Data analysis method and device, electronic equipment and storage medium

Abstract

The invention discloses a data analysis method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining group unit time workload data, unit time total benefit data and individual workload data in a current data analysis period; inputting the total unit time benefit data and the input information of each working group into a regression analysis prediction model to obtain predicted workload data; using the unit time workload data of the group to calculate the average workload data of the group; performing correlation analysis on the predicted workload data and the group average workload data to obtain a unit work assignment value of each work group on each management rule; respectively calculating the sum of the work scores of each person by using the unit work assignment value and the individual workload data of each work group on each management rule; and calculating the performance data of each person by using the total amount in the current data analysis period and the sum of the work scores of each person.

Description

Technical Field

[0001] The present application relates to the technical field of medical resource management and data analysis, and in particular to a data analysis method and device, electronic equipment, and storage medium. Background Art

[0002] Driven by science and technology, the current medical industry is undergoing an information-based and intelligent transformation. Therefore, hospitals are currently required to support the intelligent processing of big data and hope that all work involving large amounts of data can be processed in an information-based and intelligent manner.

[0003] Since there are multiple departments, divisions, and teams in a hospital, different departments and teams are responsible for different tasks, and the tasks of a department or team may include many different types of specific tasks, such as the imaging department, which performs X-ray, CT, MR, and PET-CT examinations. In addition, different team members are responsible for different tasks, and the difficulty and contribution of different tasks are usually different, so the work situation is more complicated. Due to the lack of technology to apply such complex situations, the current work performance of hospital staff is still collected manually through work data and benefits, and then the data is counted, and the performance data of each person is calculated based on the statistical results according to the set fixed rules.

[0004] Obviously, this method fails to effectively utilize the resources of big data analysis. More importantly, this method is not only relatively inefficient, but also uses manual analysis according to fixed rules, which is prone to errors and cannot accurately reflect the actual working conditions of various departments, resulting in the inability to guarantee the accuracy of the analyzed results. Summary of the invention

[0005] Based on the above-mentioned deficiencies of the prior art, the present application provides a data analysis method and device, an electronic device, and a storage medium to solve the problem that the prior art is low in efficiency and cannot guarantee the accuracy of the analysis results.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] The first aspect of the present application provides a data analysis method, comprising:

[0008] Obtain the group's workload per unit time data, total benefit per unit time data, and individual workload data within the current data analysis cycle; wherein the group's workload per unit time data includes the workload of each work group on each management rule within each unit time within the current data analysis cycle; the total benefit per unit time data includes the total benefit generated within each unit time within the current data analysis cycle; the individual workload data includes the workload of each person on each management rule within the current data analysis cycle;

[0009] The total benefit data per unit time and the input information of each working group are input into a regression analysis prediction model, and the predicted workload data is obtained through the regression analysis prediction model; wherein the predicted workload data is the predicted workload of each working group on each management rule in each unit time when the total benefit per unit time is generated; the input information of the working group is the statistical result of the input information of each person in the working group; the input information of the person is obtained based on the ability score of the person and the work participation time analysis;

[0010] Using the group workload data per unit time, calculate the group average workload data; wherein one of the group average workload data is the average of the workload of one of the management rules in each unit time of the work group;

[0011] Performing correlation analysis on the predicted workload data and the group average workload data to obtain a unit workload score for each work group under each management rule;

[0012] Using the unit work score value of each work group in each management rule and the individual workload data, respectively calculate the total work score of each person;

[0013] The performance data of each of the personnel is calculated using the total amount in the current data analysis period and the sum of the work scores of each of the personnel.

[0014] Optionally, in the above data analysis method, the step of obtaining the group's workload per unit time data, total benefit per unit time data, and individual workload data within the current data analysis cycle includes:

[0015] Query the total benefit per unit time data within the current data analysis period from the database;

[0016] Based on the pre-configured personal information of each of the personnel, the workload of each of the personnel on each of the management rules in each unit time in the current data analysis cycle is queried through a preset query configuration;

[0017] Counting the sum of the workload of each person on each management rule to obtain the personal workload data;

[0018] Based on the pre-configured group information, the workload of each person belonging to the same working group on each management rule within each unit time is counted to obtain the workload data of the group per unit time.

