Digital integrated management platform

Abstract

The invention discloses a digital comprehensive management platform, which belongs to the technical field of personnel management and is characterized in that basic information of target personnel and various calculation results calculated in the platform are acquired and stored by building a target personnel basic information base, and cultivation progress indexes corresponding to the target personnel are calculated through data processing; evaluating the cultivation state of the target personnel in combination with the factor types, and implementing hierarchical management measures; the method comprises the following steps: acquiring a corresponding culture scheme for target personnel with the same level of culture state and the same factor type, performing feasibility analysis and culture effect evaluation, ensuring the effectiveness and applicability of the scheme, screening out an optimal culture scheme and a culture scheme sequence, performing accurate culture on the target personnel, improving the culture effect and the management efficiency, and improving the management efficiency of the target personnel. By means of the steps, the platform can achieve precise cultivation of the target person.

Description

Technical Field

[0001] The present invention belongs to the technical field of personnel management. Specifically, it relates to a digital integrated management platform. Background Art

[0002] With the continuous development of technology, digital technology has promoted the transformation of the management work mode, realizing the refinement, standardization and intelligence of integrated management. For example, the introduction of artificial intelligence technology and big data analysis has optimized the resource allocation and work process of personnel management.

[0003] However, while the existing technology conducts digital management of target personnel, it often focuses on the overall average level of target personnel, and lacks personalized analysis of the special needs and behavioral characteristics of individual target personnel. On the one hand, the training effect of target personnel may be greatly reduced due to lack of pertinence. For example, for target personnel with psychological problems or special training needs, if not discovered and provided with personalized help in time, a series of problems may occur during their training. To solve the above problems, the present invention provides the following technical solutions. Summary of the Invention

[0004] The purpose of the present invention is to provide a digital integrated management platform to solve the problem of insufficient personalized analysis of the special needs and behavioral characteristics of individual target personnel in the process of digital management in the existing technology.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A digital integrated management platform, characterized in that the platform provides a method for accurately training target personnel, specifically including the following steps:

[0007] S11. Build a basic information database of target personnel, collect and store the basic information of target personnel, and the basic information of target personnel includes: identity information, training status, factor type, health score, psychological assessment score, labor score, education score;

[0008] S12. Obtain the basic information of target personnel, perform data processing and calculate the training progress index of target personnel;

[0009] S13. Determine the training status of target personnel according to the training progress index of target personnel and conduct hierarchical management of target personnel in combination with the factor type;

[0010] S14. Obtain the training plans of target personnel with the same training status and the same factor type;

[0011] S15. Evaluate the feasibility and training effect of the training plan of target personnel;

[0012] S16. Screen the optimal training plan and the training plan sequence according to feasibility and training effects, and precisely train the target personnel.

[0013] As a further solution of the present invention, the method for obtaining the basic information of the target personnel and performing data processing includes the following steps:

[0014] S21. Obtain the health score, psychological assessment score, labor score, and education score of any target personnel within a preset time t. Each score type forms a data set, and each data set contains several data.

[0015] S22. Obtain the health score data set C, C = C1, C2,..., C i , arrange the health scores in C in ascending order to obtain the sorted health score data set C ‘ = C1 ’ , C ’ 2,..., C ’ i , where i is a positive integer counting index, starting from 1, representing the total number of health scores taken within time t.

[0016] S23. Determine the pruning ratio p of C ‘ , p is a percentage, and 0% ≤ p < 100%. The specific value is determined by the staff.

[0017] S24. Calculate the number of health score values k that need to be pruned, k = |p × i|. When the calculation result is a decimal, k is rounded down to ensure that k is a positive integer.

[0018] Prune the first k minimum values and the last k maximum values from C ‘ to obtain the pruned health score data set C".

[0019] S25. According to the data processing method described in S21 - S24, obtain the pruned psychological assessment score data set D", labor score data set F", and education score data set E".

[0020] As a further solution of the present invention, the method for calculating the training progress index of the target personnel includes the following steps:

[0021] S31. Take the mean of the health scores in C" to obtain the average health score

[0022] S32. Obtain the average health score average psychological assessment score average labor score average education score Denote as Among them, j represents any one of C, D, E, and F;

[0023] S33. Normalize and set their respective weights, and then obtain the training progress index of the target person according to the calculation method of weighted average.

