A method for matching a person to a store based on a store operating pressure image and related components

Abstract

The application discloses a kind of based on store operating pressure image person store matching method and related components.The method includes: obtaining target store and candidate data;Store operating pressure image is constructed, and the stress score of each task dimension and task weight are obtained;Candidate ability image is constructed, including current ability and historical improvement ability;Based on historical improvement evidence intensity dynamic fusion to obtain comprehensive task ability;Accordingly, calculate task matching score, and generate rule matching score by combining experience, performance, stable adaptive fraction;For the candidate person with insufficient ability in key stress dimension, execute stress gate, limit matching score to the upper limit determined by ability, avoid comprehensive high score to cover key short board;Independent calculation deployment release state;Finally, the recommended result is output in combination with the matching score after gate and release state.The application effectively prevents key ability loss from being compensated by other high-score items through nonlinear capping mechanism, improving the accuracy and organizational executability of person-store matching.

Description

Technical Field

[0001] This invention relates to the field of data processing, and in particular to a method for matching people and stores based on a profile of store operational stress and related components. Background Technology

[0002] In multi-store operation organizations such as chain hotels, chain retail, and chain restaurants, the operational problems faced by different stores at the same time vary significantly: some stores mainly bear the pressure of revenue, profit, and occupancy rates, while others face insufficient membership growth, and still others need to repair their reputation for quality. When selecting a manager for a store, it is necessary to determine whether a particular candidate is suitable for solving the most critical problems currently facing the target store.

[0003] Existing personnel recommendation or job matching methods typically start from job requirements or candidate capabilities, using simple tag matching, recent performance rankings, or linearly weighted scoring to generate recommendation results. While these methods can comprehensively consider multiple aspects such as a candidate's experience, performance, and stability, the use of linear weighting to sum the scores for each item means that a candidate's high scores in experience, stability, or overall performance can compensate for or offset their shortcomings in key pressure dimensions of the target store.

[0004] As a result, a candidate with a high overall score but lacking the ability to solve the target store's current core problems may still be ranked high in the recommendation results, leading to a mismatch between a high overall score and an inability to address the store's core issues. This problem is particularly severe when the target store faces significant single operational pressures (such as a continuous decline in revenue or a deterioration in quality and reputation), directly impacting the accuracy and effectiveness of store manager selection.

[0005] Therefore, existing recommendation systems, when processing store operation data and candidate resume data with different dimensions and levels of completeness, are prone to amplifying the influence of sparse or low-confidence historical data due to the use of fixed weights for fusion. Furthermore, linear weighting can lead to ranking biases where low values ​​in the key task dimension are compensated for by high values ​​in other dimensions. How to uniformly represent and constrain the confidence levels of the aforementioned multi-source data, and suppress the compensation effect of low values ​​in the key task dimension during the ranking calculation process, is a technical problem that needs to be solved. Summary of the Invention

[0006] The purpose of this invention is to provide a method for matching people and stores based on a profile of store operational stress, aiming to improve the accuracy of this matching.

[0007] In a first aspect, embodiments of the present invention provide a method for matching people and stores based on a profile of store operational pressure, including: Acquire target store data and candidate data; A store operation pressure profile is constructed based on the target store data. The store operation pressure profile includes the pressure score of the target store in at least one task dimension, and the task weight of each task dimension is determined based on the pressure score. A candidate capability profile is constructed based on the candidate data, and the candidate capability profile includes the candidate's current capability and historical improvement capability in each of the task dimensions; Determine the strength of historical improvement evidence for the candidate in each of the task dimensions, dynamically determine the fusion weight of the historical improvement capability based on the strength of historical improvement evidence, and fuse the current capability and historical improvement capability accordingly to obtain the candidate's comprehensive task capability in each of the task dimensions. The task matching score is calculated based on the task weight and the comprehensive task capability, and a rule matching score is generated by combining the candidate's experience matching score, performance capability score and stability fit score. The task dimensions that meet the preset pressure conditions are determined as key pressure dimensions; when the candidate's overall task capability in any of the key pressure dimensions is lower than the capability threshold corresponding to the key pressure dimension, pressure gating is performed on the rule matching score to limit it to no more than the gating upper limit determined by the overall task capability, and the gating matching score is obtained. Based on the gated matching and output, the store matching recommendation results are generated.

[0008] Secondly, embodiments of the present invention provide a store-person matching system based on a profile of store operational pressure, comprising: The data acquisition unit is used to acquire target store data and candidate data. The store profile unit is used to construct a store operation pressure profile based on the target store data. The store operation pressure profile includes the pressure score of the target store in at least one task dimension, and the task weight of each task dimension is determined based on the pressure score. The candidate profiling unit is used to construct a candidate capability profile based on the candidate data. The candidate capability profile includes the candidate's current capability and historical improvement capability in each of the task dimensions. The task capability calculation unit is used to determine the strength of historical improvement evidence for the candidate in each of the task dimensions, dynamically determine the fusion weight of the historical improvement capability based on the strength of historical improvement evidence, and fuse the current capability and historical improvement capability accordingly to obtain the candidate's comprehensive task capability in each of the task dimensions. The matching score calculation unit is used to calculate the task matching score based on the task weight and the comprehensive task capability, and combine the candidate's experience matching score, performance capability score and stability fit score to generate a rule matching score. The matching score gating unit is used to determine the task dimensions that meet the preset pressure conditions as key pressure dimensions; when the candidate's comprehensive task ability in any of the key pressure dimensions is lower than the capability threshold corresponding to the key pressure dimension, pressure gating is performed on the rule matching score to limit it to no more than the gating upper limit determined by the comprehensive task ability, and the gating matching score is obtained. The recommendation unit is used to output a store matching recommendation result based on the gating and matching output.

[0009] The beneficial effects of this invention are as follows: it quantifies store needs into pressure scores and task weights in the task dimension, enabling the matching target to shift from static positions to dynamic task adaptation; it improves the robustness of the capability profile in data-sparse scenarios by dynamically integrating current and historical capabilities through historical improvement evidence strength; the non-linear capping mechanism of pressure gating effectively prevents key weaknesses from being masked by other high-scoring items, reducing the risk of mismatch; and it improves the overall accuracy, stability, and interpretability of human-store matching. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A flowchart illustrating the human-store matching method based on store operational pressure profiles provided in this embodiment of the invention. Figure 1 .

[0012] Figure 2 This is a schematic diagram of the S20 sub-process provided in an embodiment of the present invention.

[0013] Figure 3 This is a schematic diagram of the S40 sub-process provided in an embodiment of the present invention.

[0014] Figure 4 This is a schematic diagram of the S50 sub-process provided in an embodiment of the present invention.

