Method and system for evaluating tipped talents based on Internet of Things

Abstract

The invention relates to the technical field of intelligent capability evaluation, and particularly discloses a method and system for evaluating a talent in a tipped point based on the Internet of Things, and the method comprises the steps: analyzing production parameters, and calculating the stability of production equipment; determining an evaluation value containing a time label according to the stability; obtaining a worker matched with the production equipment and a matching time period according to the information collector; and for any worker, obtaining all production devices matched with the worker and matching time periods of the production devices, reading evaluation values of the matching time periods, and accumulating the evaluation values to obtain a final evaluation value. Operation parameters of the production equipment are analyzed, whether the operation parameters are stable or not is judged, related workers are obtained according to an information collector installed on the production equipment, the workload of the workers is determined according to whether the information collector is stable or not, and the process is completed by intelligent equipment installed on the production equipment. And the method is more objective, more real and extremely high in quantization degree.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent evaluation of capabilities, and specifically to an evaluation method and system for top-notch talents based on the Internet of Things. Background Art

[0002] Performance appraisal is a task that every unit needs to carry out. For some jobs where the workload can be quantified, performance appraisal is relatively easy. However, for some jobs that are difficult to quantify, performance appraisal is rather troublesome and can only be quantified through the forms of scoring by management personnel and among colleagues. The work in the production workshop belongs to this type of job.

[0003] The scheme of quantifying work through the forms of scoring by management personnel and among colleagues is highly subjective. How to provide a more objective workshop work quantification scheme is the technical problem that the present invention aims to solve. Summary of the Invention

[0004] The purpose of the present invention is to provide an evaluation method and system for top-notch talents based on the Internet of Things to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] An evaluation method for top-notch talents based on the Internet of Things, the method comprising:

[0007] Obtaining production parameters of a production device based on an Internet of Things device built in the production device, analyzing the production parameters, and calculating the stability of the production device; the stability contains a time tag;

[0008] Determining an evaluation value containing a time tag according to the stability, synchronously determining an information collection frequency, and activating an information collector built in the production device based on the information collection frequency; the information collector is used for collecting personnel information in the workshop;

[0009] Obtaining the staff members and matching time periods that match the production device according to the information collector;

[0010] For any staff member, obtaining all the production devices that match him / her and their matching time periods, reading the evaluation values of the matching time periods, and accumulating the evaluation values to obtain a final evaluation value.

[0011] As a further scheme of the present invention: the step of obtaining production parameters of a production device based on an Internet of Things device built in the production device, analyzing the production parameters, and calculating the stability of the production device includes:

[0012] Querying each Internet of Things device built in the production device and establishing a connection channel with the Internet of Things device;

[0013] Obtain the operating parameters with time tags of each Internet of Things device based on the connection channel;

[0014] Input the operating parameters into the trained fluctuation recognition model to output the fluctuation degree of the operating parameters;

[0015] Calculate the mean and standard deviation of the fluctuation degrees of all Internet of Things devices, and calculate the stability of the production equipment according to the mean and the standard deviation; wherein, after calculating the stability, use the time tag of the operating parameters as the time tag of the stability.

[0016] As a further solution of the present invention: the steps of determining the evaluation value with time tags according to the stability, synchronously determining the information collection frequency, and activating the information collector built in the production equipment based on the information collection frequency include:

[0017] Determine the evaluation value in real time according to the stability, and use the time tag of the stability as the time tag of the evaluation value;

[0018] Determine the information collection frequency regularly according to the stability, and send the information collection frequency to the information collector built in the production equipment;

[0019] Among them, the process of determining the evaluation value according to the stability is:

[0020] V = a(W - W 1 ) 2 + b; where V represents the stability, W represents the evaluation value, a and b are preset constants; W 1 is the preset first evaluation value threshold;

[0021] The process of determining the information collection frequency according to the stability is:

[0022] where f is the information collection frequency determined according to the stability, α is the preset correction coefficient, f 0 is the preset reference frequency, W 2 is the preset second evaluation value threshold.