[0024] As a further solution of the present invention, the method for normalizing includes the following steps:

[0025] S41. Obtain the maximum value C max and the minimum value C min in C", and according to the normalization formula:

[0026]

[0027] Normalize the average health score to the interval [0, 1] to obtain the normalized health score C 标 ;

[0028] S42. Obtain in the same way as described in S41: the normalized psychological assessment score D 标 , the normalized labor score E 标 , and the normalized education score F 标 .

[0029] As a further solution of the present invention, the steps for obtaining the training progress index of the target person according to the weighted average method are:

[0030] S51. Obtain the basic weights: ω C , ω D , ω E , ω F , denoted as ω j , and ω C + ω D + ω E + ω F = 1, ω j is greater than 0 and less than 1;

[0031] Set the adjustment range of any weight as Δω j , and the adjustment method of the basic weight includes the following steps:

[0032] S511. Obtain the average value j μ of each score of all target persons of the same factor type in the basic information database of the target person, and record them respectively as: the total average health score C μ , the total average psychological assessment score D μ , the total average labor score E μ , and the total average education score F μ ;

[0033] S512. Calculate the standard deviation σ of any kind of score j , and denote them as the standard deviation of health score σ C , the standard deviation of psychological assessment score σ D , the standard deviation of labor score σ E , the standard deviation of education score σ F ;

[0034] S513. For any target person Y in the same factor type, calculate the deviation coefficient of target person Y on each score type where Y is the target person number, representing any one of the total target persons in the same factor type;

[0035] S514. The formula for calculating the deviation coefficient is:

[0036]

[0037] If reduce the weight of this j - type score;

[0038] If increase the weight of this j - type score;

[0039] If the weight of this j - type score remains unchanged;

[0040] S515. Define a weight adjustment function to calculate the specific value after weight adjustment:

[0041]

[0042] where α is a preset adjustment coefficient, and 0 < α ≤ 1, determined by the staff;

[0043] For the weight ω of a certain j - type score of the target person j , through the formula:

[0044]

[0045] obtain the adjusted weight ω j ';

[0046] The four adjusted weights need to be normalized to satisfy ω' C + ω' D + ω' E + ω' F = 1;

[0047] S52. Obtain the training progress index θ of the target personnel according to the weighted summation formula; set different threshold intervals for the training progress index θ, judge the training status of the target personnel, then conduct hierarchical management of the target personnel in combination with the factor type, and obtain the training plan of the target personnel.

[0048] As a further solution of the present invention, the method for hierarchical management of the target personnel is as follows:

[0049] Set different threshold intervals for the training progress index θ calculated for the corresponding target personnel, divide the training status of the target personnel into several levels, and several levels respectively correspond to several threshold intervals, and judge the training status of the target personnel according to the training progress index of the target personnel.

[0050] As a further solution of the present invention, the method for evaluating the feasibility and training effect of the training plan of the target personnel includes the following steps:

[0051] S71. Obtain the target personnel in the same level of training status and the same factor type, record the total number of target personnel as m, and then obtain n training plans corresponding to the m target personnel, where m>n and both m and n are greater than 0;

[0052] S72. Obtain the start time point t1, the current time point or the time point t2 determined by the staff when any target personnel S executes the corresponding training plan, and calculate the duration t3=t2 - t1 for the corresponding target personnel to execute the training plan;

[0053] S73. Obtain the training progress index θ1 of the target personnel S at the time point t1 and the training progress index θ2 at the time point t2, and obtain the training effect θ3 = θ2 - θ1 within the duration t3 of executing the training plan. Use Q S =θ3÷t3 to confirm the unit time training effect Q of this training plan within the duration t3 S ;

[0054] S74. Repeat the method described in S72 - S73 to obtain the training effects and unit time training effects of all target personnel S in the same level of training status, the same factor type and the same training plan within the same duration t3, and then take the average of the obtained several unit time training effects to obtain the unit time average training effect obtained by this training plan among the target personnel in the same level of training status and the same factor type

[0055] S75. Set the unit time average training effect basic threshold Q r , and compare with Q r . If It is considered that the training plan has not achieved effective results within the duration t3 and has low feasibility;