[0015] Figure 5 This is a schematic diagram of the S60 sub-process provided in an embodiment of the present invention.

[0016] Figure 6 A flowchart illustrating the human-store matching method based on store operational pressure profiles provided in this embodiment of the invention. Figure 2 .

[0017] Figure 7 Another flowchart illustrating the human-store matching method based on store operational pressure profiles provided in this embodiment of the invention. Figure 3 .

[0018] Figure 8 This is a schematic block diagram of a human-store matching system based on a profile of store operational pressure, provided in an embodiment of the present invention.

[0019] Figure 9 A schematic block diagram of a computer device provided for an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0022] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0023] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0024] Please see Figure 1 , Figure 1 A flowchart illustrating the human-store matching method based on a store operation pressure profile provided in an embodiment of the present invention; like Figure 1 As shown, the method includes steps S10 to S70.

[0025] S10. Obtain target store data and candidate data.

[0026] S20. Construct a store operation pressure profile based on the target store data. The store operation pressure profile includes the pressure score of the target store in at least one task dimension, and determine the task weight of each task dimension based on the pressure score.

[0027] like Figure 2 As shown, one embodiment of step S20 includes: S201. Obtain the original operating indicators of the target store in at least one aspect of operation, membership, and quality from the target store data, with each aspect corresponding to a task dimension; S202. Obtain the original operating indicators corresponding to each task dimension, and normalize the original operating indicators to a unified value range to obtain the standardized indicator vector corresponding to each task dimension. S203. Calculate at least one pressure factor for the corresponding task dimension based on the standardized indicator vector. The pressure factor includes at least one of the following: recent performance pressure, year-on-year change pressure, budget gap pressure, regional comparison pressure, and trend fluctuation pressure. The recent performance pressure is determined based on the degree of decline of the current value of the task dimension indicator relative to the recent baseline value of the target store. The year-on-year change pressure is determined based on the decrease of the current value of the task dimension indicator relative to the value of the same period last year. The budget gap pressure is determined based on the gap between the actual value of the task dimension indicator and the budget target value. The regional comparison pressure is determined based on the degree of lag of the current value of the task dimension indicator relative to the benchmark value of similar stores in the same region. The trend fluctuation pressure is determined based on the fluctuation range or deteriorating trend of the task dimension indicator within the recent time window. S204. The pressure factors of the corresponding task dimension are weighted and summed to obtain the pressure score of the target store in the corresponding task dimension. S205. Obtain the store lifecycle from the target store data, and determine the weight adjustment amount of each task dimension based on the store lifecycle; S206. Normalize the pressure score of each task dimension with the corresponding weight correction amount to obtain the task weight of each task dimension.

[0028] In steps S201-S206, during the process of constructing a profile of store operational pressure, the system extracts raw operational indicators from the target store data in three aspects: operations, membership, and quality. Operational indicators may include profit completion rate, revenue completion rate, and revenue per room; membership indicators may include the number of new members, member repurchase rate, and member consumption percentage; quality indicators may include online ratings, negative review rate, and number of complaints. Each aspect naturally corresponds to a task dimension. Since the units of measurement and distribution ranges of each indicator are different, the raw indicators with defined upper and lower bounds under each task dimension are first mapped to a preset value range using min-max normalization. For raw indicators standardized using Z-score, after obtaining the standardization results, further truncation, translation, and scaling are performed according to preset boundaries to map them to a unified value range of [0,1] or [0,100], forming a standardized indicator vector. Next, multiple stress factors are calculated for each task dimension based on standardized indicator vectors, including: recent performance stress, reflecting the gap between the indicator and the target value in the past 30 days; year-on-year change stress, reflecting the degree of decline compared to the same period last year; budget gap stress, measuring the deviation between the actual value and the budget; regional comparison stress, using methods such as ranking at the bottom of the same business district or brand stores; and trend fluctuation stress, capturing the slope and standard deviation of the indicator changes in recent months. The purpose of these stress factors is to comprehensively depict the stress experienced by stores in this dimension from different time spans and comparison perspectives. Then, the stress factors under each dimension are weighted and summed according to preset weights, for example, recent performance stress 30%, year-on-year change stress 20%, budget gap stress 20%, regional comparison stress 15%, and trend fluctuation stress 15%, thus obtaining the stress score for that task dimension. Based on this, store lifecycle information, such as ramp-up, maturity, and rectification periods, is obtained from the target store data, and weight adjustments are set for each task dimension according to the lifecycle. For example, the operation and membership dimensions of stores in the ramp-up period can be positively adjusted, while the quality dimension of stores in the rectification period is adjusted more. Finally, the stress scores for each task dimension are combined with their corresponding weight adjustments, and then processed using softmax or linear normalization to obtain the task weights for each task dimension. If the stress score cannot be calculated correctly due to missing data, it reverts to the preset default weights (e.g., 0.50 for operations, 0.30 for memberships, and 0.20 for quality) to ensure the normal operation of the process.

[0029] Based on this, the solutions in steps S201-S206, through the weighted synthesis of multi-dimensional pressure factors, can comprehensively quantify the task pressure of stores from multiple perspectives such as recent performance, year-on-year changes, budget gap, regional competition, and trend fluctuations, avoiding the one-sidedness that may be brought about by a single indicator; the introduction of the store life cycle to modify the task weights makes the weight allocation more in line with the objective needs of the store's development stage, thereby providing a more accurate reference for the importance of tasks for subsequent matching and improving the targeting of matching.

[0030] S30. Construct a candidate capability profile based on the candidate data, the candidate capability profile including the candidate's current capability and historical improvement capability in each of the task dimensions; In step S30, the current capability includes the candidate's recent performance in each of the aforementioned task dimensions; the historical improvement capability includes the candidate's improvement results in at least one of the following indicators: revenue, profit, occupancy rate, membership, and quality, in the stores where the candidate has previously worked. For positive indicators such as revenue, profit, occupancy rate, and membership, the improvement result is represented by the increase in the indicator; for negative indicators such as negative review rate and complaint volume, the improvement result is represented by the decrease in the indicator. The system unifies the direction and scale of different improvement indicators, mapping historical improvement capabilities and current capabilities to the same value range.

[0031] S40. Determine the strength of historical improvement evidence for the candidate in each of the task dimensions, dynamically determine the fusion weight of the historical improvement capability based on the strength of historical improvement evidence, and fuse the current capability and historical improvement capability accordingly to obtain the candidate's comprehensive task capability in each of the task dimensions.