[0023] As a further solution of the present invention: the steps of obtaining the staff and the matching time period matching the production equipment according to the information collector include:

[0024] When the information collector is a wireless connector, based on the wireless connector, match the electronic identity card of the staff in real time, and predict the distance between the staff and the production equipment according to the matching signal strength; wherein, the wireless connector includes Bluetooth and Wifi;

[0025] When the information collector is a multi-directional camera, rotate the multi-directional camera to obtain the environmental video of the production equipment;

[0026] Identify and locate the environmental video, match the staff members, and predict the distance between the staff members and the production equipment based on the image sizes of the staff members;

[0027] Regard all staff members whose distances are less than the preset distance threshold as the matched staff members, and mark the current moment as the matching moment;

[0028] Count the matching moments of all the matched staff members to obtain the matching period;

[0029] Among them, the matching results between the production equipment and the staff members are stored in a preset relational database, and the matching results are used to represent which staff members have matching relationships with which production equipment at each moment.

[0030] As a further solution of the present invention: the step of, for any staff member, obtaining all the production equipment matched with him / her and their matching periods, reading the evaluation values of the matching periods, and accumulating the evaluation values to obtain the final evaluation value includes:

[0031] For any staff member, taking the staff member as a reference, query all the production equipment matched with him / her and their matching periods;

[0032] Read the evaluation values of the matching periods, and at the same time read the equipment weights of the production equipment as the weights of the evaluation values;

[0033] Accumulate the evaluation values based on the weights to obtain the final evaluation value.

[0034] As a further solution of the present invention: the method further includes:

[0035] Record the change states of each production equipment; the change states include normal to abnormal and abnormal to normal;

[0036] Receive the additional evaluation values of various change states input by the administrator;

[0037] Update the evaluation value determination process according to the additional evaluation values.

[0038] The technical solution of the present invention also provides an evaluation system for top-notch talents based on the Internet of Things, and the system includes:

[0039] A stability calculation module, configured to obtain the production parameters of the production equipment based on the Internet of Things devices built in the production equipment, analyze the production parameters, and calculate the stability of the production equipment; the stability contains a time tag;

[0040] A collector activation module, configured to determine the evaluation value with a time tag according to the stability, synchronously determine the information collection frequency, and activate the information collector built in the production equipment based on the information collection frequency; the information collector is used to collect the personnel information in the workshop;

[0041] A personnel matching module, configured to obtain the staff members and matching time periods that match the production equipment according to the information collector;

[0042] An evaluation value accumulation module, configured to, for any staff member, obtain all the production equipment and its matching time periods that match the staff member, read the evaluation values of the matching time periods, and accumulate the evaluation values to obtain the final evaluation value.

[0043] As a further solution of the present invention: The stability calculation module includes:

[0044] A connection channel establishment unit, configured to query each Internet of Things device built in the production equipment and establish a connection channel with the Internet of Things device;

[0045] An operating parameter acquisition unit, configured to acquire the operating parameters with time tags of each Internet of Things device based on the connection channel;

[0046] A fluctuation identification unit, configured to input the operating parameters into a trained fluctuation identification model and output the fluctuation degree of the operating parameters;

[0047] A stability determination unit, configured to calculate the mean and standard deviation of the fluctuation degrees of all Internet of Things devices, and calculate the stability of the production equipment according to the mean and the standard deviation; wherein, after calculating the stability, use the time tag of the operating parameters as the time tag of the stability.

[0048] As a further solution of the present invention: The collector activation module includes:

[0049] An evaluation value determination unit, configured to determine the evaluation value in real time according to the stability, and use the time tag of the stability as the time tag of the evaluation value;

[0050] A frequency determination unit, configured to determine the information collection frequency regularly according to the stability, and send the information collection frequency to the information collector built in the production equipment;

[0051] Wherein, the process of determining the evaluation value according to the stability is:

[0052] V = a(W - W 1 ) 2 + b; In the formula, V represents the stability, W represents the evaluation value, a and b are preset constants; W 1 is the preset first evaluation value threshold;

[0053] The process of determining the information collection frequency according to the stability is:

[0054] In the formula, f is the information collection frequency determined according to the stability, α is a preset correction coefficient, f 0is a preset reference frequency, W 2 is a preset second evaluation value threshold.