[0056] S76. Gradually increase Q r , screen out the optimal training plan, sort all training plans in descending order according to the value of the average training effect per unit time to obtain a training plan sequence;

[0057] S77. Obtain the training plan sequences corresponding to the training plans for each level of training status and each type of factor target personnel according to the methods described in S71 - S76;

[0058] Monitor the training effect per unit time of the training plan for each target personnel. If the training effect per unit time is lower than the basic threshold Q of the training effect per unit time r , then replace the training plan of this target personnel with the optimal training plan in the training plan sequence corresponding to the same level of training status and the same type of factor of this target personnel, and continuously monitor for an execution period, and the duration of the execution period is determined by the staff;

[0059] If the training effect per unit time of this training plan during the execution period is still lower than Q r , then obtain the next training plan backward in the corresponding training plan sequence, replace the current training plan, and continuously monitor for an execution period until the training effect per unit time obtained by the replaced training plan reaches the standard of Q r .

[0060] As a further solution of the present invention, the training status, factor type, health score, psychological assessment score, labor score, and education score described in S11 need to be associated and aligned with the identity information of the target personnel.

[0061] As a further solution of the present invention, the platform further includes:

[0062] A central processing unit for data analysis and processing calculation steps;

[0063] A display for real - time displaying the basic information of the target personnel and the calculation results of any data calculated in this solution to the staff;

[0064] A target personnel basic information database for storing any data calculated in this solution.

[0065] The beneficial effects of the present invention:

[0066] (1) By calculating the duration from the start of the self-cultivation plan implementation to the current time or a certain time point, the present invention can dynamically monitor the effects of the cultivation plan in different time periods, promptly discover problems and adjust the cultivation plan. This method can quickly identify ineffective cultivation plans and replace them in a timely manner, avoiding waste of resources such as manpower and material resources;

[0067] (2) The present invention provides a method of setting a threshold for the cultivation effect of the cultivation plan and gradually increasing the threshold, which can screen out the optimal cultivation plan, ensuring that the selected cultivation plan has high feasibility in practical applications. This method can evaluate multiple cultivation plans simultaneously, select the optimal plan through data comparison, avoid the blindness of a single plan, and continuously improve the cultivation effect by continuously adjusting the threshold and optimizing the plan, forming a dynamic and continuously optimized systematic method;

[0068] (3) The present invention provides a method for eliminating outliers in health scores, psychological assessment scores, labor scores, and education scores, which can effectively reduce the impact of extreme values on the overall data. Combined with normalization processing, it ensures the comparability of different score data, avoiding biases caused by different dimensions. The method of using multiple score data for multi-dimensional evaluation and assigning weights can better reflect the performance of the target personnel in various aspects, realizing personalized evaluation and avoiding the one-sidedness of a single indicator. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The present invention will be further described below with reference to the accompanying drawings.

[0070] Figure 1 is a schematic diagram of the framework structure of a digital integrated management platform of the present invention;

[0071] Figure 2 is a schematic flow chart of the method described in Embodiment 2 of the present invention;

[0072] Figure 3 is a schematic flow chart of the method described in Embodiment 4 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0073] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0074] Embodiment 1

[0075] A digital integrated management platform, as Figure 1As shown in the figure, the management platform includes:

[0076] A basic information database for target personnel, which is used to collect and store the basic information of target personnel, and the basic information database of target personnel also stores any calculation result described in the present invention;

[0077] The basic information of the target personnel includes: identity information, cultivation status, factor type, health score, psychological assessment score, labor score, and education score;

[0078] The health score, psychological assessment score, labor score, and education score are all obtained after being fairly and publicly evaluated by a professional team or experts according to the actual situation of the target personnel, and the cultivation status, factor type, health score, psychological assessment score, labor score, and education score all need to be associated and aligned in time and space with the identity information of the target personnel to ensure correspondence;

[0079] A central processing unit for processing each specific step in the method for accurately cultivating target personnel provided by this platform.

[0080] A display for displaying the basic information of the target personnel and any calculated data in the present invention, and for staff to access.