[0019] Optionally, in the above data analysis method, the correlation analysis is performed on the predicted workload data and the group average workload data to obtain the unit work score value of each work group under each management rule, including:

[0020] Using the workload of the same working group on the same management rule in the predicted workload data and the group average workload data, calculate the work completion index of each working group on each management rule;

[0021] The score value is adjusted using the adjustment coefficient corresponding to the work completion index of each working group in each management rule to obtain the unit work score value of each working group in each management rule.

[0022] Optionally, in the above data analysis method, the sum of the work scores of each person is calculated by using the unit work score value of each work group in each management rule and the personal workload data, including:

[0023] For each of the personnel, the work score of the personnel in each of the management rules is calculated based on the ratio of the unit work score of the work group to which the personnel belongs in each of the management rules to the workload of the personnel in each of the management rules;

[0024] The work scores of the personnel on each of the management rules are accumulated to obtain the total work scores of the personnel in the current data analysis cycle.

[0025] Optionally, in the above data analysis method, the performance data of each person is calculated by using the total amount in the current data analysis period and the sum of the work scores of each person, including:

[0026] Adding up the total work scores of the various personnel to obtain the total work score in the current data analysis period;

[0027] Divide the total amount in the current data analysis period by the total work score in the current data analysis period to obtain a unit score amount;

[0028] The unit score amount is multiplied by the performance data of each of the personnel respectively to obtain the performance data of each of the personnel.

[0029] A second aspect of the present application provides a data analysis device, comprising:

[0030] A data acquisition unit is used to acquire the group workload data per unit time, the total benefit data per unit time, and the individual workload data in the current data analysis cycle; wherein the group workload data per unit time includes the workload of each work group on each management rule within each unit time in the current data analysis cycle; the total benefit data per unit time includes the total benefit generated within each unit time in the current data analysis cycle; the individual workload data includes the workload of each person on each management rule in the current data analysis cycle;

[0031] A prediction unit is used to input the total benefit data per unit time and the input information of each of the working groups into a regression analysis prediction model, and obtain predicted workload data through analysis of the regression analysis prediction model; wherein the predicted workload data is the predicted workload of each of the working groups on each of the management rules in each unit time when the total benefit per unit time is generated; the input information of the working group is the statistical result of the input information of each of the personnel in the working group; the input information of the personnel is obtained based on the ability points of the personnel and the work participation time analysis;

[0032] A mean value calculation unit, used to calculate the group average workload data using the group unit time workload data; wherein one of the group average workload data is the mean value of the workload of one of the management rules of one of the working groups in each unit time;

[0033] A scoring value determination unit, used for performing correlation analysis on the predicted workload data and the group average workload data to obtain a unit workload scoring value of each work group under each management rule;

[0034] A score calculation unit, used to calculate the total work score of each person by using the unit work score assigned by each work group in each management rule and the personal workload data;

[0035] The performance data calculation unit is used to calculate the performance data of each of the personnel using the total amount in the current data analysis cycle and the sum of the work scores of each of the personnel.

[0036] Optionally, in the above-mentioned data analysis device, the data acquisition unit includes:

[0037] A benefit query unit, used for querying the total benefit data per unit time in the current data analysis cycle from a database;

[0038] A personal workload query unit, used to query the workload of each of the personnel on each of the management rules in each unit time in the current data analysis cycle through a preset query configuration based on the pre-configured personal information of each of the personnel;

[0039] A personal workload statistics unit, used to respectively count the total workload of each person on each management rule to obtain the personal workload data;

[0040] The group workload statistics unit is used to count the workload of each person belonging to the same working group on each management rule within each unit time based on pre-configured group information, so as to obtain the group workload data per unit time.