[0055] As a further solution of the present invention: the personnel matching module includes:

[0056] A first acquisition unit, when the information collector is a wireless connector, is used to match the electronic identity card of the staff in real time based on the wireless connector, and predict the distance between the staff and the production equipment according to the matched signal strength; wherein, the wireless connector includes Bluetooth and Wifi;

[0057] A second acquisition unit, when the information collector is a multi-directional camera, is used to obtain the environmental video of the production equipment based on the rotation of the multi-directional camera;

[0058] A visual recognition unit, used to identify and locate the environmental video, match the staff, and predict the distance between the staff and the production equipment according to the image size of the staff;

[0059] A moment marking unit, used to regard all staff with a distance less than the preset distance threshold as the matched staff, and mark the current moment as the matching moment;

[0060] A moment statistics unit, used to count the matching moments of all matched staff to obtain the matching period;

[0061] Among them, the matching result between the production equipment and the staff is stored in a preset relational database, and the matching result is used to represent which staff has a matching relationship with which production equipment at each moment.

[0062] Compared with the prior art, the beneficial effects of the present invention are: the present invention analyzes the operating parameters of the production equipment to judge whether it is stable, obtains the relevant staff according to the information collector installed on the production equipment, and determines the workload of the staff according to whether it is stable. This process is completed by the intelligent device installed on the production equipment, which is more objective, more real and has a very high quantification degree. Brief Description of the Drawings

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0064] Figure 1 is a flow block diagram of a top-notch talent evaluation method based on the Internet of Things.

[0065] Figure 2 is a block diagram of the composition structure of a top-notch talent evaluation system based on the Internet of Things. Detailed Embodiments

[0066] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0067] Figure 1 As a flowchart of a method for evaluating top-notch talents based on the Internet of Things, in an embodiment of the present invention, a method for evaluating top-notch talents based on the Internet of Things, the method includes:

[0068] Step S100: Obtain the production parameters of the production equipment based on the Internet of Things equipment built in the production equipment, analyze the production parameters, and calculate the stability of the production equipment; the stability contains a time tag;

[0069] The production equipment is a working equipment in the Internet of Things workshop, which is provided with a plurality of operation monitoring devices with data transmission functions, including sensors and signal recorders, etc., collectively referred to as Internet of Things equipment. Obtain the production parameters of the production equipment based on the Internet of Things equipment built in the production equipment. The production parameters are the general term for the data of the Internet of Things equipment. The production parameters are essentially a time series. By analyzing the production parameters, it can be judged whether the production equipment is in a stable state, which is represented by the parameter of stability; the higher the stability, the more stable the working state of the production equipment; the simplest way is to calculate the difference between the current production parameters and the preset standard parameters, and determine the stability according to the inverse ratio of the difference. The smaller the difference, the more stable the production equipment and the higher the stability.

[0070] Step S200: Determine an evaluation value containing a time tag according to the stability, synchronously determine the information collection frequency, and activate the information collector built in the production equipment based on the information collection frequency; the information collector is used to collect the personnel information in the workshop;

[0071] Convert the stability into an evaluation value. There is a time correspondence between the evaluation value and the stability. The stability at a certain moment corresponds to the evaluation value at that moment; the evaluation value is used to characterize the influence degree of the production equipment on the surrounding staff, which can be understood as a kind of workload, and the workloads corresponding to different stabilities are different; at the same time, determine the information collection frequency according to the stability. The higher the stability, the lower the information collection frequency. Activate the information collector built in the production equipment based on the information collection frequency. The information collector is used to obtain the identity and distance of the staff around the production equipment. The information collector includes a conventional distance sensor and a more complex visual collector, as long as it can obtain the identity and distance of the staff around the production equipment.

[0072] Step S300: Obtain the staff members and matching time periods that match the production equipment according to the information collector;

[0073] The information collector corresponds to the production equipment. The information of the staff members collected by each information collector is considered as the information related to the corresponding production equipment. In other words, the staff members detected by the information collector are the staff members that match the production equipment. Since each matching process has a time tag and is an instantaneous matching process, each matching can only obtain which production equipment and which staff members match at each moment. By integrating these moments, it can be obtained which time periods the production equipment and the staff members are matched.