[0081] Embodiment 2

[0082] This embodiment discloses a method for processing data of health score, psychological assessment score, labor score, and education score. As Figure 2 shown, the method specifically includes the following steps:

[0083] Obtain any target personnel, and clarify the factor type of the target personnel for subsequent comparison calculations. Set a preset time t according to actual needs, and obtain the health score, psychological assessment score, labor score, and education score of the current target personnel within the time t (the four score data will be replaced by "four types of score data" hereinafter, and any single score data will be replaced by "score data" hereinafter);

[0084] Take several of each of the four types of score data obtained, that is, several health scores, several psychological assessment scores, several labor scores, and several education scores;

[0085] And group the score data of the same type, with each group as a data set, which contains several score data of the same type;

[0086] For example, the health score data set contains several health scores within a preset time t in the past.

[0087] The following content is an example process for health scores, and all four types of score data are processed using this method:

[0088] Obtain a health score dataset, denoted as C;

[0089] The health score dataset C contains several health scores, expressed as C = C1, C2,..., C i ;

[0090] Arrange the health scores in the health score dataset C in ascending order of the score value. The higher the score, the healthier the target person, and the lower the score, the less healthy the target person;

[0091] Obtain the health score dataset after ascending arrangement, denoted as C ‘ = C1 ’ 、C ’ 2、...、C ’ i , where i is a positive integer counting index, starting from 1, representing the total number of health scores taken within time t;

[0092] Determine the pruning ratio p of C ‘ . Generally, the pruning ratio p takes a small percentage, for example: 5% or 10%;

[0093] According to the pruning ratio p and the total number i of health scores in C ‘ , calculate the number k of health score values to be excluded, k = |p × i|;

[0094] When the calculation result has a non - terminating division (i.e., there is a decimal part), perform a floor operation on the value of k. Specifically, the floor operation means taking the largest integer less than the calculation result. Through this operation, ensure that the value of k is always a positive integer during the process of excluding score data, thereby ensuring the accuracy and effectiveness of data processing;

[0095] Exemplarily, if k is calculated to be 5.34 after calculation, then k is 5 after floor operation;

[0096] Prune the first k minimum values and the last k maximum values from the health score dataset C ‘ after ascending arrangement, where k is a positive integer calculated according to the preset pruning ratio; after the above data processing operations, obtain the health score dataset C" after data processing;

[0097] All four types of score data are processed according to the above method to obtain the corresponding psychological assessment score dataset D", labor score dataset F", and education score dataset E" after excluding outliers.

[0098] Exemplarily, it is now necessary to evaluate the training effect of a target person. A target person X is selected, and the factor type is the first factor type. According to the actual needs, the preset time t is set to 60 days;

[0099] Within 60 days, four types of score data of the target person X are obtained: Health score: 75, 80, 90, 85, 95; Psychological assessment score: 60, 70, 70, 65, 80; Labor score: 80, 90, 90, 85, 100; Education score: 80, 75, 70, 85, 90;

[0100] Among them, 5 values are obtained for each type of score. For the 5 values in one type of score, the time interval for obtaining the values is a constant value, which is determined by the staff themselves. For example, the time interval between the first value 75 of the health score and the second value 80 of the health score is 10 days, then the time interval between the second value 80 of the health score and the third value 90 of the health score is also 10 days;

[0101] For the first to fifth values in each of the four types of score data, the acquisition times of the corresponding numbered values should be at the same moment. For example, if the acquisition time of the first value of the health score is 12:00 noon on the first day, then the acquisition times of the first values of the psychological assessment score, labor score, and education score are also 12:00 noon on the first day.

[0102] Group the score data of the same type to form data sets: Health score data set: {75, 80, 90, 85, 95}; Psychological assessment score data set: {60, 70, 70, 65, 80}; Labor score data set: {80, 90, 90, 85, 100}; Education score data set: {80, 75, 70, 85, 90};

[0103] Take the health score data set: {75, 80, 90, 85, 95} for example processing. Arrange it in ascending order according to the health score size to get C ‘ {75, 80, 85, 90, 95};

[0104] Determine the trimming ratio p = 20%, the total number of scores i = 5, and calculate the number of scores k that need to be trimmed = |p×i| = 5×0.2 = 1;

[0105] According to the calculated k value, from C ‘ Eliminate the first 1 minimum value and the last 1 maximum value to obtain the health score data set C" {80, 85, 90} after eliminating outliers. According to the above steps for processing the health score, obtain the psychological assessment score data set D", labor score data set F", and education score data set E" after eliminating outliers respectively.