[0041] Optionally, in the above-mentioned data analysis device, the scoring value determination unit includes:

[0042] An indicator calculation unit, used for calculating the work completion indicator of each working group on each management rule by using the workload of the same working group on the same management rule in the predicted workload data and the group average workload data;

[0043] The adjustment unit is used to adjust the scoring value by using the adjustment coefficient corresponding to the work completion index of each working group in each management rule, so as to obtain the unit work scoring value of each working group in each management rule.

[0044] Optionally, in the above-mentioned data analysis device, the score calculation unit includes:

[0045] A first calculation unit is used to calculate, for each of the personnel, a work score of the personnel in each of the management rules based on a ratio of a unit work score of the work group to which the personnel belongs in each of the management rules to the workload of the personnel in each of the management rules;

[0046] The second calculation unit is used to accumulate the work scores of the personnel on each of the management rules to obtain the total work scores of the personnel in the current data analysis cycle.

[0047] Optionally, in the above-mentioned data analysis device, the performance data calculation unit includes:

[0048] A total score calculation unit, used for adding up the total work scores of each of the personnel to obtain the total work score in the current data analysis cycle;

[0049] An equalization unit, used for dividing the total amount in the current data analysis period by the total work score in the current data analysis period to obtain a unit score amount;

[0050] The performance calculation unit is used to multiply the unit score amount by the performance data of each of the personnel to obtain the performance data of each of the personnel.

[0051] A third aspect of the present application provides an electronic device, including:

[0052] Memory and processor;

[0053] Wherein, the memory is used to store programs;

[0054] The processor is used to execute the program, and when the program is executed, it is specifically used to implement any of the data analysis methods described above.

[0055] A fourth aspect of the present application provides a computer storage medium for storing a computer program, which, when executed by a processor, is used to implement a data analysis method as described in any one of the above.

[0056] The present application provides a data analysis method for obtaining the group workload data per unit time, the total benefit data per unit time, and the individual workload data within the current data analysis cycle. Among them, the group workload data per unit time includes the workload of each work group on each management rule in each unit time in the current data analysis cycle. The total benefit data per unit time includes the total benefit generated in each unit time in the current data analysis cycle. The individual workload data includes the workload of each person on each management rule in the current data analysis cycle. Then the total benefit data per unit time and the input information of each work group are input into the regression analysis prediction model, and the predicted workload data is obtained through the regression analysis prediction model. Among them, the predicted workload data is the predicted workload of each work group on each management rule in each unit time when the total benefit in each unit time is generated; the input information of the work group is the statistical result of the input information of each person in the work group; the input information of the person is obtained based on the ability points of the person and the work participation time analysis, so as to consider the work input of the person. And, using the group workload data per unit time, the group average workload data is calculated. Among them, one of the data in the group average workload data is the mean of the workload of a work group on a management rule in each unit time. Then, the predicted workload data and the group average workload data are analyzed for correlation, and the unit work score of each work group in each management rule is obtained, so as to determine the unit work score considering the current actual work situation of each work group, so that the analysis results are consistent with the actual work situation of each work group, and the accuracy of the results is improved. Then, the unit work score of each work group in each management rule and the personal workload data are used to calculate the total work score of each person, and the workload is converted into a score so that the obtained score is consistent with the situation of the management rule. Finally, the total amount in the current data analysis cycle and the total work score of each person are used to calculate the performance data of each person, which is a method for automatically and accurately analyzing performance data, which not only ensures the accuracy of the analysis results, but also does not require manual intervention, thereby improving the efficiency of the analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0058] Figure 1 A flowchart of a data analysis method provided in an embodiment of the present application;

[0059] Figure 2 A flowchart of a method for obtaining data in a current data analysis cycle provided in an embodiment of the present application;

[0060] Figure 3 A flowchart of a method for determining a unit work scoring value provided in an embodiment of the present application;

[0061] Figure 4 A flowchart of a method for calculating the total work score of each person provided in an embodiment of the present application;

[0062] Figure 5 A flowchart of a method for calculating performance data of each person provided in an embodiment of the present application;

[0063] Figure 6 A schematic diagram of the architecture of a data analysis device provided in an embodiment of the present application;

[0064] Figure 7 A schematic diagram of the architecture of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0066] In this application, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0067] The present application embodiment provides a data analysis method, such as Figure 1 As shown, the specific steps include:

[0068] S101, obtaining the group's workload data per unit time, total benefit data per unit time, and individual workload data within the current data analysis cycle.