[0074] Step S400: For any staff member, obtain all the production equipment that matches him / her and the matching time periods, read the evaluation values of the matching time periods, and accumulate the evaluation values to obtain the final evaluation value;

[0075] The matching process occurs between all production equipment and all staff members. Based on the staff members, query which production equipment they have a matching relationship with and in which time periods, read the evaluation values of the production equipment that is matched during the time period, and accumulate all the evaluation values corresponding to the same staff member to obtain the final evaluation value.

[0076] In the technical solution of the present invention, the obtained final evaluation value completely records how much workload the staff member has completed in actual work. For front-line staff members, the workload in the front-line scenario largely reflects their work ability. Compared with the traditional personnel scoring system, this is more objective, more real, and more quantitative.

[0077] Regarding Step S100, the step of obtaining the production parameters of the production equipment based on the Internet of Things devices built in the production equipment, analyzing the production parameters, and calculating the stability of the production equipment includes:

[0078] Query each Internet of Things device built in the production equipment and establish a connection channel with the Internet of Things device;

[0079] Based on the connection channel, obtain the running parameters with time tags of each Internet of Things device;

[0080] Input the running parameters into the trained fluctuation recognition model and output the fluctuation degree of the running parameters;

[0081] Calculate the mean and standard deviation of the fluctuation degrees of all Internet of Things devices, and calculate the stability of the production equipment according to the mean and the standard deviation; wherein, after calculating the stability, use the time tag of the running parameters as the time tag of the stability.

[0082] The above content specifically defines the calculation process of the stability of production equipment. Query each Internet of Things device built into the production equipment, establish a connection channel with the Internet of Things device, obtain the operating parameters with time tags of each Internet of Things device based on the connection channel, input the obtained operating parameters into the trained fluctuation recognition model, and output the fluctuation degree of the operating parameters; the fluctuation recognition model is a neural network model. The staff pre-statistics the operating parameters for a period of time in advance, and then artificially calibrates its fluctuation degree as a sample. When the number of samples is large enough, the neural network model is trained based on the samples until the error rate is small enough. The response speed of this method is extremely high. The advantage of this method is that when analyzing each operating parameter, it will consider the influence of its previous operating parameters on it. Compared with the traditional threshold comparison scheme, it is more in line with the actual situation and the response speed is also high; it should be noted that the period of time in the process of the staff pre-statistics the operating parameters for a period of time in advance is generally a few minutes. Correspondingly, when inputting the operating parameters into the fluctuation recognition model, the fluctuation recognition model will also read the data a period of time ago as an independent variable to determine the fluctuation degree.

[0083] Each Internet of Things device corresponds to a fluctuation degree. The stability of the same production equipment needs to be jointly determined by the comprehensive detection results of multiple Internet of Things devices. In the technical solution of the present invention, calculate the mean and standard deviation of the fluctuation degrees of all Internet of Things devices, and calculate the stability of the production equipment according to the mean and the standard deviation; after calculating the stability, use the time tag of the operating parameter as the time tag of the stability.

[0084] Among them, the stability is inversely proportional to the mean and also inversely proportional to the standard deviation. This means that the smaller the mean value of the fluctuation degree, the more uniform the fluctuation degrees corresponding to each Internet of Things device, the better the stability, and the higher the stability.

[0085] Regarding step S200, the steps of determining the evaluation value with time tags according to the stability, synchronously determining the information collection frequency, and activating the information collector built into the production equipment based on the information collection frequency include:

[0086] Determine the evaluation value in real time according to the stability, and use the time tag of the stability as the time tag of the evaluation value;

[0087] Determine the information collection frequency regularly according to the stability, and send the information collection frequency to the information collector built into the production equipment;

[0088] Among them, the process of determining the evaluation value according to the stability is:

[0089] V=a(W-W 1 ) 2 +b; In the formula, V represents the stability, W represents the evaluation value, and a and b are preset constants; W 1is a preset first evaluation value threshold.