[0106] Example 3

[0107] This embodiment discloses a method for calculating the training progress index θ of a target person and judging the training status of the target person according to the training progress index of the target person. The method mainly includes the following steps:

[0108] Obtain the calculated results in Embodiment 2: the health score dataset C“, the psychological assessment score dataset D“, the labor score dataset F“, and the education score dataset E“. Calculate the average for each of the four obtained datasets to get: the average health score the average psychological assessment score the average labor score the average education score For the convenience of subsequent calculations and representations, here the four average scores are denoted as where j represents any one of C, D, E, and F;

[0109] For the average health score the average psychological assessment score the average labor score the average education score Perform normalization processing respectively, so that different scoring criteria can be calculated under the same dimension;

[0110] Here, taking the average health score as an example, the other three types of scores are also normalized according to this method. The specific processing steps are as follows:

[0111] First, obtain the maximum value in the health score dataset C“ and denote it as C max and the minimum value and denote it as C min , according to the normalization formula:

[0112]

[0113] Normalize the average health score to the 0, 1 interval to obtain the normalized health score C 标 ;

[0114] And so on, obtain the normalized psychological assessment score D 标 , the normalized labor score E 标 , the normalized education score F 标 ;

[0115] Then, for the average health score the average psychological assessment score the average labor score the average education score Set their respective weights. The weight setting needs to combine the different personal conditions of each target person. The specific setting method includes the following steps:

[0116] First, determine the weights of the respective proportions of the health score, psychological assessment score, labor score, and education score by experts or staff: ω C , ω D , ω E , ω F , denoted as ω j , and ω C + ω D + ω E + ω F = 1, and the values of ω C , ω D , ω E , ω F are all greater than 0 and less than 1;

[0117] Take ω C , ω D , ω E , ω F as the basic weights, and set the adjustment range of any weight to be Δω j . The adjustment method of the basic weights includes the following steps:

[0118] Obtain the average health score of all target personnel of the same factor type within the preset time t Average psychological assessment score Average labor score Average education score Take the average of the average scores of the same type of all target personnel again to obtain the average value j μ of each score of all target personnel of the same factor type, and denote them respectively as: total average health score C μ , total average psychological assessment score D μ , total average labor score E μ , total average education score F μ ;

[0119] Calculate the standard deviation σ j of any one type of score, and denote them respectively as health score standard deviation σ C , psychological assessment score standard deviation σ D , labor score standard deviation σ E , education score standard deviation σ F ;

[0120] For any target personnel Y of the same factor type, calculate the deviation coefficient of target personnel Y on each score type The deviation coefficient represents the degree of deviation of target personnel Y from the group average level on this type of score, and denote them respectively as health score deviation coefficient Psychological assessment score deviation coefficient Labor score deviation coefficient Education score deviation coefficient Among them, Y is the target personnel number, representing any one of the total target personnel numbers of the same factor type;

[0121] The formula for the deviation coefficient is:

[0122]

[0123] If the calculated deviation coefficient of the corresponding score type indicates that the target personnel's score in this j-th type is higher than the group average level. To ensure that the weight calculation is not significantly affected by a certain score, the weight of this j-th type of score should be reduced;

[0124] If indicates that the target personnel's score in this j-th type is lower than the group average level, so the weight of this j-th type of score is increased;

[0125] If indicates that there is no deviation between this type of score and the group average level, and the weight of this j-th type of score remains unchanged;

[0126] Define a weight adjustment function to calculate the specific value after weight adjustment:

[0127]

[0128] This function calculates the specific adjusted value of the weight of the corresponding j-th type of score according to the deviation coefficient where α is a preset adjustment coefficient, and 0 < α ≤ 1, which is used to control the adjustment amplitude and is determined by the staff;

[0129] For the weight ω j of a certain j-th type of score of the current target personnel, and then through the formula:

[0130]

[0131] obtain the adjusted weight ω j ′, ensuring that the adjusted weight ω j ′ is within the adjustment range Δω j . If it does not meet the requirement, modify the adjustment coefficient α until ω j ′ meets the adjustment range;