[0069] The group workload per unit time data includes the workload of each work group on each management rule in each unit time in the current data analysis cycle. For example, if the current data analysis cycle is 1 month and the unit time is 1 day, the group workload per unit time data includes the workload of each work group on each day in a month that complies with each management rule. The workload of a work group is the sum of the workloads of each person in the work group.

[0070] The total benefit per unit time data includes the total benefit generated per unit time in the current data analysis cycle, for example, the total benefit generated per day in a month.

[0071] The personal workload data includes the workload of each person on each management rule in the current data analysis cycle, for example, the workload obtained by person A on each management rule in one month.

[0072] Optionally, the working group can be divided according to the needs, such as departments, working groups or departments. The management rules are specifically set according to the workload management rules of different jobs. They can be set according to the benefits and difficulty of the work. For example, corresponding management rules can be set for X-ray shooting and CT examination.

[0073] Optionally, in another embodiment of the present application, a specific implementation of step S101 is as follows: Figure 2 As shown, the following steps are included:

[0074] S201. Query the total amount of benefit per unit time in the current data analysis cycle from the database.

[0075] Since the total benefit is usually recorded per unit time, the total benefit data per unit time in the current data analysis period can be directly found from the database.

[0076] S202: Based on the pre-configured personal information of each person, the workload of each person on each management rule in each unit time in the current data analysis cycle is queried through a preset query configuration.

[0077] It should be noted that in order to analyze the data of each person, it is necessary to configure the relevant information of each person, such as name, rank, number, age, group, etc. In addition, in order to facilitate subsequent data query, query configuration is also performed, such as SQL statements, work records and other methods, so as to obtain the workload of individuals that meet the performance management rules.

[0078] Since the workload is usually recorded with individuals as units and the workload on each management rule within unit time, it is necessary to first query the workload of each person on each management rule within each unit time in the current data analysis cycle based on the pre-configured personal information of each person through the preset query configuration, and then perform statistics through subsequent steps.

[0079] S203, respectively counting the total workload of each person on each management rule to obtain personal workload data.

[0080] S204. Based on the pre-configured group information, the workload of each person belonging to the same working group on each management rule in each unit time is counted to obtain the group workload data per unit time.

[0081] Since the workload of a working group is the sum of the workloads of each person in the working group, in order to obtain the workload of the working group on a certain management rule within a certain unit of time, it is necessary to sum up the workload of each person in the working group on the management rule within the unit of time.

[0082] S102, input the total benefit per unit time data and the input information of each working group into the regression analysis prediction model, and obtain the predicted workload data through the regression analysis prediction model.

[0083] The predicted workload data refers to the predicted workload of each working group on each management rule in each unit time when the total amount of benefits in each unit time is generated.

[0084] It should be noted that the regression analysis prediction model is an AI regression analysis prediction model, which is pre-trained through manually labeled total unit time benefit data, input information of each working group and group unit time workload data. This enables it to predict the workload of each working group in each unit time and under each management rule through the total unit time benefit and input information of each working group.

[0085] Therefore, the current total benefit per unit time data and the input information of each work group are input into the regression analysis prediction model, and the total benefit per unit time in the current total benefit per unit time data is analyzed through the regression analysis prediction model to obtain the predicted workload data. That is, the workload target that each work group needs to pay for each management rule when generating the total benefit per unit time is obtained.

