invoke the resource support strength, determine the coverage of process and upgrade requirements, analyze the priority of process support and upgrade support, calculate each matching relationship, and obtain the resource support coefficient.

7. The multi-dimensional evaluation method for digital transformation of SMEs according to claim 1, characterized in that, The steps also include: S5: Based on the resource support coefficient, calculate the number and distribution of risk points in the transformation process, analyze the scope and weight of the risk points, determine the role of key risks in the continuity of transformation, identify risk items with high impact, and obtain transformation risk indicators. The transformation risk indicators include risk classification items, impact intensity factors, and continuous interference items.

8. The multi-dimensional evaluation method for digital transformation of SMEs according to claim 7, characterized in that, The specific steps for obtaining the transformation risk indicators are as follows: S511: Based on the resource support coefficient, identify the risk points in the process of digital transformation, calculate the resource support strength of the risk points, determine their distribution characteristics in the business process, screen the risks that affect the stability of resource support, and obtain the support strength of the risk points. S512: Based on the support strength of the risk points, compare the functional nodes of each risk point in the enterprise's digital transformation system, analyze the impact range and risk weight of each risk point, screen the risks that have a key impact on the transformation, and obtain the impact magnitude of key risks. S513: Based on the impact magnitude of the key risks, determine the intensity of interference to the continuity of enterprise digital transformation, analyze the matching between the scope of impact and the strength of resource support, calculate the interference level of key risks in the transformation path, and obtain transformation risk indicators.

9. A multi-dimensional evaluation system for digital transformation of SMEs, characterized in that, The system is used to implement the multi-dimensional evaluation method for digital transformation of SMEs as described in any one of claims 1-8, and the system includes: The process structure analysis module is based on the organizational structure of small and medium-sized enterprises. It analyzes the task history sequence of each business unit, compares the actual distribution of each type of operation action with the difference in the order of process nodes, calculates the time interval offset of each node, and evaluates the gap between the actual process and the standard process by summarizing the repetition of operations and path changes, thus obtaining the process structure offset characteristics. The trend feature extraction module compares the process structure offset features with the production efficiency and equipment utilization rate sequences of each cycle, calculates the trend change direction of each sequence in each cycle, filters out the cycle segments where the direction changes, determines the length and jump frequency of continuous segments, and obtains the trend fluctuation features. Based on the aforementioned trend fluctuation characteristics, the capability adaptability analysis module analyzes the enterprise's information infrastructure, equipment digitization rate, and management process maturity, calculates the degree of matching between each element and the conditions required for digital transformation, determines whether the adaptability of each basic element meets the feasibility requirements, and obtains basic adaptability parameters. Based on the aforementioned basic adaptability parameters, the resource support assessment module screens the company's currently available funds, technology resources, and talent reserves, analyzes the resources' ability to support key aspects of digital transformation, compares the resources' coverage of process and upgrade needs, judges the resource investment's guarantee capability, and obtains the resource support coefficient. Based on the resource support coefficient, the risk assessment module calculates the number and distribution of risk points in the enterprise's digital transformation process, analyzes the scope and weight of the impact of risk points, judges the impact of key risks on the continuity of transformation, identifies risk items with critical impact, and obtains transformation risk indicators.

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