[0132] Normalize the four adjusted weights to satisfy ω′ C +ω′ D +ω′ E +ω′ F =1;

[0133] According to the weighted sum formula:

[0134] θ = (ω' C ·C 标 + ω' D ·D 标 + ω' E ·E 标 + ω' F ·F 标 ) × 100%

[0135] The training progress index θ of the target personnel is obtained, and different threshold intervals are set for the training progress index θ. The threshold intervals are determined by the staff in combination with the actual situation;

[0136] According to different threshold intervals and in combination with the training progress index θ of the target personnel, the training status of the target personnel is judged, and the target personnel are classified and managed according to the training status of the target personnel and in combination with the factor type.

[0137] Embodiment 4

[0138] This embodiment specifically discloses a method for screening out a training plan sequence among target personnel in the same-level training status and of the same factor type to achieve precise training of the target personnel. As Figure 3 shown, the specific steps are as follows:

[0139] Step 1: Obtain the total number m of target personnel in the same-level training status and of the same factor type, and obtain the respective training plans of the m target personnel. At this time, there should be m plans. Since it is difficult to implement one-to-one personalized training plan adaptation for each target personnel when the number of target personnel is large during the management process of the target personnel, the obtained m training plans are grouped according to the types of the plans, resulting in n types of training plans, and it is considered that the number of target personnel matching each type of training plan is not less than 1 person, and m > n, and both m and n are greater than 0;

[0140] Step 2: Select any target personnel S from the total number m of target personnel, obtain the start execution time point t1 of the training plan corresponding to the target personnel S, and then take the current time point or the time point t2 determined by the staff. Through t3 = t2 - t1, calculate the duration t3 of the execution of the training plan;

[0141] Step 3: Calculate the training progress index θ1 of the target personnel S at the time point t1 and the training progress index θ2 at the time point t2 respectively. Through θ3 = θ2 - θ1, obtain the training effect θ3 of the training plan during the execution duration t3, and use Q S = θ3 ÷ t3 to confirm the unit-time training effect Q of this training plan during the duration t3 S, calculating this indicator can intuitively reflect the actual effects achieved by each training plan for different target personnel;

[0142] Step 4: Repeat the methods described in Step 2 to Step 3 to obtain the training effects achieved by all training plans with the same training status, the same factor type, and the same duration t3 as the target personnel S, calculate the training effect per unit time, and then take the average of the obtained training effects per unit time to obtain the average training effect per unit time achieved by this training plan among target personnel with the same training status and the same factor type.

[0143] Step 5: Set a threshold Q for the average training effect per unit time r , used to judge whether the corresponding training plan meets the expected goal, and compare the average training effect per unit time calculated in Step 4 with the basic threshold Q r to evaluate whether it is satisfied If not satisfied, it is regarded that the corresponding training plan has not achieved effective results during the duration t3 and has low feasibility;

[0144] Step 6: Based on the experience of the staff and the requirements of the current actual environment, gradually increase it in the form of a fixed value on the basis of the basic threshold Q r , aiming to screen and distinguish training plans with insufficiently significant training effects or limited potential from excellent training plans, and sort them in descending order according to the value of the average training effect per unit time to obtain a training plan sequence;

[0145] Step 7: Repeat Step 1 to Step 6 to obtain the training plan sequences corresponding to all levels of training status and target personnel of each factor type;

[0146] Continuously monitor the training effect per unit time achieved by the training plan of each target personnel. If the training effect per unit time achieved by the training plan of a certain target personnel is lower than the basic threshold Q of the training effect per unit time r , then replace the training plan of this target personnel with the training plan with the largest value of the average training effect per unit time, that is, the optimal training plan, in the training plan sequence corresponding to the same training status and the same factor type of this target personnel, and continuously monitor an execution period, and the duration of this execution period is determined by the staff;

[0147] If it is found that the training effect per unit time of this training plan during the execution period is still lower than the basic threshold Q of the training effect per unit time r, then obtain the next training plan backward in the training plan sequence corresponding to this training plan, replace the current training plan, and continuously monitor for an execution cycle until the training effect per unit time achieved by the replaced training plan reaches Q r standards.