[0086] Among them, the input information of the working group is the statistical result of the input information of each of the personnel in the working group. The input information of each person is obtained based on the ability score of the person and the work participation time analysis. Specifically, the ability score and work participation time of the configured personnel can be analyzed. Among them, the ability score is the score obtained by scoring the work ability of the personnel. Optionally, the ability of the personnel can be evaluated according to their years of work, professional titles, and relevant skill certificates, and points can be assigned. For example, basic personnel are 100 points, senior professional titles are +30 points, deputy senior professional titles are added 20 points, intermediate professional titles are added 10 points, and years of work are added 1 point for every year. Special certificates are assigned points according to actual conditions. The work participation time is the time invested in the work, so that the contribution of personnel at work and the difficulty of work are taken into account through the ability score and work participation time.

[0087] S103. Calculate the group's average workload data using the group's workload data per unit time.

[0088] Among them, one of the data in the group average workload data is the average of the workload of a work group on a management rule in each unit time, that is, the sum of the workload of a work group on a management rule in each unit time divided by the number of unit time.

[0089] S104. Perform correlation analysis on the predicted workload data and the group average workload data to obtain the unit workload score of each work group under each management rule.

[0090] It should be noted that since different jobs have different levels of difficulty, the effort and benefits obtained for the same amount of work are different, so it is necessary to convert the workload into corresponding scores through the scoring method according to the various management rules.

[0091] Since the predicted workload data is the workload of a work group in theory, that is, the workload target, and the group average workload data is the actual work of the work group, through the direct correlation and comparison between the two, we can know the work completion status of each work group, and in order to make the analysis results consistent with the actual work situation of each work, instead of using a fixed allocation method, the results are more accurate and reasonable. Therefore, it is necessary to set the score that the unit work should get in each management rule according to the work situation of the work.

[0092] Optionally, in another embodiment of the present application, a specific implementation of step S104 is as follows: Figure 3 As shown, the following steps are included:

[0093] S301. Using the workload of the same working group under the same management rule in the predicted workload data and the group average workload data, calculate the work completion index of each working group under each management rule.

[0094] Specifically, the work completion rate may be calculated by using the ratio of the workload of the same working group under the same management rule in the predicted workload data and the group average workload data.

[0095] S302. Use the adjustment coefficient corresponding to the work completion index of each working group under each management rule to adjust the scoring value, so as to obtain the unit work scoring value of each working group under each management rule.

[0096] It should be noted that for each work group's unit work score in each management rule, an initial unit work score will be set in advance based on the corresponding work difficulty, etc. Therefore, at this time, the initial score will be adjusted using the corresponding adjustment coefficient based on the actual work situation of the work group, so as to obtain the current unit work score of each work group in each management rule.

[0097] S105. Calculate the total work score of each person by using the unit work score assigned to each work group in each management rule and the individual workload data.

[0098] Specifically, the workload of each person in the personal workload data on each management rule can be converted into corresponding scores according to the unit work score of the work group to which he belongs on each management rule, and then the scores on each management rule are summed to obtain the total work score of each person.

[0099] Optionally, in another embodiment of the present application, a specific method of step S105 is as follows: Figure 4 As shown, including:

[0100] S401. For each person, the work score of the person in each management rule is calculated based on the ratio of the unit work score of the work group to which the person belongs in each management rule and the workload of the person in each management rule.

[0101] S402: Accumulate the work scores of the personnel on each management rule to obtain the total work scores of the personnel in the current data analysis cycle.

[0102] S106. Calculate the performance data of each person using the total amount in the current data analysis cycle and the total work score of each person.

[0103] Since the sum of each person's work scores represents their contribution in the current data analysis cycle, we can use the sum of each person's work scores to determine the proportion of their contribution, and allocate the total amount in the current data analysis cycle according to the proportion to obtain each person's performance data.

[0104] Optionally, in another embodiment of the present application, a specific implementation of step S106 is as follows: Figure 5 As shown, the following steps are included:

[0105] S501. Add up the total work scores of each person to obtain the total work score in the current data analysis cycle.

[0106] S502. Divide the total amount in the current data analysis period by the total work score in the current data analysis period to obtain the unit score amount.

[0107] S503. Multiply the unit score amount by the performance data of each person to obtain the performance data of each person.