[0032] In step S40, the strength of historical improvement evidence for the candidate in each of the task dimensions is determined by: determining the strength of historical improvement evidence for the corresponding task dimension based on at least one of the following: the number of valid historical improvement indicators related to the candidate in the corresponding task dimension, the number of historical appointment records, the historical result missing rate, the historical result confidence level, and the historical result abnormal fluctuation information.

[0033] Specifically, the assessment of a candidate's historical improvement evidence strength is conducted on a task-by-task basis. For a specific task dimension (e.g., business growth), improvement data from all of the candidate's past stores is reviewed to identify which indicators related to business improvement have complete start and end values ​​and allow for calculation of improvement magnitude. These indicators are designated as valid historical improvement indicators, and their number constitutes the "number of valid historical improvement indicators." Simultaneously, the number of the candidate's past employment records is obtained as background reference, and the historical result missing rate is calculated—the proportion of all recordable improvement indicators with empty or invalid results. The confidence level of historical results is determined based on the data source. Structured data obtained directly from audited operating or financial systems has higher confidence, while data from manual reports or verbal feedback has lower confidence. Furthermore, anomaly analysis is performed on historical improvement results. If the improvement magnitude of the same candidate varies significantly across different stores, with a large standard deviation, it is considered anomaly, indicating that historical improvement capabilities may be affected by unforeseen factors, and the evidence strength should be weakened. The system can synthesize an evidence strength value from the above factors using a rule engine or scoring model. For example, a base score is preset. First, a step coefficient is applied based on the number of valid indicators (coefficient is 1.0 when the number is ≥3, 0.7 when it is 2, 0.4 when it is 1, and 0 when it is 0). Then, it is multiplied by the historical result confidence coefficient, and additional discounts are applied based on the missing rate and abnormal fluctuations. Finally, the historical improvement evidence strength for this task dimension is output. After normalization, this evidence strength value falls within the interval [0,1]. The lower the value, the more cautious the use should be.

[0034] like Figure 3 As shown, step S40, the process of confirming the candidate's comprehensive task capabilities across each task dimension, includes: S401. Determine the maximum allowed historical weight for each task dimension based on the store task type or pressure dimension corresponding to each task dimension. S402. Combine the maximum historical weight of each task dimension with the strength of historical improvement evidence to obtain the fusion weight of the historical improvement capability of each task dimension. S403. The historical improvement capability is weighted by the fusion weight, and the current capability is weighted by the difference between 1 and the fusion weight. The weighted results are summed to obtain the candidate's comprehensive task capability in each task dimension. The historical improvement evidence strength, maximum historical weight, and fusion weight are all located in the interval [0,1]. Specifically, when the number of effective historical improvement indicators is lower than the preset number or the confidence level of the historical results is lower than the preset confidence level, the fusion weight is reduced, thereby increasing the proportion of the current capability in the overall task capability.

[0035] In steps S401-S403, after determining the strength of historical improvement evidence for each task dimension, the following capability dynamic fusion stage begins.

[0036] First, based on the type of tasks or main pressure dimensions currently faced by the target store, set a maximum allowable historical weight for each task dimension. For example, if the target store is a typical growth-oriented store, the maximum historical weight for the operations dimension can be set to 0.7, while the maximum historical weight for the quality dimension can be set to 0.4; if it is a quality-improvement-oriented store, the maximum historical weight for the quality dimension is increased, and the weight for the operations dimension is decreased. This maximum historical weight reflects the upper limit of the required historical problem-solving ability of the candidate in a specific scenario.

[0037] Then, the maximum historical weight of each task dimension is combined with the previously calculated strength of historical improvement evidence, as follows: e j =n j ×r j ; λ j =λ j ^max×e j ; m j =(1-λ j )u j +λ j v j ; Among them, e j To improve the strength of evidence for historical purposes, n j The count coefficient r corresponds to the number of effective historical improvement fields. j λ represents the confidence level of historical results. j ^max represents the maximum historical weight, u j and v j These represent the current capability and historical improvement capability, respectively, which have been mapped to the same value range. j This is to enhance overall task capabilities. Therefore, when the strength of evidence is low (e.g., insufficient number of valid indicators or confidence levels below a threshold), the fusion weights are automatically and significantly reduced.

[0038] Finally, overall task capability C d Based on current ability C cur and historical improvement capabilities H d The overall task capability C is calculated based on the fusion weights, i.e., according to the following formula. d : C d =w fuse ×H d +(1-w fuse )×C cur ; Among them, when the strength of evidence is insufficient, the fusion weight decreases, making C d It relies more on current capabilities. It should be noted that the historical improvement evidence strength E described in this embodiment... d Maximum historical weight w max and the fusion weight w obtained by multiplying the two. fuse All values ​​are taken within the interval [0,1], thus ensuring the comprehensive task capability C. d This is a convex combination of current capability and historical improvement capability with weights in the range [0,1]. Its value will not exceed the range defined by the current capability and historical improvement capability, avoiding capability scores that exceed limits or become distorted during the fusion process. For example, if the maximum historical weight for the operational dimension is 0.6 and the evidence strength is 0.8, then the fusion weight is 0.48. If the historical improvement capability is 85 points and the current capability is 75 points, the overall capability is approximately 0.48 × 85 + (1 - 0.48) × 75 ≈ 79.8 points. If the evidence strength of another candidate is only 0.2, the fusion weight drops to 0.12, and the overall capability is closer to the current capability, with the historical improvement portion playing only a weak reference role. This dynamic process achieves two aspects of regulation: the task type determines the theoretical upper limit of historical capability, while the evidence strength determines the degree to which this upper limit is utilized based on the actual data quality.

[0039] Based on this, the solution in steps S401-S403 uses a two-layer adjustment mechanism that determines the maximum historical weight for task type and calibrates the actual fusion weight for evidence strength. This ensures that the candidate's historical improvement capabilities are integrated to both match the store's task requirements and are reasonably constrained by the reliability of historical data. This avoids the defects of insufficient historical data under fixed weights leading to distorted profiles or insufficient utilization when data is sufficient, thus improving the flexibility and accuracy of comprehensive task capability calculation.

[0040] S50. Calculate the task matching score based on the task weight and the comprehensive task capability, and generate the rule matching score by combining the candidate's experience matching score, performance capability score and stability fit score.

[0041] like Figure 4 As shown, one embodiment of step S50 includes: S501. The task matching score is obtained by weighting and summing the task weights of each task dimension with the candidate's comprehensive task capabilities in the corresponding task dimension. S502. Based on at least one of the candidate's brand experience, business model experience, business district customer group experience, and life cycle experience, the experience matching score is obtained. S503. The performance ability score is obtained based on at least one of the candidate's recent performance, long-term performance, task-related performance, performance trend, and performance volatility. S504. The stability fit score is obtained based on at least one of the candidate's job stability, resume continuity, and regional adaptability. S505. The task matching score, experience matching score, performance ability score and stability adaptation score are weighted and summed according to preset weight coefficients to obtain the rule matching score.