[0148] The above content is only an example and illustration of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods to replace them. As long as they do not deviate from the invention or exceed the scope defined by this claim book, they should belong to the protection scope of the present invention.

[0149] It should be stated that: all user data collected in this application is collected with the consent and authorization of the users. And the uses of the user data are all legal and compliant, and the use and processing of the user data comply with the relevant laws, regulations and standards of the relevant regions.

Claims

1. A digital integrated management platform, characterized in that, The platform provides a method for precisely cultivating target personnel, which specifically includes the following steps: S11. Build a basic information database for target personnel, collect and store the basic information of target personnel. The basic information of target personnel includes: identity information, cultivation status, factor type, health score, psychological assessment score, labor score, and education score; S12. Obtain the basic information of target personnel, perform data processing, and calculate the cultivation progress index of target personnel; S13. Determine the cultivation status of target personnel according to the cultivation progress index of target personnel, and conduct hierarchical management of target personnel in combination with the factor type; S14. Obtain the cultivation plans of target personnel with the same cultivation status and the same factor type; S15. Evaluate the feasibility and cultivation effect of the cultivation plans of target personnel; S16. Screen the optimal cultivation plan and cultivation plan sequence according to the feasibility and cultivation effect, and precisely cultivate target personnel.

2. The digital integrated management platform according to claim 1, wherein The method for obtaining the basic information of target personnel and performing data processing includes the following steps: S21. Obtain the health score, psychological assessment score, labor score, and education score of any target personnel within the preset time t. Each score type forms a data set, and each data set contains several data; S22. Obtain the health score dataset C, C = C1, C2,..., C i , arrange the health scores in C in ascending order to obtain the sorted health score dataset C ‘ = C1 ’ , C ’ 2,..., C ’ i , where i is a positive integer counting index, starting from 1, representing the total number of health scores taken within time t; S23. Determine C ‘ The trimming ratio p of is determined, where p is a percentage and 0% ≤ p < 100%, and the specific value is determined by the staff; S24. Calculate the number k of health score values that need to be trimmed, k = |p × i|. When the calculation result is a decimal, k is rounded down to ensure that k is a positive integer; From C ‘ Prune the top k minimum values and the bottom k maximum values from it to obtain the pruned healthy score dataset C " ; S25. Obtain the trimmed psychological assessment score dataset D according to the data processing method described in S21 - S24 " , labor score dataset F " , education score dataset E " .

3. The digital integrated management platform according to claim 2, wherein The method for calculating the cultivation progress index of target personnel includes the following steps: S31. Take the average of the health scores in C " to obtain the average health score S32. Obtain the average health score according to the method in S31 Average psychological assessment score Average labor score Average education score Denote as where j represents any one of C, D, E, and F; S33. Normalize and set their respective weights, and then obtain the training progress index of the target person according to the calculation method of weighted average.

4. The digital integrated management platform according to claim 3, characterized in that For The method for normalization processing includes the following steps: S41. Obtain C " the maximum value C max and the minimum value C min , according to the normalization formula: Normalize the average health score to the range of 0 to 1 to obtain the normalized health score C 标 ; S42. Obtained according to the method described in S41: the normalized psychological assessment score D 标 , the normalized labor score E 标 , the normalized education score F 标 .

5. A digital integrated management platform according to claim 4, characterized in that The steps for obtaining the cultivation progress index of target personnel according to the weighted average method are as follows: S51. Obtain the basic weights: ω C , ω D , ω E , ω F , denoted as ω j , and ω C + ω D + ω E + ω F = 1, ω j is greater than 0 and less than 1; Set the adjustment range of any weight to Δω j , and the adjustment method for the basic weight includes the following steps: S511. Obtain the average value j of each score of all target personnel of the same factor type in the target personnel basic information database μ , which are respectively denoted as: the total average health score C μ , the total average psychological assessment score D μ , the total average labor score E μ , the total average education score F μ ; S512. Calculate the standard deviation σ of any kind of score j , which are respectively denoted as the standard deviation of health score σ C , the standard deviation of psychological assessment score σ D , the standard deviation of labor score σ E , the standard deviation of education score σ F ; S513. For any target person Y in the same factor type, calculate the deviation coefficient of target person Y for each score type. Where Y is the target person number, representing any one of the total target persons in the same factor type. S514. The formula for calculating the deviation coefficient is: If reduce the weight of the score of this j-th class; If increase the weight of the score of this j category; If the weight of the score of class j remains unchanged; S515. Define a weight adjustment function to calculate the specific value after weight adjustment: where α is a preset adjustment coefficient, and 0 < α ≤ 1, which is determined by the staff; The weight ω of a certain type j score of the target person j , through the formula: Obtain the adjusted weight ω j ′ ; The four adjusted weights need to be normalized to satisfy ω ′ C + ω ′ D + ω ′ E + ω ′ F = 1; S52. Obtain the cultivation progress index θ of target personnel according to the weighted summation formula; and set different threshold intervals for the cultivation progress index θ, judge the cultivation status of target personnel, then conduct hierarchical management of target personnel in combination with the factor type, and obtain the cultivation plan of target personnel.