[0108] The embodiment of the present application provides a data analysis method, which obtains the group workload data per unit time, the total benefit data per unit time, and the individual workload data in the current data analysis cycle. Among them, the group workload data per unit time includes the workload of each work group on each management rule in each unit time in the current data analysis cycle. The total benefit data per unit time includes the total benefit generated in each unit time in the current data analysis cycle. The individual workload data includes the workload of each person on each management rule in the current data analysis cycle. Then the total benefit data per unit time and the input information of each work group are input into the regression analysis prediction model, and the predicted workload data is obtained through the regression analysis prediction model. Among them, the predicted workload data is the predicted workload of each work group on each management rule in each unit time when the total benefit in each unit time is generated; the input information of the work group is the statistical result of the input information of each person in the work group; the input information of the person is obtained based on the ability score of the person and the work participation time analysis, so as to consider the work input of the person. And, using the group workload data per unit time, the group average workload data is calculated. Among them, one of the data in the group average workload data is the mean of the workload of a work group on a management rule in each unit time. Then, the predicted workload data and the group average workload data are analyzed for correlation, and the unit work score of each work group in each management rule is obtained, so as to determine the unit work score considering the current actual work situation of each work group, so that the analysis results are consistent with the actual work situation of each work group, and the accuracy of the results is improved. Then, the unit work score of each work group in each management rule and the personal workload data are used to calculate the total work score of each person, and the workload is converted into a score so that the obtained score is consistent with the situation of the management rule. Finally, the total amount in the current data analysis cycle and the total work score of each person are used to calculate the performance data of each person, which is a method for automatically and accurately analyzing performance data, which not only ensures the accuracy of the analysis results, but also does not require manual intervention, thereby improving the efficiency of the analysis.

[0109] Another embodiment of the present application provides a data analysis device, such as Figure 6 As shown, including:

[0110] The data acquisition unit 601 is used to acquire the group's workload per unit time data, total benefit per unit time data, and individual workload data within the current data analysis cycle.

[0111] The group workload per unit time data includes the workload of each work group on each management rule in each unit time of the current data analysis cycle. The total benefit per unit time data includes the total benefit generated in each unit time of the current data analysis cycle. The individual workload data includes the workload of each person on each management rule in the current data analysis cycle.

[0112] The prediction unit 602 is used to input the total benefit per unit time data and the input information of each working group into the regression analysis prediction model, and obtain the predicted workload data through the regression analysis prediction model.

[0113] The predicted workload data is the predicted workload of each work group in each unit time and on each management rule when the total benefit in each unit time is generated. The input information of the work group is the statistical result of the input information of each person in the work group. The input information of the person is obtained based on the ability score of the person and the analysis of the work participation time.

[0114] The mean calculation unit 603 is used to calculate the average workload data of the group using the workload data per unit time of the group.

[0115] Among them, one of the data in the group average workload data is the average of the workload of a work group on a management rule in each unit time.

[0116] The scoring determination unit 604 is used to perform correlation analysis on the predicted workload data and the group average workload data to obtain the unit workload scoring value of each work group under each management rule.

[0117] The score calculation unit 605 is used to calculate the total work score of each person by using the unit work score assigned by each work group in each management rule and the personal workload data.

[0118] The performance data calculation unit 606 is used to calculate the performance data of each person using the total amount in the current data analysis cycle and the sum of the work scores of each person.

[0119] Optionally, in a data analysis device provided in another embodiment of the present application, the data acquisition unit includes:

[0120] The benefit query unit is used to query the total benefit data per unit time in the current data analysis cycle from the database.

[0121] The personal workload query unit is used to query the workload of each person on each management rule in each unit time in the current data analysis cycle through a preset query configuration based on the pre-configured personal information of each person.

[0122] The personal workload statistics unit is used to separately count the total workload of each person on each management rule to obtain personal workload data.

[0123] The group workload statistics unit is used to count the workload of each person belonging to the same work group on each management rule within each unit time based on the pre-configured group information, and obtain the group workload data per unit time.