[0042] In steps S501-S505, the rule matching score is composed of four dimensions. First, the task matching score S... task The weighted summation method is used to calculate the score. The task weight for each task dimension is multiplied by the candidate's overall task ability in that dimension, and then summed. The resulting score directly reflects the candidate's potential to solve the store's core tasks. For example, with an operational weight of 0.6, a membership weight of 0.3, and a quality weight of 0.1, and the candidate's overall operational ability of 80, membership weight of 70, and quality weight of 60, the task matching score would be 0.6 × 80 + 0.3 × 70 + 0.1 × 60 = 75. Secondly, the experience matching score is S. exp To assess the relevance of a candidate's background to the target store's attributes, the candidate's brand experience, business model experience (direct operation or franchise), business district / customer group experience (e.g., business district, scenic area), and lifecycle experience (e.g., preparation for opening, renovation) are extracted and compared with the corresponding attributes of the target store. Matching scores are accumulated, and after normalization, an experience matching score is obtained. Third, performance capability score S. perf The fourth factor is the stable fit score (S). This score is calculated by comprehensively considering the candidate's recent performance ranking and score, long-term average performance, historical performance indicators closely related to the task (such as profit achievement rate corresponding to business tasks), the direction of performance trends, and the degree of performance fluctuation. stab The candidates are scored based on their job stability (e.g., average length of service per store), job continuity (e.g., whether their job experience is continuous, whether there are frequent gaps or abnormal interruptions), and suitability for the target region (e.g., whether it is consistent with or acceptable to the location of the target store).

[0043] Finally, the pre-set weighting coefficients are calculated according to the following formula to match the four-part weighted synthesis rule S. rule : S rule =α1S task +α2S exp +α3S perf +α4S stab ; For example, the preset coefficients α1-α4 are: task matching score 0.40, experience matching score 0.25, performance ability score 0.20, and stability fit score 0.15, but these can be adjusted according to business strategies. The rule matching score allows candidates' comprehensive abilities, experience, performance, and stability fit to be compared on the same scale.

[0044] Based on this, the solution in steps S501-S505 is refined into four sub-items that can be calculated and interpreted independently, and a weighted composite rule is used to match the scores, making the matching scoring logic transparent and adjustable. Each sub-item corresponds to actual business considerations, making it easier for the human resources department to understand the composition of the scores and conduct manual review. While maintaining automation efficiency, it provides good interpretability and business adaptability.

[0045] S60. The task dimension that meets the preset pressure condition is determined as the key pressure dimension. When the candidate's comprehensive task ability in any of the key pressure dimensions is lower than the capability threshold corresponding to the key pressure dimension, pressure gating is performed on the rule matching score to limit it to no more than the gating upper limit determined by the comprehensive task ability, and the gating matching score is obtained.

[0046] like Figure 5 As shown, one embodiment of step S60 includes: S601. The task dimensions for which the pressure score is greater than the pressure threshold and the candidate's overall task ability in the corresponding task dimension is lower than the ability threshold are identified as the set of key pressure dimensions for triggering gating. S602. When the set of key pressure dimensions is empty, the rule matching score is used as the gating matching score. S603. When the set of key pressure dimensions is not empty, determine the gating upper limit according to the comprehensive task capability corresponding to each key pressure dimension in the set, and limit the rule matching score to no more than the minimum value in the gating upper limit to obtain the gating matching score. The lower the overall task capability, the lower the corresponding gating limit.

[0047] In steps S601-S603, after the rule matching score is generated, a pressure gating check is initiated. Preset pressure and capability thresholds are used, for example, a pressure threshold of 0.7 (pressure score range 0-1) and a capability threshold of 55 (capability score range 0-100). All task dimensions are iterated through, and those dimensions where the pressure score is greater than the pressure threshold and the candidate's overall task capability in that dimension is lower than the capability threshold are identified as key pressure dimensions triggering gating, forming a set of key pressure dimensions. If the set is empty, it indicates that there is no significant pressure at the store or that the candidate's capability in the high-pressure dimension meets the standard; in this case, the matching score after gating is directly equal to the rule matching score. If the set is not empty, starting from each key pressure dimension in the set, the corresponding upper limit of the gating is calculated based on the candidate's overall task capability in that dimension. The gating limit is positively correlated with the overall task capability. For example, it can be designed as a segmented mapping: in one implementation where the capability threshold is 55 points, when the overall task capability is in the range [0, 40), the gating limit is 50 points; when the overall task capability is in the range [40, 55), the gating limit is 64 points; when the overall task capability is not lower than 55 points, the pressure gating for that task dimension is not triggered. The gating limit function can also be a continuous function that monotonically does not decrease with the overall task capability. Then, the minimum value among the gating limits of all key pressure dimensions is taken as the final cap value, and the rule matching score is restricted to not exceeding this minimum value, thus obtaining the gating matching score. If the rule matching score itself is lower than this minimum value, the original score remains unchanged. For example, if a store is under extreme operational pressure and the candidate's overall operational task capability is only 48 points, the operational dimension gating is triggered, corresponding to a limit of 64; if its rule matching score is 80, it is compressed to 64. The difference between this non-linear capping and ordinary point deduction is that it does not simply reduce a fixed number of points, but sets an absolute ceiling for the score based on the weakness of the ability. The weaker the ability, the lower the ceiling, which fundamentally reduces the situation where a key weakness is covered up by a high score in other aspects.

[0048] Based on this, the solution in steps S601-S603 uses a pressure gating mechanism to directly limit the upper limit of the matching score by leveraging capability shortcomings. This forces the system to prioritize the candidate's basic ability to solve the core problems of the target store when making recommendations. This effectively avoids the compensatory effect of advantages such as high experience value and high stability on the lack of key capabilities, significantly reduces the probability of mismatch in high-pressure task dimensions, and improves the task-oriented reliability of the recommendation results.

[0049] S70. Output the store matching recommendation result after the gate control is established.

[0050] In summary, through steps S10-S70, this invention quantifies store needs into pressure scores and task weights at the task dimension, shifting the matching target from static job positions to dynamic task adaptation; by dynamically integrating current and historical capabilities through historical improvement evidence strength, it enhances the robustness of capability profiles in data-sparse scenarios; the non-linear capping mechanism of pressure gating effectively prevents key weaknesses from being masked by other high-scoring items, reducing the risk of mismatch.

[0051] In one embodiment, the person-store matching method based on store operational pressure profiles further includes: S70a. Calculate the allocation and release status of candidates independently based on the candidate data, and output the person-store matching recommendation result based on the gating matching score and the allocation and release status.