[0090] The meaning of the process of determining the evaluation value according to the stability provided in this application is that the closer the stability is to a certain value, the smaller the evaluation value. On the contrary, the farther away from a certain value, whether it is smaller or larger, the evaluation value becomes larger. The conventional solution for this relationship is the quadratic function provided in the above content. Of course, there are other functions that can describe this relationship. Its practical significance is that if the stability is too high, it means that the corresponding staff member's production equipment is too stable during work. At this time, it is considered that their workload is large and their work ability is strong. If the production equipment is unstable during the staff member's work, it means that the staff member has performed some special operations. During this series of special operations, the staff member's emergency response ability has been improved. At this time, it is also considered that they have stronger work ability.

[0091] It is worth mentioning that there is a special case, that is, the staff member deliberately damages the production equipment. This situation is too rare, and there will be other measures to manage such situations, which will not be elaborated in this application.

[0092] The process of determining the information collection frequency according to the stability is as follows:

[0093] In the formula, f is the information collection frequency determined according to the stability, α is a preset correction coefficient, f 0 is a preset reference frequency, W 2 is a preset second evaluation value threshold.

[0094] The relationship between stability and information collection frequency is that the smaller the stability, the larger the information collection frequency. However, the above solution is not like this. It is actually also a function with a low middle and high sides. In order to achieve the relationship required by this application, W 2 generally takes the maximum value of stability. When it is large enough, the smaller the stability, the larger the information collection frequency, and the higher the stability (less than the maximum value), the smaller the information collection frequency.

[0095] Regarding step S300, the step of obtaining the staff member and the matching time period matching the production equipment according to the information collector includes:

[0096] When the information collector is a wireless connector, based on the wireless connector, the electronic identity card of the staff member is matched in real time, and the distance between the staff member and the production equipment is predicted according to the matching signal strength; wherein, the wireless connector includes Bluetooth and Wifi;

[0097] When the information collector is a multi-directional camera, based on the rotation of the multi-directional camera, the environmental video of the production equipment is obtained;

[0098] Identify and locate the environmental video, match the staff, and predict the distance between the staff and the production equipment according to the image size of the staff;

[0099] There are mainly two types of information collectors. One is a wireless connector, including Bluetooth and Wifi. When the information collector is a wireless connector, based on the wireless connector, the electronic identity card of the staff is matched in real time, and the distance between the staff and the production equipment is predicted according to the matching signal strength. In this process, when the staff is matched, the identity of the staff will also be recorded as a label for the distance, indicating how far the production equipment is from which staff. When the information collector is a multi-directional camera, the environmental video of the production equipment is obtained by rotating the multi-directional camera, and with the help of image recognition algorithms, the identity of the staff and the distance between the production equipment and the staff can be obtained from a visual perspective.

[0100] All staff with a distance less than the preset distance threshold are regarded as the matched staff, and the current moment is marked as the matching moment;

[0101] Compare the distance with the preset distance threshold. When the distance is less than the preset distance threshold, the staff is regarded as the staff matched with the current production equipment. At the same time, the current moment needs to be marked as the matching moment.

[0102] Count the matching moments of all matched staff to obtain the matching period;

[0103] Among them, the matching result between the production equipment and the staff is stored in a preset relational database, and the matching result is used to represent which staff has a matching relationship with which production equipment at each moment.

[0104] Finally, taking the staff as the index, count the matching moments of each staff with the current production equipment, and summarize the matching moments to obtain the matching period; the matching period indicates in what time period each staff is matched with the current production equipment.

[0105] Regarding step S400, the steps of obtaining all the production equipment matched by any staff and their matching periods, reading the evaluation value of the matching period, and accumulating the evaluation value to obtain the final evaluation value include:

[0106] For any staff, taking the staff as the benchmark, query all the production equipment matched by them and their matching periods;

[0107] Read the evaluation value of the matching period, and at the same time read the equipment weight of the production equipment as the weight of the evaluation value;

[0108] Accumulate the evaluation value based on the weight to obtain the final evaluation value.