[0124] Optionally, in a data analysis device provided in another embodiment of the present application, the scoring value determination unit includes:

[0125] The indicator calculation unit is used to calculate the work completion indicator of each working group under each management rule by using the workload of the same working group under the same management rule in the predicted workload data and the group average workload data.

[0126] The adjustment unit is used to adjust the score value using the adjustment coefficient corresponding to the work completion index of each working group under each management rule, so as to obtain the unit work score value of each working group under each management rule.

[0127] Optionally, in a data analysis device provided in another embodiment of the present application, the score calculation unit includes:

[0128] The first calculation unit is used to calculate the work score of each person under each management rule based on the ratio of the unit work score of the work group to which the person belongs under each management rule and the workload of the person under each management rule.

[0129] The second calculation unit is used to accumulate the work scores of the personnel on each management rule to obtain the total work scores of the personnel in the current data analysis cycle.

[0130] Optionally, in a data analysis device provided in another embodiment of the present application, the performance data calculation unit includes:

[0131] The total score calculation unit is used to add up the total work scores of each person to obtain the total work score in the current data analysis cycle.

[0132] The equalization unit is used to divide the total amount in the current data analysis period by the total work score in the current data analysis period to obtain the unit score amount.

[0133] The performance calculation unit is used to multiply the unit score amount by the performance data of each person to obtain the performance data of each person.

[0134] It should be noted that the specific working process of each unit provided in the above embodiments of the present application can refer to the implementation process of the corresponding steps in the above method embodiments, and will not be repeated here.

[0135] Another embodiment of the present application provides an electronic device, such as Figure 7 As shown, including:

[0136] Memory 701 and processor 702 .

[0137] The memory 701 is used to store programs.

[0138] The processor 702 is used to execute the program stored in the memory 701. When the program is executed, it is specifically used to implement the data analysis method provided in any one of the above embodiments.

[0139] Another embodiment of the present application provides a computer storage medium for storing a computer program, which, when executed by a processor, is used to implement a data analysis method as provided in any of the above embodiments.

[0140] Computer storage media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0141] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0142] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data analysis method, characterized in that: include: Obtain the group's workload per unit time data, total benefit per unit time data, and individual workload data within the current data analysis cycle; wherein the group's workload per unit time data includes the workload of each work group on each management rule within each unit time within the current data analysis cycle; the total benefit per unit time data includes the total benefit generated within each unit time within the current data analysis cycle; the individual workload data includes the workload of each person on each management rule within the current data analysis cycle; The total benefit data per unit time and the input information of each working group are input into a regression analysis prediction model, and the predicted workload data is obtained through the regression analysis prediction model; wherein the predicted workload data is the predicted workload of each working group on each management rule in each unit time when the total benefit per unit time is generated; the input information of the working group is the statistical result of the input information of each person in the working group; the input information of the person is obtained based on the ability score of the person and the work participation time analysis; Using the group workload data per unit time, calculate the group average workload data; wherein one of the group average workload data is the average of the workload of one of the management rules in each unit time of the work group; Performing correlation analysis on the predicted workload data and the group average workload data to obtain a unit workload score for each work group under each management rule; Using the unit work score value of each work group in each management rule and the individual workload data, respectively calculate the total work score of each person; The performance data of each of the personnel is calculated using the total amount in the current data analysis period and the sum of the work scores of each of the personnel.

2. The method according to claim 1, characterized in that: The acquisition of the group's workload per unit time data, total benefit per unit time data, and individual workload data within the current data analysis cycle includes: Query the total benefit per unit time data within the current data analysis period from the database; Based on the pre-configured personal information of each of the personnel, the workload of each of the personnel on each of the management rules in each unit time in the current data analysis cycle is queried through a preset query configuration; Counting the sum of the workload of each person on each management rule to obtain the personal workload data; Based on the pre-configured group information, the workload of each person belonging to the same working group on each management rule within each unit time is counted to obtain the workload data of the group per unit time.