[0052] Step S70a aims to achieve dual-channel output of pressure gating and matching score, as well as allocation release status. These two channels are independent and do not interfere with each other, preventing candidates who do not meet the allocation criteria from being forcibly recommended due to their high matching scores, and also preventing a candidate's ability matching score from being lowered simply because they are temporarily unavailable for allocation. This ensures the compliance of the recommendation results while fully preserving the assessment information of ability matching.

[0053] More specifically, such as Figure 6 As shown, S70a includes: S70a1. Determine the type of release risk triggered by the candidate based on at least one of the following: the candidate's current tenure, willingness to transfer, eligibility for transfer, current store operating status, whether the current store and the candidate are in a good match, risk of impact from transfer, job rotation conditions, and promotion conditions. S70a2. Accumulate and deduct the preset deduction values ​​corresponding to each triggered release risk type to obtain the candidate's release score, where different release risk types correspond to their respective preset deduction values. S70a3. When a candidate meets the preset strong protection conditions, the candidate is classified as a protected non-allocation state, independent of the release score; otherwise, the candidate is classified as an allocable recommended state, a conditional allocable recommended state, or a protected non-allocation state according to the release score. S70a4. The allocation and release state does not change the gating matching score, which serves as an independent basis for candidate output stratification and recommendation interpretation.

[0054] In steps S70a1-S70a4, the calculation channel for the transfer release status is completely independent of the matching scoring channel. The system extracts information from candidate data, including current tenure, the results of the most recent transfer intention survey, transfer eligibility (e.g., whether the necessary transfer conditions stipulated by the organization have been met), current store operating status (e.g., whether it is in a ramp-up or rectification phase), whether the current store and the candidate are in a favorable match (determined through personnel-store matching assessment), the potential performance impact risk score after transfer, whether the rotation period requirement has been met, and whether the candidate is currently under promotion consideration. Based on this information, the system identifies the types of release risks triggered: for example, less than 12 months of tenure triggers "Insufficient Tenure Risk," "Unwillingness to Transfer" triggers "Insufficient Willingness Risk," failure to meet transfer eligibility requirements triggers "Ineligibility Risk," the current store being in a ramp-up phase and in a favorable match triggers "Favorable Match Protection Risk," and difficulty in succession after transfer triggers "Succession Risk." Each type of release risk corresponds to a preset deduction value, such as 15, 20, or 30 points. The system accumulates all triggered deductions from the initial release score (e.g., 100 points) to obtain the candidate's release score. Simultaneously, strong protection conditions are set, such as the candidate being included in the group's key talent retention list, or the current store being in a severe crisis and the candidate being identified as the only person who can be used to stabilize the situation. When these strong protection conditions are met, regardless of the release score, the candidate can be directly classified as protected and not allocated. For candidates who do not meet the strong protection conditions, they are stratified according to their release score: a release score of 80 or higher is a recommended allocation status, indicating organizational support for allocation; 60 to 79 is a conditional allocation recommendation status, indicating that allocation is feasible but requires additional approval or a supplementary replacement plan; and below 60 is classified as protected and not allocated. The allocation release status does not participate in any calculation or modification of the matching score after gating; it is only displayed as an independent status identifier alongside the matching score in the recommendation list.

[0055] Based on this, steps S70a1-S70a4 decouple the allocation and release status from the matching score, making "the candidate is suitable for the target store" and "the candidate can currently be allocated" two independent decision dimensions, avoiding blindly recommending candidates who are "matched but cannot be moved". The quantitative deduction of release points and the strong protection mechanism can protect the stable operation of key talents and key stores, and improve the executability and security of the recommendation results at the organizational allocation level.

[0056] In some other embodiments, such as Figure 7 As shown, the present invention's method for matching people and stores based on store operational pressure profiles also includes: S100. Generate target store demand text based on the store operation pressure profile, generate candidate profile text based on the candidate capability profile, and calculate the semantic similarity between the target store demand text and the candidate profile text. S200: Obtain cases in the historical case library that are similar to the target store scenario, and calculate the case similarity. S300: Normalize the semantic similarity and case similarity within their respective recall candidate sets. Based on the normalization results and the corresponding preset single-item increment upper limit, determine the semantic recall increment and case recall increment. When the sum of the semantic recall increment and the case recall increment is greater than the preset total recall increment upper limit, scale them proportionally according to their proportions before scaling to obtain a bounded recall increment. For candidates within the preset ranking range, use a cross-coding model to calculate the fine-ranking relevance between the target store demand text and the candidate profile text, and limit the fine-ranking correction value determined based on the fine-ranking relevance to a preset correction interval. S400. Obtain the structured risk label and task gap label corresponding to the candidate based on the candidate data, and determine the risk deduction value based on the structured risk label and task gap label. S500: The bounded recall increment and the fine-ranking correction value are added to the gating matching score, and the risk deduction value is deducted to obtain the matching score to be verified; when the candidate triggers the pressure gating, the matching score to be verified is restricted again to not exceed the corresponding gating upper limit to obtain the final matching score; S600. Output the store matching recommendation result based on the final matching score.

[0057] In steps S100-S600, after rule-based scoring and gating are completed, the system can introduce an AI enhancement module to expand the candidate discovery range. First, the system uses text generation technology to transform structured information such as stress scores and task weights from the store's operational stress profile into a target store demand text, such as "urgently need to improve operating profits and occupancy rates, and stabilize member repeat purchases." Simultaneously, it generates candidate profile text based on candidate capability profiles, describing their main capability characteristics and historical improvement highlights. A pre-trained language model is used to encode the two texts into semantic vectors, and cosine similarity is calculated as semantic similarity. Second, the system retrieves cases similar to the current target store in terms of brand, stress type, and lifecycle from the historical successful matching case library. Capability profiles of successful candidates in these cases are extracted, and the similarity between the current candidate and these successful profiles is calculated, summarizing the case similarity. Next, semantic similarity and case similarity are normalized within their respective recall candidate sets. For example, min-max normalization is used to map the similarity within the same recall candidate set to the [0,1] interval. The normalization results are then multiplied by the corresponding preset single-item increment upper limit to obtain the semantic recall increment and case recall increment, thus ensuring that the contribution of any single recall signal does not exceed its preset upper limit. Subsequently, the semantic recall increment and case recall increment are added together. When the sum of the two is greater than the preset total upper limit of recall increment, it is no longer simply truncated to the upper limit value, but is scaled proportionally according to their proportions before scaling, so that the sum after scaling equals the total upper limit and the relative proportion of the two remains unchanged, resulting in a bounded recall increment; if the sum of the two does not exceed the total upper limit, it is directly used as the bounded recall increment. Based on this, for candidates within a preset ranking range (e.g., the top candidates in the initial ranking), a cross-coding model is further used to input the target store's demand text and the candidate profile text in pairs, calculating the fine-ranking correlation between the two. The fine-ranking correction value determined based on this fine-ranking correlation is then limited to a preset correction range (e.g., [-3, +3] points) to finely correct the ranking of top candidates within a controllable range, avoiding significant fluctuations caused by the fine-ranking signal. Furthermore, the system extracts or identifies structured risk tags (including at least one of the following: persistent low performance markers, job stability risk markers, original store release risk markers, and key task capability gap markers, each derived from verifiable structured records) and task gap markers (such as "operational capability gap" and "quality capability gap") from the candidate data, each tag corresponding to a preset risk deduction value. Based on the gating matching score, a bounded recall increment and fine-ranking correction value are superimposed, and the risk deduction value is subtracted to calculate the final matching score.