[0109] In an example of the technical solution of the present invention, taking the staff as a unit, query the time periods during which he has a matching relationship with which production equipment, and then read the evaluation values of the matching production equipment during the corresponding time periods. At the same time, the above content adds weights. Different production equipment has different importance levels in the Internet of Things workshop. The higher the importance level, the greater the weight, and the greater the weight of the corresponding evaluation value. Based on the weights, the evaluation values are accumulated to obtain the final evaluation value.

[0110] As an example of the technical solution of the present invention, the method further includes:

[0111] Record the change status of each production equipment; the change status includes normal to abnormal and abnormal to normal;

[0112] Receive the additional evaluation values of various change statuses input by the administrator;

[0113] Update the evaluation value determination process according to the additional evaluation values.

[0114] In an example of the technical solution of the present invention, the concept of additional evaluation values is introduced, and the change status of each production equipment is recorded. The change status includes normal to abnormal and abnormal to normal. Normal to abnormal indicates that an abnormal problem has occurred, and abnormal to normal indicates that maintenance has been carried out. Both are valuable experiences. The surrounding staff has actually received an on-site training. Correspondingly, the evaluation value should be higher. Therefore, the present application introduces additional evaluation values to update the evaluation value determination process.

[0115] It is worth mentioning that once a change status occurs, the fluctuation value of the production equipment will increase, and the stability

[0116] Figure 2 FIG. 22 is a block diagram of the composition structure of a top-notch talent evaluation system based on the Internet of Things. In an embodiment of the present invention, a top-notch talent evaluation system based on the Internet of Things, the system 10 includes:

[0117] A stability calculation module 11, configured to obtain production parameters of a production equipment based on an Internet of Things device built in the production equipment, analyze the production parameters, and calculate the stability of the production equipment; the stability contains a time tag;

[0118] A collector activation module 12, configured to determine an evaluation value with a time tag according to the stability, synchronously determine an information collection frequency, and activate an information collector built in the production equipment based on the information collection frequency; the information collector is used to collect personnel information in the workshop;

[0119] A personnel matching module 13, configured to obtain the staff members and matching time periods that match the production equipment according to the information collector;

[0120] The evaluation value accumulation module 14 is used to obtain, for any staff member, all the production equipment matched with him and the matching time period, read the evaluation values of the matching time period, and accumulate the evaluation values to obtain the final evaluation value.

[0121] Further, the stability calculation module 11 includes:

[0122] The connection channel establishment unit is used to query each Internet of Things device built in the production equipment and establish a connection channel with the Internet of Things device;

[0123] The operating parameter acquisition unit is used to acquire the time-tagged operating parameters of each Internet of Things device based on the connection channel;

[0124] The fluctuation identification unit is used to input the operating parameters into the trained fluctuation identification model and output the fluctuation degree of the operating parameters;

[0125] The stability determination unit is used to calculate the mean and standard deviation of the fluctuation degrees of all Internet of Things devices, and calculate the stability of the production equipment according to the mean and the standard deviation; wherein, after the stability is calculated, the time tag of the operating parameters is used as the time tag of the stability.

[0126] Specifically, the collector activation module 12 includes:

[0127] The evaluation value determination unit is used to determine the evaluation value in real time according to the stability, and use the time tag of the stability as the time tag of the evaluation value;

[0128] The frequency determination unit is used to determine the information collection frequency regularly according to the stability, and send the information collection frequency to the information collector built in the production equipment;

[0129] Among them, the process of determining the evaluation value according to the stability is:

[0130] V = a(W - W 1 ) 2 + b; in the formula, V represents the stability, W represents the evaluation value, a and b are preset constants; W 1 is the preset first evaluation value threshold;

[0131] The process of determining the information collection frequency according to the stability is:

[0132] In the formula, f is the information collection frequency determined according to the stability, α is the preset correction coefficient, f 0 is the preset reference frequency, W 2 is the preset second evaluation value threshold.