6. A digital integrated management platform according to claim 5, characterized in that The method for hierarchical management of target personnel is: Set different threshold intervals for the calculated cultivation progress index θ of the corresponding target personnel, divide the cultivation status of target personnel into several levels, and several levels respectively correspond to several threshold intervals. Judge the cultivation status of target personnel according to the cultivation progress index of target personnel.

7. A digital integrated management platform according to claim 6, characterized in that, The method for evaluating the feasibility and cultivation effect of the cultivation plans of target personnel includes the following steps: S71. Obtain target personnel with the same cultivation status and the same factor type, record the total number of target personnel as m, and then obtain n cultivation plans corresponding to m target personnel, where m > n and both m and n are greater than 0; S72. Obtain the start time point t1, the current time point, or the time point t2 determined by the staff when any target personnel S executes the corresponding cultivation plan, and calculate the duration t3 = t2 - t1 for the corresponding target personnel to execute the cultivation plan; S73. Obtain the cultivation progress index θ1 of the target person S at time point t1 and the cultivation progress index θ2 at time point t2, and obtain the cultivation effect θ3 = θ2 - θ1 within the duration t3 of implementing the cultivation plan. Use Q S = θ3 ÷ t3 to confirm the cultivation effect Q per unit time of this cultivation plan within the duration t3 S ; Repeat the method described in S72 - S73 to obtain all the cultivation effects and cultivation effects per unit time of the same cultivation status, the same factor type, and the same cultivation plan for the same duration t3 as the target person S. Then, take the mean of the obtained cultivation effects per unit time to obtain the average cultivation effect per unit time achieved by this cultivation plan among target persons with the same cultivation status and the same factor type. S75. Set the basic threshold Q for the average cultivation effect per unit time r , compare with Q r . If is regarded as that the cultivation plan has not achieved effective results within the duration t3 and has low feasibility; S76. Gradually increase Q r , screen out the optimal culture plan, sort all culture plans in descending order according to the values of the average culture effect per unit time to obtain a culture plan sequence; S77. Obtain the cultivation plan sequence of the cultivation plans corresponding to target personnel of each level of cultivation status and each factor type according to the method described in S71 - S76; Monitor the training effect per unit time of the training plan for each target person. If the training effect per unit time is lower than the basic threshold Q of the training effect per unit time r , then replace the training plan of this target person with the optimal training plan in the training plan sequence corresponding to the same training status and the same factor type of this target person, and continuously monitor for an execution period, and the duration of the execution period is determined by the staff; If the per-unit-time cultivation effect during the implementation of the cultivation plan is still lower than Q r , then obtain the next cultivation plan in the corresponding cultivation plan sequence, replace the current cultivation plan, and continuously monitor an execution cycle until the per-unit-time cultivation effect achieved by the replaced cultivation plan reaches Q r 's standard.

8. A digital integrated management platform according to claim 1, characterized in that The cultivation status, factor type, health score, psychological assessment score, labor score, and education score described in S11 need to be associated and aligned with the identity information of the target person.

9. A digital integrated management platform according to any one of claims 1 to 8, characterized in that The platform further includes: A central processing unit for data analysis and processing calculation steps; A display for real-time displaying the basic information of the target person and the calculation results of any data calculated in this solution to the staff; A basic information database of the target person for storing any data calculated in this solution.

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