[0058] In a further step, for candidates discovered solely by semantic or case recall but not previously included in the rule candidate pool, the system assigns them to a supplementary discovery candidate set and sorts them within the supplementary discovery candidate set according to their independent discovery score, using a discovery score independent of the main list to avoid sharing the same scoring metric with the main list candidates.

[0059] Based on this, the scheme in steps S100-S600, on the stable baseline of rule matching, can capture implicit adaptation relationships not covered by the rules through bounded artificial intelligence increments, thus expanding the scope of candidate discovery. At the same time, through total upper limit constraints and structured risk deduction, the fluctuation impact and potential risks of AI signals on ranking are controlled, ensuring the stability and interpretability of matching results, and achieving an effective combination of rule constraints and AI flexibility.

[0060] This invention also provides a store-person matching system based on a store operational stress profile. This system is used to execute any of the aforementioned store-person matching methods based on store operational stress profiles. Specifically, please refer to... Figure 8 , Figure 8 This is a schematic block diagram of a human-store matching system based on a profile of store operational pressure provided in an embodiment of the present invention.

[0061] like Figure 8 As shown, the store-person matching system based on store operation pressure profiles includes: a data acquisition unit 801, a store profile unit 802, a candidate profile unit 803, a task capability calculation unit 804, a matching score calculation unit 805, a matching score gate control unit 806, and a recommendation unit 807.

[0062] Data acquisition unit 801 is used to acquire target store data and candidate data; The store profile unit 802 is used to construct a store operation pressure profile based on the target store data. The store operation pressure profile includes the pressure score of the target store in at least one task dimension, and the task weight of each task dimension is determined based on the pressure score. The candidate profiling unit 803 is used to construct a candidate capability profile based on the candidate data. The candidate capability profile includes the candidate's current capability and historical improvement capability in each of the task dimensions. The task capability calculation unit 804 is used to determine the strength of historical improvement evidence of the candidate in each of the task dimensions, dynamically determine the fusion weight of the historical improvement capability based on the strength of historical improvement evidence, and fuse the current capability and historical improvement capability accordingly to obtain the candidate's comprehensive task capability in each of the task dimensions. The matching score calculation unit 805 is used to calculate the task matching score based on the task weight and the comprehensive task ability, and combine the candidate's experience matching score, performance ability score and stability fit score to generate a rule matching score. The matching score gating unit 806 is used to determine the task dimensions that meet the preset pressure conditions as key pressure dimensions; when the candidate's comprehensive task ability in any of the key pressure dimensions is lower than the capability threshold corresponding to the key pressure dimension, pressure gating is performed on the rule matching score to limit it to no more than the gating upper limit determined by the comprehensive task ability, and the gating matching score is obtained. Recommendation unit 807 is used to output a store matching recommendation result based on the gating and matching output.

[0063] The system quantifies store needs into pressure scores and task weights, shifting the matching target from static positions to dynamic task adaptation. By dynamically integrating current and historical capabilities through historical improvement evidence strength, it enhances the robustness of capability profiles in data-sparse scenarios. The non-linear capping mechanism of pressure gating effectively prevents key weaknesses from being masked by other high-scoring items, reducing the risk of mismatch. Overall, it improves the accuracy, stability, and interpretability of human-store matching.

[0064] In one embodiment, the human-store matching system based on store operational pressure profiles further includes an AI-enhanced fusion unit; the AI-enhanced fusion unit is used for: Based on the store operation pressure profile, generate target store demand text; based on the candidate capability profile, generate candidate profile text; and calculate the semantic similarity between the target store demand text and the candidate profile text. Obtain cases from the historical case library that are similar to the target store scenario, and calculate the case similarity. The semantic similarity and case similarity are normalized within their respective recall candidate sets. Based on the normalization results and the corresponding preset single-item increment upper limit, the semantic recall increment and case recall increment are determined. When the sum of the semantic recall increment and the case recall increment is greater than the preset total recall increment upper limit, they are scaled proportionally according to their proportions before scaling to obtain a bounded recall increment. For candidates within a preset ranking range, the fine-ranking relevance between the target store demand text and the candidate profile text is calculated using a cross-coding model, and the fine-ranking correction value determined based on the fine-ranking relevance is restricted to a preset correction interval. Based on the candidate data, obtain the structured risk label and task gap label corresponding to the candidate, and determine the risk deduction value based on the structured risk label and task gap label; Based on the gating matching score, the bounded recall increment and the fine ranking correction value are superimposed, and the risk deduction value is deducted to obtain the final matching score; The final matching score will be used to output the store matching recommendation results.

[0065] In this embodiment, on the stable baseline of rule matching, bounded artificial intelligence increments can capture implicit adaptation relationships that are not covered by the rules, thus expanding the scope of candidate discovery. At the same time, through total upper limit constraints and structured risk deduction, the fluctuation impact and potential risks of AI signals on ranking are controlled, ensuring the stability and interpretability of the matching results, and achieving an effective combination of rule constraints and AI flexibility.

[0066] The above system can be implemented as a computer program, which can be used in, for example... Figure 9 It runs on the computer device shown.

[0067] Please see Figure 9 , Figure 9 This is a schematic block diagram of a computer device provided in an embodiment of the present invention. The computer device 900 is a server, which can be a standalone server or a server cluster composed of multiple servers.

[0068] See Figure 9 The computer device 900 includes a processor 902, a memory, and a network interface 905 connected via a system bus 901. The memory may include a non-volatile storage medium 903 and internal memory 904.

[0069] The non-volatile storage medium 903 can store an operating system 9031 and a computer program 9032. When the computer program 9032 is executed, it enables the processor 902 to execute a person-store matching method based on a store operation pressure profile.