[0133] Even further, the personnel matching module 13 includes:

[0134] The first acquisition unit is used to, when the information collector is a wireless connector, based on the wireless connector, match the electronic identity tags of the staff in real time, and predict the distance between the staff and the production equipment according to the matched signal strength; wherein, the wireless connector includes Bluetooth and Wifi;

[0135] The second acquisition unit is used to, when the information collector is a multi-directional camera, obtain the environmental video of the production equipment based on the rotation of the multi-directional camera;

[0136] The visual recognition unit is used to identify and locate the environmental video, match the staff, and predict the distance between the staff and the production equipment according to the image size of the staff;

[0137] The moment marking unit is used to regard all the staff with a distance less than the preset distance threshold as the matched staff, and at the same time mark the current moment as the matching moment;

[0138] The moment statistics unit is used to count the matching moments of all the matched staff to obtain the matching period;

[0139] Wherein, the matching result between the production equipment and the staff is stored in a preset relational database, and the matching result is used to represent which staff has a matching relationship with which production equipment at each moment.

[0140] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for evaluating top talents based on the Internet of Things, characterized in that: The method comprises: Acquire production parameters of the production equipment based on an IoT device built into the production equipment, analyze the production parameters, and calculate the stability of the production equipment; the stability contains a time tag; Determine an evaluation value containing a time tag according to the stability, synchronously determine an information collection frequency, and activate an information collector built into the production equipment based on the information collection frequency; the information collector is used to collect personnel information in the workshop; Acquire the staff and matching time period matching the production equipment according to the information collector; For any staff member, all the production equipment matched by him and their matching time periods are obtained, the evaluation value of the matching time period is read, the evaluation values ​​are accumulated, and the final evaluation value is obtained.

2. The method for evaluating top talents based on the Internet of Things according to claim 1 is characterized in that: The step of obtaining the production parameters of the production equipment based on the Internet of Things device built into the production equipment, analyzing the production parameters, and calculating the stability of the production equipment includes: Query each IoT device built into the production equipment and establish a connection channel with the IoT device; Obtain the time-stamped operating parameters of each IoT device based on the connection channel; Inputting the operating parameters into a trained fluctuation recognition model and outputting the fluctuation degree of the operating parameters; The mean and standard deviation of the volatility of all IoT devices are calculated, and the stability of the production equipment is calculated based on the mean and the standard deviation; wherein, after the stability is calculated, the time label of the operating parameter is used as the time label of the stability.

3. The method for evaluating top talents based on the Internet of Things according to claim 1 is characterized in that: The steps of determining the evaluation value containing the time tag according to the stability, synchronously determining the information collection frequency, and activating the information collector built into the production equipment based on the information collection frequency include: Determine the evaluation value in real time according to the stability, and use the time label of the stability as the time label of the evaluation value; Determine the information collection frequency according to the stability timing, and send the information collection frequency to the information collector built into the production equipment; Among them, the process of determining the evaluation value according to the stability is: V=a(W-w1) 2 +b; where V represents stability, W represents evaluation value, a and b are preset constants; W1 is the preset first evaluation value threshold; The process of determining the frequency of information collection based on stability is: Wherein, f is the information collection frequency determined according to the stability, α is the preset correction coefficient, f0 is the preset reference frequency, and W2 is the preset second evaluation value threshold.

4. The method for evaluating top talents based on the Internet of Things according to claim 1 is characterized in that: The step of obtaining the staff and matching time period matching the production equipment according to the information collector comprises: When the information collector is a wireless connector, the electronic ID card of the worker is matched in real time based on the wireless connector, and the distance between the worker and the production equipment is predicted according to the matching signal strength; wherein the wireless connector includes Bluetooth and Wifi; When the information collector is a multi-directional camera, the environment video of the production equipment is obtained based on the rotation of the multi-directional camera; Identify and locate the environmental video, match the worker, and predict the distance between the worker and the production equipment based on the worker's image size; All workers whose distance is less than the preset distance threshold are regarded as matching workers, and the current moment is marked as the matching moment; Count the matching times of all matching staff members to obtain the matching time period; The matching results between the production equipment and the staff are stored in a preset relational database, and the matching results are used to indicate which staff is matched with which production equipment at each moment.