3. The method according to claim 1, characterized in that The correlation analysis of the predicted workload data and the group average workload data is performed to obtain the unit workload score of each work group under each management rule, including: Using the workload of the same working group on the same management rule in the predicted workload data and the group average workload data, calculate the work completion index of each working group on each management rule; The score value is adjusted using the adjustment coefficient corresponding to the work completion index of each working group in each management rule to obtain the unit work score value of each working group in each management rule.

4. The method according to claim 1, characterized in that The method of using the unit work score value of each work group in each management rule and the individual workload data to calculate the total work score of each person includes: For each of the personnel, the work score of the personnel in each of the management rules is calculated based on the ratio of the unit work score of the work group to which the personnel belongs in each of the management rules to the workload of the personnel in each of the management rules; The work scores of the personnel on each of the management rules are accumulated to obtain the total work scores of the personnel in the current data analysis cycle.

5. The method according to claim 1, characterized in that The performance data of each person is calculated by using the total amount in the current data analysis period and the sum of the work scores of each person, including: Adding up the total work scores of the various personnel to obtain the total work score in the current data analysis period; Divide the total amount in the current data analysis period by the total work score in the current data analysis period to obtain a unit score amount; The unit score amount is multiplied by the performance data of each of the personnel respectively to obtain the performance data of each of the personnel.

6. A data analysis device, characterized in that: include: A data acquisition unit is used to acquire the group workload data per unit time, the total benefit data per unit time, and the individual workload data in the current data analysis cycle; wherein the group workload data per unit time includes the workload of each work group on each management rule within each unit time in the current data analysis cycle; the total benefit data per unit time includes the total benefit generated within each unit time in the current data analysis cycle; the individual workload data includes the workload of each person on each management rule in the current data analysis cycle; A prediction unit is used to input the total benefit data per unit time and the input information of each of the working groups into a regression analysis prediction model, and obtain predicted workload data through analysis of the regression analysis prediction model; wherein the predicted workload data is the predicted workload of each of the working groups on each of the management rules in each unit time when the total benefit per unit time is generated; the input information of the working group is the statistical result of the input information of each of the personnel in the working group; the input information of the personnel is obtained based on the ability points of the personnel and the work participation time analysis; A mean value calculation unit, used to calculate the group average workload data using the group unit time workload data; wherein one of the group average workload data is the mean value of the workload of one of the management rules of one of the working groups in each unit time; A scoring value determination unit, used for performing correlation analysis on the predicted workload data and the group average workload data to obtain a unit workload scoring value of each work group under each management rule; A score calculation unit, used to calculate the total work score of each person by using the unit work score assigned by each work group in each management rule and the personal workload data; The performance data calculation unit is used to calculate the performance data of each of the personnel using the total amount in the current data analysis cycle and the sum of the work scores of each of the personnel.

7. The device according to claim 6, characterized in that The data acquisition unit comprises: A benefit query unit, used for querying the total benefit data per unit time in the current data analysis cycle from a database; A personal workload query unit, used to query the workload of each of the personnel on each of the management rules in each unit time in the current data analysis cycle through a preset query configuration based on the pre-configured personal information of each of the personnel; A personal workload statistics unit, used to respectively count the total workload of each person on each management rule to obtain the personal workload data; The group workload statistics unit is used to count the workload of each person belonging to the same working group on each management rule within each unit time based on pre-configured group information, so as to obtain the group workload data per unit time.

8. The device according to claim 6, characterized in that The scoring value determination unit includes: An indicator calculation unit, used for calculating the work completion indicator of each working group on each management rule by using the workload of the same working group on the same management rule in the predicted workload data and the group average workload data; The adjustment unit is used to adjust the scoring value by using the adjustment coefficient corresponding to the work completion index of each working group in each management rule, so as to obtain the unit work scoring value of each working group in each management rule.

9. An electronic device, characterized in that: include: Memory and processor; Wherein, the memory is used to store programs; The processor is used to execute the program, and when the program is executed, it is specifically used to implement the data analysis method as described in any one of claims 1 to 5.

10. A computer storage medium, characterized in that: Used to store a computer program, which, when executed by a processor, is used to implement the data analysis method according to any one of claims 1 to 5.

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