[0070] The processor 902 provides computing and control capabilities to support the operation of the entire computer device 900.

[0071] The internal memory 904 provides an environment for the operation of the computer program 9032 in the non-volatile storage medium 903. When the computer program 9032 is executed by the processor 902, the processor 902 can execute a person-store matching method based on the store operation pressure profile.

[0072] The network interface 905 is used for network communication, such as providing data transmission. Those skilled in the art will understand that... Figure 9The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device 900 to which the present invention is applied. The specific computer device 900 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0073] Those skilled in the art will understand that Figure 9 The embodiments of the computer device shown do not constitute a limitation on the specific configuration of the computer device. In other embodiments, the computer device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. For example, in some embodiments, the computer device may include only memory and a processor. In such embodiments, the structure and function of the memory and processor are different from those shown. Figure 9 The embodiments shown are consistent and will not be repeated here.

[0074] It should be understood that, in this embodiment of the invention, the processor 902 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0075] In another embodiment of the invention, a computer-readable storage medium is provided. This computer-readable storage medium may be a non-volatile computer-readable storage medium. The computer-readable storage medium stores a computer program, wherein when executed by a processor, the computer program implements the human-store matching method based on a store operation pressure profile, as described in this embodiment of the invention.

[0076] The storage medium is a physical, non-transient storage medium, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk, or any other physical storage medium capable of storing program code.

[0077] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0078] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for matching people and stores based on a profile of store operational stress, characterized in that, include: Acquire target store data and candidate data; A store operation pressure profile is constructed based on the target store data. The store operation pressure profile includes the pressure score of the target store in at least one task dimension, and the task weight of each task dimension is determined based on the pressure score. A candidate capability profile is constructed based on the candidate data, and the candidate capability profile includes the candidate's current capability and historical improvement capability in each of the task dimensions; Determine the strength of historical improvement evidence for the candidate in each of the task dimensions, dynamically determine the fusion weight of the historical improvement capability based on the strength of historical improvement evidence, and fuse the current capability and historical improvement capability accordingly to obtain the candidate's comprehensive task capability in each of the task dimensions. The task matching score is calculated based on the task weight and the comprehensive task capability, and a rule matching score is generated by combining the candidate's experience matching score, performance capability score and stability fit score. The task dimensions that meet the preset pressure conditions are determined as key pressure dimensions; when the candidate's overall task capability in any of the key pressure dimensions is lower than the capability threshold corresponding to the key pressure dimension, pressure gating is performed on the rule matching score to limit it to no more than the gating upper limit determined by the overall task capability, and the gating matching score is obtained. Based on the gated matching and output, the store matching recommendation results are generated.

2. The method for matching people and stores based on store operational pressure profiles according to claim 1, characterized in that, Based on the target store data, a store operation pressure profile is constructed, and the task weights for each task dimension are determined based on the pressure score, including: Obtain the original operating indicators of the target store in at least one aspect of operation, membership, and quality from the target store data, with each aspect corresponding to a task dimension; Obtain the original operating indicators corresponding to each task dimension, and normalize the original operating indicators to a unified value range to obtain the standardized indicator vector corresponding to each task dimension. Calculate at least one stress factor for the corresponding task dimension based on the standardized indicator vector. The stress factor includes at least one of the following: recent performance stress, year-on-year change stress, budget gap stress, regional comparison stress, and trend fluctuation stress. The pressure score of the target store on the corresponding task dimension is obtained by weighted summation of at least one pressure factor on the corresponding task dimension. Obtain the store lifecycle from the target store data, and determine the weight adjustment amount for each of the task dimensions based on the store lifecycle; The pressure score of each task dimension is combined with the corresponding weight correction amount and then normalized to obtain the task weight of each task dimension.

3. The person-store matching method based on store operational pressure profiles according to claim 1, characterized in that, Determining the strength of historical improvement evidence for each candidate across all task dimensions includes: The strength of historical improvement evidence for the corresponding task dimension is determined based on at least one of the following: the number of valid historical improvement indicators related to the candidate and the corresponding task dimension, the number of historical appointment records, the historical result missing rate, the historical result confidence level, and the historical result abnormal fluctuation information.

4. The person-store matching method based on store operation pressure profiles according to claim 3, characterized in that, The fusion weight of the historical improvement capability is dynamically determined based on the strength of the historical improvement evidence, and the current capability and historical improvement capability are fused to obtain the candidate's comprehensive task capability in each of the task dimensions, including: Based on the store task type or pressure dimension corresponding to each task dimension, determine the maximum allowed historical weight for each task dimension. The maximum historical weight of each task dimension is combined with the strength of historical improvement evidence to obtain the fusion weight of the historical improvement capability of each task dimension. The historical improvement capability is weighted by the fusion weight, and the current capability is weighted by the difference between 1 and the fusion weight. The weighted results are summed to obtain the candidate's comprehensive task capability in each task dimension. The historical improvement evidence strength, maximum historical weight, and fusion weight are all located in the interval [0,1]. Specifically, when the number of effective historical improvement indicators is lower than the preset number or the confidence level of the historical results is lower than the preset confidence level, the fusion weight is reduced, thereby increasing the proportion of the current capability in the overall task capability.

5. The person-store matching method based on store operational pressure profiles according to claim 1, characterized in that, A task matching score is calculated based on the task weight and the comprehensive task capability. This score is then combined with the candidate's experience matching score, performance capability score, and stability fit score to generate a rule-based matching score, including: The task matching score is obtained by weighting and summing the task weights of each task dimension with the candidate’s comprehensive task capabilities in the corresponding task dimension. The experience matching score is obtained based on at least one of the candidate's brand experience, business model experience, business district customer group experience, and life cycle experience. The performance competence score is obtained based on at least one of the candidate's recent performance, long-term performance, task-related performance, performance trends, and performance volatility. The stability fit score is obtained based on at least one of the candidate's job stability, resume continuity, and regional suitability. The task matching score, experience matching score, performance ability score, and stability adaptation score are weighted and summed according to preset weight coefficients to obtain the rule matching score.

6. The person-store matching method based on store operational pressure profiles according to claim 1, characterized in that, Task dimensions that meet preset pressure conditions are identified as key pressure dimensions. When a candidate's overall task capability on any of these key pressure dimensions is lower than the capability threshold corresponding to that key pressure dimension, pressure gating is applied to the rule-matching score, limiting it to no more than the gating upper limit determined by the overall task capability, resulting in a gating matching score, including: The task dimensions for which the stress score is greater than the stress threshold and the candidate's overall task ability in the corresponding task dimension is lower than the ability threshold are identified as the set of key stress dimensions that trigger gating. When the set of key pressure dimensions is empty, the rule matching score is used as the gating matching score; When the set of key pressure dimensions is not empty, the gating upper limit is determined according to the comprehensive task capability corresponding to each key pressure dimension in the set, and the rule matching score is restricted to not exceeding the minimum value in the gating upper limit to obtain the gating matching score. The lower the overall task capability, the lower the corresponding gating limit.