5. The method for evaluating top talents based on the Internet of Things according to claim 1 is characterized in that: The steps of obtaining all matching production equipment and matching time periods for any staff member, reading the evaluation value of the matching time period, accumulating the evaluation value, and obtaining the final evaluation value include: For any worker, take the worker as the benchmark to query all the matching production equipment and their matching time periods; Read the evaluation value of the matching period, and at the same time read the equipment weight of the production equipment as the weight of the evaluation value; The evaluation values ​​are accumulated based on the weights to obtain a final evaluation value.

6. The method for evaluating top talents based on the Internet of Things according to claim 3 is characterized in that: The method further comprises: Record the change status of each production equipment; the change status includes normal to abnormal and abnormal to normal; Receive additional evaluation values ​​of various change states input by the administrator; The rating value determination process is updated according to the additional rating value.

7. A top talent evaluation system based on the Internet of Things, characterized in that: The system comprises: A stability calculation module, used to obtain production parameters of the production equipment based on an Internet of Things device built into the production equipment, analyze the production parameters, and calculate the stability of the production equipment; the stability contains a time tag; A collector activation module is used to determine the evaluation value containing the time tag according to the stability, synchronously determine the information collection frequency, and activate the information collector built into the production equipment based on the information collection frequency; the information collector is used to collect personnel information in the workshop; A personnel matching module, used to obtain the personnel and matching time period that match the production equipment according to the information collector; The evaluation value accumulation module is used to obtain all the production equipment and matching time periods matched by any staff member, read the evaluation value of the matching time period, accumulate the evaluation value, and obtain the final evaluation value.

8. The top talent evaluation system based on the Internet of Things according to claim 7 is characterized in that: The stability calculation module comprises: A connection channel establishing unit, used to query each IoT device built into the production equipment and establish a connection channel with the IoT device; An operating parameter acquisition unit, used to acquire the operating parameters containing time tags of each IoT device based on the connection channel; A fluctuation identification unit, used for inputting the operating parameter into a trained fluctuation identification model and outputting the fluctuation degree of the operating parameter; The stability determination unit is used to calculate the mean and standard deviation of the fluctuations of all IoT devices, and calculate the stability of the production equipment based on the mean and the standard deviation; wherein, after the stability is calculated, the time label of the operating parameter is used as the time label of the stability.

9. The top talent evaluation system based on the Internet of Things according to claim 7 is characterized in that: The collector activation module includes: An evaluation value determination unit, used to determine the evaluation value in real time according to the stability, and use the time tag of the stability as the time tag of the evaluation value; A frequency determination unit, used to determine the information collection frequency according to the stability timing, and send the information collection frequency to an information collector built into the production equipment; Among them, the process of determining the evaluation value according to the stability is: V=a(W-W1) 2 +b; where V represents stability, W represents evaluation value, a and b are preset constants; W1 is the preset first evaluation value threshold; The process of determining the frequency of information collection based on stability is: Wherein, f is the information collection frequency determined according to the stability, α is the preset correction coefficient, f0 is the preset reference frequency, and W2 is the preset second evaluation value threshold.

10. The top talent evaluation system based on the Internet of Things according to claim 7 is characterized in that: The personnel matching module includes: A first collection unit is used for, when the information collector is a wireless connector, matching the electronic ID card of the staff in real time based on the wireless connector, and predicting the distance between the staff and the production equipment according to the matching signal strength; wherein the wireless connector includes Bluetooth and Wifi; A second acquisition unit, for acquiring an environmental video of the production equipment based on the rotation of the multi-directional camera when the information collector is a multi-directional camera; A visual recognition unit, used to identify and locate the environmental video, match the worker, and predict the distance between the worker and the production equipment according to the image size of the worker; A time marking unit, used to treat all workers whose distance is less than a preset distance threshold as matching workers, and mark the current time as the matching time; The time statistics unit is used to count the matching time of all matching staff members to obtain the matching time period; The matching results between the production equipment and the staff are stored in a preset relational database, and the matching results are used to indicate which staff is matched with which production equipment at each moment.

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