7. The person-store matching method based on store operational pressure profiles according to claim 1, characterized in that, Also includes: The allocation and release status of candidates is calculated independently based on the candidate data, and the person-store matching recommendation result is output based on the gating matching score and the allocation and release status.

8. The person-store matching method based on store operation pressure profiles according to claim 7, characterized in that, The allocation and release status of candidates is independently calculated based on the candidate data, including: The type of release risk triggered by the candidate is determined based on at least one of the following: the candidate's current tenure, willingness to transfer, eligibility for transfer, current store operating status, whether the current store and the candidate are in a good match, risk of impact from transfer, conditions for job rotation, and conditions for promotion. The preset deduction values ​​corresponding to each triggered release risk type are cumulatively deducted to obtain the candidate's release score, where different release risk types correspond to their respective preset deduction values; When a candidate meets the preset strong protection conditions, the candidate is classified as a protected non-allocation state, independent of the release score; otherwise, the candidate is classified as an allocable recommended state, a conditional allocable recommended state, or a protected non-allocation state based on the release score. The allocation and release state does not change the gating matching score, which serves as an independent basis for candidate output stratification and recommendation interpretation.

9. The method for matching people and stores based on store operational pressure profiles according to claim 1, characterized in that, Also includes: Based on the store operation pressure profile, generate target store demand text; based on the candidate capability profile, generate candidate profile text; and calculate the semantic similarity between the target store demand text and the candidate profile text. Obtain cases from the historical case library that are similar to the target store scenario, and calculate the case similarity. The semantic similarity and case similarity are normalized within their respective recall candidate sets. Based on the normalization results and the corresponding preset single-item increment upper limit, the semantic recall increment and case recall increment are determined. When the sum of the semantic recall increment and the case recall increment is greater than the preset total upper limit of the recall increment, the two are scaled proportionally according to their proportions before scaling to obtain a bounded recall increment; for candidates within the preset ranking range, the fine-ranking relevance between the target store demand text and the candidate profile text is calculated using a cross-coding model, and the fine-ranking correction value determined based on the fine-ranking relevance is limited to the preset correction range. Based on the candidate data, obtain the structured risk label and task gap label corresponding to the candidate, and determine the risk deduction value based on the structured risk label and task gap label; The bounded recall increment and the fine-ranking correction value are superimposed on the gating matching score, and the risk deduction value is deducted to obtain the matching score to be verified; when the candidate triggers the pressure gating, the matching score to be verified is restricted again to not exceed the corresponding gating upper limit to obtain the final matching score; The final matching score will be used to output the store matching recommendation results.

10. The method for matching people and stores based on store operational pressure profiles according to claim 9, characterized in that, Also includes: For candidates that are obtained solely by semantic recall or case recall and have not entered the rule candidate set, they are assigned to the supplementary discovery candidate set and sorted within the supplementary discovery candidate set according to their independent discovery score.

11. A person-store matching system based on store operational pressure profiles, characterized in that, include: The data acquisition unit is used to acquire target store data and candidate data. The store profile unit is used to construct a store operation pressure profile based on the target store data. The store operation pressure profile includes the pressure score of the target store in at least one task dimension, and the task weight of each task dimension is determined based on the pressure score. The candidate profiling unit is used to construct a candidate capability profile based on the candidate data. The candidate capability profile includes the candidate's current capability and historical improvement capability in each of the task dimensions. The task capability calculation unit is used to determine the strength of historical improvement evidence for the candidate in each of the task dimensions, dynamically determine the fusion weight of the historical improvement capability based on the strength of historical improvement evidence, and fuse the current capability and historical improvement capability accordingly to obtain the candidate's comprehensive task capability in each of the task dimensions. The matching score calculation unit is used to calculate the task matching score based on the task weight and the comprehensive task capability, and combine the candidate's experience matching score, performance capability score and stability fit score to generate a rule matching score. The matching gating unit is used to identify the task dimensions that meet the preset pressure conditions as the key pressure dimensions. When a candidate’s overall task capability in any of the key pressure dimensions is lower than the capability threshold corresponding to that key pressure dimension, pressure gating is applied to the rule matching score to limit it to no more than the gating upper limit determined by the overall task capability, and the gating matching score is obtained. The recommendation unit is used to output a store matching recommendation result based on the gating and matching output.

12. The store-person matching system based on store operational pressure profiles according to claim 11, characterized in that, Also includes: The allocation and release unit is used to independently calculate the allocation and release status of candidates based on candidate data; The recommendation unit is also used to output a store matching recommendation result based on the gating score and the allocation and release status.

13. The human-store matching system based on store operational pressure profiles according to claim 11, characterized in that, It also includes an AI-enhanced fusion unit; the AI-enhanced fusion unit is used for: Based on the store operation pressure profile, generate target store demand text; based on the candidate capability profile, generate candidate profile text; and calculate the semantic similarity between the target store demand text and the candidate profile text. Obtain cases from the historical case library that are similar to the target store scenario, and calculate the case similarity. The semantic similarity and case similarity are normalized within their respective recall candidate sets. Based on the normalization results and the corresponding preset single-item increment upper limit, the semantic recall increment and case recall increment are determined. When the sum of the semantic recall increment and the case recall increment is greater than the preset total upper limit of the recall increment, the two are scaled proportionally according to their proportions before scaling to obtain a bounded recall increment; for candidates within the preset ranking range, the fine-ranking relevance between the target store demand text and the candidate profile text is calculated using a cross-coding model, and the fine-ranking correction value determined based on the fine-ranking relevance is limited to the preset correction range. Based on the candidate data, obtain the structured risk label and task gap label corresponding to the candidate, and determine the risk deduction value based on the structured risk label and task gap label; The bounded recall increment and the fine-ranking correction value are superimposed on the gating matching score, and the risk deduction value is deducted to obtain the matching score to be verified; when the candidate triggers the pressure gating, the matching score to be verified is restricted again to not exceed the corresponding gating upper limit to obtain the final matching score; The final matching score will be used to output the store matching recommendation results.

14. An electronic device comprising a processor and a memory, wherein the memory stores a computer program, which, when executed by the processor, implements the method according to any one of claims 1-10.

15. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method of any one of claims 1-10.

16. A computer program product comprising a computer program that, when executed by a processor, implements the method of any one of claims 1-10.

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